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0000000000000000000000000000000000000000..424c11bfe0cbdc3e05c596df5d968f825a1ff8c8 --- /dev/null +++ b/DEMO/reference_frame.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a8709fe3de45055ca8770a9284eecd12328e6cd7a399d0c22a8ace8f1594696 +size 175040 diff --git a/DEMO/salma.jpg b/DEMO/salma.jpg new file mode 100644 index 0000000000000000000000000000000000000000..c69dd44ef80bded0cb513626ca981b6316f1feed --- /dev/null +++ b/DEMO/salma.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:985a12f77ec465a48620c8b2f86e6ca2c5fe01929ea3e10b6c80e9e555c277b0 +size 32243 diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8582f088705d90f26f2efd324ac528045dc00ba9 --- /dev/null +++ b/README.md @@ -0,0 +1,77 @@ +# Adaptive Super Resolution For One-Shot Talking-Head Generation +The repository for ICASSP2024 Adaptive Super Resolution For One-Shot Talking-Head Generation (AdaSR TalkingHead) + +## Abstract +The one-shot talking-head generation learns to synthesize a talking-head video with one source portrait image under the driving of same or different identity video. Usually these methods require plane-based pixel transformations via Jacobin matrices or facial image warps for novel poses generation. The constraints of using a single image source and pixel displacements often compromise the clarity of the synthesized images. Some methods try to improve the quality of synthesized videos by introducing additional super-resolution modules, but this will undoubtedly increase computational consumption and destroy the original data distribution. In this work, we propose an adaptive high-quality talking-head video generation method, which synthesizes high-resolution video without additional pre-trained modules. Specifically, inspired by existing super-resolution methods, we down-sample the one-shot source image, and then adaptively reconstruct high-frequency details via an encoder-decoder module, resulting in enhanced video clarity. Our method consistently improves the quality of generated videos through a straightforward yet effective strategy, substantiated by quantitative and qualitative evaluations. The code and demo video are available on: https://github.com/Songluchuan/AdaSR-TalkingHead/ + +## Updates + +- [03/2024] Inference code and pretrained model are released. +- [03/2024] Arxiv Link: https://arxiv.org/abs/2403.15944. +- [COMING] Super-resolution model (based on StyleGANEX and ESRGAN). +- [COMING] Train code and processed datasets. + + +## Installation + +**Clone this repo:** +```bash +git clone git@github.com:Songluchuan/AdaSR-TalkingHead.git +cd AdaSR-TalkingHead +``` +**Dependencies:** + +We have tested on: +- CUDA 11.3-11.6 +- PyTorch 1.10.1 +- Matplotlib 3.4.3; Matplotlib 3.4.2; opencv-python 4.7.0; scikit-learn 1.0; tqdm 4.62.3 + +## Inference Code + + +1. Download the pretrained model on google drive: https://drive.google.com/file/d/1g58uuAyZFdny9_twvbv0AHxB9-03koko/view?usp=sharing (it is trained on the HDTF dataset), and put it under checkpoints/
+ + +2. The demo video and reference image are under ```DEMO/``` + + +3. The inference code is in the ```run_demo.sh```, please run it with + +``` +bash run_demo.sh +``` + +4. You can set different demo image and driven video in the ```run_demo.sh``` +``` +--source_image DEMO/demo_img_3.jpg +``` + and +``` +--driving_video DEMO/demo_video_1.mp4 +``` + + +## Video +
+ + AdaSR Talking-Head + +
+ + + +## Citation + +```bibtex +@inproceedings{song2024adaptive, + title={Adaptive Super Resolution for One-Shot Talking Head Generation}, + author={Song, Luchuan and Liu, Pinxin and Yin, Guojun and Xu, Chenliang}, + year={2024}, + organization={IEEE International Conference on Acoustics, Speech, and Signal Processing} +} +``` + +## Acknowledgments + +The code is mainly developed based on [styleGANEX](https://github.com/williamyang1991/StyleGANEX), [ESRGAN](https://github.com/xinntao/ESRGAN) and [unofficial face2vid](https://github.com/zhanglonghao1992/One-Shot_Free-View_Neural_Talking_Head_Synthesis). Thanks to the authors contribution. + diff --git a/__pycache__/animate.cpython-38.pyc b/__pycache__/animate.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..d761e7acd6ccb3eb1e5b2a8d579664004d7562e6 Binary files /dev/null and b/__pycache__/animate.cpython-38.pyc differ diff --git a/__pycache__/logger.cpython-38.pyc b/__pycache__/logger.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..95dccce24b0e240fcce4f8e044b7171ed76554b1 Binary files /dev/null and b/__pycache__/logger.cpython-38.pyc differ diff --git a/animate.py b/animate.py new file mode 100644 index 0000000000000000000000000000000000000000..74f89548ad6c95fd715d11556890cdb9e57cccbc --- /dev/null +++ b/animate.py @@ -0,0 +1,34 @@ +import os +from tqdm import tqdm + +import torch +from torch.utils.data import DataLoader + +from logger import Logger, Visualizer +import imageio +from scipy.spatial import ConvexHull +import numpy as np + +from sync_batchnorm import DataParallelWithCallback + +def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, + use_relative_movement=False, use_relative_jacobian=False): + if adapt_movement_scale: + source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume + driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume + adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) + else: + adapt_movement_scale = 1 + + kp_new = {k: v for k, v in kp_driving.items()} + + if use_relative_movement: + kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) + kp_value_diff *= adapt_movement_scale + kp_new['value'] = kp_value_diff + kp_source['value'] + + if use_relative_jacobian: + jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) + kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) + + return kp_new diff --git a/augmentation.py b/augmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..50d03203aaec2a59fb2671bdeccfae1d214f607c --- /dev/null +++ b/augmentation.py @@ -0,0 +1,345 @@ +""" +Code from https://github.com/hassony2/torch_videovision +""" + +import numbers + +import random +import numpy as np +import PIL + +from skimage.transform import resize, rotate +from skimage.util import pad +import torchvision + +import warnings + +from skimage import img_as_ubyte, img_as_float + + +def crop_clip(clip, min_h, min_w, h, w): + if isinstance(clip[0], np.ndarray): + cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip] + + elif isinstance(clip[0], PIL.Image.Image): + cropped = [ + img.crop((min_w, min_h, min_w + w, min_h + h)) for img in clip + ] + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + return cropped + + +def pad_clip(clip, h, w): + im_h, im_w = clip[0].shape[:2] + pad_h = (0, 0) if h < im_h else ((h - im_h) // 2, (h - im_h + 1) // 2) + pad_w = (0, 0) if w < im_w else ((w - im_w) // 2, (w - im_w + 1) // 2) + + return pad(clip, ((0, 0), pad_h, pad_w, (0, 0)), mode='edge') + + +def resize_clip(clip, size, interpolation='bilinear'): + if isinstance(clip[0], np.ndarray): + if isinstance(size, numbers.Number): + im_h, im_w, im_c = clip[0].shape + # Min spatial dim already matches minimal size + if (im_w <= im_h and im_w == size) or (im_h <= im_w + and im_h == size): + return clip + new_h, new_w = get_resize_sizes(im_h, im_w, size) + size = (new_w, new_h) + else: + size = size[1], size[0] + + scaled = [ + resize(img, size, order=1 if interpolation == 'bilinear' else 0, preserve_range=True, + mode='constant', anti_aliasing=True) for img in clip + ] + elif isinstance(clip[0], PIL.Image.Image): + if isinstance(size, numbers.Number): + im_w, im_h = clip[0].size + # Min spatial dim already matches minimal size + if (im_w <= im_h and im_w == size) or (im_h <= im_w + and im_h == size): + return clip + new_h, new_w = get_resize_sizes(im_h, im_w, size) + size = (new_w, new_h) + else: + size = size[1], size[0] + if interpolation == 'bilinear': + pil_inter = PIL.Image.NEAREST + else: + pil_inter = PIL.Image.BILINEAR + scaled = [img.resize(size, pil_inter) for img in clip] + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + return scaled + + +def get_resize_sizes(im_h, im_w, size): + if im_w < im_h: + ow = size + oh = int(size * im_h / im_w) + else: + oh = size + ow = int(size * im_w / im_h) + return oh, ow + + +class RandomFlip(object): + def __init__(self, time_flip=False, horizontal_flip=False): + self.time_flip = time_flip + self.horizontal_flip = horizontal_flip + + def __call__(self, clip): + if random.random() < 0.5 and self.time_flip: + return clip[::-1] + if random.random() < 0.5 and self.horizontal_flip: + return [np.fliplr(img) for img in clip] + + return clip + + +class RandomResize(object): + """Resizes a list of (H x W x C) numpy.ndarray to the final size + The larger the original image is, the more times it takes to + interpolate + Args: + interpolation (str): Can be one of 'nearest', 'bilinear' + defaults to nearest + size (tuple): (widht, height) + """ + + def __init__(self, ratio=(3. / 4., 4. / 3.), interpolation='nearest'): + self.ratio = ratio + self.interpolation = interpolation + + def __call__(self, clip): + scaling_factor = random.uniform(self.ratio[0], self.ratio[1]) + + if isinstance(clip[0], np.ndarray): + im_h, im_w, im_c = clip[0].shape + elif isinstance(clip[0], PIL.Image.Image): + im_w, im_h = clip[0].size + + new_w = int(im_w * scaling_factor) + new_h = int(im_h * scaling_factor) + new_size = (new_w, new_h) + resized = resize_clip( + clip, new_size, interpolation=self.interpolation) + + return resized + + +class RandomCrop(object): + """Extract random crop at the same location for a list of videos + Args: + size (sequence or int): Desired output size for the + crop in format (h, w) + """ + + def __init__(self, size): + if isinstance(size, numbers.Number): + size = (size, size) + + self.size = size + + def __call__(self, clip): + """ + Args: + img (PIL.Image or numpy.ndarray): List of videos to be cropped + in format (h, w, c) in numpy.ndarray + Returns: + PIL.Image or numpy.ndarray: Cropped list of videos + """ + h, w = self.size + if isinstance(clip[0], np.ndarray): + im_h, im_w, im_c = clip[0].shape + elif isinstance(clip[0], PIL.Image.Image): + im_w, im_h = clip[0].size + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + + clip = pad_clip(clip, h, w) + im_h, im_w = clip.shape[1:3] + x1 = 0 if h == im_h else random.randint(0, im_w - w) + y1 = 0 if w == im_w else random.randint(0, im_h - h) + cropped = crop_clip(clip, y1, x1, h, w) + + return cropped + + +class RandomRotation(object): + """Rotate entire clip randomly by a random angle within + given bounds + Args: + degrees (sequence or int): Range of degrees to select from + If degrees is a number instead of sequence like (min, max), + the range of degrees, will be (-degrees, +degrees). + """ + + def __init__(self, degrees): + if isinstance(degrees, numbers.Number): + if degrees < 0: + raise ValueError('If degrees is a single number,' + 'must be positive') + degrees = (-degrees, degrees) + else: + if len(degrees) != 2: + raise ValueError('If degrees is a sequence,' + 'it must be of len 2.') + + self.degrees = degrees + + def __call__(self, clip): + """ + Args: + img (PIL.Image or numpy.ndarray): List of videos to be cropped + in format (h, w, c) in numpy.ndarray + Returns: + PIL.Image or numpy.ndarray: Cropped list of videos + """ + angle = random.uniform(self.degrees[0], self.degrees[1]) + if isinstance(clip[0], np.ndarray): + rotated = [rotate(image=img, angle=angle, preserve_range=True) for img in clip] + elif isinstance(clip[0], PIL.Image.Image): + rotated = [img.rotate(angle) for img in clip] + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + + return rotated + + +class ColorJitter(object): + """Randomly change the brightness, contrast and saturation and hue of the clip + Args: + brightness (float): How much to jitter brightness. brightness_factor + is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]. + contrast (float): How much to jitter contrast. contrast_factor + is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]. + saturation (float): How much to jitter saturation. saturation_factor + is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]. + hue(float): How much to jitter hue. hue_factor is chosen uniformly from + [-hue, hue]. Should be >=0 and <= 0.5. + """ + + def __init__(self, brightness=0, contrast=0, saturation=0, hue=0): + self.brightness = brightness + self.contrast = contrast + self.saturation = saturation + self.hue = hue + + def get_params(self, brightness, contrast, saturation, hue): + if brightness > 0: + brightness_factor = random.uniform( + max(0, 1 - brightness), 1 + brightness) + else: + brightness_factor = None + + if contrast > 0: + contrast_factor = random.uniform( + max(0, 1 - contrast), 1 + contrast) + else: + contrast_factor = None + + if saturation > 0: + saturation_factor = random.uniform( + max(0, 1 - saturation), 1 + saturation) + else: + saturation_factor = None + + if hue > 0: + hue_factor = random.uniform(-hue, hue) + else: + hue_factor = None + return brightness_factor, contrast_factor, saturation_factor, hue_factor + + def __call__(self, clip): + """ + Args: + clip (list): list of PIL.Image + Returns: + list PIL.Image : list of transformed PIL.Image + """ + if isinstance(clip[0], np.ndarray): + brightness, contrast, saturation, hue = self.get_params( + self.brightness, self.contrast, self.saturation, self.hue) + + # Create img transform function sequence + img_transforms = [] + if brightness is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness)) + if saturation is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation)) + if hue is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue)) + if contrast is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast)) + random.shuffle(img_transforms) + img_transforms = [img_as_ubyte, torchvision.transforms.ToPILImage()] + img_transforms + [np.array, + img_as_float] + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + jittered_clip = [] + for img in clip: + jittered_img = img + for func in img_transforms: + jittered_img = func(jittered_img) + jittered_clip.append(jittered_img.astype('float32')) + elif isinstance(clip[0], PIL.Image.Image): + brightness, contrast, saturation, hue = self.get_params( + self.brightness, self.contrast, self.saturation, self.hue) + + # Create img transform function sequence + img_transforms = [] + if brightness is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness)) + if saturation is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation)) + if hue is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue)) + if contrast is not None: + img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast)) + random.shuffle(img_transforms) + + # Apply to all videos + jittered_clip = [] + for img in clip: + for func in img_transforms: + jittered_img = func(img) + jittered_clip.append(jittered_img) + + else: + raise TypeError('Expected numpy.ndarray or PIL.Image' + + 'but got list of {0}'.format(type(clip[0]))) + return jittered_clip + + +class AllAugmentationTransform: + def __init__(self, resize_param=None, rotation_param=None, flip_param=None, crop_param=None, jitter_param=None): + self.transforms = [] + + if flip_param is not None: + self.transforms.append(RandomFlip(**flip_param)) + + if rotation_param is not None: + self.transforms.append(RandomRotation(**rotation_param)) + + if resize_param is not None: + self.transforms.append(RandomResize(**resize_param)) + + if crop_param is not None: + self.transforms.append(RandomCrop(**crop_param)) + + if jitter_param is not None: + self.transforms.append(ColorJitter(**jitter_param)) + + def __call__(self, clip): + for t in self.transforms: + clip = t(clip) + return clip diff --git a/config/mix-resolution.yml b/config/mix-resolution.yml new file mode 100644 index 0000000000000000000000000000000000000000..745cd1f66a0b319695b42fc57e5b67dbef14b302 --- /dev/null +++ b/config/mix-resolution.yml @@ -0,0 +1,89 @@ +dataset_params: + root_dir: ../../../train/cropped_clips_512_vid/ + frame_shape: [512, 512, 3] + id_sampling: True + pairs_list: None + augmentation_params: + flip_param: + horizontal_flip: True + time_flip: True + jitter_param: + brightness: 0.1 + contrast: 0.1 + saturation: 0.1 + hue: 0.1 + + +model_params: + common_params: + num_kp: 15 + image_channel: 3 + feature_channel: 32 + estimate_jacobian: False + kp_detector_params: + temperature: 0.1 + block_expansion: 32 + max_features: 1024 + scale_factor: 0.25 + num_blocks: 5 + reshape_channel: 16384 # 16384 = 1024 * 16 + reshape_depth: 16 + he_estimator_params: + block_expansion: 64 + max_features: 2048 + num_bins: 66 + generator_params: + block_expansion: 64 + max_features: 512 + num_down_blocks: 2 + reshape_channel: 32 + reshape_depth: 16 # 512 = 32 * 16 + num_resblocks: 6 + estimate_occlusion_map: True + dense_motion_params: + block_expansion: 32 + max_features: 1024 + num_blocks: 5 + # reshape_channel: 32 + reshape_depth: 16 + compress: 4 + discriminator_params: + scales: [1] + block_expansion: 32 + max_features: 512 + num_blocks: 4 + sn: True + +train_params: + num_epochs: 200 + num_repeats: 5 + num_worker: 8 + epoch_milestones: [16,] + lr_generator: 2.0e-4 + lr_discriminator: 2.0e-4 + lr_kp_detector: 2.0e-4 + lr_he_estimator: 2.0e-4 + gan_mode: 'hinge' # hinge or ls + batch_size: 4 + scales: [1, 0.5, 0.25, 0.125] + checkpoint_freq: 1 + hopenet_snapshot: './checkpoints/hopenet_robust_alpha1.pkl' + transform_params: + sigma_affine: 0.05 + sigma_tps: 0.005 + points_tps: 5 + loss_weights: + generator_gan: 1 + discriminator_gan: 1 + feature_matching: [10, 10, 10, 10] + perceptual: [10, 10, 10, 10, 10] + equivariance_value: 10 + equivariance_jacobian: 0 + keypoint: 10 + headpose: 20 + expression: 5 + +visualizer_params: + kp_size: 5 + draw_border: True + colormap: 'gist_rainbow' diff --git a/crop_portrait.py b/crop_portrait.py new file mode 100644 index 0000000000000000000000000000000000000000..b22683ad44b4411806db9b5daa5c019e7530cc76 --- /dev/null +++ b/crop_portrait.py @@ -0,0 +1,145 @@ +# """ +# Crop upper boddy in every video frame, square bounding box is averaged among all frames and fixed. +# """ + +# import os +# import cv2 +# import argparse +# from tqdm import tqdm +# import face_recognition +# import torch +# import util +# import numpy as np +# import face_detection + +# def crop_per_image(data_dir, dest_size, crop_level): +# fa = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device='cuda') + +# image_list = util.get_file_list(os.path.join(data_dir, 'full')) +# batch_size = 5 +# frames = [] + +# for i in tqdm(range(len(image_list))): +# frame = face_recognition.load_image_file(image_list[i]) +# frames.append(frame) + +# H, W, _ = frames[0].shape + +# batches = [frames[i:i + batch_size] for i in range(0, len(frames), batch_size)] + +# for idx in tqdm(range(len(batches))): +# fb = batches[idx] +# preds = fa.get_detections_for_batch(np.asarray(fb)) + +# for j, f in enumerate(preds): +# if f is None: +# print('no face in image {}'.format(idx * batch_size + j)) +# else: +# left, top, right, bottom = f + + +# height = bottom - top +# width = right - left +# crop_size = int(height * crop_level) + +# horizontal_delta = (crop_size - width) // 2 +# vertical_delta = (crop_size - height) // 2 + +# left = max(left - horizontal_delta, 0) +# right = min(right + horizontal_delta, W) +# top = max(top - int(vertical_delta * 0.5), 0) +# bottom = min(bottom + int(vertical_delta * 1.5), H) + +# crop_f = cv2.imread(image_list[idx * batch_size + j]) +# crop_f = crop_f[top:bottom, left:right] +# crop_f = cv2.resize(crop_f, (dest_size, dest_size), interpolation=cv2.INTER_AREA) +# cv2.imwrite(os.path.join(data_dir, 'crop', os.path.basename(image_list[idx * batch_size + j])), crop_f) + + +# if __name__ == '__main__': +# parser = argparse.ArgumentParser(description='Process some integers.') +# parser.add_argument('--data_dir', type=str, default=None) +# parser.add_argument('--dest_size', type=int, default=256) +# parser.add_argument('--crop_level', type=float, default=1.0, help='Adjust crop image size.') +# parser.add_argument('--vertical_adjust', type=float, default=0.3, help='Adjust vertical location of portrait in image.') +# args = parser.parse_args() +# util.create_dir(os.path.join(args.data_dir,'crop')) +# util.create_dir(os.path.join(args.data_dir, 'crop_region')) +# crop_per_image(args.data_dir, dest_size=args.dest_size, crop_level=args.crop_level) + + +import os +import cv2 +import argparse +from tqdm import tqdm +import face_recognition +import numpy as np +import face_detection +import util + +def crop_per_frame_and_make_video(data_dir, dest_size, crop_level, video_out_path, fps=30): + # Initialize face alignment + fa = face_detection.FaceAlignment(face_detection.LandmarksType._2D, flip_input=False, device='cuda') + + # Get list of images (frames) + image_list = util.get_file_list(os.path.join(data_dir, 'full')) + batch_size = 5 + frames = [] + + # Load frames + for image_path in tqdm(image_list, desc='Loading images'): + frame = cv2.imread(image_path) + frames.append(frame) + + H, W, _ = frames[0].shape + batches = [frames[i:i + batch_size] for i in range(0, len(frames), batch_size)] + cropped_frames = [] + + for idx, fb in enumerate(tqdm(batches, desc='Processing batches')): + preds = fa.get_detections_for_batch(np.asarray(fb)) + + for j, f in enumerate(preds): + if f is None: + print(f'No face in image {idx * batch_size + j}') + continue # Skip frames with no detected face + + left, top, right, bottom = f + height = bottom - top + width = right - left + crop_size = int(height * crop_level) + + horizontal_delta = (crop_size - width) // 2 + vertical_delta = (crop_size - height) // 2 + + left = max(left - horizontal_delta, 0) + right = min(right + horizontal_delta, W) + top = max(top - int(vertical_delta * 0.5), 0) + bottom = min(bottom + int(vertical_delta * 1.5), H) + + crop_f = fb[j][top:bottom, left:right] + crop_f = cv2.resize(crop_f, (dest_size, dest_size), interpolation=cv2.INTER_AREA) + cropped_frames.append(crop_f) + + # Define the codec and create VideoWriter object + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + out = cv2.VideoWriter(video_out_path, fourcc, fps, (dest_size, dest_size)) + + # Write frames to video + for frame in tqdm(cropped_frames, desc='Compiling video'): + out.write(frame) + + # Release everything when job is finished + out.release() + cv2.destroyAllWindows() + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Crop video frames and compile into a video.') + parser.add_argument('--data_dir', type=str, required=True, help='Directory with video frames to process.') + parser.add_argument('--dest_size', type=int, default=256, help='Destination size of cropped images.') + parser.add_argument('--crop_level', type=float, default=1.0, help='Adjust crop size relative to face detection.') + parser.add_argument('--video_out_path', type=str, required=True, help='Output path for the resulting video.') + parser.add_argument('--fps', type=int, default=30, help='Frames per second for the output video.') + args = parser.parse_args() + + util.create_dir(os.path.join(args.data_dir, 'crop')) + crop_per_frame_and_make_video(args.data_dir, dest_size=args.dest_size, crop_level=args.crop_level, video_out_path=args.video_out_path, fps=args.fps) diff --git a/demo.py b/demo.py new file mode 100644 index 0000000000000000000000000000000000000000..c915e76725d0b447704c1eb5370da4ef67c2e724 --- /dev/null +++ b/demo.py @@ -0,0 +1,313 @@ +# python demo.py --config config/vox-256-spade.yml --checkpoint checkpoints/00000189-checkpoint.pth.tar --source_image /home/cxu-serve/p61/rzhu14/lsong11_workspace/Thin-Plate-Spline-Motion-Model/assets/test.png --driving_video /home/cxu-serve/p61/rzhu14/lsong11_workspace/Thin-Plate-Spline-Motion-Model/assets/driving.mp4 --relative --adapt_scale --find_best_frame --gen spade +import matplotlib +matplotlib.use('Agg') +import os, sys +import yaml +from argparse import ArgumentParser +from tqdm import tqdm + +import imageio +import numpy as np +from skimage.transform import resize +from skimage import img_as_ubyte +import torch +import torch.nn.functional as F +from sync_batchnorm import DataParallelWithCallback + +from modules.generator import OcclusionAwareGenerator, OcclusionAwareSPADEGenerator +from modules.keypoint_detector import KPDetector, HEEstimator +from animate import normalize_kp +from scipy.spatial import ConvexHull +import warnings +warnings.filterwarnings("ignore") + + +if sys.version_info[0] < 3: + raise Exception("You must use Python 3 or higher. Recommended version is Python 3.7") + +def load_checkpoints(config_path, checkpoint_path, gen, cpu=False): + + with open(config_path) as f: + config = yaml.load(f) + + if gen == 'original': + generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + elif gen == 'spade': + generator = OcclusionAwareSPADEGenerator(**config['model_params']['generator_params'], + **config['model_params']['common_params']) + + if not cpu: + generator.cuda() + + kp_detector = KPDetector(**config['model_params']['kp_detector_params'], + **config['model_params']['common_params']) + if not cpu: + kp_detector.cuda() + + he_estimator = HEEstimator(**config['model_params']['he_estimator_params'], + **config['model_params']['common_params']) + if not cpu: + he_estimator.cuda() + + if cpu: + checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) + else: + checkpoint = torch.load(checkpoint_path) + + generator.load_state_dict(checkpoint['generator']) + kp_detector.load_state_dict(checkpoint['kp_detector']) + he_estimator.load_state_dict(checkpoint['he_estimator']) + + if not cpu: + generator = DataParallelWithCallback(generator) + kp_detector = DataParallelWithCallback(kp_detector) + he_estimator = DataParallelWithCallback(he_estimator) + + generator.eval() + kp_detector.eval() + he_estimator.eval() + + return generator, kp_detector, he_estimator + + +def headpose_pred_to_degree(pred): + device = pred.device + idx_tensor = [idx for idx in range(66)] + idx_tensor = torch.FloatTensor(idx_tensor).to(device) + pred = F.softmax(pred) + degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 99 + + return degree + +''' +# beta version +def get_rotation_matrix(yaw, pitch, roll): + yaw = yaw / 180 * 3.14 + pitch = pitch / 180 * 3.14 + roll = roll / 180 * 3.14 + + roll = roll.unsqueeze(1) + pitch = pitch.unsqueeze(1) + yaw = yaw.unsqueeze(1) + + roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll), + torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll), + torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1) + roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3) + + pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch), + torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch), + -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1) + pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3) + + yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw), + torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw), + torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1) + yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3) + + rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat) + + return rot_mat + +''' +def get_rotation_matrix(yaw, pitch, roll): + yaw = yaw / 180 * 3.14 + pitch = pitch / 180 * 3.14 + roll = roll / 180 * 3.14 + + roll = roll.unsqueeze(1) + pitch = pitch.unsqueeze(1) + yaw = yaw.unsqueeze(1) + + pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch), + torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch), + torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1) + pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3) + + yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw), + torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw), + -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1) + yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3) + + roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll), + torch.sin(roll), torch.cos(roll), torch.zeros_like(roll), + torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1) + roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3) + + rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat) + + return rot_mat + +def keypoint_transformation(kp_canonical, he, estimate_jacobian=True, free_view=False, yaw=0, pitch=0, roll=0): + kp = kp_canonical['value'] + if not free_view: + yaw, pitch, roll = he['yaw'], he['pitch'], he['roll'] + yaw = headpose_pred_to_degree(yaw) + pitch = headpose_pred_to_degree(pitch) + roll = headpose_pred_to_degree(roll) + else: + if yaw is not None: + yaw = torch.tensor([yaw]).cuda() + else: + yaw = he['yaw'] + yaw = headpose_pred_to_degree(yaw) + if pitch is not None: + pitch = torch.tensor([pitch]).cuda() + else: + pitch = he['pitch'] + pitch = headpose_pred_to_degree(pitch) + if roll is not None: + roll = torch.tensor([roll]).cuda() + else: + roll = he['roll'] + roll = headpose_pred_to_degree(roll) + + t, exp = he['t'], he['exp'] + + rot_mat = get_rotation_matrix(yaw, pitch, roll) + + # keypoint rotation + kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp) + + # keypoint translation + t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1) + kp_t = kp_rotated + t + + # add expression deviation + exp = exp.view(exp.shape[0], -1, 3) + kp_transformed = kp_t + exp + + if estimate_jacobian: + jacobian = kp_canonical['jacobian'] + jacobian_transformed = torch.einsum('bmp,bkps->bkms', rot_mat, jacobian) + else: + jacobian_transformed = None + + return {'value': kp_transformed, 'jacobian': jacobian_transformed} + +def make_animation(source_image, driving_video, generator, kp_detector, he_estimator, relative=True, adapt_movement_scale=True, estimate_jacobian=True, cpu=False, free_view=False, yaw=0, pitch=0, roll=0): + with torch.no_grad(): + predictions = [] + source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) + if not cpu: + source = source.cuda() + driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3) + kp_canonical = kp_detector(source) + he_source = he_estimator(source) + he_driving_initial = he_estimator(driving[:, :, 0]) + + kp_source = keypoint_transformation(kp_canonical, he_source, estimate_jacobian) + kp_driving_initial = keypoint_transformation(kp_canonical, he_driving_initial, estimate_jacobian) + # kp_driving_initial = keypoint_transformation(kp_canonical, he_driving_initial, free_view=free_view, yaw=yaw, pitch=pitch, roll=roll) + + for frame_idx in tqdm(range(driving.shape[2])): + driving_frame = driving[:, :, frame_idx] + if not cpu: + driving_frame = driving_frame.cuda() + he_driving = he_estimator(driving_frame) + kp_driving = keypoint_transformation(kp_canonical, he_driving, estimate_jacobian, free_view=free_view, yaw=yaw, pitch=pitch, roll=roll) + + # np.save('all_kps/%05d.npy'%frame_idx, kp_driving['value'].cpu().detach().numpy()) + # import pdb; pdb.set_trace() + kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving, + kp_driving_initial=kp_driving_initial, use_relative_movement=relative, + use_relative_jacobian=estimate_jacobian, adapt_movement_scale=adapt_movement_scale) + out = generator(source, frame_idx, kp_source=kp_source, kp_driving=kp_norm) + + predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]) + return predictions + +def find_best_frame(source, driving, cpu=False): + import face_alignment + + def normalize_kp(kp): + kp = kp - kp.mean(axis=0, keepdims=True) + area = ConvexHull(kp[:, :2]).volume + area = np.sqrt(area) + kp[:, :2] = kp[:, :2] / area + return kp + + # fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True, + # device='cpu' if cpu else 'cuda') + + fa = face_alignment.FaceAlignment(face_alignment.LandmarksType.TWO_D, flip_input=True, + device='cpu' if cpu else 'cuda') + kp_source = fa.get_landmarks(255 * source)[0] + kp_source = normalize_kp(kp_source) + norm = float('inf') + frame_num = 0 + for i, image in tqdm(enumerate(driving)): + kp_driving = fa.get_landmarks(255 * image)[0] + kp_driving = normalize_kp(kp_driving) + new_norm = (np.abs(kp_source - kp_driving) ** 2).sum() + if new_norm < norm: + norm = new_norm + frame_num = i + return frame_num + +if __name__ == "__main__": + parser = ArgumentParser() + parser.add_argument("--config", default='config/vox-256.yaml', help="path to config") + parser.add_argument("--checkpoint", default='', help="path to checkpoint to restore") + + parser.add_argument("--source_image", default='', help="path to source image") + parser.add_argument("--driving_video", default='', help="path to driving video") + parser.add_argument("--result_video", default='./results_hq.mp4', help="path to output") + + parser.add_argument("--gen", default="spade", choices=["original", "spade"]) + + parser.add_argument("--relative", dest="relative", action="store_true", help="use relative or absolute keypoint coordinates") + parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", help="adapt movement scale based on convex hull of keypoints") + + parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true", + help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)") + + parser.add_argument("--best_frame", dest="best_frame", type=int, default=None, + help="Set frame to start from.") + + parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.") + + parser.add_argument("--free_view", dest="free_view", action="store_true", help="control head pose") + parser.add_argument("--yaw", dest="yaw", type=int, default=None, help="yaw") + parser.add_argument("--pitch", dest="pitch", type=int, default=None, help="pitch") + parser.add_argument("--roll", dest="roll", type=int, default=None, help="roll") + + + parser.set_defaults(relative=False) + parser.set_defaults(adapt_scale=False) + parser.set_defaults(free_view=False) + + opt = parser.parse_args() + + source_image = imageio.imread(opt.source_image) + reader = imageio.get_reader(opt.driving_video) + fps = reader.get_meta_data()['fps'] + driving_video = [] + try: + for im in reader: + driving_video.append(im) + except RuntimeError: + pass + reader.close() + + source_image = resize(source_image, (512, 512))[..., :3] + driving_video = [resize(frame, (512, 512))[..., :3] for frame in driving_video] + generator, kp_detector, he_estimator = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, gen=opt.gen, cpu=opt.cpu) + + with open(opt.config) as f: + config = yaml.load(f) + estimate_jacobian = config['model_params']['common_params']['estimate_jacobian'] + print(f'estimate jacobian: {estimate_jacobian}') + + if opt.find_best_frame or opt.best_frame is not None: + i = opt.best_frame if opt.best_frame is not None else find_best_frame(source_image, driving_video, cpu=opt.cpu) + print ("Best frame: " + str(i)) + driving_forward = driving_video[i:] + driving_backward = driving_video[:(i+1)][::-1] + predictions_forward = make_animation(source_image, driving_forward, generator, kp_detector, he_estimator, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, estimate_jacobian=estimate_jacobian, cpu=opt.cpu, free_view=opt.free_view, yaw=opt.yaw, pitch=opt.pitch, roll=opt.roll) + predictions_backward = make_animation(source_image, driving_backward, generator, kp_detector, he_estimator, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, estimate_jacobian=estimate_jacobian, cpu=opt.cpu, free_view=opt.free_view, yaw=opt.yaw, pitch=opt.pitch, roll=opt.roll) + predictions = predictions_backward[::-1] + predictions_forward[1:] + else: + predictions = make_animation(source_image, driving_video, generator, kp_detector, he_estimator, relative=opt.relative, adapt_movement_scale=opt.adapt_scale, estimate_jacobian=estimate_jacobian, cpu=opt.cpu, free_view=opt.free_view, yaw=opt.yaw, pitch=opt.pitch, roll=opt.roll) + imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps) diff --git a/environment.yaml b/environment.yaml new file mode 100644 index 0000000000000000000000000000000000000000..4004bb3a996c0816223f9a38a243eab78edcb917 --- /dev/null +++ b/environment.yaml @@ -0,0 +1,106 @@ +name: mesh-video +channels: + - pytorch + - conda-forge + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=5.1=1_gnu + - blas=1.0=mkl + - bzip2=1.0.8=h7b6447c_0 + - ca-certificates=2023.01.10=h06a4308_0 + - certifi=2022.12.7=py38h06a4308_0 + - cudatoolkit=11.3.1=h9edb442_10 + - flit-core=3.8.0=py38h06a4308_0 + - freetype=2.12.1=h4a9f257_0 + - giflib=5.2.1=h5eee18b_3 + - gmp=6.2.1=h295c915_3 + - gnutls=3.6.15=he1e5248_0 + - intel-openmp=2021.4.0=h06a4308_3561 + - jpeg=9e=h5eee18b_1 + - lame=3.100=h7b6447c_0 + - lcms2=2.12=h3be6417_0 + - lerc=3.0=h295c915_0 + - libdeflate=1.17=h5eee18b_0 + - libedit=3.1.20221030=h5eee18b_0 + - libffi=3.2.1=hf484d3e_1007 + - libgcc-ng=11.2.0=h1234567_1 + - libgomp=11.2.0=h1234567_1 + - libidn2=2.3.2=h7f8727e_0 + - libopus=1.3.1=h7b6447c_0 + - libpng=1.6.39=h5eee18b_0 + - libstdcxx-ng=11.2.0=h1234567_1 + - libtasn1=4.19.0=h5eee18b_0 + - libtiff=4.5.0=h6a678d5_2 + - libunistring=0.9.10=h27cfd23_0 + - libuv=1.44.2=h5eee18b_0 + - libvpx=1.7.0=h439df22_0 + - libwebp=1.2.4=h11a3e52_1 + - libwebp-base=1.2.4=h5eee18b_1 + - lz4-c=1.9.4=h6a678d5_0 + - mkl=2021.4.0=h06a4308_640 + - mkl-service=2.4.0=py38h7f8727e_0 + - mkl_fft=1.3.1=py38hd3c417c_0 + - mkl_random=1.2.2=py38h51133e4_0 + - ncurses=6.4=h6a678d5_0 + - nettle=3.7.3=hbbd107a_1 + - numpy-base=1.23.5=py38h31eccc5_0 + - openh264=2.1.1=h4ff587b_0 + - openssl=1.1.1t=h7f8727e_0 + - pillow=9.4.0=py38h6a678d5_0 + - pip=23.0.1=py38h06a4308_0 + - python=3.8.0=h0371630_2 + - pytorch=1.10.1=py3.8_cuda11.3_cudnn8.2.0_0 + - pytorch-mutex=1.0=cuda + - readline=7.0=h7b6447c_5 + - setuptools=65.6.3=py38h06a4308_0 + - six=1.16.0=pyhd3eb1b0_1 + - sqlite=3.33.0=h62c20be_0 + - tk=8.6.12=h1ccaba5_0 + - torchaudio=0.10.1=py38_cu113 + - torchvision=0.11.2=py38_cu113 + - typing_extensions=4.4.0=py38h06a4308_0 + - wheel=0.38.4=py38h06a4308_0 + - x264=1!157.20191217=h7b6447c_0 + - xz=5.2.10=h5eee18b_1 + - zlib=1.2.13=h5eee18b_0 + - zstd=1.5.4=hc292b87_0 + - pip: + - cffi==1.14.6 + - cycler==0.10.0 + - decorator==5.1.0 + - face-alignment==1.3.5 + - ffmpeg==1.4 + - imageio==2.9.0 + - imageio-ffmpeg==0.4.5 + - importlib-metadata==6.0.0 + - joblib==1.2.0 + - kiwisolver==1.3.2 + - llvmlite==0.39.1 + - matplotlib==3.4.3 + - networkx==2.6.3 + - numba==0.56.4 + - numpy==1.20.3 + - nvidia-cublas-cu11==11.10.3.66 + - nvidia-cuda-nvrtc-cu11==11.7.99 + - nvidia-cuda-runtime-cu11==11.7.99 + - nvidia-cudnn-cu11==8.5.0.96 + - opencv-python==4.7.0.72 + - pandas==1.3.3 + - pycparser==2.20 + - pyparsing==2.4.7 + - python-dateutil==2.8.2 + - pytube==12.1.3 + - pytz==2021.1 + - pywavelets==1.1.1 + - pyyaml==5.4.1 + - scikit-image==0.18.3 + - scikit-learn==1.0 + - scipy==1.7.1 + - threadpoolctl==3.1.0 + - tifffile==2023.2.28 + - torch==1.13.1 + - tqdm==4.62.3 + - typing-extensions==4.5.0 + - zipp==3.15.0 +prefix: /home/songlc/miniconda3/envs/mesh-video diff --git a/frames_dataset.py b/frames_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0bf0b141b9c8239c077314c5e68ea2546558921c --- /dev/null +++ b/frames_dataset.py @@ -0,0 +1,280 @@ +#CUDA_VISIBLE_DEVICES=1 python run.py --config log_TH1K/finetune-th1k-spade.yml --device_ids 0 --checkpoint log_TH1K/00000001-checkpoint.pth.tar +import os +from skimage import io, img_as_float32 +from skimage.color import gray2rgb +from sklearn.model_selection import train_test_split +from imageio import mimread +from functools import partial +from skimage.transform import resize + + +import torch +import random +import numpy as np +from torch.utils.data import Dataset +import pandas as pd +from augmentation import AllAugmentationTransform +import glob +import math + +import pickle +from basicsr.data.degradations import circular_lowpass_kernel, random_mixed_kernels +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor + + + +def read_video(name, frame_shape): + """ + Read video which can be: + - an image of concatenated frames + - '.mp4' and'.gif' + - folder with videos + """ + + if os.path.isdir(name): + + frames = sorted(os.listdir(name)) + num_frames = len(frames) + video_array = np.array( + [img_as_float32(io.imread(os.path.join(name, frames[idx]))) for idx in range(num_frames)]) + elif name.lower().endswith('.png') or name.lower().endswith('.jpg'): + image = io.imread(name) + + if len(image.shape) == 2 or image.shape[2] == 1: + image = gray2rgb(image) + + if image.shape[2] == 4: + image = image[..., :3] + + image = img_as_float32(image) + + video_array = np.moveaxis(image, 1, 0) + + video_array = video_array.reshape((-1,) + frame_shape) + video_array = np.moveaxis(video_array, 1, 2) + + elif name.lower().endswith('.gif') or name.lower().endswith('.mp4') or name.lower().endswith('.mov'): + video = np.array(mimread(name)) + if len(video.shape) == 3: + video = np.array([gray2rgb(frame) for frame in video]) + if video.shape[-1] == 4: + video = video[..., :3] + video_array = img_as_float32(video) + else: + raise Exception("Unknown file extensions %s" % name) + + return video_array + + +class FramesDataset(Dataset): + """ + Dataset of videos, each video can be represented as: + - an image of concatenated frames + - '.mp4' or '.gif' + - folder with all frames + """ + + def __init__(self, root_dir, frame_shape=(256, 256, 3), id_sampling=False, is_train=True, + random_seed=0, pairs_list=None, augmentation_params=None): + self.root_dir = root_dir + + tmp_file = open(root_dir + 'train_file_list.pickle','rb') + self.train_files_list = pickle.load(tmp_file) + + self.videos = os.listdir(root_dir) + self.frame_shape = tuple(frame_shape) + self.pairs_list = pairs_list + self.id_sampling = id_sampling + if os.path.exists(os.path.join(root_dir, 'train')): + assert os.path.exists(os.path.join(root_dir, 'test')) + print("Use predefined train-test split.") + if id_sampling: + # train_videos = {os.path.basename(video).split('#')[0] for video in + # os.listdir(os.path.join(root_dir, 'train'))} + # train_videos = list(train_videos) + train_videos = list(self.train_files_list.keys()) + else: + train_videos = os.listdir(os.path.join(root_dir, 'train')) + test_videos = os.listdir(os.path.join(root_dir, 'test')) + self.root_dir = os.path.join(self.root_dir, 'train' if is_train else 'test') + else: + print("Use random train-test split.") + train_videos, test_videos = train_test_split(self.videos, random_state=random_seed, test_size=0.2) + + if is_train: + self.videos = train_videos + else: + self.videos = test_videos + + self.is_train = is_train + + if self.is_train: + self.transform = AllAugmentationTransform(**augmentation_params) + + #### for degradation #### + + + self.kernel_range = [2 * v + 1 for v in range(1,3)] + self.pulse_tensor = torch.zeros(11, 11).float() + self.pulse_tensor[5, 5] = 1 + + self.resize_range = [0.15, 1.5] + + # blur settings for the first degradation + self.blur_kernel_size = 7 + self.kernel_list = ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] + self.kernel_prob = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] # a list for each kernel probability + self.blur_sigma = [0.1, 0.5] + self.betag_range = [0.2, 1] # betag used in generalized Gaussian blur kernels + self.betap_range = [0.5, 1.2] # betap used in plateau blur kernels + self.sinc_prob = 0.1 # the probability for sinc filters + + # blur settings for the second degradation + self.blur_kernel_size2 = 7 + self.kernel_list2 = ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso'] + self.kernel_prob2 = [0.45, 0.25, 0.12, 0.03, 0.12, 0.03] + self.blur_sigma2 = [0.1, 0.5] + self.betag_range2 = [0.2, 1] + self.betap_range2 = [1, 1.2] + self.sinc_prob2 = 0.1 + else: + self.transform = None + + def __len__(self): + return len(self.videos) + + def __getitem__(self, idx): + if self.is_train and self.id_sampling: + # name = self.videos[idx] + # path = np.random.choice(glob.glob(os.path.join(self.root_dir, name + '*.mp4'))) + name = self.videos[idx] + choice_list = self.train_files_list[name] + # if len(choice_list) == 0: + # name = self.videos[idx-1] + # choice_list = self.train_files_list[name] + paths = np.random.choice(choice_list) + else: + name = self.videos[idx] + paths = os.path.join(self.root_dir, name) + + video_name = os.path.basename(paths) + if self.is_train and os.path.isdir(paths): + frames = os.listdir(paths) + num_frames = len(frames) + frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2)) + + + if self.frame_shape is not None: + resize_fn = partial(resize, output_shape=self.frame_shape) + else: + resize_fn = img_as_float32 + video_array = [resize_fn(img_as_float32(io.imread(paths + '/' + '%06d.jpg'%(idx) ))) for idx in frame_idx] + + + else: + video_array = read_video(paths, frame_shape=self.frame_shape) + num_frames = len(video_array) + frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2)) if self.is_train else range( + num_frames) + video_array = video_array[frame_idx] + + if self.transform is not None: + video_array = self.transform(video_array) + + out = {} + if self.is_train: + source = np.array(video_array[0], dtype='float32') + driving = np.array(video_array[1], dtype='float32') + out['driving'] = driving.transpose((2, 0, 1)) + out['source'] = source.transpose((2, 0, 1)) + + # if self.degradation: + ############ run degradation ############ + # ---- Generate kernels (used in the first degradation) ---- # + kernel_size = random.choice(self.kernel_range) + if np.random.uniform() < 0.1: + # this sinc filter setting is for kernels ranging from [7, 21] + if kernel_size < 11: + omega_c = np.random.uniform(np.pi / 3, np.pi) + else: + omega_c = np.random.uniform(np.pi / 5, np.pi) + kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) + else: + kernel = random_mixed_kernels( + self.kernel_list, + self.kernel_prob, + kernel_size, + self.blur_sigma, + self.blur_sigma, [-math.pi, math.pi], + self.betag_range, + self.betap_range, + noise_range=None) + # pad kernel + pad_size = (21 - kernel_size) // 2 + kernel = np.pad(kernel, ((pad_size, pad_size), (pad_size, pad_size))) + + + # ----- Generate kernels (used in the second degradation) ---- # + kernel_size = random.choice(self.kernel_range) + if np.random.uniform() < 0.1: + if kernel_size < 13: + omega_c = np.random.uniform(np.pi / 3, np.pi) + else: + omega_c = np.random.uniform(np.pi / 5, np.pi) + kernel2 = circular_lowpass_kernel(omega_c, kernel_size, pad_to=False) + else: + kernel2 = random_mixed_kernels( + self.kernel_list2, + self.kernel_prob2, + kernel_size, + self.blur_sigma2, + self.blur_sigma2, [-math.pi, math.pi], + self.betag_range2, + self.betap_range2, + noise_range=None) + # pad kernel + pad_size = (21 - kernel_size) // 2 + kernel2 = np.pad(kernel2, ((pad_size, pad_size), (pad_size, pad_size))) + + # ---- the final sinc kernel ---- # + if np.random.uniform() < 0.8: + kernel_size = random.choice(self.kernel_range) + omega_c = np.random.uniform(np.pi / 3, np.pi) + sinc_kernel = circular_lowpass_kernel(omega_c, kernel_size, pad_to=11) + sinc_kernel = torch.FloatTensor(sinc_kernel) + else: + sinc_kernel = self.pulse_tensor + + # BGR to RGB, HWC to CHW, numpy to tensor + # img_gt = img2tensor([img_gt], bgr2rgb=True, float32=True)[0] + kernel = torch.FloatTensor(kernel) + kernel2 = torch.FloatTensor(kernel2) + ######################################### + + out['kernel'] = kernel + out['kernel2']= kernel2 + out['sinc_kernel'] = sinc_kernel + + else: + video = np.array(video_array, dtype='float32') + out['video'] = video.transpose((3, 0, 1, 2)) + + out['name'] = video_name + + return out + + +class DatasetRepeater(Dataset): + """ + Pass several times over the same dataset for better i/o performance + """ + + def __init__(self, dataset, num_repeats=100): + self.dataset = dataset + self.num_repeats = num_repeats + + def __len__(self): + return self.num_repeats * self.dataset.__len__() + + def __getitem__(self, idx): + return self.dataset[idx % self.dataset.__len__()] diff --git a/logger.py b/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..87b49898dc37e4b31f53735594e4b14330c1bc23 --- /dev/null +++ b/logger.py @@ -0,0 +1,194 @@ +import numpy as np +import torch +import torch.nn.functional as F +import imageio + +import os +from skimage.draw import circle + +import matplotlib.pyplot as plt +import collections + + +class Logger: + def __init__(self, log_dir, checkpoint_freq=100, visualizer_params=None, zfill_num=8, log_file_name='log.txt'): + + self.loss_list = [] + self.cpk_dir = log_dir + self.visualizations_dir = os.path.join(log_dir, 'train-vis') + if not os.path.exists(self.visualizations_dir): + os.makedirs(self.visualizations_dir) + self.log_file = open(os.path.join(log_dir, log_file_name), 'a') + self.zfill_num = zfill_num + self.visualizer = Visualizer(**visualizer_params) + self.checkpoint_freq = checkpoint_freq + self.epoch = 0 + self.best_loss = float('inf') + self.names = None + + def log_scores(self, loss_names): + loss_mean = np.array(self.loss_list).mean(axis=0) + + loss_string = "; ".join(["%s - %.5f" % (name, value) for name, value in zip(loss_names, loss_mean)]) + loss_string = str(self.epoch).zfill(self.zfill_num) + ") " + loss_string + + print(loss_string, file=self.log_file) + self.loss_list = [] + self.log_file.flush() + + def visualize_rec(self, inp, out): + image = self.visualizer.visualize(inp['driving'], inp['source'], out) + imageio.imsave(os.path.join(self.visualizations_dir, "%s-rec.png" % str(self.epoch).zfill(self.zfill_num)), image) + + def save_cpk(self, emergent=False): + cpk = {k: v.state_dict() for k, v in self.models.items()} + cpk['epoch'] = self.epoch + cpk_path = os.path.join(self.cpk_dir, '%s-checkpoint.pth.tar' % str(self.epoch + 1).zfill(self.zfill_num)) + if not (os.path.exists(cpk_path) and emergent): + torch.save(cpk, cpk_path) + + @staticmethod + def load_cpk(checkpoint_path, generator=None, discriminator=None, kp_detector=None, he_estimator=None, + optimizer_generator=None, optimizer_discriminator=None, optimizer_kp_detector=None, optimizer_he_estimator=None): + checkpoint = torch.load(checkpoint_path) + if generator is not None: + generator.load_state_dict(checkpoint['generator']) + if kp_detector is not None: + kp_detector.load_state_dict(checkpoint['kp_detector']) + if he_estimator is not None: + he_estimator.load_state_dict(checkpoint['he_estimator']) + if discriminator is not None: + try: + discriminator.load_state_dict(checkpoint['discriminator']) + except: + print ('No discriminator in the state-dict. Dicriminator will be randomly initialized') + if optimizer_generator is not None: + optimizer_generator.load_state_dict(checkpoint['optimizer_generator']) + if optimizer_discriminator is not None: + try: + optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator']) + except RuntimeError as e: + print ('No discriminator optimizer in the state-dict. Optimizer will be not initialized') + if optimizer_kp_detector is not None: + optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector']) + if optimizer_he_estimator is not None: + optimizer_he_estimator.load_state_dict(checkpoint['optimizer_he_estimator']) + + return checkpoint['epoch'] + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + if 'models' in self.__dict__: + self.save_cpk() + self.log_file.close() + + def log_iter(self, losses): + losses = collections.OrderedDict(losses.items()) + if self.names is None: + self.names = list(losses.keys()) + self.loss_list.append(list(losses.values())) + + def log_epoch(self, epoch, models, inp, out): + self.epoch = epoch + self.models = models + if (self.epoch + 1) % self.checkpoint_freq == 0: + self.save_cpk() + self.log_scores(self.names) + self.visualize_rec(inp, out) + + +class Visualizer: + def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbow'): + self.kp_size = kp_size + self.draw_border = draw_border + self.colormap = plt.get_cmap(colormap) + + def draw_image_with_kp(self, image, kp_array): + image = np.copy(image) + spatial_size = np.array(image.shape[:2][::-1])[np.newaxis] + kp_array = spatial_size * (kp_array + 1) / 2 + num_kp = kp_array.shape[0] + for kp_ind, kp in enumerate(kp_array): + rr, cc = circle(kp[1], kp[0], self.kp_size, shape=image.shape[:2]) + image[rr, cc] = np.array(self.colormap(kp_ind / num_kp))[:3] + return image + + def create_image_column_with_kp(self, images, kp): + image_array = np.array([self.draw_image_with_kp(v, k) for v, k in zip(images, kp)]) + return self.create_image_column(image_array) + + def create_image_column(self, images): + if self.draw_border: + images = np.copy(images) + images[:, :, [0, -1]] = (1, 1, 1) + images[:, :, [0, -1]] = (1, 1, 1) + return np.concatenate(list(images), axis=0) + + def create_image_grid(self, *args): + out = [] + for arg in args: + if type(arg) == tuple: + out.append(self.create_image_column_with_kp(arg[0], arg[1])) + else: + out.append(self.create_image_column(arg)) + return np.concatenate(out, axis=1) + + def visualize(self, driving, source, out): + images = [] + + # Source image with keypoints + source = source.data.cpu() + kp_source = out['kp_source']['value'][:, :, :2].data.cpu().numpy() # 3d -> 2d + source = np.transpose(source, [0, 2, 3, 1]) + images.append((source, kp_source)) + + # Equivariance visualization + if 'transformed_frame' in out: + transformed = out['transformed_frame'].data.cpu().numpy() + transformed = np.transpose(transformed, [0, 2, 3, 1]) + transformed_kp = out['transformed_kp']['value'][:, :, :2].data.cpu().numpy() # 3d -> 2d + images.append((transformed, transformed_kp)) + + # Driving image with keypoints + kp_driving = out['kp_driving']['value'][:, :, :2].data.cpu().numpy() # 3d -> 2d + driving = driving.data.cpu().numpy() + driving = np.transpose(driving, [0, 2, 3, 1]) + images.append((driving, kp_driving)) + + # Result + prediction = out['prediction'].data.cpu().numpy() + prediction = np.transpose(prediction, [0, 2, 3, 1]) + images.append(prediction) + + ## Occlusion map + if 'occlusion_map' in out: + occlusion_map = out['occlusion_map'].data.cpu().repeat(1, 3, 1, 1) + occlusion_map = F.interpolate(occlusion_map, size=source.shape[1:3]).numpy() + occlusion_map = np.transpose(occlusion_map, [0, 2, 3, 1]) + images.append(occlusion_map) + + ## Mask + if 'mask' in out: + for i in range(out['mask'].shape[1]): + mask = out['mask'][:, i:(i+1)].data.cpu().sum(2).repeat(1, 3, 1, 1) # (n, 3, h, w) + # mask = F.softmax(mask.view(mask.shape[0], mask.shape[1], -1), dim=2).view(mask.shape) + mask = F.interpolate(mask, size=source.shape[1:3]).numpy() + mask = np.transpose(mask, [0, 2, 3, 1]) + + if i != 0: + color = np.array(self.colormap((i - 1) / (out['mask'].shape[1] - 1)))[:3] + else: + color = np.array((0, 0, 0)) + + color = color.reshape((1, 1, 1, 3)) + + if i != 0: + images.append(mask * color) + 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0000000000000000000000000000000000000000..9bc9dce8b80d99f769dd5b7b1b79fd0c50a74d0d --- /dev/null +++ b/modules/dense_motion.py @@ -0,0 +1,128 @@ +from torch import nn +import torch.nn.functional as F +import torch +from modules.util import Hourglass, make_coordinate_grid, kp2gaussian + +from sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d + + +class DenseMotionNetwork(nn.Module): + """ + Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving + """ + + def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress, + estimate_occlusion_map=False): + super(DenseMotionNetwork, self).__init__() + # self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(feature_channel+1), max_features=max_features, num_blocks=num_blocks) + self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks) + + self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3) + + self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1) + self.norm = BatchNorm3d(compress, affine=True) + + if estimate_occlusion_map: + # self.occlusion = nn.Conv2d(reshape_channel*reshape_depth, 1, kernel_size=7, padding=3) + self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3) + else: + self.occlusion = None + + self.num_kp = num_kp + + + def create_sparse_motions(self, feature, kp_driving, kp_source): + bs, _, d, h, w = feature.shape + identity_grid = make_coordinate_grid((d, h, w), type=kp_source['value'].type()) + identity_grid = identity_grid.view(1, 1, d, h, w, 3) + coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 1, 3) + + k = coordinate_grid.shape[1] + + # if 'jacobian' in kp_driving: + if 'jacobian' in kp_driving and kp_driving['jacobian'] is not None: + jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian'])) + jacobian = jacobian.unsqueeze(-3).unsqueeze(-3).unsqueeze(-3) + jacobian = jacobian.repeat(1, 1, d, h, w, 1, 1) + coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1)) + coordinate_grid = coordinate_grid.squeeze(-1) + ''' + if 'rot' in kp_driving: + rot_s = kp_source['rot'] + rot_d = kp_driving['rot'] + rot = torch.einsum('bij, bjk->bki', rot_s, torch.inverse(rot_d)) + rot = rot.unsqueeze(-3).unsqueeze(-3).unsqueeze(-3).unsqueeze(-3) + rot = rot.repeat(1, k, d, h, w, 1, 1) + # print(rot.shape) + coordinate_grid = torch.matmul(rot, coordinate_grid.unsqueeze(-1)) + coordinate_grid = coordinate_grid.squeeze(-1) + # print(coordinate_grid.shape) + ''' + driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 1, 3) # (bs, num_kp, d, h, w, 3) + + #adding background feature + identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1, 1) + sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1) + + # sparse_motions = driving_to_source + + return sparse_motions + + def create_deformed_feature(self, feature, sparse_motions): + bs, _, d, h, w = feature.shape + feature_repeat = feature.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp+1, 1, 1, 1, 1, 1) # (bs, num_kp+1, 1, c, d, h, w) + feature_repeat = feature_repeat.view(bs * (self.num_kp+1), -1, d, h, w) # (bs*(num_kp+1), c, d, h, w) + sparse_motions = sparse_motions.view((bs * (self.num_kp+1), d, h, w, -1)) # (bs*(num_kp+1), d, h, w, 3) + sparse_deformed = F.grid_sample(feature_repeat, sparse_motions) + sparse_deformed = sparse_deformed.view((bs, self.num_kp+1, -1, d, h, w)) # (bs, num_kp+1, c, d, h, w) + return sparse_deformed + + def create_heatmap_representations(self, feature, kp_driving, kp_source): + spatial_size = feature.shape[3:] + gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01) + gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01) + heatmap = gaussian_driving - gaussian_source + + # adding background feature + zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.type()) + heatmap = torch.cat([zeros, heatmap], dim=1) + heatmap = heatmap.unsqueeze(2) # (bs, num_kp+1, 1, d, h, w) + return heatmap + + def forward(self, feature, kp_driving, kp_source): + bs, _, d, h, w = feature.shape + + feature = self.compress(feature) + feature = self.norm(feature) + feature = F.relu(feature) + + out_dict = dict() + sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source) + deformed_feature = self.create_deformed_feature(feature, sparse_motion) + + heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source) + + input = torch.cat([heatmap, deformed_feature], dim=2) + input = input.view(bs, -1, d, h, w) + + # input = deformed_feature.view(bs, -1, d, h, w) # (bs, num_kp+1 * c, d, h, w) + + prediction = self.hourglass(input) + + mask = self.mask(prediction) + mask = F.softmax(mask, dim=1) + out_dict['mask'] = mask + mask = mask.unsqueeze(2) # (bs, num_kp+1, 1, d, h, w) + sparse_motion = sparse_motion.permute(0, 1, 5, 2, 3, 4) # (bs, num_kp+1, 3, d, h, w) + deformation = (sparse_motion * mask).sum(dim=1) # (bs, 3, d, h, w) + deformation = deformation.permute(0, 2, 3, 4, 1) # (bs, d, h, w, 3) + + out_dict['deformation'] = deformation + + if self.occlusion: + bs, c, d, h, w = prediction.shape + prediction = prediction.view(bs, -1, h, w) + occlusion_map = torch.sigmoid(self.occlusion(prediction)) + out_dict['occlusion_map'] = occlusion_map + + return out_dict diff --git a/modules/discriminator.py b/modules/discriminator.py new file mode 100644 index 0000000000000000000000000000000000000000..3ceed5287516a5c3e6daf2482cdbe1c5ef8ea757 --- /dev/null +++ b/modules/discriminator.py @@ -0,0 +1,90 @@ +from torch import nn +import torch.nn.functional as F +from modules.util import kp2gaussian +import torch + + +class DownBlock2d(nn.Module): + """ + Simple block for processing video (encoder). + """ + + def __init__(self, in_features, out_features, norm=False, kernel_size=4, pool=False, sn=False): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size) + + if sn: + self.conv = nn.utils.spectral_norm(self.conv) + + if norm: + self.norm = nn.InstanceNorm2d(out_features, affine=True) + else: + self.norm = None + self.pool = pool + + def forward(self, x): + out = x + out = self.conv(out) + if self.norm: + out = self.norm(out) + out = F.leaky_relu(out, 0.2) + if self.pool: + out = F.avg_pool2d(out, (2, 2)) + return out + + +class Discriminator(nn.Module): + """ + Discriminator similar to Pix2Pix + """ + + def __init__(self, num_channels=3, block_expansion=64, num_blocks=4, max_features=512, + sn=False, **kwargs): + super(Discriminator, self).__init__() + + down_blocks = [] + for i in range(num_blocks): + down_blocks.append( + DownBlock2d(num_channels if i == 0 else min(max_features, block_expansion * (2 ** i)), + min(max_features, block_expansion * (2 ** (i + 1))), + norm=(i != 0), kernel_size=4, pool=(i != num_blocks - 1), sn=sn)) + + self.down_blocks = nn.ModuleList(down_blocks) + self.conv = nn.Conv2d(self.down_blocks[-1].conv.out_channels, out_channels=1, kernel_size=1) + if sn: + self.conv = nn.utils.spectral_norm(self.conv) + + def forward(self, x): + feature_maps = [] + out = x + + for down_block in self.down_blocks: + feature_maps.append(down_block(out)) + out = feature_maps[-1] + prediction_map = self.conv(out) + + return feature_maps, prediction_map + + +class MultiScaleDiscriminator(nn.Module): + """ + Multi-scale (scale) discriminator + """ + + def __init__(self, scales=(), **kwargs): + super(MultiScaleDiscriminator, self).__init__() + self.scales = scales + discs = {} + for scale in scales: + discs[str(scale).replace('.', '-')] = Discriminator(**kwargs) + self.discs = nn.ModuleDict(discs) + + def forward(self, x): + out_dict = {} + for scale, disc in self.discs.items(): + scale = str(scale).replace('-', '.') + key = 'prediction_' + scale + feature_maps, prediction_map = disc(x[key]) + out_dict['feature_maps_' + scale] = feature_maps + out_dict['prediction_map_' + scale] = prediction_map + return out_dict diff --git a/modules/generator.py b/modules/generator.py new file mode 100644 index 0000000000000000000000000000000000000000..efac74e85302fbb1c913c791461ea4b0595265fb --- /dev/null +++ b/modules/generator.py @@ -0,0 +1,277 @@ +import torch +from torch import nn +import torch.nn.functional as F +from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d, ResBlock3d, SPADEResnetBlock +from modules.dense_motion import DenseMotionNetwork + +import torchvision + + +class OcclusionAwareGenerator(nn.Module): + """ + Generator follows NVIDIA architecture. + """ + + def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth, + num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): + super(OcclusionAwareGenerator, self).__init__() + + if dense_motion_params is not None: + self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel, + estimate_occlusion_map=estimate_occlusion_map, + **dense_motion_params) + else: + self.dense_motion_network = None + + self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(7, 7), padding=(3, 3)) + + down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.down_blocks = nn.ModuleList(down_blocks) + + self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1) + + self.reshape_channel = reshape_channel + self.reshape_depth = reshape_depth + + self.resblocks_3d = torch.nn.Sequential() + for i in range(num_resblocks): + self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1)) + + out_features = block_expansion * (2 ** (num_down_blocks)) + self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True) + self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1) + + self.resblocks_2d = torch.nn.Sequential() + for i in range(num_resblocks): + self.resblocks_2d.add_module('2dr' + str(i), ResBlock2d(out_features, kernel_size=3, padding=1)) + + up_blocks = [] + for i in range(num_down_blocks): + in_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i))) + out_features = max(block_expansion, block_expansion * (2 ** (num_down_blocks - i - 1))) + up_blocks.append(UpBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.up_blocks = nn.ModuleList(up_blocks) + + self.final = nn.Conv2d(block_expansion, image_channel, kernel_size=(7, 7), padding=(3, 3)) + self.estimate_occlusion_map = estimate_occlusion_map + self.image_channel = image_channel + + def deform_input(self, inp, deformation): + _, d_old, h_old, w_old, _ = deformation.shape + _, _, d, h, w = inp.shape + if d_old != d or h_old != h or w_old != w: + deformation = deformation.permute(0, 4, 1, 2, 3) + deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear') + deformation = deformation.permute(0, 2, 3, 4, 1) + return F.grid_sample(inp, deformation) + + def forward(self, source_image, kp_driving, kp_source): + # Encoding (downsampling) part + out = self.first(source_image) + for i in range(len(self.down_blocks)): + out = self.down_blocks[i](out) + out = self.second(out) + bs, c, h, w = out.shape + # print(out.shape) + feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w) + feature_3d = self.resblocks_3d(feature_3d) + + + + # Transforming feature representation according to deformation and occlusion + output_dict = {} + if self.dense_motion_network is not None: + dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving, + kp_source=kp_source) + output_dict['mask'] = dense_motion['mask'] + + if 'occlusion_map' in dense_motion: + occlusion_map = dense_motion['occlusion_map'] + output_dict['occlusion_map'] = occlusion_map + else: + occlusion_map = None + deformation = dense_motion['deformation'] + out = self.deform_input(feature_3d, deformation) + + bs, c, d, h, w = out.shape + out = out.view(bs, c*d, h, w) + out = self.third(out) + out = self.fourth(out) + + if occlusion_map is not None: + if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]: + occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear') + out = out * occlusion_map + + # output_dict["deformed"] = self.deform_input(source_image, deformation) # 3d deformation cannot deform 2d image + + # Decoding part + out = self.resblocks_2d(out) + for i in range(len(self.up_blocks)): + out = self.up_blocks[i](out) + out = self.final(out) + out = F.sigmoid(out) + + output_dict["prediction"] = out + + return output_dict + + +class SPADEDecoder(nn.Module): + def __init__(self): + super().__init__() + ic = 256 + oc = 64 + norm_G = 'spadespectralinstance' + label_nc = 256 + + self.fc = nn.Conv2d(ic, 2 * ic, 3, padding=1) + self.G_middle_0 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.G_middle_1 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.G_middle_2 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.G_middle_3 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.G_middle_4 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.G_middle_5 = SPADEResnetBlock(2 * ic, 2 * ic, norm_G, label_nc) + self.up_0 = SPADEResnetBlock(2 * ic, ic, norm_G, label_nc) + self.up_1 = SPADEResnetBlock(ic, oc, norm_G, label_nc) + self.conv_img = nn.Conv2d(oc, 3, 3, padding=1) + self.up = nn.Upsample(scale_factor=2) + + def forward(self, feature): + seg = feature + x = self.fc(feature) + x = self.G_middle_0(x, seg) + x = self.G_middle_1(x, seg) + x = self.G_middle_2(x, seg) + x = self.G_middle_3(x, seg) + x = self.G_middle_4(x, seg) + x = self.G_middle_5(x, seg) + x = self.up(x) + x = self.up_0(x, seg) # 256, 128, 128 + x = self.up(x) + x = self.up_1(x, seg) # 64, 256, 256 + + x = self.conv_img(F.leaky_relu(x, 2e-1)) + # x = torch.tanh(x) + x = F.sigmoid(x) + + return x + + +class OcclusionAwareSPADEGenerator(nn.Module): + + def __init__(self, image_channel, feature_channel, num_kp, block_expansion, max_features, num_down_blocks, reshape_channel, reshape_depth, + num_resblocks, estimate_occlusion_map=False, dense_motion_params=None, estimate_jacobian=False): + super(OcclusionAwareSPADEGenerator, self).__init__() + + if dense_motion_params is not None: + self.dense_motion_network = DenseMotionNetwork(num_kp=num_kp, feature_channel=feature_channel, + estimate_occlusion_map=estimate_occlusion_map, + **dense_motion_params) + else: + self.dense_motion_network = None + + self.first = SameBlock2d(image_channel, block_expansion, kernel_size=(3, 3), padding=(1, 1)) + + down_blocks = [] + for i in range(num_down_blocks): + in_features = min(max_features, block_expansion * (2 ** i)) + out_features = min(max_features, block_expansion * (2 ** (i + 1))) + down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1))) + self.down_blocks = nn.ModuleList(down_blocks) + + self.second = nn.Conv2d(in_channels=out_features, out_channels=max_features, kernel_size=1, stride=1) + + self.reshape_channel = reshape_channel + self.reshape_depth = reshape_depth + + self.resblocks_3d = torch.nn.Sequential() + for i in range(num_resblocks): + self.resblocks_3d.add_module('3dr' + str(i), ResBlock3d(reshape_channel, kernel_size=3, padding=1)) + + out_features = block_expansion * (2 ** (num_down_blocks)) + self.third = SameBlock2d(max_features, out_features, kernel_size=(3, 3), padding=(1, 1), lrelu=True) + self.fourth = nn.Conv2d(in_channels=out_features, out_channels=out_features, kernel_size=1, stride=1) + + self.estimate_occlusion_map = estimate_occlusion_map + self.image_channel = image_channel + + self.decoder = SPADEDecoder() + + def deform_input(self, inp, deformation): + _, d_old, h_old, w_old, _ = deformation.shape + _, _, d, h, w = inp.shape + if d_old != d or h_old != h or w_old != w: + deformation = deformation.permute(0, 4, 1, 2, 3) + deformation = F.interpolate(deformation, size=(d, h, w), mode='trilinear') + deformation = deformation.permute(0, 2, 3, 4, 1) + return F.grid_sample(inp, deformation) + + def forward(self, source_image, frame_idx, kp_driving, kp_source): + # Encoding (downsampling) part + + # import pdb; pdb.set_trace() + # import torchvision.utils as vutils, torchvision.utils.save_image(feature_3d[0][1:2,:3,], 'feature.png') + + out = self.first(source_image) + # torchvision.utils.save_image(out[0][:1,], 'ablation_features/feature_1_%05d.png'%frame_idx) + + for i in range(len(self.down_blocks)): + out = self.down_blocks[i](out) + + # torchvision.utils.save_image(out[0][:1,], 'ablation_features/feature_2_%05d.png'%frame_idx) + + out = self.second(out) + # torchvision.utils.save_image(out[0][:1,], 'ablation_features/feature_3_%05d.png'%frame_idx) + bs, c, h, w = out.shape + # print(out.shape) + feature_3d = out.view(bs, self.reshape_channel, self.reshape_depth, h ,w) + feature_3d = self.resblocks_3d(feature_3d) + # torchvision.utils.save_image(feature_3d[0][1:2,:1,], 'ablation_features/feature_4_%05d.png'%frame_idx) + + + + + + # Transforming feature representation according to deformation and occlusion + output_dict = {} + if self.dense_motion_network is not None: + dense_motion = self.dense_motion_network(feature=feature_3d, kp_driving=kp_driving, + kp_source=kp_source) + output_dict['mask'] = dense_motion['mask'] + + if 'occlusion_map' in dense_motion: + occlusion_map = dense_motion['occlusion_map'] + output_dict['occlusion_map'] = occlusion_map + + else: + occlusion_map = None + deformation = dense_motion['deformation'] + out = self.deform_input(feature_3d, deformation) + + # torchvision.utils.save_image(out[0][1:2,:1,], 'ablation_features/feature_5_%05d.png'%frame_idx) + + bs, c, d, h, w = out.shape + out = out.view(bs, c*d, h, w) + out = self.third(out) + # torchvision.utils.save_image(out[:1,:1,], 'ablation_features/feature_6_%05d.png'%frame_idx) + out = self.fourth(out) + # torchvision.utils.save_image(out[:1,:1,], 'ablation_features/feature_7_%05d.png'%frame_idx) + + if occlusion_map is not None: + if out.shape[2] != occlusion_map.shape[2] or out.shape[3] != occlusion_map.shape[3]: + occlusion_map = F.interpolate(occlusion_map, size=out.shape[2:], mode='bilinear') + out = out * occlusion_map + + # Decoding part + # torchvision.utils.save_image(out[:1,:1,], 'ablation_features/feature_8_%05d.png'%frame_idx) + out = self.decoder(out) + # torchvision.utils.save_image(out[:1,:1,], 'ablation_features/feature_9_%05d.png'%frame_idx) + output_dict["prediction"] = out + # + + return output_dict \ No newline at end of file diff --git a/modules/hopenet.py b/modules/hopenet.py new file mode 100644 index 0000000000000000000000000000000000000000..c9e0b74ca67ddf19664e62b811050821cddd84fe --- /dev/null +++ b/modules/hopenet.py @@ -0,0 +1,171 @@ +import torch +import torch.nn as nn +from torch.autograd import Variable +import math +import torch.nn.functional as F + +class Hopenet(nn.Module): + # Hopenet with 3 output layers for yaw, pitch and roll + # Predicts Euler angles by binning and regression with the expected value + def __init__(self, block, layers, num_bins): + self.inplanes = 64 + super(Hopenet, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc_yaw = nn.Linear(512 * block.expansion, num_bins) + self.fc_pitch = nn.Linear(512 * block.expansion, num_bins) + self.fc_roll = nn.Linear(512 * block.expansion, num_bins) + + # Vestigial layer from previous experiments + self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + pre_yaw = self.fc_yaw(x) + pre_pitch = self.fc_pitch(x) + pre_roll = self.fc_roll(x) + + return pre_yaw, pre_pitch, pre_roll + +class ResNet(nn.Module): + # ResNet for regression of 3 Euler angles. + def __init__(self, block, layers, num_classes=1000): + self.inplanes = 64 + super(ResNet, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc_angles = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc_angles(x) + return x + +class AlexNet(nn.Module): + # AlexNet laid out as a Hopenet - classify Euler angles in bins and + # regress the expected value. + def __init__(self, num_bins): + super(AlexNet, self).__init__() + self.features = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(64, 192, kernel_size=5, padding=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + nn.Conv2d(192, 384, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(384, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2), + ) + self.classifier = nn.Sequential( + nn.Dropout(), + nn.Linear(256 * 6 * 6, 4096), + nn.ReLU(inplace=True), + nn.Dropout(), + nn.Linear(4096, 4096), + nn.ReLU(inplace=True), + ) + self.fc_yaw = nn.Linear(4096, num_bins) + self.fc_pitch = nn.Linear(4096, num_bins) + self.fc_roll = nn.Linear(4096, num_bins) + + def forward(self, x): + x = self.features(x) + x = x.view(x.size(0), 256 * 6 * 6) + x = self.classifier(x) + yaw = self.fc_yaw(x) + pitch = self.fc_pitch(x) + roll = self.fc_roll(x) + return yaw, pitch, roll diff --git a/modules/keypoint_detector.py b/modules/keypoint_detector.py new file mode 100644 index 0000000000000000000000000000000000000000..7f7b0e804ee1a7c64fc501e59b30f35f099a9020 --- /dev/null +++ b/modules/keypoint_detector.py @@ -0,0 +1,178 @@ +from torch import nn +import torch +import torch.nn.functional as F + +from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d +from modules.util import KPHourglass, make_coordinate_grid, AntiAliasInterpolation2d, ResBottleneck + + +class KPDetector(nn.Module): + """ + Detecting canonical keypoints. Return keypoint position and jacobian near each keypoint. + """ + + def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, reshape_channel, reshape_depth, + num_blocks, temperature, estimate_jacobian=False, scale_factor=1, single_jacobian_map=False): + super(KPDetector, self).__init__() + + self.predictor = KPHourglass(block_expansion, in_features=image_channel, + max_features=max_features, reshape_features=reshape_channel, reshape_depth=reshape_depth, num_blocks=num_blocks) + + # self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=7, padding=3) + self.kp = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=num_kp, kernel_size=3, padding=1) + + if estimate_jacobian: + self.num_jacobian_maps = 1 if single_jacobian_map else num_kp + # self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=7, padding=3) + self.jacobian = nn.Conv3d(in_channels=self.predictor.out_filters, out_channels=9 * self.num_jacobian_maps, kernel_size=3, padding=1) + ''' + initial as: + [[1 0 0] + [0 1 0] + [0 0 1]] + ''' + self.jacobian.weight.data.zero_() + self.jacobian.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0, 0, 0, 1] * self.num_jacobian_maps, dtype=torch.float)) + else: + self.jacobian = None + + self.temperature = temperature + self.scale_factor = scale_factor + if self.scale_factor != 1: + self.down = AntiAliasInterpolation2d(image_channel, self.scale_factor) + + def gaussian2kp(self, heatmap): + """ + Extract the mean from a heatmap + """ + shape = heatmap.shape + heatmap = heatmap.unsqueeze(-1) + grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) + value = (heatmap * grid).sum(dim=(2, 3, 4)) + kp = {'value': value} + + return kp + + def forward(self, x): + if self.scale_factor != 1: + x = self.down(x) + + feature_map = self.predictor(x) + prediction = self.kp(feature_map) + + final_shape = prediction.shape + heatmap = prediction.view(final_shape[0], final_shape[1], -1) + heatmap = F.softmax(heatmap / self.temperature, dim=2) + heatmap = heatmap.view(*final_shape) + + out = self.gaussian2kp(heatmap) + + if self.jacobian is not None: + jacobian_map = self.jacobian(feature_map) + jacobian_map = jacobian_map.reshape(final_shape[0], self.num_jacobian_maps, 9, final_shape[2], + final_shape[3], final_shape[4]) + heatmap = heatmap.unsqueeze(2) + + jacobian = heatmap * jacobian_map + jacobian = jacobian.view(final_shape[0], final_shape[1], 9, -1) + jacobian = jacobian.sum(dim=-1) + jacobian = jacobian.view(jacobian.shape[0], jacobian.shape[1], 3, 3) + out['jacobian'] = jacobian + + return out + + +class HEEstimator(nn.Module): + """ + Estimating head pose and expression. + """ + + def __init__(self, block_expansion, feature_channel, num_kp, image_channel, max_features, num_bins=66, estimate_jacobian=True): + super(HEEstimator, self).__init__() + + self.conv1 = nn.Conv2d(in_channels=image_channel, out_channels=block_expansion, kernel_size=7, padding=3, stride=2) + self.norm1 = BatchNorm2d(block_expansion, affine=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + + self.conv2 = nn.Conv2d(in_channels=block_expansion, out_channels=256, kernel_size=1) + self.norm2 = BatchNorm2d(256, affine=True) + + self.block1 = nn.Sequential() + for i in range(3): + self.block1.add_module('b1_'+ str(i), ResBottleneck(in_features=256, stride=1)) + + self.conv3 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=1) + self.norm3 = BatchNorm2d(512, affine=True) + self.block2 = ResBottleneck(in_features=512, stride=2) + + self.block3 = nn.Sequential() + for i in range(3): + self.block3.add_module('b3_'+ str(i), ResBottleneck(in_features=512, stride=1)) + + self.conv4 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=1) + self.norm4 = BatchNorm2d(1024, affine=True) + self.block4 = ResBottleneck(in_features=1024, stride=2) + + self.block5 = nn.Sequential() + for i in range(5): + self.block5.add_module('b5_'+ str(i), ResBottleneck(in_features=1024, stride=1)) + + self.conv5 = nn.Conv2d(in_channels=1024, out_channels=2048, kernel_size=1) + self.norm5 = BatchNorm2d(2048, affine=True) + self.block6 = ResBottleneck(in_features=2048, stride=2) + + self.block7 = nn.Sequential() + for i in range(2): + self.block7.add_module('b7_'+ str(i), ResBottleneck(in_features=2048, stride=1)) + + self.fc_roll = nn.Linear(2048, num_bins) + self.fc_pitch = nn.Linear(2048, num_bins) + self.fc_yaw = nn.Linear(2048, num_bins) + + self.fc_t = nn.Linear(2048, 3) + + self.fc_exp = nn.Linear(2048, 3*num_kp) + + def forward(self, x): + out = self.conv1(x) + out = self.norm1(out) + out = F.relu(out) + out = self.maxpool(out) + + out = self.conv2(out) + out = self.norm2(out) + out = F.relu(out) + + out = self.block1(out) + + out = self.conv3(out) + out = self.norm3(out) + out = F.relu(out) + out = self.block2(out) + + out = self.block3(out) + + out = self.conv4(out) + out = self.norm4(out) + out = F.relu(out) + out = self.block4(out) + + out = self.block5(out) + + out = self.conv5(out) + out = self.norm5(out) + out = F.relu(out) + out = self.block6(out) + + out = self.block7(out) + + out = F.adaptive_avg_pool2d(out, 1) + out = out.view(out.shape[0], -1) + + yaw = self.fc_roll(out) + pitch = self.fc_pitch(out) + roll = self.fc_yaw(out) + t = self.fc_t(out) + exp = self.fc_exp(out) + + return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} diff --git a/modules/model.py b/modules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..99c80faa7da34cc4a02cc4cf4ed99e833a84739a --- /dev/null +++ b/modules/model.py @@ -0,0 +1,568 @@ +from torch import nn +import torch +import torch.nn.functional as F +from modules.util import AntiAliasInterpolation2d, make_coordinate_grid_2d +from torchvision import models +import numpy as np +from torch.autograd import grad +import modules.hopenet as hopenet +from torchvision import transforms + +import random +from basicsr.data.degradations import random_add_gaussian_noise_pt, random_add_poisson_noise_pt +from prefetch_generator import BackgroundGenerator +from basicsr.utils.img_process_util import filter2D +from basicsr.utils import DiffJPEG, USMSharp + + +class Vgg19(torch.nn.Module): + """ + Vgg19 network for perceptual loss. + """ + def __init__(self, requires_grad=False): + super(Vgg19, self).__init__() + vgg_pretrained_features = models.vgg19(pretrained=True).features + self.slice1 = torch.nn.Sequential() + self.slice2 = torch.nn.Sequential() + self.slice3 = torch.nn.Sequential() + self.slice4 = torch.nn.Sequential() + self.slice5 = torch.nn.Sequential() + for x in range(2): + self.slice1.add_module(str(x), vgg_pretrained_features[x]) + for x in range(2, 7): + self.slice2.add_module(str(x), vgg_pretrained_features[x]) + for x in range(7, 12): + self.slice3.add_module(str(x), vgg_pretrained_features[x]) + for x in range(12, 21): + self.slice4.add_module(str(x), vgg_pretrained_features[x]) + for x in range(21, 30): + self.slice5.add_module(str(x), vgg_pretrained_features[x]) + + self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))), + requires_grad=False) + self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))), + requires_grad=False) + + if not requires_grad: + for param in self.parameters(): + param.requires_grad = False + + def forward(self, X): + X = (X - self.mean) / self.std + h_relu1 = self.slice1(X) + h_relu2 = self.slice2(h_relu1) + h_relu3 = self.slice3(h_relu2) + h_relu4 = self.slice4(h_relu3) + h_relu5 = self.slice5(h_relu4) + out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] + return out + + +class ImagePyramide(torch.nn.Module): + """ + Create image pyramide for computing pyramide perceptual loss. + """ + def __init__(self, scales, num_channels): + super(ImagePyramide, self).__init__() + downs = {} + for scale in scales: + downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale) + self.downs = nn.ModuleDict(downs) + + def forward(self, x): + out_dict = {} + for scale, down_module in self.downs.items(): + out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x) + return out_dict + + +class Transform: + """ + Random tps transformation for equivariance constraints. + """ + def __init__(self, bs, **kwargs): + noise = torch.normal(mean=0, std=kwargs['sigma_affine'] * torch.ones([bs, 2, 3])) + self.theta = noise + torch.eye(2, 3).view(1, 2, 3) + self.bs = bs + + if ('sigma_tps' in kwargs) and ('points_tps' in kwargs): + self.tps = True + self.control_points = make_coordinate_grid_2d((kwargs['points_tps'], kwargs['points_tps']), type=noise.type()) + self.control_points = self.control_points.unsqueeze(0) + self.control_params = torch.normal(mean=0, + std=kwargs['sigma_tps'] * torch.ones([bs, 1, kwargs['points_tps'] ** 2])) + else: + self.tps = False + + def transform_frame(self, frame): + grid = make_coordinate_grid_2d(frame.shape[2:], type=frame.type()).unsqueeze(0) + grid = grid.view(1, frame.shape[2] * frame.shape[3], 2) + grid = self.warp_coordinates(grid).view(self.bs, frame.shape[2], frame.shape[3], 2) + return F.grid_sample(frame, grid, padding_mode="reflection") + + def warp_coordinates(self, coordinates): + theta = self.theta.type(coordinates.type()) + theta = theta.unsqueeze(1) + transformed = torch.matmul(theta[:, :, :, :2], coordinates.unsqueeze(-1)) + theta[:, :, :, 2:] + transformed = transformed.squeeze(-1) + + if self.tps: + control_points = self.control_points.type(coordinates.type()) + control_params = self.control_params.type(coordinates.type()) + distances = coordinates.view(coordinates.shape[0], -1, 1, 2) - control_points.view(1, 1, -1, 2) + distances = torch.abs(distances).sum(-1) + + result = distances ** 2 + result = result * torch.log(distances + 1e-6) + result = result * control_params + result = result.sum(dim=2).view(self.bs, coordinates.shape[1], 1) + transformed = transformed + result + + return transformed + + def jacobian(self, coordinates): + new_coordinates = self.warp_coordinates(coordinates) + grad_x = grad(new_coordinates[..., 0].sum(), coordinates, create_graph=True) + grad_y = grad(new_coordinates[..., 1].sum(), coordinates, create_graph=True) + jacobian = torch.cat([grad_x[0].unsqueeze(-2), grad_y[0].unsqueeze(-2)], dim=-2) + return jacobian + + +def detach_kp(kp): + return {key: value.detach() for key, value in kp.items()} + + +def headpose_pred_to_degree(pred): + device = pred.device + idx_tensor = [idx for idx in range(66)] + idx_tensor = torch.FloatTensor(idx_tensor).to(device) + pred = F.softmax(pred) + degree = torch.sum(pred*idx_tensor, axis=1) * 3 - 99 + + return degree + +''' +# beta version +def get_rotation_matrix(yaw, pitch, roll): + yaw = yaw / 180 * 3.14 + pitch = pitch / 180 * 3.14 + roll = roll / 180 * 3.14 + + roll = roll.unsqueeze(1) + pitch = pitch.unsqueeze(1) + yaw = yaw.unsqueeze(1) + + roll_mat = torch.cat([torch.ones_like(roll), torch.zeros_like(roll), torch.zeros_like(roll), + torch.zeros_like(roll), torch.cos(roll), -torch.sin(roll), + torch.zeros_like(roll), torch.sin(roll), torch.cos(roll)], dim=1) + roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3) + + pitch_mat = torch.cat([torch.cos(pitch), torch.zeros_like(pitch), torch.sin(pitch), + torch.zeros_like(pitch), torch.ones_like(pitch), torch.zeros_like(pitch), + -torch.sin(pitch), torch.zeros_like(pitch), torch.cos(pitch)], dim=1) + pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3) + + yaw_mat = torch.cat([torch.cos(yaw), -torch.sin(yaw), torch.zeros_like(yaw), + torch.sin(yaw), torch.cos(yaw), torch.zeros_like(yaw), + torch.zeros_like(yaw), torch.zeros_like(yaw), torch.ones_like(yaw)], dim=1) + yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3) + + rot_mat = torch.einsum('bij,bjk,bkm->bim', roll_mat, pitch_mat, yaw_mat) + + return rot_mat +''' + +def get_rotation_matrix(yaw, pitch, roll): + yaw = yaw / 180 * 3.14 + pitch = pitch / 180 * 3.14 + roll = roll / 180 * 3.14 + + roll = roll.unsqueeze(1) + pitch = pitch.unsqueeze(1) + yaw = yaw.unsqueeze(1) + + pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch), + torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch), + torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1) + pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3) + + yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw), + torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw), + -torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1) + yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3) + + roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll), + torch.sin(roll), torch.cos(roll), torch.zeros_like(roll), + torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1) + roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3) + + rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat) + + return rot_mat + +def keypoint_transformation(kp_canonical, he, estimate_jacobian=True): + kp = kp_canonical['value'] # (bs, k, 3) + yaw, pitch, roll = he['yaw'], he['pitch'], he['roll'] + t, exp = he['t'], he['exp'] + + yaw = headpose_pred_to_degree(yaw) + pitch = headpose_pred_to_degree(pitch) + roll = headpose_pred_to_degree(roll) + + rot_mat = get_rotation_matrix(yaw, pitch, roll) # (bs, 3, 3) + + # keypoint rotation + kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp) + + # keypoint translation + t = t.unsqueeze_(1).repeat(1, kp.shape[1], 1) + kp_t = kp_rotated + t + + # add expression deviation + exp = exp.view(exp.shape[0], -1, 3) + kp_transformed = kp_t + exp + + if estimate_jacobian: + jacobian = kp_canonical['jacobian'] # (bs, k ,3, 3) + jacobian_transformed = torch.einsum('bmp,bkps->bkms', rot_mat, jacobian) + else: + jacobian_transformed = None + + return {'value': kp_transformed, 'jacobian': jacobian_transformed} + +class GeneratorFullModel(torch.nn.Module): + """ + Merge all generator related updates into single model for better multi-gpu usage + """ + + def __init__(self, kp_extractor, he_estimator, generator, discriminator, train_params, estimate_jacobian=True): + super(GeneratorFullModel, self).__init__() + self.kp_extractor = kp_extractor + self.he_estimator = he_estimator + self.generator = generator + self.discriminator = discriminator + self.train_params = train_params + self.scales = train_params['scales'] + self.disc_scales = self.discriminator.scales + self.pyramid = ImagePyramide(self.scales, generator.image_channel) + if torch.cuda.is_available(): + self.pyramid = self.pyramid.cuda() + + self.loss_weights = train_params['loss_weights'] + + self.estimate_jacobian = estimate_jacobian + + if sum(self.loss_weights['perceptual']) != 0: + self.vgg = Vgg19() + if torch.cuda.is_available(): + self.vgg = self.vgg.cuda() + + self.L1 = nn.L1Loss().cuda() + + if self.loss_weights['headpose'] != 0: + self.hopenet = hopenet.Hopenet(models.resnet.Bottleneck, [3, 4, 6, 3], 66) + print('Loading hopenet') + hopenet_state_dict = torch.load(train_params['hopenet_snapshot']) + self.hopenet.load_state_dict(hopenet_state_dict) + if torch.cuda.is_available(): + self.hopenet = self.hopenet.cuda() + self.hopenet.eval() + + self.jpeger = DiffJPEG(differentiable=False).cuda() + self.usm_sharpener = USMSharp().cuda() + + self.resize_prob = [0.2, 0.7, 0.1] + self.resize_range= [0.5, 1.2] + self.noise_range = [1, 10] + self.poisson_scale_range =[0.05, 1] + self.jpeg_range = [10, 25] + + self.opt_scale = 1 + + self.resize_prob2 = [0.3, 0.4, 0.3] + self.resize_range2= [0.5, 1.2] + self.noise_range2 = [1, 10] + self.poisson_scale_range2 = [0.05, 1.0] + self.jpeg_range2 = [10, 25] + + + def forward(self, x, config): + kp_canonical = self.kp_extractor(x['source']) # {'value': value, 'jacobian': jacobian} + + he_source = self.he_estimator(x['source']) # {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} + he_driving = self.he_estimator(x['driving']) # {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} + + # {'value': value, 'jacobian': jacobian} + kp_source = keypoint_transformation(kp_canonical, he_source, self.estimate_jacobian) + kp_driving = keypoint_transformation(kp_canonical, he_driving, self.estimate_jacobian) + if config['train_params']['low_quality_train']: + # ----------------------- The first degradation process ----------------------- # + # blur + # x_source = self.usm_sharpener(x['source']) + x_source = x['source'] + x_source = filter2D(x_source, x['kernel']) + + # random resize + updown_type = random.choices(['up', 'down', 'keep'], self.resize_prob)[0] + if updown_type == 'up': + scale = np.random.uniform(1, self.resize_range[1]) + elif updown_type == 'down': + scale = np.random.uniform(self.resize_range[0], 1) + else: + scale = 1 + mode = random.choice(['area', 'bilinear', 'bicubic']) + x_source = F.interpolate(x_source, scale_factor=scale, mode=mode) + + # add noise + gray_noise_prob = 0.4 + if np.random.uniform() < 0.5: + x_source = random_add_gaussian_noise_pt( + x_source, sigma_range=self.noise_range, clip=True, rounds=False, gray_prob=gray_noise_prob) + else: + x_source = random_add_poisson_noise_pt( + x_source, + scale_range=self.poisson_scale_range, + gray_prob=gray_noise_prob, + clip=True, + rounds=False) + # JPEG compression + jpeg_p = x_source.new_zeros(x_source.size(0)).uniform_(*self.jpeg_range) + x_source = torch.clamp(x_source, 0, 1) # clamp to [0, 1], otherwise JPEGer will result in unpleasant artifacts + x_source = self.jpeger(x_source, quality=jpeg_p) + + # ----------------------- The second degradation process ----------------------- # + # blur + if np.random.uniform() < 0.8: + x_source = filter2D(x_source, x['kernel2'].cuda()) + # random resize + updown_type = random.choices(['up', 'down', 'keep'], self.resize_prob2)[0] + if updown_type == 'up': + scale = np.random.uniform(1, self.resize_range2[1]) + elif updown_type == 'down': + scale = np.random.uniform(self.resize_range2[0], 1) + else: + scale = 1 + mode = random.choice(['area', 'bilinear', 'bicubic']) + x_source = F.interpolate( + x_source, size=(int(config['dataset_params']['frame_shape'][0] / self.opt_scale * scale), int(config['dataset_params']['frame_shape'][1] / self.opt_scale * scale)), mode=mode) + # add noise + gray_noise_prob = 0.4 + if np.random.uniform() < 0.5: + x_source = random_add_gaussian_noise_pt( + x_source, sigma_range=self.noise_range2, clip=True, rounds=False, gray_prob=gray_noise_prob) + else: + x_source = random_add_poisson_noise_pt( + x_source, + scale_range=self.poisson_scale_range2, + gray_prob=gray_noise_prob, + clip=True, + rounds=False) + + # JPEG compression + the final sinc filter + # We also need to resize images to desired sizes. We group [resize back + sinc filter] together + # as one operation. + # We consider two orders: + # 1. [resize back + sinc filter] + JPEG compression + # 2. JPEG compression + [resize back + sinc filter] + # Empirically, we find other combinations (sinc + JPEG + Resize) will introduce twisted lines. + if np.random.uniform() < 0.5: + # resize back + the final sinc filter + mode = random.choice(['area', 'bilinear', 'bicubic']) + x_source = F.interpolate(x_source, size=(config['dataset_params']['frame_shape'][0] // self.opt_scale, config['dataset_params']['frame_shape'][1] // self.opt_scale), mode=mode) + x_source = filter2D(x_source, x['sinc_kernel'].cuda()) + # JPEG compression + jpeg_p = x_source.new_zeros(x_source.size(0)).uniform_(*self.jpeg_range2) + x_source = torch.clamp(x_source, 0, 1) + x_source = self.jpeger(x_source, quality=jpeg_p) + else: + # JPEG compression + jpeg_p = x_source.new_zeros(x_source.size(0)).uniform_(*self.jpeg_range2) + x_source = torch.clamp(x_source, 0, 1) + x_source = self.jpeger(x_source, quality=jpeg_p) + # resize back + the final sinc filter + mode = random.choice(['area', 'bilinear', 'bicubic']) + x_source = F.interpolate(x_source, size=(config['dataset_params']['frame_shape'][0] // self.opt_scale, config['dataset_params']['frame_shape'][1] // self.opt_scale), mode=mode) + x_source = filter2D(x_source, x['sinc_kernel'].cuda()) + + # clamp and round + lq = torch.clamp((x_source * 255.0).round(), 0, 255) / 255. + lq_img = lq.contiguous() + + + generated = self.generator(lq_img, kp_source=kp_source, kp_driving=kp_driving) + else: + generated = self.generator(x['source'], kp_source=kp_source, kp_driving=kp_driving) + generated.update({'kp_source': kp_source, 'kp_driving': kp_driving}) + + loss_values = {} + + pyramide_real = self.pyramid(x['driving']) + pyramide_generated = self.pyramid(generated['prediction']) + + if sum(self.loss_weights['perceptual']) != 0: + value_total = 0 + for scale in self.scales: + x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)]) + y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)]) + + for i, weight in enumerate(self.loss_weights['perceptual']): + value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean() + value_total += self.loss_weights['perceptual'][i] * value + loss_values['perceptual'] = value_total + + if self.loss_weights['generator_gan'] != 0: + discriminator_maps_generated = self.discriminator(pyramide_generated) + discriminator_maps_real = self.discriminator(pyramide_real) + value_total = 0 + for scale in self.disc_scales: + key = 'prediction_map_%s' % scale + if self.train_params['gan_mode'] == 'hinge': + value = -torch.mean(discriminator_maps_generated[key]) + elif self.train_params['gan_mode'] == 'ls': + value = ((1 - discriminator_maps_generated[key]) ** 2).mean() + else: + raise ValueError('Unexpected gan_mode {}'.format(self.train_params['gan_mode'])) + + value_total += self.loss_weights['generator_gan'] * value + loss_values['gen_gan'] = value_total + + if sum(self.loss_weights['feature_matching']) != 0: + value_total = 0 + for scale in self.disc_scales: + key = 'feature_maps_%s' % scale + for i, (a, b) in enumerate(zip(discriminator_maps_real[key], discriminator_maps_generated[key])): + if self.loss_weights['feature_matching'][i] == 0: + continue + value = torch.abs(a - b).mean() + value_total += self.loss_weights['feature_matching'][i] * value + loss_values['feature_matching'] = value_total + + if (self.loss_weights['equivariance_value'] + self.loss_weights['equivariance_jacobian']) != 0: + transform = Transform(x['driving'].shape[0], **self.train_params['transform_params']) + transformed_frame = transform.transform_frame(x['driving']) + + transformed_he_driving = self.he_estimator(transformed_frame) + + transformed_kp = keypoint_transformation(kp_canonical, transformed_he_driving, self.estimate_jacobian) + + generated['transformed_frame'] = transformed_frame + generated['transformed_kp'] = transformed_kp + + ## Value loss part + if self.loss_weights['equivariance_value'] != 0: + # project 3d -> 2d + kp_driving_2d = kp_driving['value'][:, :, :2] + transformed_kp_2d = transformed_kp['value'][:, :, :2] + value = torch.abs(kp_driving_2d - transform.warp_coordinates(transformed_kp_2d)).mean() + loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value + + ## jacobian loss part + if self.loss_weights['equivariance_jacobian'] != 0: + # project 3d -> 2d + transformed_kp_2d = transformed_kp['value'][:, :, :2] + transformed_jacobian_2d = transformed_kp['jacobian'][:, :, :2, :2] + jacobian_transformed = torch.matmul(transform.jacobian(transformed_kp_2d), + transformed_jacobian_2d) + + jacobian_2d = kp_driving['jacobian'][:, :, :2, :2] + normed_driving = torch.inverse(jacobian_2d) + normed_transformed = jacobian_transformed + value = torch.matmul(normed_driving, normed_transformed) + + eye = torch.eye(2).view(1, 1, 2, 2).type(value.type()) + + value = torch.abs(eye - value).mean() + loss_values['equivariance_jacobian'] = self.loss_weights['equivariance_jacobian'] * value + + if self.loss_weights['keypoint'] != 0: + # print(kp_driving['value'].shape) # (bs, k, 3) + value_total = 0 + for i in range(kp_driving['value'].shape[1]): + for j in range(kp_driving['value'].shape[1]): + dist = F.pairwise_distance(kp_driving['value'][:, i, :], kp_driving['value'][:, j, :], p=2, keepdim=True) ** 2 + dist = 0.1 - dist # set Dt = 0.1 + dd = torch.gt(dist, 0) + value = (dist * dd).mean() + value_total += value + + kp_mean_depth = kp_driving['value'][:, :, -1].mean(-1) + value_depth = torch.abs(kp_mean_depth - 0.33).mean() # set Zt = 0.33 + + value_total += value_depth + loss_values['keypoint'] = self.loss_weights['keypoint'] * value_total + + if self.loss_weights['headpose'] != 0: + transform_hopenet = transforms.Compose([transforms.Resize(size=(224, 224)), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]) + driving_224 = transform_hopenet(x['driving']) + + yaw_gt, pitch_gt, roll_gt = self.hopenet(driving_224) + yaw_gt = headpose_pred_to_degree(yaw_gt) + pitch_gt = headpose_pred_to_degree(pitch_gt) + roll_gt = headpose_pred_to_degree(roll_gt) + + yaw, pitch, roll = he_driving['yaw'], he_driving['pitch'], he_driving['roll'] + yaw = headpose_pred_to_degree(yaw) + pitch = headpose_pred_to_degree(pitch) + roll = headpose_pred_to_degree(roll) + + value = torch.abs(yaw - yaw_gt).mean() + torch.abs(pitch - pitch_gt).mean() + torch.abs(roll - roll_gt).mean() + loss_values['headpose'] = self.loss_weights['headpose'] * value + + if self.loss_weights['expression'] != 0: + value = torch.norm(he_driving['exp'], p=1, dim=-1).mean() + loss_values['expression'] = self.loss_weights['expression'] * value + + loss_values['reconstruction'] = self.loss_weights['reconstruction'] * self.L1(generated['prediction'], x['driving']) + + return loss_values, generated + + +class DiscriminatorFullModel(torch.nn.Module): + """ + Merge all discriminator related updates into single model for better multi-gpu usage + """ + + def __init__(self, kp_extractor, generator, discriminator, train_params): + super(DiscriminatorFullModel, self).__init__() + self.kp_extractor = kp_extractor + self.generator = generator + self.discriminator = discriminator + self.train_params = train_params + self.scales = self.discriminator.scales + self.pyramid = ImagePyramide(self.scales, generator.image_channel) + if torch.cuda.is_available(): + self.pyramid = self.pyramid.cuda() + + self.loss_weights = train_params['loss_weights'] + + self.zero_tensor = None + + def get_zero_tensor(self, input): + if self.zero_tensor is None: + self.zero_tensor = torch.FloatTensor(1).fill_(0).cuda() + self.zero_tensor.requires_grad_(False) + return self.zero_tensor.expand_as(input) + + def forward(self, x, generated): + pyramide_real = self.pyramid(x['driving']) + pyramide_generated = self.pyramid(generated['prediction'].detach()) + + discriminator_maps_generated = self.discriminator(pyramide_generated) + discriminator_maps_real = self.discriminator(pyramide_real) + + loss_values = {} + value_total = 0 + for scale in self.scales: + key = 'prediction_map_%s' % scale + if self.train_params['gan_mode'] == 'hinge': + value = -torch.mean(torch.min(discriminator_maps_real[key]-1, self.get_zero_tensor(discriminator_maps_real[key]))) - torch.mean(torch.min(-discriminator_maps_generated[key]-1, self.get_zero_tensor(discriminator_maps_generated[key]))) + elif self.train_params['gan_mode'] == 'ls': + value = ((1 - discriminator_maps_real[key]) ** 2 + discriminator_maps_generated[key] ** 2).mean() + else: + raise ValueError('Unexpected gan_mode {}'.format(self.train_params['gan_mode'])) + + value_total += self.loss_weights['discriminator_gan'] * value + loss_values['disc_gan'] = value_total + + return loss_values diff --git a/modules/util.py b/modules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..5ccc086dfbc248e2c5c8a777552200f22ac92984 --- /dev/null +++ b/modules/util.py @@ -0,0 +1,483 @@ +from torch import nn + +import torch.nn.functional as F +import torch + +from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d +from sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d + +import torch.nn.utils.spectral_norm as spectral_norm +import re + + +def kp2gaussian(kp, spatial_size, kp_variance): + """ + Transform a keypoint into gaussian like representation + """ + mean = kp['value'] + + coordinate_grid = make_coordinate_grid(spatial_size, mean.type()) + number_of_leading_dimensions = len(mean.shape) - 1 + shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape + coordinate_grid = coordinate_grid.view(*shape) + repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 1) + coordinate_grid = coordinate_grid.repeat(*repeats) + + # Preprocess kp shape + shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 1, 3) + mean = mean.view(*shape) + + mean_sub = (coordinate_grid - mean) + + out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance) + + return out + +def make_coordinate_grid_2d(spatial_size, type): + """ + Create a meshgrid [-1,1] x [-1,1] of given spatial_size. + """ + h, w = spatial_size + x = torch.arange(w).type(type) + y = torch.arange(h).type(type) + + x = (2 * (x / (w - 1)) - 1) + y = (2 * (y / (h - 1)) - 1) + + yy = y.view(-1, 1).repeat(1, w) + xx = x.view(1, -1).repeat(h, 1) + + meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2) + + return meshed + + +def make_coordinate_grid(spatial_size, type): + d, h, w = spatial_size + x = torch.arange(w).type(type) + y = torch.arange(h).type(type) + z = torch.arange(d).type(type) + + x = (2 * (x / (w - 1)) - 1) + y = (2 * (y / (h - 1)) - 1) + z = (2 * (z / (d - 1)) - 1) + + yy = y.view(1, -1, 1).repeat(d, 1, w) + xx = x.view(1, 1, -1).repeat(d, h, 1) + zz = z.view(-1, 1, 1).repeat(1, h, w) + + meshed = torch.cat([xx.unsqueeze_(3), yy.unsqueeze_(3), zz.unsqueeze_(3)], 3) + + return meshed + + +class ResBottleneck(nn.Module): + def __init__(self, in_features, stride): + super(ResBottleneck, self).__init__() + self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features//4, kernel_size=1) + self.conv2 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features//4, kernel_size=3, padding=1, stride=stride) + self.conv3 = nn.Conv2d(in_channels=in_features//4, out_channels=in_features, kernel_size=1) + self.norm1 = BatchNorm2d(in_features//4, affine=True) + self.norm2 = BatchNorm2d(in_features//4, affine=True) + self.norm3 = BatchNorm2d(in_features, affine=True) + + self.stride = stride + if self.stride != 1: + self.skip = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=1, stride=stride) + self.norm4 = BatchNorm2d(in_features, affine=True) + + def forward(self, x): + out = self.conv1(x) + out = self.norm1(out) + out = F.relu(out) + out = self.conv2(out) + out = self.norm2(out) + out = F.relu(out) + out = self.conv3(out) + out = self.norm3(out) + if self.stride != 1: + x = self.skip(x) + x = self.norm4(x) + out += x + out = F.relu(out) + return out + + +class ResBlock2d(nn.Module): + """ + Res block, preserve spatial resolution. + """ + + def __init__(self, in_features, kernel_size, padding): + super(ResBlock2d, self).__init__() + self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.norm1 = BatchNorm2d(in_features, affine=True) + self.norm2 = BatchNorm2d(in_features, affine=True) + + def forward(self, x): + out = self.norm1(x) + out = F.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = F.relu(out) + out = self.conv2(out) + out += x + return out + + +class ResBlock3d(nn.Module): + """ + Res block, preserve spatial resolution. + """ + + def __init__(self, in_features, kernel_size, padding): + super(ResBlock3d, self).__init__() + self.conv1 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.conv2 = nn.Conv3d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, + padding=padding) + self.norm1 = BatchNorm3d(in_features, affine=True) + self.norm2 = BatchNorm3d(in_features, affine=True) + + def forward(self, x): + out = self.norm1(x) + out = F.relu(out) + out = self.conv1(out) + out = self.norm2(out) + out = F.relu(out) + out = self.conv2(out) + out += x + return out + + +class UpBlock2d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(UpBlock2d, self).__init__() + + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + + def forward(self, x): + out = F.interpolate(x, scale_factor=2) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + +class UpBlock3d(nn.Module): + """ + Upsampling block for use in decoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(UpBlock3d, self).__init__() + + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm3d(out_features, affine=True) + + def forward(self, x): + # out = F.interpolate(x, scale_factor=(1, 2, 2), mode='trilinear') + out = F.interpolate(x, scale_factor=(1, 2, 2)) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + + +class DownBlock2d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + self.pool = nn.AvgPool2d(kernel_size=(2, 2)) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + out = self.pool(out) + return out + + +class DownBlock3d(nn.Module): + """ + Downsampling block for use in encoder. + """ + + def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): + super(DownBlock3d, self).__init__() + ''' + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups, stride=(1, 2, 2)) + ''' + self.conv = nn.Conv3d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, + padding=padding, groups=groups) + self.norm = BatchNorm3d(out_features, affine=True) + self.pool = nn.AvgPool3d(kernel_size=(1, 2, 2)) + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = F.relu(out) + out = self.pool(out) + return out + + +class SameBlock2d(nn.Module): + """ + Simple block, preserve spatial resolution. + """ + + def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1, lrelu=False): + super(SameBlock2d, self).__init__() + self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, + kernel_size=kernel_size, padding=padding, groups=groups) + self.norm = BatchNorm2d(out_features, affine=True) + if lrelu: + self.ac = nn.LeakyReLU() + else: + self.ac = nn.ReLU() + + def forward(self, x): + out = self.conv(x) + out = self.norm(out) + out = self.ac(out) + return out + + +class Encoder(nn.Module): + """ + Hourglass Encoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Encoder, self).__init__() + + down_blocks = [] + for i in range(num_blocks): + down_blocks.append(DownBlock3d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), + min(max_features, block_expansion * (2 ** (i + 1))), + kernel_size=3, padding=1)) + self.down_blocks = nn.ModuleList(down_blocks) + + def forward(self, x): + outs = [x] + for down_block in self.down_blocks: + outs.append(down_block(outs[-1])) + return outs + + +class Decoder(nn.Module): + """ + Hourglass Decoder + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Decoder, self).__init__() + + up_blocks = [] + + for i in range(num_blocks)[::-1]: + in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) + out_filters = min(max_features, block_expansion * (2 ** i)) + up_blocks.append(UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.up_blocks = nn.ModuleList(up_blocks) + # self.out_filters = block_expansion + self.out_filters = block_expansion + in_features + + self.conv = nn.Conv3d(in_channels=self.out_filters, out_channels=self.out_filters, kernel_size=3, padding=1) + self.norm = BatchNorm3d(self.out_filters, affine=True) + + def forward(self, x): + out = x.pop() + # for up_block in self.up_blocks[:-1]: + for up_block in self.up_blocks: + out = up_block(out) + skip = x.pop() + out = torch.cat([out, skip], dim=1) + # out = self.up_blocks[-1](out) + out = self.conv(out) + out = self.norm(out) + out = F.relu(out) + return out + + +class Hourglass(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): + super(Hourglass, self).__init__() + self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) + self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features) + self.out_filters = self.decoder.out_filters + + def forward(self, x): + return self.decoder(self.encoder(x)) + + +class KPHourglass(nn.Module): + """ + Hourglass architecture. + """ + + def __init__(self, block_expansion, in_features, reshape_features, reshape_depth, num_blocks=3, max_features=256): + super(KPHourglass, self).__init__() + + self.down_blocks = nn.Sequential() + for i in range(num_blocks): + self.down_blocks.add_module('down'+ str(i), DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), + min(max_features, block_expansion * (2 ** (i + 1))), + kernel_size=3, padding=1)) + + in_filters = min(max_features, block_expansion * (2 ** num_blocks)) + self.conv = nn.Conv2d(in_channels=in_filters, out_channels=reshape_features, kernel_size=1) + + self.up_blocks = nn.Sequential() + for i in range(num_blocks): + in_filters = min(max_features, block_expansion * (2 ** (num_blocks - i))) + out_filters = min(max_features, block_expansion * (2 ** (num_blocks - i - 1))) + self.up_blocks.add_module('up'+ str(i), UpBlock3d(in_filters, out_filters, kernel_size=3, padding=1)) + + self.reshape_depth = reshape_depth + self.out_filters = out_filters + + def forward(self, x): + out = self.down_blocks(x) + out = self.conv(out) + bs, c, h, w = out.shape + out = out.view(bs, c//self.reshape_depth, self.reshape_depth, h, w) + out = self.up_blocks(out) + + return out + + + +class AntiAliasInterpolation2d(nn.Module): + """ + Band-limited downsampling, for better preservation of the input signal. + """ + def __init__(self, channels, scale): + super(AntiAliasInterpolation2d, self).__init__() + sigma = (1 / scale - 1) / 2 + kernel_size = 2 * round(sigma * 4) + 1 + self.ka = kernel_size // 2 + self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka + + kernel_size = [kernel_size, kernel_size] + sigma = [sigma, sigma] + # The gaussian kernel is the product of the + # gaussian function of each dimension. + kernel = 1 + meshgrids = torch.meshgrid( + [ + torch.arange(size, dtype=torch.float32) + for size in kernel_size + ] + ) + for size, std, mgrid in zip(kernel_size, sigma, meshgrids): + mean = (size - 1) / 2 + kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2)) + + # Make sure sum of values in gaussian kernel equals 1. + kernel = kernel / torch.sum(kernel) + # Reshape to depthwise convolutional weight + kernel = kernel.view(1, 1, *kernel.size()) + kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) + + self.register_buffer('weight', kernel) + self.groups = channels + self.scale = scale + inv_scale = 1 / scale + self.int_inv_scale = int(inv_scale) + + def forward(self, input): + if self.scale == 1.0: + return input + + out = F.pad(input, (self.ka, self.kb, self.ka, self.kb)) + out = F.conv2d(out, weight=self.weight, groups=self.groups) + out = out[:, :, ::self.int_inv_scale, ::self.int_inv_scale] + + return out + + +class SPADE(nn.Module): + def __init__(self, norm_nc, label_nc): + super().__init__() + + self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False) + nhidden = 128 + + self.mlp_shared = nn.Sequential( + nn.Conv2d(label_nc, nhidden, kernel_size=3, padding=1), + nn.ReLU()) + self.mlp_gamma = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + self.mlp_beta = nn.Conv2d(nhidden, norm_nc, kernel_size=3, padding=1) + + def forward(self, x, segmap): + normalized = self.param_free_norm(x) + segmap = F.interpolate(segmap, size=x.size()[2:], mode='nearest') + actv = self.mlp_shared(segmap) + gamma = self.mlp_gamma(actv) + beta = self.mlp_beta(actv) + out = normalized * (1 + gamma) + beta + return out + + +class SPADEResnetBlock(nn.Module): + def __init__(self, fin, fout, norm_G, label_nc, use_se=False, dilation=1): + super().__init__() + # Attributes + self.learned_shortcut = (fin != fout) + fmiddle = min(fin, fout) + self.use_se = use_se + # create conv layers + self.conv_0 = nn.Conv2d(fin, fmiddle, kernel_size=3, padding=dilation, dilation=dilation) + self.conv_1 = nn.Conv2d(fmiddle, fout, kernel_size=3, padding=dilation, dilation=dilation) + if self.learned_shortcut: + self.conv_s = nn.Conv2d(fin, fout, kernel_size=1, bias=False) + # apply spectral norm if specified + if 'spectral' in norm_G: + self.conv_0 = spectral_norm(self.conv_0) + self.conv_1 = spectral_norm(self.conv_1) + if self.learned_shortcut: + self.conv_s = spectral_norm(self.conv_s) + # define normalization layers + self.norm_0 = SPADE(fin, label_nc) + self.norm_1 = SPADE(fmiddle, label_nc) + if self.learned_shortcut: + self.norm_s = SPADE(fin, label_nc) + + def forward(self, x, seg1): + x_s = self.shortcut(x, seg1) + dx = self.conv_0(self.actvn(self.norm_0(x, seg1))) + dx = self.conv_1(self.actvn(self.norm_1(dx, seg1))) + out = x_s + dx + return out + + def shortcut(self, x, seg1): + if self.learned_shortcut: + x_s = self.conv_s(self.norm_s(x, seg1)) + else: + x_s = x + return x_s + + def actvn(self, x): + return F.leaky_relu(x, 2e-1) \ No newline at end of file diff --git a/results_hq_1_1.mp4 b/results_hq_1_1.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..807ca234876c2aba060b2b06ac17553642050ff7 Binary files /dev/null and b/results_hq_1_1.mp4 differ diff --git a/results_hq_test_crop 512.mp4 b/results_hq_test_crop 512.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..c0b0118bb50974eab36f0a39557c050216b25436 Binary files /dev/null and b/results_hq_test_crop 512.mp4 differ diff --git a/run_demo.sh b/run_demo.sh new file mode 100644 index 0000000000000000000000000000000000000000..27536a2d7b15940c939a1b49bb3138206cd32ff2 --- /dev/null +++ b/run_demo.sh @@ -0,0 +1,20 @@ + +target_dir="./DEMO/data" + +video_dir="./data/AdamKinzinger2_3.mp4" + +ffmpeg -hide_banner -y -i $video_dir -r 25 $target_dir/full/%05d.png + +# crop and resize video frames +python crop_portrait.py \ + --data_dir $target_dir \ + --crop_level 1.5 \ + --vertical_adjust 0.2 + +# python demo.py \ +# --config config/mix-resolution.yml \ +# --checkpoint checkpoints/mix-train.pth.tar \ +# --source_image DEMO/00018.png \ +# --driving_video DEMO/AdamKinzinger2_3.mp4 \ +# --video_out_path \ +# --relative \ No newline at end of file diff --git a/sync_batchnorm/__init__.py b/sync_batchnorm/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc8709d92c610b36e0bcbd7da20c1eb41dc8cfcf --- /dev/null +++ b/sync_batchnorm/__init__.py @@ -0,0 +1,12 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/sync_batchnorm/__pycache__/__init__.cpython-36.pyc b/sync_batchnorm/__pycache__/__init__.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..03653db6f3662660cc8d8a9956e86c4c4719908e Binary files /dev/null and b/sync_batchnorm/__pycache__/__init__.cpython-36.pyc differ diff --git a/sync_batchnorm/__pycache__/__init__.cpython-37.pyc 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MIT License. + +import collections + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm +from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast + +from .comm import SyncMaster + +__all__ = ['SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d'] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dementions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + + return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) diff --git a/sync_batchnorm/comm.py b/sync_batchnorm/comm.py new file mode 100644 index 0000000000000000000000000000000000000000..922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b --- /dev/null +++ b/sync_batchnorm/comm.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {'master_callback': self._master_callback} + + def __setstate__(self, state): + self.__init__(state['master_callback']) + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/sync_batchnorm/replicate.py b/sync_batchnorm/replicate.py new file mode 100644 index 0000000000000000000000000000000000000000..b71c7b8ed51a1d6c55b1f753bdd8d90bad79bd06 --- /dev/null +++ b/sync_batchnorm/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/sync_batchnorm/unittest.py b/sync_batchnorm/unittest.py new file mode 100644 index 0000000000000000000000000000000000000000..0675c022e4ba85d38d1f813490f6740150909524 --- /dev/null +++ b/sync_batchnorm/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest + +import numpy as np +from torch.autograd import Variable + + +def as_numpy(v): + if isinstance(v, Variable): + v = v.data + return v.cpu().numpy() + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3): + npa, npb = as_numpy(a), as_numpy(b) + self.assertTrue( + np.allclose(npa, npb, atol=atol), + 'Tensor close check failed\n{}\n{}\nadiff={}, rdiff={}'.format(a, b, np.abs(npa - npb).max(), np.abs((npa - npb) / np.fmax(npa, 1e-5)).max()) + ) diff --git a/upsampler/app_gradio.py b/upsampler/app_gradio.py new file mode 100644 index 0000000000000000000000000000000000000000..f774ecdeaeee5f607d7c086f46eace4a9b8be395 --- /dev/null +++ b/upsampler/app_gradio.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +import argparse +import pathlib +import torch +import gradio as gr + +from webUI.app_task import * +from webUI.styleganex_model import Model + + +DESCRIPTION = ''' +
+

+ Face Manipulation with StyleGANEX +

+

For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. +Duplicate Space

+

+example +

+''' +ARTICLE = r""" +If StyleGANEX is helpful, please help to ⭐ the Github Repo. Thanks! +[![GitHub Stars](https://img.shields.io/github/stars/williamyang1991/StyleGANEX?style=social)](https://github.com/williamyang1991/StyleGANEX) +--- +📝 **Citation** +If our work is useful for your research, please consider citing: +```bibtex +@article{yang2023styleganex, + title = {StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces}, + author = {Yang, Shuai and Jiang, Liming and Liu, Ziwei and and Loy, Chen Change}, + journal = {arXiv preprint arXiv:2303.06146}, + year={2023}, +} +``` +📋 **License** +This project is licensed under S-Lab License 1.0. +Redistribution and use for non-commercial purposes should follow this license. + +📧 **Contact** +If you have any questions, please feel free to reach me out at williamyang@pku.edu.cn. +""" + +FOOTER = '
visitor badge
' + + +def main(): + + device = 'cuda' if torch.cuda.is_available() else 'cpu' + print('*** Now using %s.'%(device)) + model = Model(device=device) + + torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/234_sketch.jpg', + '234_sketch.jpg') + torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/output/ILip77SbmOE_inversion.pt', + 'ILip77SbmOE_inversion.pt') + torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE.png', + 'ILip77SbmOE.png') + torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE_mask.png', + 'ILip77SbmOE_mask.png') + torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/pexels-daniel-xavier-1239291.jpg', + 'pexels-daniel-xavier-1239291.jpg') + torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/529_2.mp4', + '529_2.mp4') + torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/684.mp4', + '684.mp4') + torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/pexels-anthony-shkraba-production-8136210.mp4', + 'pexels-anthony-shkraba-production-8136210.mp4') + + with gr.Blocks(css='style.css') as demo: + gr.Markdown(DESCRIPTION) + with gr.Tabs(): + with gr.TabItem('Inversion for Editing'): + create_demo_inversion(model.process_inversion, allow_optimization=True) + with gr.TabItem('Image Face Toonify'): + create_demo_toonify(model.process_toonify) + with gr.TabItem('Video Face Toonify'): + create_demo_vtoonify(model.process_vtoonify, max_frame_num=5000) + with gr.TabItem('Image Face Editing'): + create_demo_editing(model.process_editing) + with gr.TabItem('Video Face Editing'): + create_demo_vediting(model.process_vediting, max_frame_num=5000) + with gr.TabItem('Sketch2Face'): + create_demo_s2f(model.process_s2f) + with gr.TabItem('Mask2Face'): + create_demo_m2f(model.process_m2f) + with gr.TabItem('SR'): + create_demo_sr(model.process_sr) + gr.Markdown(ARTICLE) + gr.Markdown(FOOTER) + + demo.queue(concurrency_count=1) + demo.launch(server_port=8088, server_name="0.0.0.0", debug=True) + +if __name__ == '__main__': + main() + diff --git a/upsampler/configs/__init__.py b/upsampler/configs/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/configs/data_configs.py b/upsampler/configs/data_configs.py new file mode 100644 index 0000000000000000000000000000000000000000..7624ed6ccb0054030afafe0cf049cf210129b812 --- /dev/null +++ b/upsampler/configs/data_configs.py @@ -0,0 +1,48 @@ +from configs import transforms_config +from configs.paths_config import dataset_paths + + +DATASETS = { + 'ffhq_encode': { + 'transforms': transforms_config.EncodeTransforms, + 'train_source_root': dataset_paths['ffhq'], + 'train_target_root': dataset_paths['ffhq'], + 'test_source_root': dataset_paths['ffhq_test'], + 'test_target_root': dataset_paths['ffhq_test'], + }, + 'ffhq_sketch_to_face': { + 'transforms': transforms_config.SketchToImageTransforms, + 'train_source_root': dataset_paths['ffhq_train_sketch'], + 'train_target_root': dataset_paths['ffhq'], + 'test_source_root': dataset_paths['ffhq_test_sketch'], + 'test_target_root': dataset_paths['ffhq_test'], + }, + 'ffhq_seg_to_face': { + 'transforms': transforms_config.SegToImageTransforms, + 'train_source_root': dataset_paths['ffhq_train_segmentation'], + 'train_target_root': dataset_paths['ffhq'], + 'test_source_root': dataset_paths['ffhq_test_segmentation'], + 'test_target_root': dataset_paths['ffhq_test'], + }, + 'ffhq_super_resolution': { + 'transforms': transforms_config.SuperResTransforms, + 'train_source_root': dataset_paths['ffhq'], + 'train_target_root': dataset_paths['ffhq1280'], + 'test_source_root': dataset_paths['ffhq_test'], + 'test_target_root': dataset_paths['ffhq1280_test'], + }, + 'toonify': { + 'transforms': transforms_config.ToonifyTransforms, + 'train_source_root': dataset_paths['toonify_in'], + 'train_target_root': dataset_paths['toonify_out'], + 'test_source_root': dataset_paths['toonify_test_in'], + 'test_target_root': dataset_paths['toonify_test_out'], + }, + 'ffhq_edit': { + 'transforms': transforms_config.EditingTransforms, + 'train_source_root': dataset_paths['ffhq'], + 'train_target_root': dataset_paths['ffhq'], + 'test_source_root': dataset_paths['ffhq_test'], + 'test_target_root': dataset_paths['ffhq_test'], + }, +} diff --git a/upsampler/configs/dataset_config.yml b/upsampler/configs/dataset_config.yml new file mode 100644 index 0000000000000000000000000000000000000000..f7addabb39ff776e4b899a2e41080ff242e7ae01 --- /dev/null +++ b/upsampler/configs/dataset_config.yml @@ -0,0 +1,60 @@ +# dataset and data loader settings +datasets: + train: + name: FFHQ + type: FFHQDegradationDataset + # dataroot_gt: datasets/ffhq/ffhq_512.lmdb + dataroot_gt: ../../../../share/shuaiyang/ffhq/realign1280x1280test/ + io_backend: + # type: lmdb + type: disk + + use_hflip: true + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + out_size: 1280 + scale: 4 + + blur_kernel_size: 41 + kernel_list: ['iso', 'aniso'] + kernel_prob: [0.5, 0.5] + blur_sigma: [0.1, 10] + downsample_range: [4, 40] + noise_range: [0, 20] + jpeg_range: [60, 100] + + # color jitter and gray + #color_jitter_prob: 0.3 + #color_jitter_shift: 20 + #color_jitter_pt_prob: 0.3 + #gray_prob: 0.01 + + # If you do not want colorization, please set + color_jitter_prob: ~ + color_jitter_pt_prob: ~ + gray_prob: 0.01 + gt_gray: True + + crop_components: true + component_path: ./pretrained_models/FFHQ_eye_mouth_landmarks_512.pth + eye_enlarge_ratio: 1.4 + + # data loader + use_shuffle: true + num_worker_per_gpu: 6 + batch_size_per_gpu: 4 + dataset_enlarge_ratio: 1 + prefetch_mode: ~ + + val: + # Please modify accordingly to use your own validation + # Or comment the val block if do not need validation during training + name: validation + type: PairedImageDataset + dataroot_lq: datasets/faces/validation/input + dataroot_gt: datasets/faces/validation/reference + io_backend: + type: disk + mean: [0.5, 0.5, 0.5] + std: [0.5, 0.5, 0.5] + scale: 1 diff --git a/upsampler/configs/paths_config.py b/upsampler/configs/paths_config.py new file mode 100644 index 0000000000000000000000000000000000000000..2d5d7e14859e90ecd4927946f2881247628fddba --- /dev/null +++ b/upsampler/configs/paths_config.py @@ -0,0 +1,25 @@ +dataset_paths = { + 'ffhq': 'data/train/ffhq/realign320x320/', + 'ffhq_test': 'data/train/ffhq/realign320x320test/', + 'ffhq1280': 'data/train/ffhq/realign1280x1280/', + 'ffhq1280_test': 'data/train/ffhq/realign1280x1280test/', + 'ffhq_train_sketch': 'data/train/ffhq/realign640x640sketch/', + 'ffhq_test_sketch': 'data/train/ffhq/realign640x640sketchtest/', + 'ffhq_train_segmentation': 'data/train/ffhq/realign320x320mask/', + 'ffhq_test_segmentation': 'data/train/ffhq/realign320x320masktest/', + 'toonify_in': 'data/train/pixar/trainA/', + 'toonify_out': 'data/train/pixar/trainB/', + 'toonify_test_in': 'data/train/pixar/testA/', + 'toonify_test_out': 'data/train/testB/', +} + +model_paths = { + 'stylegan_ffhq': 'pretrained_models/stylegan2-ffhq-config-f.pt', + 'ir_se50': 'pretrained_models/model_ir_se50.pth', + 'circular_face': 'pretrained_models/CurricularFace_Backbone.pth', + 'mtcnn_pnet': 'pretrained_models/mtcnn/pnet.npy', + 'mtcnn_rnet': 'pretrained_models/mtcnn/rnet.npy', + 'mtcnn_onet': 'pretrained_models/mtcnn/onet.npy', + 'shape_predictor': 'shape_predictor_68_face_landmarks.dat', + 'moco': 'pretrained_models/moco_v2_800ep_pretrain.pth.tar' +} diff --git a/upsampler/configs/transforms_config.py b/upsampler/configs/transforms_config.py new file mode 100644 index 0000000000000000000000000000000000000000..0af0404f4f59c79e5f672205031470bdab013622 --- /dev/null +++ b/upsampler/configs/transforms_config.py @@ -0,0 +1,242 @@ +from abc import abstractmethod +import torchvision.transforms as transforms +from datasets import augmentations + + +class TransformsConfig(object): + + def __init__(self, opts): + self.opts = opts + + @abstractmethod + def get_transforms(self): + pass + + +class EncodeTransforms(TransformsConfig): + + def __init__(self, opts): + super(EncodeTransforms, self).__init__(opts) + + def get_transforms(self): + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.RandomHorizontalFlip(0.5), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': None, + 'transform_test': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + } + return transforms_dict + + +class FrontalizationTransforms(TransformsConfig): + + def __init__(self, opts): + super(FrontalizationTransforms, self).__init__(opts) + + def get_transforms(self): + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((256, 256)), + transforms.RandomHorizontalFlip(0.5), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': transforms.Compose([ + transforms.Resize((256, 256)), + transforms.RandomHorizontalFlip(0.5), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_test': transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + } + return transforms_dict + + +class SketchToImageTransforms(TransformsConfig): + + def __init__(self, opts): + super(SketchToImageTransforms, self).__init__(opts) + + def get_transforms(self): + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor()]), + 'transform_test': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor()]), + } + return transforms_dict + + +class SegToImageTransforms(TransformsConfig): + + def __init__(self, opts): + super(SegToImageTransforms, self).__init__(opts) + + def get_transforms(self): + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': transforms.Compose([ + transforms.Resize((320, 320)), + augmentations.ToOneHot(self.opts.label_nc), + transforms.ToTensor()]), + 'transform_test': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((320, 320)), + augmentations.ToOneHot(self.opts.label_nc), + transforms.ToTensor()]) + } + return transforms_dict + + +class SuperResTransforms(TransformsConfig): + + def __init__(self, opts): + super(SuperResTransforms, self).__init__(opts) + + def get_transforms(self): + if self.opts.resize_factors is None: + self.opts.resize_factors = '1,2,4,8,16,32' + factors = [int(f) for f in self.opts.resize_factors.split(",")] + print("Performing down-sampling with factors: {}".format(factors)) + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((1280, 1280)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': transforms.Compose([ + transforms.Resize((320, 320)), + augmentations.BilinearResize(factors=factors), + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_test': transforms.Compose([ + transforms.Resize((1280, 1280)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((320, 320)), + augmentations.BilinearResize(factors=factors), + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + } + return transforms_dict + + +class SuperResTransforms_320(TransformsConfig): + + def __init__(self, opts): + super(SuperResTransforms_320, self).__init__(opts) + + def get_transforms(self): + if self.opts.resize_factors is None: + self.opts.resize_factors = '1,2,4,8,16,32' + factors = [int(f) for f in self.opts.resize_factors.split(",")] + print("Performing down-sampling with factors: {}".format(factors)) + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': transforms.Compose([ + transforms.Resize((320, 320)), + augmentations.BilinearResize(factors=factors), + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_test': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((320, 320)), + augmentations.BilinearResize(factors=factors), + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + } + return transforms_dict + + +class ToonifyTransforms(TransformsConfig): + + def __init__(self, opts): + super(ToonifyTransforms, self).__init__(opts) + + def get_transforms(self): + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((1024, 1024)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_test': transforms.Compose([ + transforms.Resize((1024, 1024)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + } + return transforms_dict + +class EditingTransforms(TransformsConfig): + + def __init__(self, opts): + super(EditingTransforms, self).__init__(opts) + + def get_transforms(self): + transforms_dict = { + 'transform_gt_train': transforms.Compose([ + transforms.Resize((1280, 1280)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_source': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_test': transforms.Compose([ + transforms.Resize((1280, 1280)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]), + 'transform_inference': transforms.Compose([ + transforms.Resize((320, 320)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + } + return transforms_dict \ No newline at end of file diff --git a/upsampler/criteria/__init__.py b/upsampler/criteria/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/criteria/id_loss.py b/upsampler/criteria/id_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..1608ec1eb575e88035aba73c5b6595b4722db5b8 --- /dev/null +++ b/upsampler/criteria/id_loss.py @@ -0,0 +1,44 @@ +import torch +from torch import nn +from configs.paths_config import model_paths +from models.encoders.model_irse import Backbone + + +class IDLoss(nn.Module): + def __init__(self): + super(IDLoss, self).__init__() + print('Loading ResNet ArcFace') + self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se') + self.facenet.load_state_dict(torch.load(model_paths['ir_se50'])) + self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112)) + self.facenet.eval() + + def extract_feats(self, x): + x = x[:, :, 35:223, 32:220] # Crop interesting region + x = self.face_pool(x) + x_feats = self.facenet(x) + return x_feats + + def forward(self, y_hat, y, x): + n_samples = x.shape[0] + x_feats = self.extract_feats(x) + y_feats = self.extract_feats(y) # Otherwise use the feature from there + y_hat_feats = self.extract_feats(y_hat) + y_feats = y_feats.detach() + loss = 0 + sim_improvement = 0 + id_logs = [] + count = 0 + for i in range(n_samples): + diff_target = y_hat_feats[i].dot(y_feats[i]) + diff_input = y_hat_feats[i].dot(x_feats[i]) + diff_views = y_feats[i].dot(x_feats[i]) + id_logs.append({'diff_target': float(diff_target), + 'diff_input': float(diff_input), + 'diff_views': float(diff_views)}) + loss += 1 - diff_target + id_diff = float(diff_target) - float(diff_views) + sim_improvement += id_diff + count += 1 + + return loss / count, sim_improvement / count, id_logs diff --git a/upsampler/criteria/lpips/__init__.py b/upsampler/criteria/lpips/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/criteria/lpips/lpips.py b/upsampler/criteria/lpips/lpips.py new file mode 100644 index 0000000000000000000000000000000000000000..1add6acc84c1c04cfcb536cf31ec5acdf24b716b --- /dev/null +++ b/upsampler/criteria/lpips/lpips.py @@ -0,0 +1,35 @@ +import torch +import torch.nn as nn + +from criteria.lpips.networks import get_network, LinLayers +from criteria.lpips.utils import get_state_dict + + +class LPIPS(nn.Module): + r"""Creates a criterion that measures + Learned Perceptual Image Patch Similarity (LPIPS). + Arguments: + net_type (str): the network type to compare the features: + 'alex' | 'squeeze' | 'vgg'. Default: 'alex'. + version (str): the version of LPIPS. Default: 0.1. + """ + def __init__(self, net_type: str = 'alex', version: str = '0.1'): + + assert version in ['0.1'], 'v0.1 is only supported now' + + super(LPIPS, self).__init__() + + # pretrained network + self.net = get_network(net_type).to("cuda") + + # linear layers + self.lin = LinLayers(self.net.n_channels_list).to("cuda") + self.lin.load_state_dict(get_state_dict(net_type, version)) + + def forward(self, x: torch.Tensor, y: torch.Tensor): + feat_x, feat_y = self.net(x), self.net(y) + + diff = [(fx - fy) ** 2 for fx, fy in zip(feat_x, feat_y)] + res = [l(d).mean((2, 3), True) for d, l in zip(diff, self.lin)] + + return torch.sum(torch.cat(res, 0)) / x.shape[0] diff --git a/upsampler/criteria/lpips/networks.py b/upsampler/criteria/lpips/networks.py new file mode 100644 index 0000000000000000000000000000000000000000..3a0d13ad2d560278f16586da68d3a5eadb26e746 --- /dev/null +++ b/upsampler/criteria/lpips/networks.py @@ -0,0 +1,96 @@ +from typing import Sequence + +from itertools import chain + +import torch +import torch.nn as nn +from torchvision import models + +from criteria.lpips.utils import normalize_activation + + +def get_network(net_type: str): + if net_type == 'alex': + return AlexNet() + elif net_type == 'squeeze': + return SqueezeNet() + elif net_type == 'vgg': + return VGG16() + else: + raise NotImplementedError('choose net_type from [alex, squeeze, vgg].') + + +class LinLayers(nn.ModuleList): + def __init__(self, n_channels_list: Sequence[int]): + super(LinLayers, self).__init__([ + nn.Sequential( + nn.Identity(), + nn.Conv2d(nc, 1, 1, 1, 0, bias=False) + ) for nc in n_channels_list + ]) + + for param in self.parameters(): + param.requires_grad = False + + +class BaseNet(nn.Module): + def __init__(self): + super(BaseNet, self).__init__() + + # register buffer + self.register_buffer( + 'mean', torch.Tensor([-.030, -.088, -.188])[None, :, None, None]) + self.register_buffer( + 'std', torch.Tensor([.458, .448, .450])[None, :, None, None]) + + def set_requires_grad(self, state: bool): + for param in chain(self.parameters(), self.buffers()): + param.requires_grad = state + + def z_score(self, x: torch.Tensor): + return (x - self.mean) / self.std + + def forward(self, x: torch.Tensor): + x = self.z_score(x) + + output = [] + for i, (_, layer) in enumerate(self.layers._modules.items(), 1): + x = layer(x) + if i in self.target_layers: + output.append(normalize_activation(x)) + if len(output) == len(self.target_layers): + break + return output + + +class SqueezeNet(BaseNet): + def __init__(self): + super(SqueezeNet, self).__init__() + + self.layers = models.squeezenet1_1(True).features + self.target_layers = [2, 5, 8, 10, 11, 12, 13] + self.n_channels_list = [64, 128, 256, 384, 384, 512, 512] + + self.set_requires_grad(False) + + +class AlexNet(BaseNet): + def __init__(self): + super(AlexNet, self).__init__() + + self.layers = models.alexnet(True).features + self.target_layers = [2, 5, 8, 10, 12] + self.n_channels_list = [64, 192, 384, 256, 256] + + self.set_requires_grad(False) + + +class VGG16(BaseNet): + def __init__(self): + super(VGG16, self).__init__() + + self.layers = models.vgg16(True).features + self.target_layers = [4, 9, 16, 23, 30] + self.n_channels_list = [64, 128, 256, 512, 512] + + self.set_requires_grad(False) \ No newline at end of file diff --git a/upsampler/criteria/lpips/utils.py b/upsampler/criteria/lpips/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..3d15a0983775810ef6239c561c67939b2b9ee3b5 --- /dev/null +++ b/upsampler/criteria/lpips/utils.py @@ -0,0 +1,30 @@ +from collections import OrderedDict + +import torch + + +def normalize_activation(x, eps=1e-10): + norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True)) + return x / (norm_factor + eps) + + +def get_state_dict(net_type: str = 'alex', version: str = '0.1'): + # build url + url = 'https://raw.githubusercontent.com/richzhang/PerceptualSimilarity/' \ + + f'master/lpips/weights/v{version}/{net_type}.pth' + + # download + old_state_dict = torch.hub.load_state_dict_from_url( + url, progress=True, + map_location=None if torch.cuda.is_available() else torch.device('cpu') + ) + + # rename keys + new_state_dict = OrderedDict() + for key, val in old_state_dict.items(): + new_key = key + new_key = new_key.replace('lin', '') + new_key = new_key.replace('model.', '') + new_state_dict[new_key] = val + + return new_state_dict diff --git a/upsampler/criteria/moco_loss.py b/upsampler/criteria/moco_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..4e6f04dde1a929862012395e4b873804ef2bbc00 --- /dev/null +++ b/upsampler/criteria/moco_loss.py @@ -0,0 +1,69 @@ +import torch +from torch import nn +import torch.nn.functional as F +from configs.paths_config import model_paths + + +class MocoLoss(nn.Module): + + def __init__(self): + super(MocoLoss, self).__init__() + print("Loading MOCO model from path: {}".format(model_paths["moco"])) + self.model = self.__load_model() + self.model.cuda() + self.model.eval() + + @staticmethod + def __load_model(): + import torchvision.models as models + model = models.__dict__["resnet50"]() + # freeze all layers but the last fc + for name, param in model.named_parameters(): + if name not in ['fc.weight', 'fc.bias']: + param.requires_grad = False + checkpoint = torch.load(model_paths['moco'], map_location="cpu") + state_dict = checkpoint['state_dict'] + # rename moco pre-trained keys + for k in list(state_dict.keys()): + # retain only encoder_q up to before the embedding layer + if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'): + # remove prefix + state_dict[k[len("module.encoder_q."):]] = state_dict[k] + # delete renamed or unused k + del state_dict[k] + msg = model.load_state_dict(state_dict, strict=False) + assert set(msg.missing_keys) == {"fc.weight", "fc.bias"} + # remove output layer + model = nn.Sequential(*list(model.children())[:-1]).cuda() + return model + + def extract_feats(self, x): + x = F.interpolate(x, size=224) + x_feats = self.model(x) + x_feats = nn.functional.normalize(x_feats, dim=1) + x_feats = x_feats.squeeze() + return x_feats + + def forward(self, y_hat, y, x): + n_samples = x.shape[0] + x_feats = self.extract_feats(x) + y_feats = self.extract_feats(y) + y_hat_feats = self.extract_feats(y_hat) + y_feats = y_feats.detach() + loss = 0 + sim_improvement = 0 + sim_logs = [] + count = 0 + for i in range(n_samples): + diff_target = y_hat_feats[i].dot(y_feats[i]) + diff_input = y_hat_feats[i].dot(x_feats[i]) + diff_views = y_feats[i].dot(x_feats[i]) + sim_logs.append({'diff_target': float(diff_target), + 'diff_input': float(diff_input), + 'diff_views': float(diff_views)}) + loss += 1 - diff_target + sim_diff = float(diff_target) - float(diff_views) + sim_improvement += sim_diff + count += 1 + + return loss / count, sim_improvement / count, sim_logs diff --git 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new file mode 100644 index 0000000000000000000000000000000000000000..746ae7e032ec5dbdfb459b335a7d3bd11197042f --- /dev/null +++ b/upsampler/data/pexels-daniel-xavier-1239291.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf23e680736ce94fc54b06a9242138a84472a9ef0a0c4adc4e7d363abb9d9cb4 +size 43900 diff --git a/upsampler/datasets/__init__.py b/upsampler/datasets/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/datasets/augmentations.py b/upsampler/datasets/augmentations.py new file mode 100644 index 0000000000000000000000000000000000000000..2e0507f155fa32a463b9bd4b2f50099fd1866df0 --- /dev/null +++ b/upsampler/datasets/augmentations.py @@ -0,0 +1,110 @@ +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F +from torchvision import transforms + + +class ToOneHot(object): + """ Convert the input PIL image to a one-hot torch tensor """ + def __init__(self, n_classes=None): + self.n_classes = n_classes + + def onehot_initialization(self, a): + if self.n_classes is None: + self.n_classes = len(np.unique(a)) + out = np.zeros(a.shape + (self.n_classes, ), dtype=int) + out[self.__all_idx(a, axis=2)] = 1 + return out + + def __all_idx(self, idx, axis): + grid = np.ogrid[tuple(map(slice, idx.shape))] + grid.insert(axis, idx) + return tuple(grid) + + def __call__(self, img): + img = np.array(img) + one_hot = self.onehot_initialization(img) + return one_hot + + +class BilinearResize(object): + def __init__(self, factors=[1, 2, 4, 8, 16, 32]): + self.factors = factors + + def __call__(self, image): + factor = np.random.choice(self.factors, size=1)[0] + D = BicubicDownSample(factor=factor, cuda=False) + img_tensor = transforms.ToTensor()(image).unsqueeze(0) + img_tensor_lr = D(img_tensor)[0].clamp(0, 1) + img_low_res = transforms.ToPILImage()(img_tensor_lr) + return img_low_res + + +class BicubicDownSample(nn.Module): + def bicubic_kernel(self, x, a=-0.50): + """ + This equation is exactly copied from the website below: + https://clouard.users.greyc.fr/Pantheon/experiments/rescaling/index-en.html#bicubic + """ + abs_x = torch.abs(x) + if abs_x <= 1.: + return (a + 2.) * torch.pow(abs_x, 3.) - (a + 3.) * torch.pow(abs_x, 2.) + 1 + elif 1. < abs_x < 2.: + return a * torch.pow(abs_x, 3) - 5. * a * torch.pow(abs_x, 2.) + 8. * a * abs_x - 4. * a + else: + return 0.0 + + def __init__(self, factor=4, cuda=True, padding='reflect'): + super().__init__() + self.factor = factor + size = factor * 4 + k = torch.tensor([self.bicubic_kernel((i - torch.floor(torch.tensor(size / 2)) + 0.5) / factor) + for i in range(size)], dtype=torch.float32) + k = k / torch.sum(k) + k1 = torch.reshape(k, shape=(1, 1, size, 1)) + self.k1 = torch.cat([k1, k1, k1], dim=0) + k2 = torch.reshape(k, shape=(1, 1, 1, size)) + self.k2 = torch.cat([k2, k2, k2], dim=0) + self.cuda = '.cuda' if cuda else '' + self.padding = padding + for param in self.parameters(): + param.requires_grad = False + + def forward(self, x, nhwc=False, clip_round=False, byte_output=False): + filter_height = self.factor * 4 + filter_width = self.factor * 4 + stride = self.factor + + pad_along_height = max(filter_height - stride, 0) + pad_along_width = max(filter_width - stride, 0) + filters1 = self.k1.type('torch{}.FloatTensor'.format(self.cuda)) + filters2 = self.k2.type('torch{}.FloatTensor'.format(self.cuda)) + + # compute actual padding values for each side + pad_top = pad_along_height // 2 + pad_bottom = pad_along_height - pad_top + pad_left = pad_along_width // 2 + pad_right = pad_along_width - pad_left + + # apply mirror padding + if nhwc: + x = torch.transpose(torch.transpose(x, 2, 3), 1, 2) # NHWC to NCHW + + # downscaling performed by 1-d convolution + x = F.pad(x, (0, 0, pad_top, pad_bottom), self.padding) + x = F.conv2d(input=x, weight=filters1, stride=(stride, 1), groups=3) + if clip_round: + x = torch.clamp(torch.round(x), 0.0, 255.) + + x = F.pad(x, (pad_left, pad_right, 0, 0), self.padding) + x = F.conv2d(input=x, weight=filters2, stride=(1, stride), groups=3) + if clip_round: + x = torch.clamp(torch.round(x), 0.0, 255.) + + if nhwc: + x = torch.transpose(torch.transpose(x, 1, 3), 1, 2) + if byte_output: + return x.type('torch.ByteTensor'.format(self.cuda)) + else: + return x diff --git a/upsampler/datasets/ffhq_degradation_dataset.py b/upsampler/datasets/ffhq_degradation_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..b43ff6b1d82c1c491900f119a62f259ac4294b61 --- /dev/null +++ b/upsampler/datasets/ffhq_degradation_dataset.py @@ -0,0 +1,235 @@ +import cv2 +import math +import numpy as np +import os.path as osp +import torch +import torch.utils.data as data +from basicsr.data import degradations as degradations +from basicsr.data.data_util import paths_from_folder +from basicsr.data.transforms import augment +from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor +from basicsr.utils.registry import DATASET_REGISTRY +from torchvision.transforms.functional import (adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation, + normalize) + + +@DATASET_REGISTRY.register() +class FFHQDegradationDataset(data.Dataset): + """FFHQ dataset for GFPGAN. + It reads high resolution images, and then generate low-quality (LQ) images on-the-fly. + Args: + opt (dict): Config for train datasets. It contains the following keys: + dataroot_gt (str): Data root path for gt. + io_backend (dict): IO backend type and other kwarg. + mean (list | tuple): Image mean. + std (list | tuple): Image std. + use_hflip (bool): Whether to horizontally flip. + Please see more options in the codes. + """ + + def __init__(self, opt): + super(FFHQDegradationDataset, self).__init__() + self.opt = opt + # file client (io backend) + self.file_client = None + self.io_backend_opt = opt['io_backend'] + + self.gt_folder = opt['dataroot_gt'] + self.mean = opt['mean'] + self.std = opt['std'] + self.out_size = opt['out_size'] + + self.crop_components = opt.get('crop_components', False) # facial components + self.eye_enlarge_ratio = opt.get('eye_enlarge_ratio', 1) # whether enlarge eye regions + + if self.crop_components: + # load component list from a pre-process pth files + self.components_list = torch.load(opt.get('component_path')) + + # file client (lmdb io backend) + if self.io_backend_opt['type'] == 'lmdb': + self.io_backend_opt['db_paths'] = self.gt_folder + if not self.gt_folder.endswith('.lmdb'): + raise ValueError(f"'dataroot_gt' should end with '.lmdb', but received {self.gt_folder}") + with open(osp.join(self.gt_folder, 'meta_info.txt')) as fin: + self.paths = [line.split('.')[0] for line in fin] + else: + # disk backend: scan file list from a folder + self.paths = paths_from_folder(self.gt_folder) + + # degradation configurations + self.blur_kernel_size = opt['blur_kernel_size'] + self.kernel_list = opt['kernel_list'] + self.kernel_prob = opt['kernel_prob'] + self.blur_sigma = opt['blur_sigma'] + self.downsample_range = opt['downsample_range'] + self.noise_range = opt['noise_range'] + self.jpeg_range = opt['jpeg_range'] + + # color jitter + self.color_jitter_prob = opt.get('color_jitter_prob') + self.color_jitter_pt_prob = opt.get('color_jitter_pt_prob') + self.color_jitter_shift = opt.get('color_jitter_shift', 20) + # to gray + self.gray_prob = opt.get('gray_prob') + + logger = get_root_logger() + logger.info(f'Blur: blur_kernel_size {self.blur_kernel_size}, sigma: [{", ".join(map(str, self.blur_sigma))}]') + logger.info(f'Downsample: downsample_range [{", ".join(map(str, self.downsample_range))}]') + logger.info(f'Noise: [{", ".join(map(str, self.noise_range))}]') + logger.info(f'JPEG compression: [{", ".join(map(str, self.jpeg_range))}]') + + if self.color_jitter_prob is not None: + logger.info(f'Use random color jitter. Prob: {self.color_jitter_prob}, shift: {self.color_jitter_shift}') + if self.gray_prob is not None: + logger.info(f'Use random gray. Prob: {self.gray_prob}') + self.color_jitter_shift /= 255. + + @staticmethod + def color_jitter(img, shift): + """jitter color: randomly jitter the RGB values, in numpy formats""" + jitter_val = np.random.uniform(-shift, shift, 3).astype(np.float32) + img = img + jitter_val + img = np.clip(img, 0, 1) + return img + + @staticmethod + def color_jitter_pt(img, brightness, contrast, saturation, hue): + """jitter color: randomly jitter the brightness, contrast, saturation, and hue, in torch Tensor formats""" + fn_idx = torch.randperm(4) + for fn_id in fn_idx: + if fn_id == 0 and brightness is not None: + brightness_factor = torch.tensor(1.0).uniform_(brightness[0], brightness[1]).item() + img = adjust_brightness(img, brightness_factor) + + if fn_id == 1 and contrast is not None: + contrast_factor = torch.tensor(1.0).uniform_(contrast[0], contrast[1]).item() + img = adjust_contrast(img, contrast_factor) + + if fn_id == 2 and saturation is not None: + saturation_factor = torch.tensor(1.0).uniform_(saturation[0], saturation[1]).item() + img = adjust_saturation(img, saturation_factor) + + if fn_id == 3 and hue is not None: + hue_factor = torch.tensor(1.0).uniform_(hue[0], hue[1]).item() + img = adjust_hue(img, hue_factor) + return img + + def get_component_coordinates(self, index, status): + """Get facial component (left_eye, right_eye, mouth) coordinates from a pre-loaded pth file""" + components_bbox = self.components_list[f'{index:08d}'] + if status[0]: # hflip + # exchange right and left eye + tmp = components_bbox['left_eye'] + components_bbox['left_eye'] = components_bbox['right_eye'] + components_bbox['right_eye'] = tmp + # modify the width coordinate + components_bbox['left_eye'][0] = self.out_size - components_bbox['left_eye'][0] + components_bbox['right_eye'][0] = self.out_size - components_bbox['right_eye'][0] + components_bbox['mouth'][0] = self.out_size - components_bbox['mouth'][0] + + # get coordinates + locations = [] + for part in ['left_eye', 'right_eye', 'mouth']: + mean = components_bbox[part][0:2] + mean[0] = mean[0] * 2 + 128 ######## + mean[1] = mean[1] * 2 + 128 ######## + half_len = components_bbox[part][2] * 2 ######## + if 'eye' in part: + half_len *= self.eye_enlarge_ratio + loc = np.hstack((mean - half_len + 1, mean + half_len)) + loc = torch.from_numpy(loc).float() + locations.append(loc) + return locations + + def __getitem__(self, index): + if self.file_client is None: + self.file_client = FileClient(self.io_backend_opt.pop('type'), **self.io_backend_opt) + + # load gt image + # Shape: (h, w, c); channel order: BGR; image range: [0, 1], float32. + gt_path = self.paths[index] + img_bytes = self.file_client.get(gt_path) + img_gt = imfrombytes(img_bytes, float32=True) + + # random horizontal flip + img_gt, status = augment(img_gt, hflip=self.opt['use_hflip'], rotation=False, return_status=True) + h, w, _ = img_gt.shape + + # get facial component coordinates + if self.crop_components: + locations = self.get_component_coordinates(index, status) + loc_left_eye, loc_right_eye, loc_mouth = locations + + # ------------------------ generate lq image ------------------------ # + # blur + kernel = degradations.random_mixed_kernels( + self.kernel_list, + self.kernel_prob, + self.blur_kernel_size, + self.blur_sigma, + self.blur_sigma, [-math.pi, math.pi], + noise_range=None) + img_lq = cv2.filter2D(img_gt, -1, kernel) + # downsample + scale = np.random.uniform(self.downsample_range[0], self.downsample_range[1]) + img_lq = cv2.resize(img_lq, (int(w // scale), int(h // scale)), interpolation=cv2.INTER_LINEAR) + # noise + if self.noise_range is not None: + img_lq = degradations.random_add_gaussian_noise(img_lq, self.noise_range) + # jpeg compression + if self.jpeg_range is not None: + img_lq = degradations.random_add_jpg_compression(img_lq, self.jpeg_range) + + # resize to original size + img_lq = cv2.resize(img_lq, (int(w // self.opt['scale']), int(h // self.opt['scale'])), interpolation=cv2.INTER_LINEAR) + + # random color jitter (only for lq) + if self.color_jitter_prob is not None and (np.random.uniform() < self.color_jitter_prob): + img_lq = self.color_jitter(img_lq, self.color_jitter_shift) + # random to gray (only for lq) + if self.gray_prob and np.random.uniform() < self.gray_prob: + img_lq = cv2.cvtColor(img_lq, cv2.COLOR_BGR2GRAY) + img_lq = np.tile(img_lq[:, :, None], [1, 1, 3]) + if self.opt.get('gt_gray'): # whether convert GT to gray images + img_gt = cv2.cvtColor(img_gt, cv2.COLOR_BGR2GRAY) + img_gt = np.tile(img_gt[:, :, None], [1, 1, 3]) # repeat the color channels + + # BGR to RGB, HWC to CHW, numpy to tensor + #img_gt, img_lq = img2tensor([img_gt, img_lq], bgr2rgb=True, float32=True) + img_gt = img2tensor(img_gt, bgr2rgb=True, float32=True) + img_lq = img2tensor(img_lq, bgr2rgb=True, float32=True) + + # random color jitter (pytorch version) (only for lq) + if self.color_jitter_pt_prob is not None and (np.random.uniform() < self.color_jitter_pt_prob): + brightness = self.opt.get('brightness', (0.5, 1.5)) + contrast = self.opt.get('contrast', (0.5, 1.5)) + saturation = self.opt.get('saturation', (0, 1.5)) + hue = self.opt.get('hue', (-0.1, 0.1)) + img_lq = self.color_jitter_pt(img_lq, brightness, contrast, saturation, hue) + + # round and clip + img_lq = torch.clamp((img_lq * 255.0).round(), 0, 255) / 255. + + # normalize + normalize(img_gt, self.mean, self.std, inplace=True) + normalize(img_lq, self.mean, self.std, inplace=True) + + ''' + if self.crop_components: + return_dict = { + 'lq': img_lq, + 'gt': img_gt, + 'gt_path': gt_path, + 'loc_left_eye': loc_left_eye, + 'loc_right_eye': loc_right_eye, + 'loc_mouth': loc_mouth + } + return return_dict + else: + return {'lq': img_lq, 'gt': img_gt, 'gt_path': gt_path} + ''' + return img_lq, img_gt + + def __len__(self): + return len(self.paths) \ No newline at end of file diff --git a/upsampler/datasets/gt_res_dataset.py b/upsampler/datasets/gt_res_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..8892efabcfad7b902c5d49e4b496001241e7ed99 --- /dev/null +++ b/upsampler/datasets/gt_res_dataset.py @@ -0,0 +1,32 @@ +#!/usr/bin/python +# encoding: utf-8 +import os +from torch.utils.data import Dataset +from PIL import Image + + +class GTResDataset(Dataset): + + def __init__(self, root_path, gt_dir=None, transform=None, transform_train=None): + self.pairs = [] + for f in os.listdir(root_path): + image_path = os.path.join(root_path, f) + gt_path = os.path.join(gt_dir, f) + if f.endswith(".jpg") or f.endswith(".png"): + self.pairs.append([image_path, gt_path.replace('.png', '.jpg'), None]) + self.transform = transform + self.transform_train = transform_train + + def __len__(self): + return len(self.pairs) + + def __getitem__(self, index): + from_path, to_path, _ = self.pairs[index] + from_im = Image.open(from_path).convert('RGB') + to_im = Image.open(to_path).convert('RGB') + + if self.transform: + to_im = self.transform(to_im) + from_im = self.transform(from_im) + + return from_im, to_im diff --git a/upsampler/datasets/images_dataset.py b/upsampler/datasets/images_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..62bb3e3eb85f3841696bac02fa5fb217488a43cd --- /dev/null +++ b/upsampler/datasets/images_dataset.py @@ -0,0 +1,33 @@ +from torch.utils.data import Dataset +from PIL import Image +from utils import data_utils + + +class ImagesDataset(Dataset): + + def __init__(self, source_root, target_root, opts, target_transform=None, source_transform=None): + self.source_paths = sorted(data_utils.make_dataset(source_root)) + self.target_paths = sorted(data_utils.make_dataset(target_root)) + self.source_transform = source_transform + self.target_transform = target_transform + self.opts = opts + + def __len__(self): + return len(self.source_paths) + + def __getitem__(self, index): + from_path = self.source_paths[index] + from_im = Image.open(from_path) + from_im = from_im.convert('RGB') if self.opts.label_nc == 0 else from_im.convert('L') + + to_path = self.target_paths[index] + to_im = Image.open(to_path).convert('RGB') + if self.target_transform: + to_im = self.target_transform(to_im) + + if self.source_transform: + from_im = self.source_transform(from_im) + else: + from_im = to_im + + return from_im, to_im diff --git a/upsampler/datasets/inference_dataset.py b/upsampler/datasets/inference_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..de457349b0726932176f21814c61e34f15955bb7 --- /dev/null +++ b/upsampler/datasets/inference_dataset.py @@ -0,0 +1,22 @@ +from torch.utils.data import Dataset +from PIL import Image +from utils import data_utils + + +class InferenceDataset(Dataset): + + def __init__(self, root, opts, transform=None): + self.paths = sorted(data_utils.make_dataset(root)) + self.transform = transform + self.opts = opts + + def __len__(self): + return len(self.paths) + + def __getitem__(self, index): + from_path = self.paths[index] + from_im = Image.open(from_path) + from_im = from_im.convert('RGB') if self.opts.label_nc == 0 else from_im.convert('L') + if self.transform: + from_im = self.transform(from_im) + return from_im diff --git a/upsampler/image_translation.py b/upsampler/image_translation.py new file mode 100644 index 0000000000000000000000000000000000000000..d59bf87f57d2dc3d5bb3f197b2723bfabac1df5e --- /dev/null +++ b/upsampler/image_translation.py @@ -0,0 +1,163 @@ +import os +#os.environ['CUDA_VISIBLE_DEVICES'] = "0" + +from models.psp import pSp +import torch +import dlib +import cv2 +import PIL +import argparse +from tqdm import tqdm +import numpy as np +import torch.nn.functional as F +import torchvision +from torchvision import transforms, utils +from argparse import Namespace +from datasets import augmentations +from scripts.align_all_parallel import align_face +from latent_optimization import latent_optimization +from utils.inference_utils import save_image, load_image, visualize, get_video_crop_parameter, tensor2cv2, tensor2label, labelcolormap + +class TestOptions(): + def __init__(self): + + self.parser = argparse.ArgumentParser(description="StyleGANEX Image Translation") + self.parser.add_argument("--data_path", type=str, default='./data/ILip77SbmOE.png', help="path of the target image") + self.parser.add_argument("--ckpt", type=str, default='pretrained_models/styleganex_sr32.pt', help="path of the saved model") + self.parser.add_argument("--output_path", type=str, default='./output/', help="path of the output images") + self.parser.add_argument("--cpu", action="store_true", help="if true, only use cpu") + self.parser.add_argument("--use_raw_data", action="store_true", help="if true, input image needs no pre-procssing") + self.parser.add_argument("--resize_factor", type=int, default=32, help="super resolution resize factor") + self.parser.add_argument("--number", type=int, default=4, help="output number of multi-modal translation") + self.parser.add_argument("--parsing_model_ckpt", type=str, default='pretrained_models/faceparsing.pth', help="path of the parsing model") + + def parse(self): + self.opt = self.parser.parse_args() + args = vars(self.opt) + print('Load options') + for name, value in sorted(args.items()): + print('%s: %s' % (str(name), str(value))) + return self.opt + + +if __name__ == "__main__": + + parser = TestOptions() + args = parser.parse() + print('*'*98) + + device = "cpu" if args.cpu else "cuda" + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), + ]) + + + ckpt = torch.load(args.ckpt, map_location='cpu') + opts = ckpt['opts'] + opts['checkpoint_path'] = args.ckpt + opts['device'] = device + opts = Namespace(**opts) + pspex = pSp(opts).to(device).eval() + pspex.latent_avg = pspex.latent_avg.to(device) + + + image_path = args.data_path + save_name = '%s/%s_%s'%(args.output_path, os.path.basename(image_path).split('.')[0], os.path.basename(args.ckpt).split('.')[0]) + + modelname = 'pretrained_models/shape_predictor_68_face_landmarks.dat' + if not os.path.exists(modelname): + import wget, bz2 + wget.download('http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2', modelname+'.bz2') + zipfile = bz2.BZ2File(modelname+'.bz2') + data = zipfile.read() + open(modelname, 'wb').write(data) + landmarkpredictor = dlib.shape_predictor(modelname) + + if opts.dataset_type == 'ffhq_seg_to_face' and not args.use_raw_data: + from models.bisenet.model import BiSeNet + maskpredictor = BiSeNet(n_classes=19) + maskpredictor.load_state_dict(torch.load(args.parsing_model_ckpt)) + maskpredictor.to(device).eval() + to_tensor = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + + if opts.dataset_type == 'ffhq_super_resolution': + frame = cv2.imread(image_path) + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + if args.use_raw_data: + x, y = frame.shape[0:2] + tmp = PIL.Image.fromarray(np.uint8(frame)).resize((int(y) * args.resize_factor // 4, int(x) * args.resize_factor // 4)) + frame = np.array(tmp) + paras = get_video_crop_parameter(frame, landmarkpredictor) + assert paras is not None, 'StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ + You can try several times or use other videos until a face is detected, \ + then switch back to the original video.' + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + if not args.use_raw_data: + x1 = PIL.Image.fromarray(np.uint8(frame)) + x1 = augmentations.BilinearResize(factors=[args.resize_factor // 4])(x1) + x1.save(save_name + '_input.png') + x1_up = x1.resize((W, H)) + x2_up = align_face(np.array(x1_up), landmarkpredictor) + x1_up = transforms.ToTensor()(x1_up).unsqueeze(dim=0).to(device) * 2 - 1 + else: + x1_up = transform(frame).unsqueeze(0).to(device) + x2_up = align_face(frame, landmarkpredictor) + x2_up = transform(x2_up).unsqueeze(dim=0).to(device) + x1 = x1_up + x2 = x2_up + elif opts.dataset_type == 'ffhq_sketch_to_face': + # no pre-processing supported, only accept one-channel sketch image + x1 = transforms.ToTensor()(PIL.Image.open(image_path)).unsqueeze(0).to(device) + x2 = None + elif opts.dataset_type == 'ffhq_seg_to_face': + if not args.use_raw_data: + frame = cv2.imread(image_path) + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + paras = get_video_crop_parameter(frame, landmarkpredictor) + assert paras is not None, 'StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ + You can try several times or use other videos until a face is detected, \ + then switch back to the original video.' + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + # convert face image to segmentation mask + x1 = to_tensor(frame).unsqueeze(0).to(device) + # upsample image for precise segmentation + x1 = F.interpolate(x1, scale_factor=2, mode='bilinear') + x1 = maskpredictor(x1)[0] + x1 = F.interpolate(x1, scale_factor=0.5).argmax(dim=1) + cv2.imwrite(save_name+'_input.png', x1.squeeze(0).cpu().numpy()) + x1 = F.one_hot(x1, num_classes=19).permute(0, 3, 1, 2).float().to(device) + else: + x1 = PIL.Image.open(image_path) + x1 = augmentations.ToOneHot(opts.label_nc)(x1) + x1 = transforms.ToTensor()(x1).unsqueeze(dim=0).float().to(device) + x1_viz = transform(tensor2label(x1[0], 19)/192) + save_image(x1_viz, save_name+'_input_viz.jpg') + x2 = None + else: + assert False, 'The input model %s does not support image translation task'%(args.ckpt) + + print('Load models successfully!') + + with torch.no_grad(): + if opts.dataset_type == 'ffhq_super_resolution': + y_hat = torch.clamp(pspex(x1=x1, x2=x2, use_skip=pspex.opts.use_skip, resize=False), -1, 1) + save_image(y_hat[0].cpu(), save_name+'.jpg') + else: + pspex.train() + for i in range(args.number): + y_hat = pspex(x1=x1, x2=x2, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=pspex.opts.use_skip, + inject_latent = pspex.decoder.style(torch.randn(1, 512).to(device)).unsqueeze(1).repeat(1,18,1) * 0.7) + y_hat = torch.clamp(y_hat, -1, 1) + save_image(y_hat[0].cpu(), save_name+'_%d.jpg'%(i)) + pspex.eval() + + print('Image translation successfully!') diff --git a/upsampler/inference_playground.ipynb b/upsampler/inference_playground.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..a62993a79bb43e4e0a97b15c1d882f3a0d8cfa68 --- /dev/null +++ b/upsampler/inference_playground.ipynb @@ -0,0 +1,11155 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Jpeb3w3R1Bxx" + }, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/williamyang1991/StyleGANEX/blob/master/inference_playground.ipynb)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "This colab contains three parts\n", + "\n", + "- PART I: Build a web demo with Gradio UI for easy use\n", + "- PART II: Face manipulation with Colab UI where you can look into the code details and easily modify the code" + ], + "metadata": { + "id": "r1x_6TZ-b1jf" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "TRW3eNYd1Bx0", + "outputId": "cd8e4abd-1bc3-4ffe-a088-e12fc93afb49", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting gradio\n", + " Downloading gradio-3.21.0-py3-none-any.whl (15.8 MB)\n", + "\u001b[2K 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"metadata": { + "id": "ey7bK3OJ1Bx1" + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ['CUDA_VISIBLE_DEVICES'] = \"0\"\n", + "os.chdir('../')\n", + "CODE_DIR = 'StyleGANEX'\n", + "device = 'cuda'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "_I_TDhFG1Bx2", + "outputId": "9efccfe0-afdb-4d8d-d46a-4451ba7e2f1d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'StyleGANEX'...\n", + "remote: Enumerating objects: 220, done.\u001b[K\n", + "remote: Counting objects: 100% (100/100), done.\u001b[K\n", + "remote: Compressing objects: 100% (53/53), done.\u001b[K\n", + "remote: Total 220 (delta 52), reused 89 (delta 46), pack-reused 120\u001b[K\n", + "Receiving objects: 100% (220/220), 15.57 MiB | 15.98 MiB/s, done.\n", + "Resolving deltas: 100% (61/61), done.\n" + ] + } + ], + "source": [ + "!git clone https://github.com/williamyang1991/StyleGANEX.git $CODE_DIR\n", + "os.chdir(f'./{CODE_DIR}')" + ] + }, + { + "cell_type": "code", + "source": [ + "!wget https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip\n", + "!sudo unzip ninja-linux.zip -d /usr/local/bin/\n", + "!sudo update-alternatives --install /usr/bin/ninja ninja /usr/local/bin/ninja 1 --force " + ], + "metadata": { + "id": "lvstV2wr1uRt", + "outputId": "ebedb646-7273-41a0-99d8-2d77866e08cf", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2023-03-15 13:26:14-- https://github.com/ninja-build/ninja/releases/download/v1.8.2/ninja-linux.zip\n", + "Resolving github.com (github.com)... 20.205.243.166\n", + "Connecting to github.com (github.com)|20.205.243.166|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: 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100%[===================>] 76.03K --.-KB/s in 0.001s \n", + "\n", + "2023-03-15 13:26:15 (63.4 MB/s) - ‘ninja-linux.zip’ saved [77854/77854]\n", + "\n", + "Archive: ninja-linux.zip\n", + " inflating: /usr/local/bin/ninja \n", + "update-alternatives: using /usr/local/bin/ninja to provide /usr/bin/ninja (ninja) in auto mode\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# PART I - Face Manipulation with Gradio UI" + ], + "metadata": { + "id": "Ni8brc4yb-gf" + } + }, + { + "cell_type": "code", + "source": [ + "from webUI.app_task import *\n", + "from webUI.styleganex_model import Model\n", + "import torch" + ], + "metadata": { + "id": "lri38alScB3A" + }, + "execution_count": 5, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/234_sketch.jpg',\n", + " '234_sketch.jpg')\n", + "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/output/ILip77SbmOE_inversion.pt',\n", + " 'ILip77SbmOE_inversion.pt')\n", + "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE.png',\n", + " 'ILip77SbmOE.png')\n", + "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/ILip77SbmOE_mask.png',\n", + " 'ILip77SbmOE_mask.png')\n", + "torch.hub.download_url_to_file('https://raw.githubusercontent.com/williamyang1991/StyleGANEX/main/data/pexels-daniel-xavier-1239291.jpg',\n", + " 'pexels-daniel-xavier-1239291.jpg')\n", + "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/529_2.mp4',\n", + " '529_2.mp4')\n", + "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/684.mp4',\n", + " '684.mp4')\n", + "torch.hub.download_url_to_file('https://github.com/williamyang1991/StyleGANEX/raw/main/data/pexels-anthony-shkraba-production-8136210.mp4',\n", + " 'pexels-anthony-shkraba-production-8136210.mp4')" + ], + "metadata": { + "id": "1V-HpMsOcUHI", + "outputId": "32cb8f1e-094e-4b70-e052-c29809ed6678", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 273, + "referenced_widgets": [ + "a561b0a49ee343aaaf568cfa0e501736", + "e21e48fe3cc0461a9749b9ced08f78cb", + "8b290003bbb14f87a2925523e8f08a1d", + "10bcb07fd2844b62a8f852515a2bc141", + "5810ca4c2561480bb7adf991edbb7f4f", + "71d12c80831646a7b47530c4f2b3075d", + "66c0d29da18e4b84b0b7b0b42474f570", + "5500dd4e879a4e39bf8d1fd3b1442335", + "5227887d34974d6f924da62b8c3fd9a8", + "eb4d2d9266264ae7b9176c60ac76f7a9", + "45881539cc1f470dadd7e5cce27f76f3", + "345327215e8c4526810e5452e16e9cbd", + "032cf8b008af4faf862fde474f73cb21", + "c0fd24844b764dca81eab021bfc54c47", + "2d96b70d175a46c68858750a2699c43f", + 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\n", + " Face Manipulation with StyleGANEX\n", + "

\n", + "

For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. \n", + "\"Duplicate

\n", + "

\n", + "\"example\"\n", + "\n", + "'''\n", + "ARTICLE = r\"\"\"\n", + "If StyleGANEX is helpful, please help to ⭐ the Github Repo. Thanks! \n", + "[![GitHub Stars](https://img.shields.io/github/stars/williamyang1991/StyleGANEX?style=social)](https://github.com/williamyang1991/StyleGANEX)\n", + "---\n", + "📝 **Citation**\n", + "If our work is useful for your research, please consider citing:\n", + "```bibtex\n", + "@article{yang2023styleganex,\n", + " title = {StyleGANEX: StyleGAN-Based Manipulation Beyond Cropped Aligned Faces},\n", + " author = {Yang, Shuai and Jiang, Liming and Liu, Ziwei and and Loy, Chen Change},\n", + " journal = {arXiv preprint arXiv:2303.06146},\n", + " year={2023},\n", + "}\n", + "```\n", + "📋 **License**\n", + "This project is licensed under S-Lab License 1.0. \n", + "Redistribution and use for non-commercial purposes should follow this license.\n", + "\n", + "📧 **Contact**\n", + "If you have any questions, please feel free to reach me out at williamyang@pku.edu.cn.\n", + "\"\"\"\n", + "\n", + "FOOTER = '

\"visitor
'" + ], + "metadata": { + "id": "O-SPsJihcVTB" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = Model(device) " + ], + "metadata": { + "id": "YTSZQBmZcWh6", + "outputId": "1d2691fc-d48b-464b-80e5-5277221667ca", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 184, + "referenced_widgets": [ + "7118e064cd784f45a95e0781f51a0107", + "55188a14af3446b6a60554c2d0b5aa4f", + "72c0b5954c5d4693bdfe3d5d7e573f59", + "b3f968128c00412eafb74dcbc61105da", + "3fd46dd8b19b4366ae104646a97b2e01", + "f93a83e03c1a4c109d16c1bc524815f2", + "677329deeede4a16b9b063c75fb8569d", + "3c9d504610ee4d95ba2a5149bf6ddc5f", + "87d3bc8717244d948cf952f9a1ac633c", + "0254a998070849928413d48582b73137", + "51775e3cfaf74243b22d8d2810ce696d", + "08589cc2b7524b15a733c2637cfeab69", + "9268205e97134baf9b29cf7280a7957f", + "8ee5c3aecc074f758f43c98abd3879b3", + "2d890e27269946fd91e2643232c11235", + "1296048ba98841f689a4579a49616bad", + "f5c3465cc49c400696c2c8bc5d982c4a", + "2836133f17314540aa6d8dc2230d90a3", + "b445adafdd1f45c4a8705bfb5623756d", + "aa0a8880365d431a964fe9d4a8871f38", + "6af2eff9b07f4a9a83cb18f7399e86f1", + "7d093ddc320d49d7956342e0df6a1286", + "d4bfeb71cf514b45ab957c45f1c7f5d6", + "96a8d55656024b82b9082b72a97d38af", + "b8242eed472141159664c0e93d7d1078", + "74d91095374c4731aecf468d6b83e51e", + "b6d66e71173940e19940fa62f7d473fa", + "0246f18bbd8e476fa37325cd790c3c6c", + "6c2019a29a4048609203cfdd86ed380c", + "1582f568d53e43ec99a5902f51a330d7", + "cfd5169efbdc4193aa45a1461b358f48", + "52c66ddb644b47519dff8800a8b45594", + "5b48c6199e3b4699af7d9901985bc858", + "0ab2fc9229024bef94254669f724d3b3", + "3ab68e51caef4570be61ffdf304b216a", + "ecedcee5ea42446b93065b09a155498a", + "c63fdb02b4d648039daf78ee4a0baf20", + "591cdc8fb71549579b0dc80a230b37cc", + "745ad66f193e4d9ca3d7d353cf528acb", + "4f83ce259f8841cfb181d4d6ea9dd28b", + "369c506aa1fd4050a7d81a68d42f25fd", + "c168b1ec8e39407a8b2113991bb3b75e", + "c2afdfbc4c0943ef96b583eb3a7e07ef", + "34018631038643fb88ccea65e8b3891f" + ] + } + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading (…)8_face_landmarks.dat: 0%| | 0.00/99.7M [00:00" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "erYJ2Roa1Bx5" + }, + "source": [ + "We will download the pre-trained model to perform StyleGANEX inversion,\n", + "and perform style mixing and domain transfer to this embedded image.\n", + "- Style mixing: applying random color and texture to the target image\n", + "- Domain transfer: load a StyleGAN-NADA model to generate stylized image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "y6__MTcZ1Bx6" + }, + "outputs": [], + "source": [ + "task = 'inversion'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JbYyCa1A1Bx6", + "outputId": "1705b917-dfbf-492b-cc46-902e06427460", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "96752008b98f49808601cfdb919be175", + "3d2895bf0b7b44619d612195b0df3ec6", + "f18ac32b88d14a8a92510a6d1f1e26ab", + "02f32b3137e24533b7a611add79f87c4", + "12006e02d4134474b20fb1946dae17c7", + "fc6964bf71eb44498a095978d6ed0677", + "5b7c6bcccbae49e8aa4200bb1f91ed9b", + "88f65d88ea3648a780d3e632e83823fb", + "e36f0be0d6d34f87ad91402f53520461", + "81c366376faa43938f4a1011da6849ea", + "20c18622bddf4851b23487f4215dc618" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading (…)leganex_inversion.pt: 0%| | 0.00/1.20G [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "viz = torchvision.utils.make_grid(torch.clamp(torch.cat([y, y_init, y_hat], dim=0), -1, 1), 3, 2) \n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RiuVjtei1Bx7", + "outputId": "d1e18c87-3198-4615-f96a-20ea5f673367", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 249 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# style mixing\n", + "torch.manual_seed(32)\n", + "y_hats = []\n", + "with torch.no_grad():\n", + " for j in range(4):\n", + " mixed_style = pspex.decoder.style(torch.randn(1, 512).to(device)).unsqueeze(1).repeat(1,18,1) * 0.6\n", + " mixed_style[:,0:8] = wplus_hat[:,0:8]\n", + " y_hat, _ = pspex.decoder([mixed_style], input_is_latent=True, randomize_noise=False, \n", + " first_layer_feature=f_hat, noise=noises_hat) \n", + " y_hat = torch.clamp(y_hat, -1, 1)\n", + " y_hats += [y_hat]\n", + "\n", + "viz = torchvision.utils.make_grid(torch.cat(y_hats, dim=0), 5, 2) \n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rVdKkgLd1Bx8" + }, + "outputs": [], + "source": [ + "# domain transfer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0dAodQw71Bx8" + }, + "outputs": [], + "source": [ + "model_repos = {\"pixar\": (\"rinong/stylegan-nada-models\", \"pixar.pt\"),\n", + " \"disney_princess\": (\"rinong/stylegan-nada-models\", \"disney_princess.pt\"),\n", + " \"edvard_munch\": (\"rinong/stylegan-nada-models\", \"edvard_munch.pt\"),\n", + " \"vintage_comics\": (\"rinong/stylegan-nada-models\", \"vintage_comics.pt\"),\n", + " \"modigliani\": (\"rinong/stylegan-nada-models\", \"modigliani.pt\")}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "db6lAT-z1Bx8", + "outputId": "5605c842-19c2-41b8-bd37-82c26904b07c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "2d63d1722034459fa667a6e587d3c450", + "a032afe33e4c4a279c8140d1bc1265e0", + "738c47a9ca504adbbe4daf216ed7cedd", + "61b9f768920e4d40be983decf3587d2b", + "ff9afd6b1cf645eb8871f3cee6eabc1d", + "82a1bc03b96340e0b66e86c53df9f3ed", + "6523fd64ee9347e1a1dc1c0c5606595c", + "1ff36b0e27014bc485a811f74cff6384", + "419d2550eda64fbbb17fe840fadd0878", + "a9bf29b521d741f88e035743184d50d2", + "79e1b51f125d4c6f9e11ee1042c31a38" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading vintage_comics.pt: 0%| | 0.00/133M [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "viz = torchvision.utils.make_grid(torch.clamp(torch.cat([y, y_hat], dim=0), -1, 1), 3, 2) \n", + "visualize(viz, 80) " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ohR4hsRi1Bx8" + }, + "source": [ + "# Face Super Resolution\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HElwPCV11Bx8" + }, + "source": [ + "We will download the pre-trained models to perform face super resolution on two settings:\n", + "- Specific SR: SR with a specific model trained on 32x SR\n", + "- General SR: SR with a model trained on 4x-48x SR" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "otaWDb8N1Bx8" + }, + "source": [ + "Perform 32x face super resolution " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UGPBkY8Z1Bx9" + }, + "outputs": [], + "source": [ + "task = 'sr-32'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "G4F1P8L11Bx9", + "outputId": "00e3c637-c9ec-48ac-80eb-2ea899969865", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "20ade13ebbd542b2a711622212ba5d49", + "41e0530417354b87956784afcda8ed09", + "33e87b8a902e43fdb7c8bf213481bf5a", + "59df3d56865d442c80f4dee5c8d84f50", + "f13b2e46917e466b9ff1a083e0edf6ed", + "b370f15d102a48b1850af925f80b2894", + "2c2ee02036b246dc9d2cca878a76ff7c", + "a5c6d4677f064e0790d6232746fac229", + "e7d9ea5b68cc49de87b1474b983e140e", + "201db17f5873470d9291beb466cf3225", + "1f8a5e55647a4cd99c2fcc6416ead734" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading styleganex_sr32.pt: 0%| | 0.00/1.41G [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "visualize(torch.cat((F.interpolate(x1_up, scale_factor=4)[0].cpu(), y_hat[0].cpu()), dim=2), 60)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nqEaSU1X1Bx9" + }, + "outputs": [], + "source": [ + "#save the result to files\n", + "#save_image(y_hat[0].cpu(), './output/%s_sr.jpg'%(os.path.basename(image_path).split('.')[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NNJPzIeJ1Bx-" + }, + "source": [ + "Perform 16x, 32x and 48x face super resolution with a single model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pDZSLBGe1Bx-" + }, + "outputs": [], + "source": [ + "task = 'sr'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Q2NHp-iM1Bx-", + "outputId": "c041a02b-d154-4b11-f432-b4c016377b05", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "f0f6b4ad273243ad93ff19fca0d0b677", + "91bed5819b784245af6500a401607df7", + "32387e85540e42a1a0d67b9e7354454c", + "af8451dfb1074bc2a4557c7af7fd45c5", + "adef71d652a54debbb9932b98811581d", + "246fb1c5e96741a6b35ada7b68657db2", + "570b087a6c8946e9a397933236b500f4", + "bc02869b16ce483da705aba0c67f7f65", + "719f25e89c85486f8d029648e6b6eceb", + "820a09151eaf42458639ff1a09f5516c", + "64ce7900167340b8a5523b5337acfab4" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading styleganex_sr.pt: 0%| | 0.00/1.37G [00:00" + ], + "image/png": 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rda1tq5Z+8j1LqMgMJEgyXgRhAWEBMKeMJSbNBdKENo2lh5LwwyVuRZrA4eAkLsSWMsVFWMnv6hY2q9XossieOsacGUvMCE4SQpwLQp+NQuO9w27X4/33nuHdd29xc7PBbrsBiDHNM5bM2F7dYjNs4b3kVKTMyEia/1EpqqhTCsDibWLEqMp0mjFNR8zHPfKySGjISVxPLA3xeGXnxbUMAExACAXMAUtiichMSExIiTAbsXtx55MHPBEyMZxCrcGbxZQr/LSgZw9zwQp+knu+qgqoq9gqlKjWadWQ9bmfvKB9gcqfa3HTvlHPrcoZnVzn5LsNmHM7czY+S4ZqlI+16tDgig2FUemuPAafvNRaVR4sSmQ132taA1e+rfkXNhgu3h/JkpYUrURA5IwFDjETIgtmKZwDsFCh0E7nhH48SR4RKCN4SOy2jNc1Y1alAWad58KX1ABvmaMnMOItgCslNaYRcCYgEMh7bLYb3Nxe4+b2Ga6urtAPPcg5xLhgPI6YphlLXMDOY7h9hqubZ9htd+hDj0AeHr4GwomFwVISq0IfZpknzNMR07jHeHjEMh4RlxlZ3WbOB4R+QDcMCJ0IF+8IwTuE4MUy9xKnMit9CAEbfUl8SASkJ7cqbxCCqXTNLTERnRHgr/N4e6x47XIssav2+BUH9ivFqk+/+xbF5Je5T9V9DTgBcw/rCauzdRCrv5/A5XZgQLl+AjMhgxFZAHfKjDkmzDEiZ0uQ8RojdeicQ/YO8CQgS5JPwaw4K2omVgvVav36PK178sxVqSGX4lJ1hGzKoXMIBPRdh/fevcF3v/MRvv2tD3H77Bab3QZd32NJEYfjEcdpgeu3GHZXCCGIFp+TWogEIl8zNNVVzZCQTooR0zxhmmbM84h5usNyeACmCcgsCX6hg+8HuDAAwaELDuw8IotF6uCQQkDXDdj6AZsgrz4E9F2HPgQEciVxKgjmNt4UKqGVFakp89YkqvV8mxJXwrrtly+SbMvxJsSbUxtjrCpGZ6bp+fcuHURnbLSm5HNl4fJ4W4u8vYK9c46kbPNpb1L7Hf3Jl69n55p9vPJ4mZW7UpB5fXn9nVhDNAa6BdhJLaGq+Bk9Qj0HmTMSExZzM2fzsGjEX93JwTlxGTuxchksyYOnyZDNWItOn6sMttG08ses9aeOJwE3LxFwqjEzI3QO3nXYbLd497138d4H7+H29ha7qyv0wwbOe6SUMM8zplldSyAM17fYvvMuhu01QrdBoADHCrdOFliSPSJSTuKiWhZM4xHjYY/D/h7HxwfM4wEpRhAR+m7AsAvwPmDotxg2G4SuhyOHJWf0S0TYzujmBSkEDDe3uL19hvevbvBss8VNP2ATAnotqWlr/sTqRRM7O8kurCbTk5P6mzo0ZC9ueSVYVywk/E4t79/EwavfVbPkjAxLXGDjhyrtWkuV1sywEg5kwlq/szJFGGBlZGZ1hUm4ISYpaVmi1BM6ImQnP51zgPcg9vAs4ZdMjesp04q2Go3uBOgt0UTjG0xwuubl2awswcn3WeOmfRew2/b49sfv4W9+79v41rc+wjvvvYfdzS26YUBmxjSNCMMRYV4AFzBspT6eyZfaRUsgLEqmgn6KSSoPphHTOGI8PmJ8eIXl8R48T2I9dF1RhofNNcJmhzAMoBAA57EwMGfGAkIOPTbDFrvNFkO/wdD16EJA570ozqpAWNxYQNficDIHhU+hYz+zx6jyrArydez8lHUaFGD5u/FD6LUYzCdoUehOrVOzUtGQ11eQG3TBqmxGi9MEMDr9rbWAz02vaklSA82tjEN7fctCblzNZ6Arf64Un4afKo/Wv9vRExSMm+uZMUFch1Jt3YYPGMqrjKzJU1Kxop4pBhIDpPW0HpIESd4V/HFaUG6u8Gpb1+eh8kiVV1e42sqetyzxk4Dr9SJSowQAPUIXsLva4d333sMHH36Im9tbbLdbdP0GzgdkSPLEPC84jhNiZvS7a2xv3oHvBjCrBqR0kHSQOSfMy4xlHpGWBcs8YTwcMR4ecXi8x+HhAXEeAQa6rkffb0Ur3lzhaneDzXaHvu9B3iMxsE0Zw7xgu0Rk7zFcX+P6+hrvKuDuuh6Ds8Lm9cQWzausaGXo1bFe99/8YRpd60o1yGlX/hLo/ipW65ccv85kMmoBqDla1i3WX2Pt2nstkxctufVT1Tud2QlqNALl+vY1Uj6QnCO2RheayCFKdwVn8RiL9iylBVQw3ISHAUO5hTErt89ma2uJGidWrhIgMWvJmwDR1a7Hdz55H3/0h9/Fd7/7Hdy+9wE2V9cYNjuQc5iXBRK5ckCYwT6g6zfw3QCQl0YF5TYSm85JSo2WZcE4HTEejxiPe4z7e4z3r3B48xppnhGcw263Q+i32N7cYvfsHVHIt1v40IF8QCbCwsCibnq4DkO/wUZjyV0IWhbkEbxDp9atJLnU0IkkdBrQ6oy0gGGLeupNUL4t7Ks+UbPuVieawnaKWy0aclne8h0Ziyt5AHWdn9aJm8ut/j6V3pfE0MUPYOOo973sVVqDd50zoCaJcKU3VGVFKiIYNvcGiGyvhiUJ6rpFG8qpXGiuaZO/FXbb52yVILkm2nPthhpg5cx6PQKQlU19o99Wy5VKeaWlSTVG19kUG000Hkf7xKz5J9b5ScAdNpbuDHjv0Pce202Pq6stbm6ucXtzg93VNfphg9ANcMEDBKSc4LsFcB4xMbrNFn0/wPlOYmIqUJKWMMQ4YzrucXy4F9fxNIk7eZwwjyOmwx7T8QjOCUM/IHQ9Ntsdrq5vcHVzi6vdLTbbLbpOOuNkAENmhCGijwnZeQy7HXa7Ha76AZuuwxACehIXtCV8VfxsZm8Vw6q//Pog5usdrauxuDSqyt2+8dsd12/hnpfv0IBuw91FuJRFrYB2+UrrOE17VE+HCndCLa9hASRnoFr8mxYvcgoK6v5f/dfco7kvtcNV5CPH5c0aI6wuZTDDBY/rqx7f+ugWf+P3P8J3f//beO/Dj9DvbrHZXknyFDN8zgh9hw4M9g5MHhQ6hK6XEFJmxCRlRRKrZSzzgmmacDgcMB4OGA+POD7c4fjmFQ53b7BMEzrvsNtu4bsBm6srXD+7xc3tDbZX1wjDBj50gNbExwxEZq2P9wjdgL4fEIIBrpM63CbBpZQkoibmtD/LYhWzrRHVihKtkVlo1tbtlCJahscpbZhEbRCUeXX+itJagNZzCIxzA7lan+1YT39d/bwQj67wZLRiSWRtYlSFURQF0LwELRSeHPaZAk7rCaxzb9jZyCtU8KrnQ5OleKVMAUApfuH6ndW82F9cn4OgWcgOCBA8cmytLgBykGob0qYqjuCI5Xxf+xk41yoQaK7O9TnbwVDzrF9ihT0JuLe3W1EWsmjQm22Pq90GV7sNdjvRSPt+gA89nA/wzsNqmJ1P8N4j26PmBHYeDCCTgG1MC+Zpwnh4xP7+DR5evcDh/h7zOEmsNkbkmBBnaXPXeY/tsJM2jdc3uL59huubZ9hurzD0G3gf4JyUHkRmuJQRtG6y22yw7XsMoUPvfNOdac1rNr1l0i4S8+/maHHDNMFK1JCyF+Cvx2B/jceXQ7ky3crCBYxNmFqr+Onvt/eq1oqGPrRoXlyaLIk7JHFTA1zLmvfeCy167f7lAOe4ALa5skuM0d5jNAzeqs2NZlVcWYzgHbZDKA3Thr7D++9c4dufvIuPP/kAN++8i257jW7YaDtKB3CG7zoEImQS93cShkVxxbJYAuY2X+YF4zjiOB5w3D/geH+P/Zs3eHj1Gof7e/AywxOAvkPaSC5FCB26fiOvbkDoJHESzoNB8AwElppkcl6AtusRQiitSiXxzMI9KkyxBhvCSajnhAdWxTiEC8ktl2KqDQq2878S+WsFt7iO2Twf8n4NHdSvlUQjOgFdu9eJzFnfjbBqSHE2dj2D6u8mJ0wxY0LNli5zo7NptFinrLl+4/mrhm89sZyroZBcy0XNc5NPrMGa4GfXrfHbqrTI37UBjjZwQV1/B0laDCSxWUl2QmnAkeFUUTY+huYESBVB5wkhSF6QVRxYbkRphESkSb3NpFB9NpsWk0WZ15Rmx5OA+9HH74EzY14iMjN8FzBsOnR9QBcCQufhg/R3dUH6rZaaXHVFpxhh7gWEGdk5RM6YlwXjeMT+4R73b97g4dVL3L98ifHxEWmJQM5lQqE9N90wACwaStd1GDYbbLYD+mFA1/fwLoBIOo0Qs/TYNcANAZ0LysBUmNitKUVpg7Fyk7bEe65f/UrH+RWBSy7ar2JBqlPsggD5L+tYCwF7Rwm+dVOZO6yduhWInVu7rQwx5ndOUvw67WRDALzWfRutEGm5AyQho/faKYmgsUaWbmbON9ryEw9WLI3TcXP5FQzcXm/wex+/I0pszgie8O7tDrc3V7i6utaYrCaTgCVERNIEw4eAwDLmnKTDU05z6QIVY8Y4z5hmUYqPhz32j2+wv3uDh5evcffqDtP+AKQEzwnOiY3SLakkq6QMMKRVpNOWdxJHcwhsTj4pLwzqPq4gK+VT0nHOLB+1bLm6flfNXpoF5KcoxD5v/zg5t8Ant++1v13irubkE9FRO6OtD7WXKiBftI3se1x/N0DkUyBGA9YF+ooyx+VuCrrtlc1qqxPbKApNcuap7rGG7fK3JL/mVXjEFMVsobHmMjUStNZ8ramKPUthEX1GKU/12kgpIGiLGQcHz0KD1bo1ulqX7jlkdA4IgRoL19VwRVGKUUJCrlEyzhIdIXk2T8ngJwH3u9/7FmJKGI+SiZiYETov7jNVN6we1zstnM8ZOUnS0zJPmKYJjBHwR3AIYOewpITj8YiHh3u8efkSr754jvsXr7F/c4dlnODJo9es4hCCTI4j+X6K0qOS1B3gpfE7eQciX4SfxdRgLekaMWfNz0+xtpDOqYvojMh++0frPrscq2lts/+y4LaN05y7ZasaVJWSKlrKYZahMT9feDV3BKD9jj3YBXQUIBm7ElMsTR0a4rD7OyKNPULrcAHHGY64NLE3K7e+yCRfpWE6V8gKczPjndtrfPfb7yNqM/iUE7bbHsFCN1qcn3MC5ablHHM5f4lJQFVBco4JMWbMMWkG8ojj/hH3d3e4f/ka96/uRSmeZyAlOCmSALFHXjLcYcbD44Tt9YTdNGNeIoaU4VMGOSm9MEvKBKYn7YBFtFKGV3yqhl/RR1CpvAj6Mod10khpQda8gSda04vN/fnBVT8rNzunFTRvm9VV2l2irvEp/7Z3PKNZU55pfWZ57pUhUN8oaoQqjUb39iepElofaT2h1BAf2byWh1OrvH1WG7mGUFrXamUv4zHJF249TlXuViV4Vc/MpWfUSkkhVcoCecB1IOrgXICHQwQhQDLtRbey0E+tRnGQ3v6EjI7EyvUO4vl063Nby/+ikteAbREvuHw8CbiffOdbiDHieBxxOBwxTjNyZvjOSyvFFJFyhM9J+leCEWNEnGcs0yglPYcj5piwZGkgEMHSdvFwxOP9A+5evcKbly+xf3OP48MeeUnofEDsBwxdh6HPQAjwvQdnRowJyzJjmScsi7xc6EDJg7017JLGBAnyIjStxVom058lPf7JKdIzfwNxyl/miiv+q74YVC34CYHwazlOIeDt83KxsUUjcL7unLZClrB+ujIyQmPdrs0UaWG8grCTAZugESbzzkuijwvwTn5m3TziNFpjJGRM6kl2KZJONknCF/Lh2bJYCUVtJ0pnz3o63KvdBh999F7t8JRSoYJlWTDPM8KwwPkgmccpKRAnzEvEPM84TjPGacK8JCzahzzmhONxxP5wwP7hEY93j7h7dYf963vEedI+0RlIEcwZWTdYyJCmMw/3e+yud9heXaHf7iRZ0mnOhEViXe3TLEllqqygcRsXa4Ea6kZZn5YeUHjY5vMSS9f1OiU7ufapsloVvacSYAxTK8hVSFjF+lZr2SpU5/TfhoJPv12vWJMCnzpvPR/6dBZWaeawtj/EuTZTn6yqLry+pk0ONbKIYd3PrNTT8NNAtPg9y63Ymi+XzwRsCWgsXWsfafwZBGxdB+c7CW3Cw7N0L+TGci+bFxjgMoMgm5N4ZG18wasNPKryVJ+5VYjFtYWiQBjNumaeTo8nAfe9Dz/REgAB3OPhgHEaEboejIR5HuG7TnaOcF4Ad4lak3cQwD3u8bg/Yn8ccThOOM4zjuOMcZwxjRPmacIyTUAmdKFHknJ4pJwQk0NIHiEwrPtUjAuOxwP2Dw8YtjuApKNIyhm+S4CXPpqJpWdrSln6SaeExVxnmbWeWWNxzTP/+iH113eYdlUsIpjmfHKO/v7X+Vm+8nHhIc7A1piRqiPxTD42qnQR5s1rLaBIS2482Avget8BPoC9eGls4k/keRHZZKCUM4iTdjNjjQm1rdgrtFozlkx2f6l7zRYLpPXNhqHDe++/A+aMJUYsi7zgnJTnTTNcN0oT+XlGBEm5REyYFZDHacTheMQ0izU6LjOO04L94xGP90ccHvY4PBwwH0fkuIA4a8KNuAzBGR5iCYAl2epwOODN6zfYbDr4vgf5DpkCIjt0PeB9BlySZC3blIA82Cetg1TFsSVklfsSf9Q3L1qkp8RC53HbJ2jKPrwUOmooo35Gdd3Kt9X9apmup6qvM9dksRrrzwraF6zPkxG1eNiqHquxAGXbQqNx1vko12vln9HzKfja2SVJrIJntVLroAlWSqc5yStjhxsNxVWgKvfQ+G8BMNTvNUMpynAIIBcUbOXlyaMjyRXIFp4hWoGlI6qbimQH4gzHCY5yOa9s50e2biRboha1CQVgTxU1PluxejwJuNc37yHnhM0yY9iM6IcHdIdHdaVlzMsEGiVu65yH1OhFTLPU5y2TAOq4f8TD3QPuHx5x/3iQcqHIYCaEEDB0W2z6nTRPjwviPINTEj+5I/G9h1BKfuZ5xmH/iO5+ANS9N6cE3y9A6KSTDVwJ0DuXwQT46BBDQOJc3IHckOsFW6yu8JccT7Up/FWOWssHJdLKVF6fu7jZymjr4lP551yj/7L7nj7LWqh83Sd5+72+9Hjb/VrNF8VAlWuvztE50qw+214OrQwwQUCANPx3suVWCCAfQKETAJbNZlXLtQmu1nURErrlF7IqSlgnTp3Pg6wxnbxcXdzVMQwDbm5vwQQsUUrx5mkWQI0J43gEE2GeZ1FMQbptpXSGGqcJj/s9Hh4POBxlj9zDYcbxOGM8TpjHRTYgyRnOMXznNK4KQOvlU4ZYEtoBigEsMePh4YCuey1JUsRIyIj5FiH28CHAhyDxbOfRA3AuIaUA701rEQHfWhfGB+08y6+kIHAC1OVbtP7zjKDeToPtFVaA1HzX6NjB7GQNHWhjFNfsIlOX/wRMW0u5HVf5QRd4nM5At/7FCqItsLenGMjbgKjhR2pOrU9/7vaGXQHGPJlZ+ozby5RP5LJG5WoG/myRz7UiLKAr8tp22wJR4U2nCrELAS50cK5DcF42H4Da0ManVjEAWgEuMgneMJetMMuzk+2XbvZ4KyNQ583me6UYfE3A9WEQH7cLAJx28ciIMQJESDFinifp/OQ8ACmKnxVo4yLdo8BcmrkHAL0P6LuA0G+w2V5hu93BdwGELG6u/SOmwx5xnkEpg7QHsuuCnOc9Yko4Hg5SegSgWxa4zRYIPbIPyOTBJN1/Ot8hAXDkMISAJXfo1HJwDNQ6zb9ex1PuWNstyJit3SnoEhP/JsfzS16puSawYm6q97F/337XwtVor7TuVKNafWlwYczeSgVuxoOi6MGYOgRQEOYm8oB3OFcWqI6ZGZxI9p0ngCjpFn38lucSoXAKukXZMu1Kj67vsdldIROhY4afJrhwxDhOknsxHmWf2sVDdvbKmOMsG2fPEx4eR9w/HvH4MOI4LljmiEVjuAxC13lsdwNC0Kx+L803wBk5LpiXGeM4YZoTQE6zsz3IO8wx4/XdXmpuiTHlhOuY0G+vEcJQEi29D0iZwfBwlOCdQyoAVddvFdMt68TtzJ3Q1BpoW2VM9KoKROf4vAamtklEe0WzRivo1t8trMDaO9s8Fpa4sx70ZXX/wuj0bFqNpUDvCT2aw9Zon1mf+kt1XFVgLn9S5red/VLDTA0PMVA2mCdta0pcS93KN6vCXHinCf+VnAtTEoikfM15wKnXSfejdT6IwVUAV+exyTy2RDxiB3ACJzW9ykYFOl/6hBm1J4X8MBki750q0VTevzzRT3easokhJz2QtS8yoC6DzFiWBTElkG04oDFWAVvp8dqFDtvNBlLK0CExodvuMFzdYHf9DJvdNbouAMSYxyPu79/g8e4NDnf3mI8H6U879Oi2GwyalQznscQFOO4RCfBxAU0juBuQfYfkROMJocPQyUYHgQhz32FJCdFnBJI4W43b/Vrx6dd+FOFDzlp8AoDWjFUNGrDn+FLO+mt7nJf4nHxeGLNqx0UBbQi/vAi1bMoYuIB183drDJFTYdlo0mrhku1/uR5U+SWzZoISg6MydAas0Vyb/MRnl1hbQqWCtzC2nCJKu1QHeOek5zlrJ6x0xDiNWNIibR9zwjQetZZ2wn4/4nBccJwWzEtGjBmZSDq3bbfo+h5936MfOnQKpF3wEqtNESkuOB4OeNgfQY9HEDkMfYeh6zRjWZKz7vcj5uev8bhk3EwZVzcL+s0VQugQfEDoOmxSRtasZdcIRAen9c18qmusAfYihTxB+y1AniRRnZ1agLFVAOtAqPn7PN3J9KOnY4H1qlSGVWxRbsd0CXRPfl54gOLSNsFmWseFK3H7VzNH8lm1YHl1Zku7DYiSuIWraxgV+ECQDTEqbwL2rCweAbseM5iTAjTLbliWcKgKMaula6Gf7AWESw2taQEGtlbmo3FiJAazQ2YC5zaqbRviSFZQZvMW2LOYp4Uq+Daz8jYseRJwzXduvnjSLfqyDwq4AsqcE0q7/yTvk2q8XejBG9nNpO977HYZ8EHaPV7fYtjdYNju4EMAwFimEdurLa52WzxsNzg+PICY0fcDhs0Wm90W3dDbvAPksMSIOI7gGJHDhOQCsu/guh79MIBzggdj9g5zHARwc0ZyTq3coiOu4idf5/htNH4oh2rPF7c3/iou2l/z8Ztwp7/tYBg+GtmfWLj2O1UxIbt9rJunV3e0XtfcXdzYR+a+0rptAV/5uQLcBmzNVcYqOdmLq4w4F7dDyZ58y7QZQ5dxnFg2sjNXlm0tyUk9fEhihTuPZZ6kHzokiWocR+wPR+wPRxwOM6Y5IbFsKtBve3R9j9B32Gw32Gw26PsBfd8jhE7KiJwDckKKCSnO2B8OcP0jun6Lvu+wG3r0XVAXtyZmLRHTwhhf3+MwZ1zPMzZXR3TDgC6IAr/FggUZtoeYrZmkWAl/Zlc3G1lbhrZ2a9JfT+sFa7f9uADoqTu6OYFQmi6V79ratPBn+lBxnfJK0BdNCfZDRfxJBwxL+mutyFN1+hQY365gKxRcRAK68Kp3Olcl2nFC+UiUPQO4Ettd6bMWnyXtbHwyalvD5kuc7TotoBN0g+eq+Co/kpb0SJ288qfyMHCyz7m0T9S91Yt2u3pyczCdanj1rbW13vLnU8z99OYFaQHAmgEpmZAMgnNBXQism1YnLXKWw5nW7TOCD+g6j2HTy45AcPD9BsP1LYara4ROerhaG7Q4DOiHDrvtBle7LQ4PD8gxwjmPrusxbHfohl7XXVzciTNyyohpQp5mROfA3sP1Axxv4ZEQCVhCwJxmzElAt3Net2fiWurEvzzo/jYOWVjbzUPf/Os62F/HcfJsrbup7Id84dzWAhAQNjWbUXfkaZKm7HNzJTUWL1HTNcqSmVwVnKsb2h+sQteJ9lx2sS7aPuvvrURqn89Y2ISzCJnW1Zw5Y4kLyPRx5pPPJacCGViWiHlOmOaMac6IGWDn0XmPru/QKdAO2y022x2GzRZ9v0E/DOIqdh7ekfa2BOZpkt7lzuPm6grbzYDtZkDXdZJnsVizjAnjHHGMEXGa8HCXcZxndEMPP/ToNhtc0YLoGdlD+xVQWRPK4qI2L1RxxzbgeIo7reXYLk9LSmLsrfosneJmPVf/WDWTaK7U5lqcJWiZooQq6FcXbX5YFT03N1sDuo3tDOZRwKG5lryrtFQ1wGYyqgV8ht1ljNw8uPGIopBdh8rlKj9q2AsQVjEvlDPL3gC0GVIreNuyIKvddRAAJt8oBiXUUkMxsjujk9wCWwOylo3NBjUZYE2SciQ9z00ngq2oJHeU9W3Bl9s3azC6TtFFi+ht++EeDvLAOUnWcIyyzyXrAwFIiRGXJJo2xL0ZvC+N3F0gOE8ILKUJ5DqEzRbb62for67hfQ9AelvmnBGSRxcIfSeF8H3XIS0LAIfgO/Tq7pJ7WwLIjCXOwDLLJvVgZOeBuOgG4lm2anIOUwg4hlA2myd0YN27yBNBQ0dfqjf+9cG4cx30XIX/aodZqpes1dpO8NyafZt129Yirgdon5/d6CuNtTC2AuXpepVXy9CrmO3J9fT8E6O3Ga66dJXJVwltl08v3aguek7YhEq9CLfjQ1NWQK3ArvePKWGcJrjECDmDHCHFXPo855iwzBFJN1sY5yTlP1E3/vYefe8xDAH9MGgjma1uubnFsNlhGDbaxlK6PzkixGWRn1oW5MDY7XbSU73rEHPGssw4jqOUFI4zNnPEHDNmzpjmiAUk+RlxxjHPGHNE5AymtaXLIYOzuLODFui2STwCnLaAlzjVzlSLxdYGWLmUW9A5I0lUeuLm/HJyA4IVTOo3ymYQBs4NLVB7ql5AFOrz+68Ge+FXoJ2b9sHMkJCfK5A1pW6N3s117fv2po58xdfVeq/FPg4OScCW0CgbqmiSpiFVDaHwn112VWrDLFuRKgg6G47yGWwryQZ80fCerZGBLsD6viluVL4OPCGGShx5Pb0lUbMVK2+RY08C7uvXr6rQApcG6tC9N5kZ8zRjmicpv4GAbO4k5uNbjU4+LTFIcTd38L4DwSFnRgJA7MCsMaOuA282SKEDkUcIHfphA99pYX9cikCXnYYYWBYgaQOuZQFiAmIGJ8aSGSMZrYibLyFjQIceXrYB49pfecVEFxjxd3fYyjZa4n9Jx1tjt1+OyS3YVjlmOelVE7189fbnyX3L1dt3L2s3wsMsnghaGQPr66rmXwVLa3nXYRb3I5VdnLEsCw6HPVxYEPoewXvEmBFTxJIilhgxjhPGOWGao+7gJclRMhdZDFaWBgE5A5wYWXo9qqdKlN4QqoWbYgKRtHAcugDvPTbbLTbbDfouSO/lGKQbXD9gGEYcpwmHOeIwS8ONCJLeNEvGsh9LHDnmjHTF2iKWQdyXbnDsHQKcbs8n82FW5bmGvNZw2prValGdU8AlvLGVLm88hcgGYaaUqjZAp2Ojc1hUYrgwqCfObU3LMkZ6khqJzGtSqXilz8Es0gZ1Lh0t0Jb45WX6NwBd85pGlospqRu8t66BpgGGPJcFjuRheXXHZsUakDXruoKqKTt0trbt2CTUYzxZXeOnMYvTJ1pdjy0x7bKgehJwP/vsuewvG0TDNKISd3GHlBjjOGKcJCsSTPBeSn26TjpFedWKS6vsTCAXJakqRniy6zLACTlH5FSzm4lImV2aq3edxJOSavHeZWQXZRNqBihnUIygzCDp6wXOjJQYc5aJTCwNMTLJpsUJjIwOPUkJA7egW+byRBO8ZAU9gQSXEhouW4VPXbxhYlQyrJdtz7/EtL+d48lY7i+jEVyyNN9+9/IvoXU9YjUdX/uyeGp2W7BdX/GpeSgyuVxEV/LJuHJd70tjPo5H3N29Rug3cKGHcwE5Z0zjhOPxiP3hgMNhxDgtmJeEeV4QYxLeIsB5DysJSagN3lkTxYhk316CPE8iAEl2DBJv1wJwlIxibkpD1H0XOqd9bFlc65iw5BkhExKybsIkbSSBiDf3j4iZxRWngjFA3f1dABgloYp9Xe+zFuItCxQw4mpltQD1Nu1tRULnLSqKEmTA31h/pUyosWyLpXfCD2/1EOk/jX5dbHVG28rVJqG1/8sN5CJUz+QySXXyzHvCzfgKmdppDICcKIYN8JWfjELLrOORJr25uU8rLjW3QufPmUJka2TKpyqlTFybd6DB7fpLo9RUEG4f1aaC0YAwNdxWlKWSIdJiaVFZqlpQZUG7sk8t65OA++Mffoqu89htN9htB3Rd0FaKkpSREmM8jjiOI+Z50Y3hBSD7vsd2O2Czkd0/yDklDul247sOzjvkQZqpM0Pcw7OULCzziDjPSDGK8PRBJ61mwHGOyDnCsuFIffXSWEDiyrwsSJANiTMzImcsOem2gKgLCQCO5T5QogI0V02jMKqFXdQ5v6IUf3ty0Ve7yGUttvn06yKK3f0runO/+ve/PtCuwK150BP5WX5XcYbKBlXBoeacqv2eAN8T9376A7NEL+sRNb5mjFyZmpWrq+Wt/6oVwNzQo7nRNMmkCp56HA4jXnzxGv1mA6dbX85zxDRFzUY+4ngYsSwLUkpIWfYJzRpc8l1QS3hBmBcMc8RiDWJSKrW94yC8L0lTGcs8Y5mOmOcRxOpejhHTNCOmDNZubzElxCQOp5wgiSkZoMxgZOlOxwLwKTF4jjg8HvAqMxwI4ZlHB9nkoCPZ+zZnJ80MspaamIuQajYsFWluysLayhSFSEXmZb2sgFn7YSGZU532hA7aBB+Q0SPhotxYEU+rGfJ5GOLsVlTGdWkcRbe7qAAWzhDabJCwpc12WKW0TifZ+jO3Skc7ELEQ7R11A+vHpdmM3q9sRIC2EVuzPqZsNBanrC1WZVsi/uxBqgu/8OWKDlqV1n5UxaScVyxWBdjVVFYkbyNERQe4cDwJuD/50acYhg43t9d45/Yam82AruvhvBT+x5hxPB5xPI4YpxnLsoCZ4YPHZhhwfX2F6+sNNhupuyOSTh1OLeI5zhh2I7phAxAhRWmUPo1HybBcFiAzvA9NuCCDSDrpxCWKpp2izJX3IN2EnmKU/RBBkuw1T4iQ3pmBJb3cBWkF5khjuJ3WzIEAyvAqJKuCRCsi+V0Yk7VUgU5W/ndn2V4+fvmxMBqwpYbh7coFP6kwa3Eh2au1ZrgB5hKoacD3LePAGXN9yUyvtOhT6wYqqFqa0nvZzVrBwrbjCENib1IDa8fxsODliwf4/gg4j5wtUSpjmmccjxPGcUSKGSlnJEbZ1J7B4qkK8uqHgGEzYZxnbDY9xmnEbhrRD0d0/Uaylb2XfI5lQlwmxHlGcFJ7n7I02CCrjdRnSClJm8klYZwWaR2ZsmSGJpa2juS0sQGQImMaF9zd3WPre1xdExYiRE8InpByTRwDuHyvZKQCZS/fM3opq240RWXeV6DV8FVZ69bd2wjfNmxhxEbUfNjaQ6dyowDWZTI6VeounmngV4j97UdlK6Xl1vWKavW1GFZwj4BSQqkg1LpT19a7bHtH1pUMrUfuxIAh2cWKuBTANcC7bv8IapQI+0mk/KErXBaF6t3K2JoF45M5tRvxWjKc6VcXkNQAtgXar12H++LFawxDj3mOSEvCbrfFMAxw3kPCpRIjOh5HjOOEeVmQOcM7j2HocTxOOBy32G4HDH0n9aMsvSrD4x7dw0aSoIZB9rG1TQ+mUdxVKctemKFDmCaEvpeOIq7WAcuEaNelIF1GXMjSwzOJgOKcZVulnOBzgtem7pwl2SsnaQqQvAgmZxob1XR+67DTuipPl+UpC+mXtRxXbuSvdY3K6L/u46uO4xKtfS3791SXOFMu9KfxUPNxy/h0ct7pXbk950uP9hxj7pOxmZLQjkX/q03XV5c4YXS7jQHtSTypsXQfDxM+e3EP50RwGn+lnBEXidsuMYIZ2sFHSyD0JwApo/Ae4egQuhHDZsKw6bHdbbA9TthuDthsNhj6vii+Kc6YpyPSsiA4h+1mwjAtssH8qkZZxpISMEfGnDMmBVywNJ0RHJP8D3K2jgTWxKu4DMg+InVBrHSfdCtE9R4w16zTZs2r6691dbc00QDaKkOpAVsF2RX8NmBawbNBUGqVp8q75mq16o5LPNq+YyG4cqZe0y5dSblCaH1rbdHW35vnbHSHNnZblVsqNy68pLdpu0UZ+LbWe2v1mjKcW2nJuc6fKSmmkZqJaKM4MfOtwYZ5fddS2HisacRxAsIruWTPS6cf2NfqdVfXbJR2ucwa4ItCgcvHk4B7PE6IUUqBcgbGKWK7WeC89mpdEqZxwjhNOE6zuJWZ4T2h6wL2hyM2D+JWHoYeQeO5RAR4adcY+g6hV0YFBAhjBDjBW/eaEMSNHSSJw3kREtKMo0foelDo4IOAOmw/ipTAKSLGBSlFES5dh9AP6LoBne8QyMODSllIVoFlgEsgtXTrRDvXCNvfILhdOgrz/Iru3/8sDl67acQNpa+iPTega/8RqWLExXXVfgagaspyFkpEpmH4hl+xXmO6wKQnQk/fK3jauJdLkgjJvS22d65LsyiMp4Crz3W/H/HZF/cyD5pzAFVEpa1pLrqKPVbOuXy/PI1z2mpR2jqGLqAbevSDAu92wDB06LsO5BzibO1XI7oQME49un7WPYBlH2AGpC4SEsPLICQiRG112DsPBKmX9CTfY+fhERA0QTI4JwIbbgWK4pDW+nmqvYpWc0/t+V+i3LVA2nxHrC8+e6/1Lp1Zlry2yCwBx2iLwevv44yymr9JFa72+uth13KfNTmeXccgnOpnxeOj168jQlUaSaPErU5BWrzELR1xUQqoAFP7YBU4WytYTsti2FjsfrV2l9bxBOBMPje/r56tVSKK3DbNuHH1n4F7IwgIq3G1PC17LjQaTPl5meqeBNw5aq9UjNpVKmEcF9Ewc5bi9mnBNE8Y50W1aWEoHxyOxwldF9AP0oEmeEmisoUHkbR3C163+FPwg1i2IQjjuaZZPOmWfP0wYNhuMWyvABfQBYL3PagTN7EoCVI2xMuMvMwgAnw/oNtIfWEfBvQ+KOi6IlcZGuuC6GhMDl63FWPVrh0RoMX47bx+Xfj9ZeB6df6lYOIvqQP8qg0sVoxycp2Ve6XEz7/afdcfmVg4cTXB3LRUElVO7ZNmMKurPTVdTwrpix+YKKii9nTsVRM2gfC2m3Dzgiof9ZoMYFkS7h/2YOUXAV1TL+q1W4Vl5abWMThH4tlxTrbtS1l4/TBh/3AQHu5l32vvNUEqZ9mGMHg8+gldN6p7utNuXA7W7tGFANd3IB/UA9WhG7T/rfNwLsC5DmxVDMFh6zcY3AYdidxw2rqvlHzY85Gm47CsdU34afv7lkctXt6Km5fpzhSxAkyrpRGeWykthqG8gthGpqC+02gAp/h5dr0VheOJxhXtFajKhKIhnD4X1Wu1N2yAZ4UbQLU0VakDUC1jrrtUscpoke9NLSznMhmsN5ZKnupGXm2D19CxxZfp7HkqbxCoKBMtIFfL9gQQVzNRL8d2PzQKcsuotiZUlZR2joqycU415Xi68QVLzBVqucYloesneOfEtZSk8H5ZIuZojTFksSkR5iXCTw7+KBtMeycM2D4z6XshSBF+0L0Ig2Y7++BFM2ft1kOErg/Y7Ha4zgC5Hl3PoMHD+Q6hG+BCLz2WcwbiDPYdcgggAH6zRRh26Psthr7HEHrZe9d5AV6yll7qcsustYGysb1TYvZORL5XLdyI9Ldqd/4fwcrFGpOEIVpBV7XYknhRXIeSqlHyb5XhqyA2pmks3AYUrX6yZbAqmFoBhWIklUGWnycCuHyfy1jtVT63wbZgbd8q5qrERyV/ISGqW895ayWIMub6FbOO82rszjm4zNLtTVGJweDsNDSTEOdF96U2Xpbv5ZjgfESco/ayneGCJFa6END1PTrn0IPQhx7DIMpuN3QIXUDwfWmXmdUzBUfY9j22QWrxnZYLFEuWXAH00iSDAL6ARgTA62fFumtAt0jy1kSummABUFuT9rSy3NV/jTbpzZR3qXzQRDtXr3VKLna+3aExhM8AcAUWVJ9lLeRrYtK50toAuWHpCoD1asVdzHWq9DPSGZFEVQaZUQJSoE3VvX4GhoC5q0mtZiKncV9U0DXeyBcAsgCjDV6UsaIItM9hD7q6fzMBxn91IU40koq17cRZDJn179YMWOcs1+NJwC3NLRR0OUlpgWQGCnMk1Yhz0s2ubWiZkCgj6mo6RwjktEyoafyuLtrgPYbgSzkRB9n/Vtxjsu1XZgYckFIHH3qkZFq61AV3oUMXBrhuAHyQjRdCkN1eYgcioBs26PoNuq5H1/XoQxCg99q6T4WNxYehlq6jhKyLSwSkTGAvOyR53ai4CEt6Wrupq/fVwPJtVuPbrvW1Sg9OLeSveHzd753S+eVjpec/PY8twIFWmnIr0Mq/pxb3ybWYT8bX8OWZtCtA3AqRRgnQ661e7ZOZdKP1HBar/OQ9136vDBiSmxAFcEnDHDlxaWNn2bimpLQgAHU3g+Q6CKHUr5Pu/MUuF2sk5wx2BPIOiTNIFeeawmWcL8IWTlQdFwK6YYPtdourq2tst5IH0vWdtnbs4LU8KYOQ9CE7chi8U8XWkr14FfJxjq2MwMKCJ+F0c7PLoojCQuvFL7RyukjNhJsCdspD5X4VkMo41XVfBmTAfVbD1GB80QbKV1YjaYEPJ89xxidn/C7ffZILWhpfgdTJNBBAbCVJzXuVJeR9Df94iQkglWsIQBvDGVZaE5I6n8ZZbd5DBeDVo54sqJxKhSfqTfSqZ/PafFvxyjywq2uvlIP1GNbualSl98LxJOCG4Mr6OmXYnCI4kZb52ONq/VTRCDVTkAG2/rWZ4DwKsEkykvZi5oScGNnJeUQSpw1dh9B1ABFC1i31iNENPTa7Hba7Kym432wwDAP6bkDXD/ChB/sAD4bPUoCfcgQBWssrCSDOEchLJywbDykxW2u8rC0riXSbJj3HC/fKtmSAlEwooUoG31cA3V/haI2or17n+8S1fklL+a1u4699FMmIM0g+Bbw1Jpc1M45qZ97i8ESVKeQSai+1zGfa+Mn71P7z5CM29zz5hO3aDN2suvnbvk11LVv3mrnbL81tzkKfQnpnD1KSdHIbZwOkE5XyKRHATjcqIXlRjHBe+jObtWaKsp2WYULVjAFSl3KQPsnDBv1GWkVe7Xa4utpid3WF3XaLYdNj6HspNeokZCSWqu5hzQzKosia5ZGZxWOVMzgREgMeLI1ybAILMdSpdSRj7W2JHErv9LMFK7h7uvgNbrZfy1xuy6CiuFlCZ2apGc3aBYwyIyOL7CySvtJmidY27mqz3i/yRTt+Wv2ykg82PacA3h4V6yuPGLiuN9AFmodGUeeYmzmqYCwWvgJVeV8UJVq5Gk49PhDL2caBkwcwQXyG+iiW8QoA29PKs8i7Vn9es7PXc2j3Kha3WeZU479yO67g/hZZ8STgDiHo7UU4MXPJuS8WBenuHtLXXDpGKVMaIxAB3jls+g677SAJVMEDDohR+jQTIFZu12HYDNhsBmw2W/RDD/Ie2SxPZvg+4ObmFrfP3sHVzS0GBd5+s5H+rrp3biZCQgBzD4ZmQloili4ma9P0TAATl7ZyGUBSJmeVTAVwwfAEJHLIXK0JsDbl/g2CrdAm19/xJBv+Z3LQye9PPM0lsP3S61VGM8u3ANuZNdMcFya2xmbsg1PdeD1+OUMd1cXFyMXSKZ8XIVJdY8bMzrmS9NU+mvE0QZtCmLCCCRsT4HI4oOwNTWQZy/YMEh7KGpdlklpX5+uYyrlEsm2hl9yL0Hey4UEnlmroBnTDgND36PtBFOHNgO12i91ui512oxo2A/oQSsjIObOWScJUKWu3KwFyJpIudJmBnIWTFZTpRMjlsgIiSJ1z2MA8alyA7kyA2+rVfyqeXCKR5rsyr9xoUs05xY1vFrqU+1d5rDFNAzVbY/VOFHotq8wXx2Q0/eVb79n1G8wpHNLEQZufZnTVeWrfELlnSlGJe6+Itn22Sp8gS3xDSSSUShDrXQ4FONIxULOlZr2+jbuZPuOGs4deDevslFYonMgRKqrCyTWN57Sc1HbP/WUs3KHzK+bOyXomV4S3JIWcSQAuiRBI2WIWuq/mZoOb6ys8e3aNq530QyZPUnivhflEhOADNhvp6zpsttI8PQSw7uyTcgZ5h6urK1zf3GK7u8aw2aqreJCkjeDFHe6c9oxXAQaYDiklFJkRYwJRlDKnJI07MiSGG7PVDJqSUZfRO0JHDn0Qd1i736JoUFwJrF3SS1bh17QwV0L8/BZrDf13dLzN2r1Yx6ZvfelU2HkwgVbn9DTCUy5YNGi7xPlN3gL1XzKW5o5FgjWgy6sPaxyV7MN619UYGq26Aqlp2atTBHy4NoFw+oE9cUqSsFIBmJDJMp9NkMlP7z02mqHc9Z3u4SquWasM6Icew3aDftOj6wf0Xachmg5dJ1tiytZ+vfDxIKA7bAZsBtmVyHuvSZSSMOlIGlQkn+FSwhITUsyIDNmBjKRpTWDZGAYkdZ4glkYYuswJsk9EYiBBQlU36ABikKcTI4RgHrn13KOALvA2uqB6Bq8VLFujzBmOSeK4YJQAARlF2rcMfo0u1nH46rVodE9uga2+WUpnVu+tn4PLLY2eGgXyBIQK35QbtzNgdGhuVjFqrJ7g7MbNxauOcQ6TrYfn5JaFbdZKAJ2Nu3xiF2WIwqgfV4VKnl0iltTMOSqgntz/spyisq6m9F06ngTcbe8BSIcXgJESIUfpw+p0eyRRFEnjrYToGC5lRJKONt57XF/t8O67z/D+B+/hgw/ex7Nnt9hsNyDvsOSIaV6wxLqv7tAP6BVAQz/AB6+7rjDmKFGejTLxRkE5dD2CF8u27ubiFAi9lhhoYwB16+XMmOeEJTGcS4CrE5U5I7I0fc+6rZoJPUeAdx69c9jmsLKaHZ0u/K/5MK1Zd8oBoAT+uwfZX8dB5d+GowrHtCeZ1ZCLFShCTU4wpYfZtuQzBm+UFH1Rc7uCgY1FunqhfV1eaC7CTs8z0F/59MzyuaBoryZEn0X3PC7uskKLpDsY2cvVrH6gxGBJcxFkfARiSXQBhL+7rsP19TVubm9wdXMlTW56D9d1sPAKSDrEyVaZsnXf0AX4EKRWPgRpw+olIarvBVy7riu/+y6UMdq2h450zDDBLQvEnCRHJEYkZjiX4ZIDUZY4MYlFkcFa6ylW8sKMKSbElLHpe6QrgDgg9B6BqKyPAdp59mujtJItYYNuDepVbyvBgvfMdX0z6043JuCbxa7PiVUp78kIUIhUx8BmqZcz61dXmMj1Kuu2sDgjuAr+9jGtPr10tO+eA7/NQT1TDDD7vIUkLu+J14ekv0s2BZXKea1S3SofLcDaX+U73MaBTw4y1m6V5Hat13qx3cXOK6EZtOfV57l0vMWlLL1UvWYeJAckzbhz3hfmZ9Z4UpKMUCE+2VS6HzrcPrvFR598iI8/+QQfffwhnr3zDjbbDZz3iJwxLwumRUCXWeKsoevhfQff1WL6DMayyCYFIXgMnTBx30nihXO+bEws1oDtYypt6awxQEwSJ8o5y09OYCRkouJSZmbELMlaydzKukCkmnMfHDL36HzApuuQ21VZMc+vdpxaxZll/G1NrmOu5PhL3vfrxoK/asz2K8V6T29T5u/tD9NMAbQ68MTGbG/wm1NKqlFU73Euwi1uzBfog2F+sbNRrrT9Jm4E8bR4zRomkl7mRLYnqA4nAQlZYq+sFrAmszjnsdls8MGH7+H9D97Hs3ef4fr6CpvtBqEPCF1fvD0M22HIYrADQvDa7tUXL490sLIyImkHG0Io2cgoioM+udIDOQdPKpTJITFpFyvpZgWXAfIAkmaGEsACtAnSDzqmjClGbTEJLDFj03W49oTcXSgmK+xCuCQgK9+fIVRDbeUNkx5FhrTJXo4hVR8t6KK5BMStWuLBQAkZnBDE+XsFjKn8eXrYrdvdbVaXxKka+Rb+WbnL5aNiSHKTl68KSIlF68+y/MUD1db2aohDFQsDNV6No4K4/WmKU2WvE5/X2bS1sfJ2vtv5O1c8ajLc0/LvbZ66JwG3Ky5lIQBzFAASbyUncRLLJk4kRJ6YtXaVsN1t8c777+CT73wbn3zr23j/g/dxfXuLbhikrACQnqtZerdmFteVxHV0k2G1phmWrSxZisH54ppyzhVXmu2HKIJHan+Dk0pbBuCcAFZMsgdvyiJQFpYYbtakEgHcrIBbNVYAslNKCHAg7PoGlFWLe8L2eXoVvsJhjJuVILMmbVBmsOMyxl8teemvw8Grn0U+EVVBuzpb32kBi9ZnQAVfjd3S6jNhVK6S6ATs7WsnG5mc38eEivJKET3le/ILkUGnZhJrMSmXsQC2J64YOHT2clp36yDdlqRkR+let9TRPUBkqBbIhTaUYPEUffzx+/j2dz7Bx598hGfvPMP2aofNZoPQS01tTBk5J+285jXZSRIPRcEV5bvMEVHxLFGxZGV89vuqdINZMqD1OWVPoAzvGYGBjCybGiRG1l7sTCIscwZSlhLGJYsHLOpYraEH5wTODjE5JE91F7OiJdW1PoWY8tOsnhNJasBqMWIDWBPkKWd48rJNXXu+xYmprrERVHGPmlA/ocV2wwUZVuEAW4EVtjBOn6VlAW4+sPtCNglovl2GZ/TZTkAD1u1TrO/OFVv1iuZgbyPvJeSivxfezYzEWb04xsfcgB8qfzeAKPTYDJ30uoLqBcwBLjhmyc1VyVr/bJ/NWPqSZH9KCj8JuJtBk6Y01pM9wWchCK/1fgYAKel+nMxwieCY4EPAZrfF7Tvv4v2PP8YHn3yCZ+++i+3uCiF0xRJllvhM0r1soZ1l6mRIwwmGxEQyy56fpu9718RODXTRJHQVISWCxsGSJxg5AVEzoM3iTboQKTNSUu2exT1pzd+9kwzuqYulZGllya2CKEoEq9X4+qBoRJjZmtBzSTbIWVPwIYKhzIV9920q11uOr1SW9Gs4CgsXjmyRzZ6pCmkrhkdz+qkb8IK9rrxIZy65c46pXPT2p17f00TJZRf/2iYqX+R2bk/oqLFnLRfBbueJEBzBOxSwdeQ0814VVenJBCJCIgZxVi+VuJ+f3d7ik08+xO9999v4+JOPcXNzg2GzEfdv8ALaOZdMW+dqjS0V1zU1tNks4QkvltpZEjM2oxjaKJanrrNzUtGQMiOwPEdKKLLUspZZEpdFZCcpX0qWDcyVb23Os8611ZK2UFWXqa5eu44lhIOWJriyuroUWsXY7rGiE6qhqyrXG2XRVt5i022dcAMQlRIaWqEWANY0SAY2BkTQhK1ymZpUZtbfClpaxbQ1a3OdLRuXbTCzjrDJwDJESTSLt1WGy31sHsW6gFNlsXht+KTKwEDX7m/PrHK+6NLNApp1zppv1M6DJROadV6Pupqt82H1Hs7lb3s8CbhXV5uqaGW1/mxnHmAFSiDNwCNxb3pI44p+s8H25hpX77yLq3fexe76GbbbLYIPMFdZ5lwygksJw6qVndCyWJ9aHpRzQ8OElQQ1xrW4F6gsvBG3g4R5PUmZkjdhwZZgQiiBF5JFEbrKQLZ63Urc1fYvahTWzLBa6690nAKcKmXaBERdRIQi5Ko4aEHkLXc7URBWSVhn+sEl4llR6S91rJ5Rx1vW3IQjlDlO7mfPnJVZTYDbeKtoMSGBhlHr3+VKJsy40e4bplrJzLPn0LGXd6pWbLhzkSJO5rmQfxEg8k/dLL3ePXiPTd/BESMEK7dTK9KLaumdR/DCW0vKsuk7SdgnhA7Xt9d494P38cGHH+K9997H7upaM49DQ+PqOTFr1nn1OlXlx9YhGX+imWJdjxqLt7XRHujNgxcBTUCyNBFVrr3+DgCcgUCE7ExwyzabpIFqzhkgcVF7zigZ3Sp5y7jOloLLnK9h9cklK6vCYLW8jU9rnXJNuKT6nGR8dVlOlAcz2WKWdGOItF+os3pGss15BtNrVaOy4emVGyuysTgLb9izNwojLJeCWploXKwqyEo+UlGODHQNDIW3xcgiziD24p4vYz7hZfs9C722O/8U6VGedaUK6BzS2fvtSOs9z0G3laeXpb8cTwLu9bUAbkrV3cbNna0NXIwJbolgZ4lPEQ5Sl9cNA/rdFfqra/S7a/SbHfpuEJe0cleGMKozC1eD3Fkn36zHxIxMom2DfGMByyO2Qq+kcROac9aHI8ns9N6h04Uyq50BeGYkJ1a9jEF2SEokVfbmIlubWjrxb7Fgvw7oro5Ge05sAESNldCM4WRI7b2fuvYvY3V/leNibNjuqT/bsZ+CVxMRaq8K08JLFx+9jWvOWAkeE+qMGhtr8Rb1C/WeOpkXF4zf8hdWCUDtsDW5Fqub210bT0m1JKiVb+VCXdfh5uoK3kudbKdJS7JndAcmjWkuEeO84DDN4GlRZZXQ9x02uwFX11e4urnB9koy/oPuOS2AW1rZFAUZpLkSVLs/ZdaSjly9QMJHtQ7YbI/2sWwNbR5MObbEKkfZ9OciSwlAcBq7tY6BkFK94KENakS2BAd4YlhP6koGlWDsmoSGDk1nvnCsqLlYoQpkSoiW7IPmVetLWwVEaeLiXQz8+PQtrEfe/GqM0Iz9omequaeBTXupsloNj54CrZ3XXt3qqdtYeTVGTtUBvRwXyhCQzVkUJ9Y6ZuVXx+Lh9Cr7pJ/2SiWpY6ZmUrhVwE8A1JQCBljzAbxdrfAv0K7QqXJSr0fl87eJ0qeTpvpOJoPEZWqbDfjg4TVuA5Za2sM043F/BLkHzLOk9JN38F1AGHqEYYMwDPBdDx80yUnXJAMg1UjLdlugYnFKYlP1HZEj7QRVH9gmoIrJmlzSTo0xbTuNDhoPYwevPWLtKlJv28RMDd2o2TmIKkHVZT9dp/UKtH99na5Qdg9GbT5QCFp0lYbWqhLS3OzJ65dRXPj4q7iT6S3XPn+C9W8tIPIZulyYu4sEze0ZJ4PjRsg0EuNUO36LJX/pyarwbk9thQqdvOoDPF2ph0aROJ2TOorNEHBzvYFzhC4EbHc9dpsBt7c32F1fw3U9UgaO04L7/QFfvLrD8xcvcTiMyEzoeinvGbQ2vuskSVGaUXidCq8yq1otRNWV3AqyzBKLdcRAJnEr5irs5Ot0toAljEmmpMg5BrreiUJu4E9K4MTGtwAyo3NYCXtyhOAAhwRwWs2pxYCpWDUVqMhckFQTjc7Ct9RAR/meIRadPEvjYWvWncp8NHNRUE9ntew/C8W6U+m1nsP2b25J8AQkoKVK9Z4nsMEnNMcNnl/ihGYAhneVvahci4pMqgMsw2OU5LKs2/VxoyiJvNNzFJAFrE/GczIX9tTtfYDVo5VXG8ApvHwiE9bX4fUHduWztarHk4DrCrDJ1XwI2GyvsL3aYbfdou9lq76UEh6PR7y5uweFgMNxxHychFeDk1KdTkp2ShIFUemRSkrRDOlVXB6aNcaQZe9MI9a1xlwnr5Wf5flZLFlWMGq9DlXYC3c5xxeuKXHfnMW6Zkdac4yysOJK4pNtFN+i4vyyR7l+JWycPNM5aHwVAPzljrUAOn1jfbRgXJRmU4Bai675Kb+bpKGy/vX57HcVTE+PtDmV7c4rsC1KSuFAuW45W81StvHZd962zFynpg6zicWS5R6cXqSKAOt7LDWzudzXMuVD18l2mH3AzfUO7737DB9+9D6evfc++u0O2TlM04LH/REv39zhp59+jp98+gs8Ph5Fee4HuG4D8h2c1x23vJTuUJ2UZufAqmSq1gkqIo9LFysCQFl4ypToOhlQvqm5FRWIuMyP9F53IA3eyrIYzYjc8BAvViAH5IzEBA8GyIO8xLdtQ3MuVreK1paMQA3rtDSHcu8zb0U9UxLBGtljn7AiLblmvZtXPbsqY6TPZBeUMq7ia2luaqCs92jop+Ggk5/N5/p1S0It/THbcwut13BLwyyV5U/Yz7xOYvHW0r3VOeWdrHHgZrbt+wzJ7FYcymxx/2oVN8t4dvClzwrWn2goen6GsX8DviffJ/tOq9nweh2fOt7SS5nKRYkcumGD3c0tbt95hpvrW2x3WykbyBmH8YjNzR0yOTzuj4hv7uA7L1NSiBylANwerhqJpJsiNFNkyQ+NFmifluQHoLgFSq4bGy0J01hyZqENLiL3zCJrk1IAgDXjOSNLIX1GAdtyTjO5T4n9iwtfxvNLgOIThF6YwZ6jAbrGg1XH0xBVXZXza59rayeg3io6p/RJdIbFFetqzPZUY6zpD61gQhHOKwAuQHRJwaXme+2HXMfeSqCTo1XO1u+ezsP5ccroX47SJuOqNUalhdB6FM47dH0Px0AXOuy2W7z77jN8+OH7eOfDjxC2V0DokDPjeBxx++YNrq622F7t8KOf/BzjNMF1UuMudawGRLVsZ8UPxfODAh4GnABKAh8h6zVyiWGegZlORqULW0NN/AFLuMe5um4G6Ha/IvjlcwcRZlljy95LpYTTbGm9QtPqEtXNWya/js7kE4OaDlVPru7qmvaRWdPQUq3TWHaZE6q0vVb7ZfK44YZCByfkWuTpiXxbyzmTjHUDi/UnDYjaZKgreV0fUGepqIcsO6lxY3noKqtyZRqoekVaoUAkHsxyHZthXXtmgCxEoek1bMre+n4XDwNQomavilzes2eg9pm4TnDb5Ol0vZo/9Lpv5+8nARdEukE1gUKHzdUNbt95D++89z5unz3DdrtD6DowA9dxwe72HZDzOI4jEBzGMSLHiDjPSEtESvpyXtyx7CQJ4kTRKMX6tNYubBLMxVyShRqiauO6BEjwnKTbi/GnAa4kHnGxWOQm6zoxs7eNYZNj2fC7TK1qY3r/i4D61un/akcLyiVeoUKsdM/Rh66xw/W9qRCGEKoz96AxXhHyTd3xyVXWyQ6NNoo1mZVkEB3fqQVqGnBun+1EQySIMgVNetELi3DVsi/ZYSSXrRILX6EV6wSCAMCp4mY3XgcFGCayftW1O40+U/tLKwRWSonGqFjdqAwwi7+00rjWxHYBLmd0ncNm0+Pm9hq3776L3e07CJstKPRgImyuE/rNVjaJ91LT/pOffSYJi2nBHBfMMaJLUUp4WHfpcRVYUeaW0Aqq+lR1HU1EQ71Upy5xy61gmBtxDTMONaLaJiiaUoAixKH0IPPnQQgqU5wj7XmesVbqdIbL9kF1nU+9NsW1eCajUGhppcgxgEzSGIjyaicZk6dWPVGufyLn2u5zJYm44aEz3jQlgUy2CU9Vo8TOaX4Cmv1sfEqQbPbTa1e3bYEb0xnAjeejnm9/c7lX5cbiWkaVrQWSV8oDlWcgliSpdk4shtuW39VlWxHrGY4UZejMU1YBk+sXK+03M0Mr+q+SZqUAPSE8nrZwLa7ZAb7fYHt1javbd3Dzznu4vr3FdruD950gOzO2V1eIKeNh/4glZ7z84jU4ZyzTiOl4wHQ8YvBB3D8AcnaafPHUCNqDyyRnziUb0vbCbBeYGwL0aqF6aAs4vVZW5m3r5mryRmMRacxWvinxpFp+gyLk176VCg6/ylH57nzl2sSgtXuKqqbVuGPre3U2s6Xat8Oneu2yiTsqy8ijVsWmuOgaF0Jxl+pDlLG105HrnFtajrsgfE7BqjwvqBFc8kkLuIDG9prv2ACqvST/Es5n+K0rd+HD6iE4HbX9xWU+vpoKVkEf9lurGKqA8k72pA2dh+86DJsN+t0O/XaHMGzhugFMhI4BFzpk57FkxuMU8bA/Yk4L9scj9uOI7TzDhx6ZCcE7BC0vct4LuRuq6JS3T2EdlWzE9nditUoV2EgtDDCkXh6VzmC8a25LNpe60kmph09w5Mu8F4uJJH7sIBnMXltSmhJplmLl9QxxYTVTvloepZfVetvzryCy/OYAsHPFestAkTu1ftoVWmhBbHWs+JWbW3AZJzcKagW3RhbyuXxblVrqjcvfdF7DTwVwCwxVwC2gI+dY+ZjIllyo3kBZyoEq79mGOOa6b4MrxWBgLpsLOJNVei1v14eGDaooqEYHVVWhXWac/l6UqupNsL9ahazKFxSeqGWna1nzlPh/q0uZvIMjj24rvY2H7VZ+Djv0wxbOhUqj5HBz+y7e+/Bj7I8jDocZ43HCOB7wePcaj1dX6LUFInOGI1936UF1ZTnNphKisVo6IaKcpRSp1M42s3cJcE07S7Adgaicly8SpJIiibbnQFqUL0sdQk1WgqPSzrHt+VnWrvnt9PgqbuR6SpUCRpTWVcg5h+C1q49zRdNnhpZGANZY3TJGc85YUsQ8R8xL1H7RUss5DD22g2xbaDXMRYzaOkDaXpZOXTrnrVxYxSaprkcrN9bEZkyoVsml01ql065lTMZVCJI1qydj5koQpxZ0sfp5fbN2vPVbzcCqmn8yzmpJVeG+1nalfO78udrLFvVA9aX2Pi0ME0F6h2uHNQoB7ALIB3jdyIOdB3JGGDbYXiVcP3sHN48HPHtvj+cvvsCbxz1u7u8xbHqQAzJvEUKHLnv47OCzuHfNOoNzIPNIkrmiVRDmWoNaBDAztDsqSl6zI4DFXZyJdPMQaIxWFsRcv0JfWcI6mdW9J/NQEqyaeRLglTplTzbXddbkT1ECMmcFAVrTQ7M4Z6RqdES2zlXhsnWzDO7EGRGq7DdCuSwugApyLRWhEPYaIKQPdgHdBgrWnj6UbnSmrFj9/krBrqMAyJUxGg07orLVnshDlAx1IgY5J5u+6LUBk20VokUFUoKxhhrygOW6xOu634YDitxpr926eKnOevmsKNi8vtTpUQEa5Zf1itfyQomtV0lACuxCi3YhVy/4hIh/a9IUeQfnA/q+k02jgyRW+NDBu07aJepgvO8xbK/w7J338fB4wOs3D9L5ZZ7weP8Gj1dX2A4DOu/Buas1gyQuGEfSOYq57pdrPYOtCFoAN5dXZSEqmkjJYCYAjiTL2GmdsAkHRr0uTlybsJgQiZUP0ajg5NzOc9kVKGic6BRI1lDbrvaXA+3lo11o0r0eUZgyxdQklNi2h1p47musKHHGsiw4ThMe90fc7w8YlwWsZSK319d49/Ya19sNEKQHdbGudPRSM50QU8KSc6kNrnNXlYP61G2MVZ+l0QytU5LQqi+lTmXGGhASoSWxzSLmqLrJjens2wSqHu0i+OjESsEJmzVzbRa1/n0KtpcOU+gK2DYvRt295+I9YSBvMap6zXoRtSgUdJxp8mR9j/U5rbMTnNTP+gDXDxh2V9je3CDcP2A/zXjz8IB+6AB1MQ/9Fovv4H2QzlKeamcp5tqvmdb5E/W5W9CtlpU9HzPBO1GxHBESOemVrMlhlmxj1QG1MY3ttpPh2JUJFT2AQNlwQyxcDwO/pk8etSBna3ACmxUVsaaS+l1SejpVxkRuyA5kc2LpBMcMi/1US5LRpCPptev1mQhjZDzOjE0fEDT/hanWrBo/FNnHucxXauYuJtmIpVq9p1DegJVtxFLeZVVghE8dyz7FVCz5ymtW716V9DLBzTyxEYF6PLggn/BKA76mTaPSjyma5fNWmaF6Dtr7lWvZ8tIKj8swGQVUC12ovFF9oZKGgiqDNATSXr+538nxNOB6KtqMM6GjBOG1PyrBlSw3x0DfbbC7usHN7TPcPHsHy5IQQkCaZ0yHPabDI8YQkFJfdgohkjIj7wJYNzywhvy5gEd1KTO3DK7PaGur0s1Cw0Zc7cEnv7fWbYUGVNA1Ye9EmyGi0rs4kINtzHR+l1NW/OWPam3LRgmRGTFGLMuEZVowHyfEJSInAM6JQtQFuOBKrXHoAhjAcTzi/vERdw+PuHs8YFoWEHkMmwHjkhBzxnGasemC1kvLMwevcT1Ii82YEuacBXSzrY0hjIFG8wzNXLQuYefk2p0PStRWdlKB/m3zIrubVa125WI3LqJmHey8Bqi/lhpE7U86/+zEemjp1NC3xPbsOybEda6pPMf68q11IooOIcMpP6jnAdosjwiscW4L+DORWr8duk2PYbfDnGbMS8R+f4An2fHrahvRdT1C6JG5RwodOobwrD2PCmd7PvM+5aSej5yQGiEPFaxUHkRsy5wd2JmgRsOH1VqT7TKlRtP2VxWAqXtZO5VLrB1hXBmf0YKBUlXi7GVjarnWftqzrpaZ1meBmhg8AQQPkMcSMxxlxC4j5iTr66QpScqylSkzwzvCEDy8k71+v3h9j59+8RJ/9dOf4Ec/+xk+/NZ38MkHH+HZzTXeu73FO5sBm45q1zyduJwJ0QwTFg9DTAmz7sDUdsWrTUca5aHkRtQufZ4InYN2NXMIHmA4AWFulCzUHgYWDmhhj8jWVS1bYxYde8VOA1g23bJJAVEFi3ndRbCMv2VAUuWq5X27RKsotL8Z1tgYTxSrBmiLXGEZs6M2fcuI/Px4GnAN6ZmRUkROESmlYjk57+DgQexK5pgPAV3fY7u7xrN33gUniaN6IuRlwTIeMXcBnGXbPRcCnAvIPgjTKftarZ/t7pNb1/LqoS4flZBoPdl1ztdatAkFaDyAUeLLK+HHYiU7Pd8rcVaLWQjEndzzbQvwVY6SNQoCaSOO4zji7u4Br1++xovPX2H/sEfOgO8H7G5ucP3sFqGXrcm6LuDqagffeTzuH/Hy9Wvc3z/g8TBiSVlKvoYt5iVhnGdshwF98NqcXqyc7dBh23fwkomClKVR/BwTIifpd6tMDnApganKmlE7FyZ3WvoxdAHc65xrZrqUcXGzjqfOI5nguj7tfXSdCxOYFtwCm/1i88tv3U+Unvzj/MS60pWmqzJSbQtTTYyhRdhZN6cERydlNfa9YkVm7dQm20vGVNsr5sxl675iiTID5OBCh27osbnaoUs9hmEL7wcwHFKKGKe5NJ5xTiyc7EktUCqoX92YNQExKw1wztXiyhYbo7q2EC+Ft2vY9VSg5Wykwiv3dJ2DojsVxck5lo3p1cKttop+jzOYnc6f8ZSswRpTRcoUxQLV8pG35PfiWSjwoTkBnJAzME0JccnY9h0igOMyYn+c8fruEZ9//hrHKWKz2+C96yv88e99gECET3/+Of7NX/wVPv3sJxgPGW+ev8RPhh/hgz/8Oxh62T7x3ZsbfPzx+/ib3/0W3r/dwrOU1uRsPeAl7DaniDnOOC4LlmVBTIt6qIDa4lRtNFVQXBNO886hdx4b77HpHHrfbNlKhCQ7ySMlWR+zqG3Gy/qsuBXlvq0beaWcF8WsmlaWoErNfLtmLK1CbWtRrsoN8LagW5QAk0uK7lzebR/izKBAe/9iZatwecJYeEsM18sNdYusOM+I84S4zNLMnLP0XitzSiDyCKHHsNni5vYZkAnEGVstrHcgyE71Ueprsvr3SVwlOetG8U4Yb9Wsf8U4QCsd1+4RO6EuZesSLF6Qxk1qFnTp59l+k6iZXN1jV11clj9VsgJNuKK6MVdOo6IQPyWxTZA1I6AqQJclYn884s39A169foMvvniJ5589x/NfvMD+YQ+Gw7C7wjsfvI93YkS/GZBZAPd6juj7gMN0wONxxH6csD+OWGKC9wFxkXWepglD12kyjjSq74ceV9sBV5sBfRfgnZQWiOYcpRd2zkg5KW20gOtqk5BWo9V5CMGBua/N+AMJ4KACrpLXGdBVC8Pm6mn9slCDrWfDpL+sKtQu45cY4yttv2Hni6Ns41CmGJzSjNFsTAu86zAvsiVdSkleOYFSgiNXXNilaQWRlPld7eAY2G23uN5usN30su7eowsewQWthyVtVMMXxtCU6emY2oQd835I1YekwEi5D0OSr7n0szXF1a5dXMrZ/s7wJTAkcypekqLilvi4BwqNFaVH/y1dilhi0LK5QLVi+ETzql4vE6r1fiJTqkvTEn0iA4f9iGU84Cef/gw/+/w5XnzxHNurLT78+Lv44fd/jg+ur/HB+8/w8Ufvw3HGs5sb+O98gt3g8P9+/jN8Nk7ALNc+vnqF7fvP8I/+5I/xuJ/x/e//HJ9+9gZ/8Dc+wR9890NsOyegy4wlM6YlCdDOM+ZlxpgW2eow5WIFq4AHnF8rterZ9M6j9wGxC+DcAb1s1yghhaxzTUU+mWiVcK6UU7YaqIWBVL9tZvcUeLl0gxOlkVFZvbGGXZ33dbLmV1SQec23hgUy/gqykrzXrnmlilWwjNyKdy8db4nhuuImQlwk23g8YBoPmOcR/TCAg0wsN3535zz6fsD17gYOHh4k1tF2wG63xdAPsl1XCHA+gJzsKVs3LGD5vTCuWrhNZmuxKE/njxs35AXhfKpN2XXa3XdKAkZjNRlgENQCzlLvJyn8Naac2CHp7R0pgD8Jrifja9xURbXQ93LOmGPE/cMen3/xAr/4xef4xWef4/nzL/D65Ws83j0ixoiuH7DjjM3VFtN0VdpmTjMhpoh+I1nlXddhsxkwTzM4JnCMWDKDlwVxHHHoxLr1fYd+GNBvNhiXGft5wdB36IIkvInFkEpSRtbYLmt8vc6fU+OuKk1CkgyfdN9l7zS0IF3MHKqW2MaFTzVK00jOZ7mFuMKVawKwdQZwbkeeX+00oeKpE8u4VNjYsxttFoFjYHPaCxxoGLd93qrfx8zisnQkG3+kiBjllWIEp4Tss/xU2dpuXWl74AYi7DYDdtsdNkOHPviyGYJkKktSnrMeyo0saZMQi+LaGAviZlwnOLo6IfqZa767TpAxugIkJlpAk1sqqhaO82q5qZVr7max8LmAeyZGdtCSE91UtABpldZFlFBVvltZYJ6D8vyQHIdxXjA+HPDmJz/B859+ihev93j16g7T8Q3+9p/9I/zh3/wD5DHj73x8iw8+eB/f+/1v4eWrV2AQhi4AnDBOCTe3t9jf7ZHzgvHzn8PdDnj+s5/i2fU1NjniZz/8KV5/9gVev/ge/v7f+R6eXQ9ISRRf4oyeJORATKAEcCLkRNITk1PjhVgKvXrjK4jRlUOASx06AMFpz27vEKyjUFkPU7akKyBnsxTrZgWVdBp+Lpyn5T+EYkzR6rXOA7G6hJIk2XAkrb58wqetcmgWdHGFV6lRsOTEw3R6uXaM5Rb8tFH1dB2upgsxZ+SYEJcZ8zhiHo+YpiP6YaMDCABcSXAiEILvsNGyoc577Dayh+bQy+bUIdhWXUG4pGitjc3RaECr0p1GaK/msQVbu8aZUDy3aVrNmlH3xLWzXOsqgCoWJJnPIGvanvTlkIq2Teqe5oYCLh8mcAg109MUgZRlz+DD4YiXr17jZ5/+HD/9yc/wi88+x8tXr3DcH5BjKvuQco6Iy4TpeJC6ZwW/8SjdhbZXWwxDL9sbMkAxacwnIhEQHcEFDxc8uqFH1M28lxhxnCL6vkPfd5Iw5qVXrRg/Ov/aGcaEEDEhIxUhaZnjMiMMlxO8I8xdh84ndD6rBdSsVIuX5gau+jjQMO05/ZysvgGhWZDl25fo5UsOPv2TVm+Wq7WW+uq95nXhEIup2h5UJkKVsGVBcIxIJDG6ZUZcZiwxIqQEpCgijaiAmXNONhbpOvCWEbzDdrPFbrvFtu/Vsm02tdd9paEux5qRuRJDQMM3ZXIqehahVsG1KiTtF03eWmywbtbB4sKENVeolg2RhcCaRCrN2ndFoarWrbwIjnUzex1HNjqimmBVRtV4uupay6NlAlJmzEvE437Ej3/4Kb7/F/8Rjy+f4/H1HV58/gbj4RHXzzYYjxn/83//z/Dw4nO88yd/iA8/eQ//w//0z/Dy7g7Ptht8+8P38P0f/AjH8YgwOHifMe8PiHFCH7+Lf/gn/yfkxNjPjL/6y7/CnoE0HvDi5z/H3/gb38Z3v/sJttsBTmV3FwJq+U2Eo4gEIMaMyADYgThD6t0JcFRErG0rmIkQO4sBB3UfO90K1XjUFSVLpiarInwur+s82m4TDViuwLYpRWrpv7kil3PXYF5/5fqdBkNa79lpyZ2Qrli1RY6trnnhoDqKpz2YbwFcc0OV2ecMzgkpLljmCfM0yqJ4GVjOQM5J3nMOXTfAu4Ch67DbboplFEqyFGmXG1morL5eC4SX8hu7/Vt8dm9NrqkrWeaFmgU2QM9loivYOdXcoFv7ARCL1VEpdcjMWFLCHBM67+FydbUDKAx/KtBJ/fyWIZ04I0UB2JgyYoyY5xnTOOFxf8Dd3T1evXyFzz/7HC9fvMR0HBG8FytF9wZ2zsF3HnEa8fD6lZSF6FpmAlwIuHl2g6ubKyzjhMPDI8bDEUIkYr1EkvpLHwKIgc4HsCVqzRHLHDCHDqHzCMFpwpMkfDhHCC6U8ADnkx62bEkwbDMPD8aiexIna7/HlUmqRntJ6z0lhFbsX7ZZG8xuMMOEwrk2fMq/hSap/ehp+qsqgbnGask/WuYno+MT4XRBo7Zb5iQbiMxYELWxTIwLUlwk5yI66T+rfcpFiIgXqgs9AKALAcMwYDMM2AwCuN6AtiiaKDvhkP2+GthaSV2lj1ApBpJnpbVi0vqdxC140laSLS6s1q9+to7H1/rWEpslc4MbgK+V68wZmdpM20YjKsDRgCxDPRFUz3MOS8x4c/eIzz//Ai+ff4EXP/sMrz5/gTevXmPc3yFxxrxMCB3w3re/hT//l/9S55/wz4+v8JNPf4Qf//BH+ON/8KfI96+AeMDd3RtwjkCaMATG8zevsNl1+Af/4I8Q5yP+/D98Hz/4wU/xvU/ex3sffYSrd97B/jjh+edv8KOfvsD3vvct/OH3PkIXArR/SVUkmAHKgA8g8uLBdEk8CG13R61pJmri8sbDts6qOJXfFSIr8LZKF2BYbO0ZK380xhRbTjmKi9YA2Uiu6GolWamMSK5mQFsoq4VlI4TcPJeBbi4AW/TFgj1VZvHJZ6d4vZYN6+NJwI1RMwwzwwW7kmxGnRYBXcDBBUly4kxNUpXsFuS9xzAMGDYbDH0vgtk3m8WT1uAx5LswjZXKFkxVzK4ncXUoeK3eQuvfr0LWLlMEYWFA+SBxW9JRBT9Q9+8ESaG4ZU7GRFicgC6Zdlz60Yqbug3sl6VjcZdHBex5sfrYBeM44rA/4PHhEXdv7vD69RvcvbnDw90DxnGE9x63N7fShL4fQACWecYyT4jjiPGwlyf0HpkJU0qAcxiPR0zHW8R5xsOr11imGaHr0XW9eh0Azl7i1N4D/QJ4L93CADjvsYQAH4J4K/qAvgvog/z0KrBZ3Xi2dy+YwVq7W+J0BLCGLmwLxJxr4k8VxU8DbMsIT9FISwMwbbn+0VzvBF1PKKreUpTMdj3PsJstDtV+u70K66j1ZwmjtJ9fGIK5mjMjxoQcpGNbnhPyIt3dUozIKcEHzRZWRSbFiBQTAGmY4ahDFzps1PvUdUHCBVQBt32s3E4RWT6ExOtbtxzY0ockh6OAK53UIJcJsVmp4GZ/t2UsXK7diFdq+BxNzFs/k2vI97ICrSROoWxI4lQgWO7F6XOviVFuNseMz3/xGX7y45/h8e4OTpPUlsc9Hu4f8fB4j7gcMSVgTAsOj4+Y/tOPEJzD65cv0HU97l8/x2c/+Sk+/M4nOLx5jeO8xxcvv8A8Rbx5+Rrf+taHyGlGTDNyznj9/FP8/Ic/wM9/8Rq762tsO8bD81/gR3/x58gMdKHHlDzuP3+OH/7HK/ydP/4DfOfbH6LvSOVURuckaY6yh2MgGOgANUGv9UKR5lQo3Z0qmdw0IqpeRkNEhjXgKfxra8MGuBV4i+uZTF4aFqz5tCaRro9iIJLsiFW6aamyROQKsFv1SwHfnKWEiyTbOznAZ5YtIDUrmNFUzRhNAlWe2U88fTwJuMfjrA/O6LzqJDmBs2Qsy2sRzZ28gGbMyCkCllBEBOelC04IkohjDRpUDZV4igJbxTddpHbyy3fYvrrSkVs91ZjNkfVTbQCXxSUsE1MniJsxFLCtZAVbYVO+i+YPqTecU4JPsXCrWNKuEEglDhll1K0NlxgxLQvGOWKcZ4zjhOPhiMPjHo+Pj3i8e8Tjfo/D/oDxOIEhe5kyIAAJklhMzljmGdNxxDxPiMsiBOg8EjOmmJABxGnCvN8jLQv29/fgzLi6vlEr2bZFIxAz0jLj8JgxH0cBUCelF063gHNdgO8CvPfoOo/tMOBqO2DogpYRBDhIFm2CNACQki1105QSBHMVopQzZCfxpKcObv4zBertepkCQGu5qJBdC5EW7NbCHYwmsa44IAFe019rCaDx0tR7EM4HyeUmJflIuKt6q5oj5YQUE1IEyHmN3yYB4ZTllRnMqQBX0pid1FYSSNctaJlf8OopKQ1d6nOXRCOqLRarvtPURpaZrg9NaD09xrE1JFBLD5vV5crHxWJoBP5psgrINdc+UbDRZlFLnFXESFYetQYYa7QtG5jbvR1hWhI+/ekv8IO/+iF+8ZOf4uXnX4CXBX3nMcUIgHBzc42/9fEf4fWrO/zwB98Hc8Y8TzgegNAN6vlJOO4f8OydG2y2O/z4+z+Ax4y/9bd+H59/+go5RjhE3N29xvHxHtvNDX70/R/hzf2EHBPevHqF6XBAyox33nmG7W6AowQsEYcXr/H4qsNP/+oH+Jt/+2/g7//p38XVzU6yjPseSfdHThprrcBhdFjBSGrkTVm9pC7KvLFmpps1aHKTNJeleJX0fadewrVhRLW8hkjrvh1s55m1xal0wBVombPEq9kXBY3IxggwJ1X8ZbzGI6lY79bMJ4MzITsCZ4dMWk9t92vziTIDfo0XtV/z+fEk4D48HOC9Q987+KAx2pzAOUmmMevvIOkCw9KAIacE1gch58vUQ4ulnfVS1pcAk2glxa9O2mOVdMGI1y206JyhCpPov965UjtmICJqg2pepTfpqXBcx4JbWcftm5qcYd9fUgRFdV6QgKEDq1uLSpIbkyUyLRjnGcdpxmGccBxnjNOI4/6Ax7sH7O/v8Xj/iMP+KPsMe4d+GNANA3JMmKYJx3HENE5gZqQlYp5GLPOEHCOQLW5KEl9KESkDy/GA412HuERMxyO6rsPQ9XBXV+i8JEtJHCdjmSYcHh+QUpZEia5H13cSLugC4D3YS/ydvMNuu8F8c42b6ytc7zYCxuZ9YIAygV3W8I4wVPDWJcuJYNdOYj5rH+/KWuWnAZKVi1mcfL1R+8nPlfIGkyCNUavgUCzTer9WALEWeVvJkbnUqNyvxrJFANUXgddEJKp+uRc39MRc3y3/NmCQMyOnhJwdYk4aihCX8rIsWOICnh2YPJgIiUlrtVktGHXDkrRvdMabzpfEGbNomDW5yKyS3NC5gS2f8CcEZLMpy1wFqdNdfOSetQuc8WlW13dZo0bDXuW0nggBU2yLlaXjY7VqzcLNmWTfXKf93HVrP+meJM9oWdkMiOs4Mz798S/wH/7Nv8OP/uNf4fnPf4a8LAAkBBP6DjFljOOEl18E/P73fh/XN9fYbLcYpyMYjMfHI4AjhqFH3zlMyLi9vQEvM376w5/gO7/3DPu7O3zx/AvcvHON+7sHvHr5CvN8QOYrfPHyHsf9iIe7PY6HPa6ur/DRRx8gxRkvnz8iZ0a36eGcQ7/bYrPd4kd/8Zf44X/8Pv7eP/oH+KO/87ekWiRneC8Wr5TxrRussHpDyLyVtG7BaSU6rXKZsmTIc86Fh4wvnBk9jZpU6AGoBhgA8xcbrXjnwLk6obPeG1mA3LGEosiStVQ7k3pn8xg1ShtDDZSke7pr0mfOQMpwxBrmNA9klQcmc9oM/DoHjaG20v7Xx5OAe3+/R997MAZsNp0+pGWgCbBmH00eScvFJGnnOWmWGkzDVoFI2p/V6leVoS2OYhpW0ZKIQVR3DFoZPLTWJMgYkHWhSFov+mLl1ljqaflI6TTVukwa0F0dF4yTDFnEJUYQpOzBOwcPFSq20EqYc1xwOE7YH484TjOO44TD/ojD/hGPd/e4e/UaD2/e4LA/Yp4XkHMYtlt0fS/1y9YNhiFWzrJgniYcDweJracEjyq0M3Npx7iMwBES/8uc4XY7IKWqbGSJCy7zhGkccTwesSwLfAgYNhtshgHDZgvfBbAjScBgAI4wbbfIMWl5F8DbDYKT2lLnpf7SZQbDye/eaXaytg9h8ZIkJCSqjTFWRqL93jA8IG5NY3HzktR6vuqeMtdjq0mtaKgFWtP89T4lMaxddwNhNMqBCgThAwVcrMsbzo5G2VslCZZRrOmONTN+iQm9dv3hlGTDkGXGMs8iGsmJ257FrQiOJ09cJ6JkQVPTCrWda001tvIOS5KsigOaa6H8rPymCU5FCT7diMI1ruGqDBGo6CWtpVWUpALyckI7xWaFVbCV4qRk/YFcFoFOUllgSr9TozllwvPPX+LP/+1f4ouffYpP/9N/AseId9+9AXPG8TDj/v4R94+PIOeRc0Z30+NhPGI/zcgpISVGnGfM84RhMyBlCb10ocfj4x7HwwjOEd/6+EP8+Me/wOP+AX0PvJon7O8eADC6MODh5ZviAdtd7bAbOhzu7xFTxmY74Ob2Bl3o8Hg44PWLNwDdYRgGjFPE/+e//x/x85/+FH/yD/8rvPfBe9JBzOUS8sklJ0V675H3kOYksi94zZA3ehA8YCKV+Voqqsla0D1tG5KAdqlZ5dBU8K1rqURYOUrbAZcuUBqCQmYE1s0vshpRhh05NVSgssHworVys3qEcu23nVjChYGrQmHKFzfvla1iDWiNU9/C508C7t3dAzbbHiEQMm8L4SNzEfLSBqy2cUvRagBrwX5OUqKAzIX5yFUBaIBXOuJoTRu0f2rVnOuCk/LaeZJJzVh0BnpUa0ENK11jMRsgZVuIUyJZy6SVOG5PY4bUuJG2PdR7S+tH2TQ5pYxpWbA/jnjY7/F4FECN84LDwyPuX77Emxcv8eqLF7h/8wbzvMh4fcA4HNANg4Cul2SoEAKICQuAtCzIMWI+HpGXWbRMorIHMQpAR1EMyEnGcdC62pTE3ZwzxmnC8bDHOB5FcHOGDx2WaULabsEpI3QdsnY3SiDAB6QlFcCOMePmJuJ6u8Vm6EDkdUwM57IArrqNi/chMxInLEB5XxtpNhrk+UsYyRQpLrRKkEirXN+Scqjl6rKYpWSn3Axot7UxjZaQzPwpIJSrOargIMJIytlMANkJ1Z1dDz6xbrl8F0BJLCweoEKErLyiSgcEUHNaEOMCcl68ELoDVM4CyiKMZPKoEY5WTiP1jQZ+kJr4LDtlJbXwCdb2FJXW1JIxq3ZVvrUC9fPPSZm67I9blqpxbdcTz3jRFAWbCWcC1tZOnyETI0kjZZkviAxjA2qS51yYsH8Y8YvPnuPzH3+K//2f/wu8/vwFbt99F+989B5CF0DE2B5n9EOPN3f3eHzY43AYsX/Y47B/xO077yCmhPEwat91YDoeEYIH8QbBAYEYx8cH7LY9Xr+4x89/9jnGZcG7tzvsDxM4Lri6vQXPjMgTHBF2uy0oZ/ASQZ3D9dUWfd/h8PAoViuArgsg5+Fpxvvv7vDy1QM++09/ieef/gx/9Cf/Ff7wj/8QV1cbeK87USlYWStb0p1bHDKs+5MBUuUFuVfWKo2YIpaYlLPUY2k1y2bRFlptFMlGlhbjyjkgy5Z/ShmF36nwgoCuz9LLgZzwHTmClZYaWBibZONNcymnau0ic9n3XEa3rr9twdaqShqHvPLI+plOj6ddyo97ZE64uhp0b2IqTJFTEpeD8yDpjd4U3UdNPFJXQU5AI3DW8Viof59KCnplIKcuHkiPVGW4pwwEW7C2JVn70+n1Mux+1WI20LXf1+rXyQ3snEbwmZg3QjT3SiKHSNpZJzPmWcD28XDE4+GAwzgizgvSNOHx9Ru8ef4FXn72HC9fvMDjwwNiSrIhuHZ88l2HrpcEJwPfMpicdTvECXGaAU7a1lG/76TelWNEnheEvkPfyQuAlpQsmOcZx8MBj/sHLNMMBmuPa4Z3DikEpGUG1JWTsmR7wmkoISUtJZLuU0QOvuvgvdPzVMN12gDBFKEssZMM0T6902xpnb+kgGFJVSWLvlmKamEClg18YsMpIyoTM8rv5TqM0hShwTX9MFcQLnRTW1sKf0sPX6hHwRjZXGxndFWeo9aht+ALNBp9JbmG6lC3SWPp+rMsC7q4SJ07FLBBNcbGDGirQXAuQrWWYaxdygRUPiQUoAXsbwk5tS7kAqpQEC7nGzC76lKm6k62ZgpFThSMtcxoXTtS6x11IKyKgAnDOscGFpooBZEruQyKy17dCRnLwnjx4g2+/+d/iVe/+Aw/+Pf/AW9ePcL1HeLLF3j5xXOACCkmqcjoPYJ3uHl2ja7r8OqLF3j5/CXu7x/w3gcfYJ6PIGQE7/Bw2MOT1D47T+IRigt83+OHP/gU+8OIEALmacF8nDFsBgTnEeMM53rs9yOIGVe7DZa4YJqB/X6EDw7b7Qai+0pFyDQt2D/OuLs/Asy42m7xeP8K//6f/3/x6vkv8Ed/+qf48KMP0XW1gxRrRYbgUlqFV7j0ai4niPcsJSxLRFy0uYZ2nRJ+MAXY4vyyFhkAZXNPG49pExEIjxktMSv9MFX+zPp9BV3nREElR7pxC8EsTaFhWgOuNogpHbIKjzaCxWitFQ4w0EXjZauNP8waPtElyvEk4M5LRL9EpKTDNZcPJL6XUgQlp20OUYRtjrG4Z4l9w8RYWRIXwazZo7Iwp3FcsVJsf4+sc1pr58Sa0eJsV0uMatJUTddYWTg6raz/FoOF18MzhWldVnT+KGb1SHICMOeIZVlwOI64f3jE4TgK0WYgzQsO9/e4e/kCLz5/jlfPn+Ph/h7HcUQ0wiGCbWLtvUfoOvS9xHOdk3jDPE9Y5ql0GeKc4FTP8cjIJDHdHBMIjOA9+q5D8B4pLjjs9+pKnjGOI6ZxBHOWLOhhg81WXr02LiEnRfQEqEsnIs8ZU4rqUnbwvsNmu0V/xeg8gZ20Ag2E0jKQSGJ2kcW1w6x9W4ODSx5Zhf6SpM9zLMkOWjFd1qJwFgBukpt0VXW+Cw2a9YPKNJWxqPVOrQhBsbyhFWtnV9XFzGLlrvZbbjWChn5K9iebhX5udVfgV+Ao/NzGLPXe2fhwQfKh1JoyuRL3tVyMDIC60CigVlhZLclT0HVZS8wAZNa+4rqOzkn7R6fxUsrKf+3craxaqt8r3hgqscNWObcQCgyQjYutdATUzGnlSrHDqCR8VcUYSASxYkiSN6cY8Xic8YtPP8OP/uOfgw8j/n//67/EtEgOBfKCaYrwzmOJM6KCDDPQa9vFrutxc3uDN69e4eHNawEtMHJcyojG4wHzcYOu75FSRgg9xsOIh4dH5CRlhcuckDLgwyA11/OC+7s9GBnX2y2mcQbcgr4LyBCQfXx8RGbxCmVOcEFc146A0PVYOGKzEwPqZz/4Ed68eoO//2f/Nb77ve8hdFpW6AlErFUj3NC7zKN5M1nd70zq2dPGK0Z/pDkkKDX13C5OYa4VfyiLOt2EvtRqE2ncvS0oM++KrHTpDV2Zp15Tgd8ULk7WSMnKgsTLw8ySa9R4wIz9W9rRgEojM1QOFT5suXZ9PF2Hm62lWtU+issuN+Cq6dKcGJyivJilXRjnpg6sTnAVLroGRXycD7NlTAF9S6SqDGhauYMr8aEVYz95PSrjYJ3ZYmWsQLeI9NVPmEYNrhq9AmRmIGZGymI1Ho4jHvePuL9/xLwkbPoBHTnkecL+zRvcvXiJNy9f4O7uDtM0YUlRNwkQF22JnzGX5gVeG4l4LW6f50ncIxACygwQJWBJDfEQgtPWfcEDBEzThJSPIkC0WxHnBB88+r7HZrfF1e4Km+1WrF1As10lBsfQnr5xQZyAHCM8OfR9j8PVFbrdjF7XKpBmrhMpC2s7uhSFsTMjOafNN4IwGluts86JuoBM2Wot2QpmgrBF62xcQTI9DXgxVFDZesrXndUYYk1DLa3WsEYFR3nZ1nRVKahaMel7FdSMOFuwqEcbz4XSG8qGGqLg2nxkOE7gtAC5E8FIXtbIErnSot3AtO1jRgkDOdPQWwsT6maEWromO43yladQgI8L7zmiIgyl6TxWiVJl/RrvlVjF+rfNL4nQtzmqu0XpqtncmbJvPKmTJoJc1p1d0atEMSBgWhIeDjN+/INP8cO//HMsx0f8+C//CknnOS0z5llpJkWASdy3KUpIhjsAhMfHPVwGvA/IS8SrL75Av9kgz7PsiKUlOCku2Gw2UgY4jjgeDwBnOI2DSvc6xhxnMBPiMsM5j03fg8FwXmji4XEPBuPhkQGSbPN+6NEPHbyW7zFItkt9eVfApx+2mA6P+Nf/04gXnz3HH/zdP8a77z0DA/CedHchU74kxi4Z3lncTUk8UBmMWVtIisWYtB+D8Z7NtVmqXBMTWegRWUtv9GRzPFipIFg35GACZZHzgBpgQi3ama5wlBISF/IxmWVAywZuDUebUXbKf8b/Ky4vPFBfjPreZbj9kv1whd7NNSaZkFldx5QiQIBzCcwkyQhJ6gCzrL3401nLcIriXgPLtH6uJwZ5aQqeGvTFXy+edP55myCD8uxPjaldhpUW3iSbzHFBmhcF2nscxwmLlg7kuGBOGYeHe9y/fo2HhzvMi7hwJZlMtclkzcGTJMUkIWZS4Apdh66X7Q6TunJZs/YIEpvNJsQN7IKUfuSYsIwTlqzAXoiQtb2fJGiBGXFZMKkwK5ZbQTep91x0GzA/9wjkMIaAxy4AnNFtBpBz6LuA1PfYDJ2UDwVZCXvGnDK883BdgEsJTgF5WmSjhCXG4goCm9CtilNhZFtTtkzDWrawftU1ZwDEaqU14rwycKv9VkVvrdleIhXT1O288/PplBmeOAx4rT41p6y9hKnZnUeEtUtZhJPTViMZAGuIJyfZRi4tSGlBzr3QTXkuAUaooGw9VdQsvbxnseRGmQU04U2+5wC1dqqSUYEaKg/oTCa0ilThS2pn0L5gCVx0aXqLUi9rLfc0D0eKGcdxwWc/+ww/+It/h44yfv6zn2PYbLEsjyIZvFPZlrDZbjBst4izKNNpiTgej9g/7uFDD44zAjl4H6RD2/4gbTIdJJbrpE1iignztGB/OMAhoQ8eC4DNZotlipijZJWP44jQeey2A662PXLO2O8jplli9XAOV9dX2Gw28KEHOZaNE/Zz2Rc75Yxh6LEbepTsX04Y93f4q3/3H/D885f4w7/3d/Hx738Hu8Fj6CShMWmZpymXcRF5EoKXDlucME+jbIygzVeS+uppxUMiOxRBz1zVtsBGgygeIm3co1avA8Gzg+xUJz/JlFeS84osqCuO8muVDA3tmCembfZiCmTTQ3lFUa21rYop1Ts+hUBPAq51D2KWrkdLjIiL/PQxiouFGdlJL+XMWqagvVuJUKwQu30RN8pczM37J0zSuoCN7VpbomaP1e8LWLG2Z6Qyre2l23eKACFaXbMNhNfX2mVlgrYsrrrELAM754xlmnHY7/H65Su8ef0GmTM2Vzv0nUdeJkwPe7x5+RKvX73C4XAAeY9hty2dgXKWnq92/YxWQAqgxsUjL1IKYPFE58w9qJYWaszMB+mNCzDmcZTaXLWGzWXtgrTfdD4ALOdNh2ONARJKUprz2l+Xs4I2I3Q9AhEmAh7SgunhHr7v4LtOtgHc7XB9vcPt7Q2834JIGp9Y0l10GX4JcF0Hlx1ilrjkvCyY1WWec66ufWoEs5G8Wm66u6qUSVmtoGrVYtkBnGtcV6FCbe/6QsOElaE1bkjnbt4i4klb4AGQnG4051aAMp4oLGvEyQ33mJXL1ZVtw5K/RXFJMSMtEUQTyCdx1XovHiuNB0vMSoAX9SmFtp00ridr1WSZpc2LC49ymRtaTRNh9caKCxufVvlCw+f6nBZyKBt/FyWJ1COxKjQpIrTQgQ6tbNKX5aLVgyXhjMO04M2rO3z/L/8cj69f4HB/j7/3X/8Zxoc7/Og//QCff/4C07iAnLhmyTvM0whOufQc2O22WJaI8ThiHif0oUPX9TiOoyQksuSTBG3+Y5Ue8zgCOSJ4j3mJ6PoeDtBSF0ZcRgybgJurHYIP2O+PiDFjyQmMjO32ClfXV+LmnhfE5SiPGRx2ux02u510hAsBXQiI04zD4YBxHLEsC9LjPaYF+OkPf4Qf/Plf4Pf/3p/gj/7xP8R3P3kPt1tf6Nli3wBAnpCkNAPLMmtnM3XPJjEMiHzdX8Y8DieK6doD2yjASUoHMxM4iac1M2kNuGx96MlrDN+VHIUiobkKhkKC5R5GBgQmFk+bIw0DUZMwWOnSEvkMBao30+6h8rZQN57ceexJwO1Dh+ClvV9KEquYlxn9MsMHr0kHWVyKCp5WTMyiUhUh11jgZ0erb5RzaP3HpbFz+wvVX+1l/VLrexfv1LBqY+kU4MX52aqNy59UBX7jos7M0lhiv8f96ze4v7tX7ddju9sCKeN4/4A3X7zAq+df4O7NGxyPI6CxNrJSGscI3gStZr2y7sqjmd9QQHKuSSAJAT64VeMISyZzOv4UFyxJU+CzxGJCCEDXibIEaWYSFwH3pAkRAEDOieAIttlAtRgTAzku0st1nnC8vytJX67rMGw3uL69xvTOMyBGOGZ0QyduHraM34wlJbhFtPek7uQp1jhR5eVmDXQRy0qbUsbWrD1plm7dDMMEdy5uKtmvdG3hKh1SZTY7PElsunhDihXGRQlSFwMqYT/FDc19ipv59PNGW1eCJRIBHaOUh83TCPFEZHjNVqUcxBOYxSouSonODwNNy0bSrN2a9dsOn07ZAKyxVwJYkx2LS9rJ/OuXqwBshGPhtTY0g6r2qo8xQUE3125RdR6qZF0l6jDDNiWwTFcjGCbGOC14+fI1/uLf/ns8//EP8fKLO/yT/8f/Hb/30Xv43/7nH+L1y5d4fHxEjIvUKMeEcZ/LXEhtsyhxIQQMww4hdIjzAuaETb/B/f4enXPYbAbpU02M3gcsy4KcFt0IQCxQEKRzWIyIccZ226Pve+SU8bg/ILFk0W6uNri6vpX69ZQAYgxXAzabrXR/0x3aklrY+/tHqTJICxiEvgt49uwWMQHHccTV1iEte/z0X/8z3H/+Y0z/9J/ib/3tP8DV7Q69Y0hb3yw70WeHxBFgxhIXTZqVhNnMUd39ALLG5W1tRHdr6A41GbAIXxRQTEUpkjpyA9pMTnjOueINtFdNtKNKo6q0UuNhYRYezY6AJN8z70PpI+4EgE2LM4W0lfctqK/49gkWfxJwh2FA14mGk2LCPM+YpxFzLxmnRIBX7dl8/KwuAYYwmWVpCkEac7bCxDIzCdVz//R4V7LnhLnssDaC4oaobgnoBIOr9Wr6sW3Fa2UDWcMUdXvAtcYiz8A1iaOZbGbpIjUeJ7x5fYfXL15hPI4ACJ0LcJkxzwe8+uILfP7Tn+H1y5d4eNhjniPMfZcWSVhwRGBl0JgjUlwEbBVIpPYyI2GRmLm6jDsi9NrMwObN1imnjCXNkk2dxLLNKkXNQ7HMrhCuuHSKKdhsZq/AvwIREdQ5RsRpwh6VsUCygXm/3eD69gb7D95HHo/gZcL1s2fod1vJSnayookZ47Lo71LjPC+LNAFhlhpnnfNSWM8o5pcBie3NWgC3vBjIUo/J8MWilf2tHDIcEmSbN9YsVhQvRlGZwd6X8p3iyrY5dQ7Ilpldcw6qHVZfhWEbDVsS4qqG3iZmmUBgiBWwHCfsHw/o+gGMgF1m9JnhUyet/BzqlpcWywXpLlfrrG8o2JpCKd4qGzo3oNXwYnmTYQU35mUyvijqbWOhG49xe//ypFQAHpA5TYA25q9zsda0WoFXmLMqDYUfgCVFvH79Bv/2X/8rPP/hD/Hiizf40//Lf4N/8o//FP/+X/0rfP6L53j15q64Y0M/IHRe9/0eELxYlcs0YzoeMe6P2D88IOZYOnltNgMyX4Eo4tnVgGe3t3AEzMuCu7s9brY9gveYphmkiuk0TkjLgtA5bPsemSULPOhuTturaxBJdzHfObguYJ4S8jQhJUa+t6Qn8Qwt0yweoql6xpz36Lwrv/dDj5t3n2F3fY3x5XP87//jf4e7Lz7HH/zZn+H6ZodNirjqlPaZtY4/FQ9ojAuWLC1FTR4KX3LNPK92iq61KsWrfIqaV5FQa6YzS6wWaECXvCwktTk7CpRY2UXlbybdn112M5Tn8ZId7cnBu9ofQHjMF35ntM9VlVNH65fJ8UvH04Dbd2rB6DZ9UTJtl3mSOjICkpddashpYwM2xhIuscJiAYm8ZogitNTF0wBny3gN29SjfcMym1EXUTbllgxq34Iu1oy9uq5pVlmEfenp2xBAcyuYkCyYq8Ipp4xlXrDfH3B//4CHh0cQA5tBGfQ4Yv9wh5fPn+OLzz/Hw/0D5iVKW0wbm4EpWyZdEjdyipL6DpR9SgPZ7xJz9T6g73t0fV+6rZgsypmxaBewqPXSLV3kLJYkoAkGRQALkXbewwdXOlKxTmhp8KBehZQz4rJI8pXGgAiSSDJsN8jjEZQieq1V5pxxjYx+u4ULoXgJ4rJInZ/2m46aAe0B7eRVidxch1wErK6d0mAB2pI4oSoXeSQiqb8DQaFct21T4Giw0TVrbRTK3q8IS8BeGk445yogt5bimeWqCmRRIlyzbR+dn08E8gHOO6QYMc8Jh/2Ert/DOY/MCd0UBSSGAaFPcN7LZvVZvBrkHFxMiFHmCGph1uE09zW9ClIfZJZJmYO1f7CqElT5jWFCDsUj0CrG0rugpIQ13yNpDaqfV4Cu/bdZ49iredbvMgPFxlUwyCybDvyL/+1f4bPv/xXAHf7+P/m/4v/23/yfsX/1Gf79v/yX+OL5Fxg2W1HEvDT6n44zgAmcH8EA5mlBMppk2Vs4xgWPR3ElDx3wwbvvYT7e42Y74JP3rtEPW3zx6hWm44iuk9K+PkhS0v44IqeIbpBWqTlL3Nf3Unvv+wFxTsg5gohwnA66x21GTMD04g4ZWVt0Qru1MXKOYEQBG+EqgDyYxJUd44L9/ghyXkqO5gV/8b/+c/z0Jz/D7/3ZP8Uf/cHvowtOPEA5a6MMQkzagyEpQjKKx8uAl5WfrKWu4QCbskRQd7EqfsZ7TEjwCrwZDrKdayYPtrAPSWY7nC89B6xyA1CwN5lqN1eytjI2RwT2pEl91pRFX8qThW0bJd9ozsrbLKwoIHzK33I8HcPt1GUYfHFXJu2m5CfJnAtBWv45H+BS9WFDS3dyjlIukmv7rKzuz7XqbqDQMC3b+7ZhXo0hVDdSc679oqCfs9TUmTDNZN1Sm3uYVtQkh5gFIKCbxW3BJxo3sBYoUFcjS2OJw36P/f0DxsMROSUMfY/tdoO8zNrY4nO8+uIL7Pd7TNMsMTcrwjY3qKar55QRtQQhJ4nrdI4whIBtHzBovDX4gGA9q7seLsjSWl3s/5+zP3uyJbvOO8HfHnw4Q0TcIW8mEgABkqKooWQtq+pqmaz5Ug8lk9rarNusH+q/7Od6UpV1V1cXSxQpSKQITgAJApnIzDtGxBncfU/9sNbe7nGBZBX7AJH33hhOnOO+915rfev7vpViIsQ14NTrmco6u4dkmslBTDL5qdov+r5j6DuGcWS3E6G91T5+WBaFMhfishCCZNQiiVBWdOcZhp7dfsd+HOitJU0Tp3fvJEsOC8dnd+xvb/HDSCpFLAsLzQ6zMn5XfbVthgJWK6lK8JC3pC5Pyp5c6f+QrW1mCMlAMvL5ZJ2uFYNDNLVuk6Bt/2zxqOjBsin2ba32q8C8rOulyd2efKzQZ+UBmM2h9HFjxRgjrmNeq3PriDkzTzPePWJKJO0inZrf2zhgXKcHlZU+lnP4TvzRV+7C063VqlJW+VPZ/Pvjz5VatdR/1z3VnqvaCRhJfFx1gLJkmzHZtueuu1USpI0spCWj9YWumW99teuVXRPOWukYa7jOM//pP/2YL/7653z+g9/lv/oX/0cGa3j/zRf8j//9v+XHf/pTpln8qbeBoRt6ldIJV2UOCyUl8VI3hs4KKngYd5AjvTf0NnO83fPiuOe7r56TsSzzTAoLL+5uCCnz7t0DD49nem/Zjzu8auxDkvcnvA5LDFH9DgRDKEVg7ZASQR3jhrHn9nbHJ6+es98NWGM5P54IIYqcK0lCPE0zOJlNPgwd5/OJGBK7XUdKC9eHMw/v3/Pm9Wve/d5/w3/5z/4x37ntOTq58En7tSUllQZK4maNwbVgVy1618BV74Ue13qX9MNIBybX94zKt6wTWSGObPRDu/MZ9f9WlE8aGIoabdZRg7qRwlg8/AXVBNr85JprttdaqyrNALcQcg24xmzVMY018CuPb+/haoXbDx3dMMghbh0lQ0iZor09752wIa1tV9E4q1lskKBbJPBW0k3OlT25+YWl/Uf+qTewBl20Ql4b72vo3FYb9S5m1DPVWLJVycMasterpheryvlzRoXbmZTXsWalWPXPlYvcNu/25pRCmBfOD4+c7h+JS9CJSTL27HyaefPLX/L1L37B4+nEPM9SaWpALKn+Xg0QeYVES5YNba1h8Jab3cjNcc9+7Ok7T9fVQNsJ2clYIW6FhWUJzLP86YKh84kYrVa51btWnXhIRGproOCMo3eGse/Y7UZ2+z27/Y5hHOg7BxSm68TFQA6BKQbiMim0ZHC+YxgGdrtR7Oh2e8b9jn7oycvM47v3zNOV6/nMMk0YDPs7MclIOctm04qohqkG31QPXv2c9DNXks22yi31gEKy2mKNVuNiTRk12bHWkozAyg6DK0Yt3ngSSMx2R9bftVmLbYltN5VGXPNk8Wy/uAbWlZyhnzObJ9S1a4zBe09xnq7vMK4jF8O8ZGAmpkIfF3xcsDGA68F6ihUozptO9qWGw6wks/peP/Ybb5Vo3kyHqXu6IgeKdNTqs+bN24S6dhmeBmfa76jfs4WaK/RYSpEDefv5sp4F9Qpu6VRPL7Uw6l9//Zof/dF/4vPv/yb/5v/+f+X85pfYkvnDf/eH/PF/+M/cP1wE3ncO34uelQJRh43kJK8nzcIENiWTMUwmsO97nh1H7m5fYNPC2HmOO8/3PnshATYmYrhh11tGb0m50JO53XXgDEtYyDjmRYcMFMPlGgnLBEYTTi/8gBAilMiud9x1PcM4st/v6IeBwXmsohc3+wMUMSd5+PBIKgu3NzfKaI8MDm4/e8U47sRv2xiu08L9w4kPr7/gJ//9/5P0+l/yX//e/5nvvryhJyuPRMpaKRIExq59fGtyY0NsZ2sZNNAa2dxFd1TlEDQYGUMyhSLj1sSi1HoSloQk3akadSDIZEUaeCIb03p6XWhtrdR1hybszsm5sq4WXbfbOo210jXqYliDrd2cP7/u8a0Bd3+zx3vPuBvYHUbGcZDg62WzFj2gY4xiYmDtGvlRr+QUQXH+9pHlArYeZKkbTwXIutu20o22Q+sF/DaAvF0lOfrqIOtWLFNvcn2uLQuaBhtXItE2g39yteuPP/nZIkMFponT/QPn0yPGGsbdgDWG6/mR969f8+aXX/Hmq6+ZlkUHPgirNG9Ol4L2h5G+Ak4N0q2hd7AfPM9uDzy/PXLcj4x9h++lV4cV9l4uK7RbA+48C8xb5+2GuHVbkSo7ZamEg45ntFaNNjpP753AykYholo5aH5qjQyk74xKSvT6lrzO9zXGkHJimly7vjhHN448vv/AdJ149umnjDc32GHEdK5tGan6Gp6AYdVrNrCEdUN8FNHk38bI+nW6oa0hZUM0KosyBmccBgm0vqzEMmOrflB/Rw0iGwSkbs5a3a1fWyuujwDpzWJ6Wls+XXPbqhics3T96j622w2M48DYu9YzW0Im5gUXEr6PuL6HrqdYr+ukkIZREtQifPY6fqxpcnVt1yS0OWsps71+rjKfy2ZP1338JDmuFU2RqrW+QblUldTUSogngTTnJNpjTRHWa67trJZQm7Ye6l6Xc0lu3Oky8Z9/9J/JyfOv/2//hhcj3M8zX/zN3/InP/oTHh9POOcxTqdjGUNcFpWcAEoinK+BsAS6ztJ3HUPn2PUdx6HnOHbcdoZht8c7w8tne149O/L87kC2Mjnteu0hRoyBzmSeH0fO88wSO5aYGDw8nicuUyCFGd8kh3JtnYXdrqeMnfRhdcSiMYU4XQmTJAio49gyy1mQUpRkdZpFkeA8OQdiLKQgqJJ1DmM9Xee5Oe6ZpoWf/S//H6b7t/yf/tt/zXc/uWVvnbjNJSO941IapNo4Ns1LZbN619vezuSi37OFirNk0vozkign54jW4bDyUVZUUkhQwjxe9fG6B7V4W+NO/VxlG7SV01acpgGbRHoTwDW41jXdKl5TuTx/zwr39tkdvuvY7Uf2hx3DMOA7DwadUiI9sZQLpcig8aoiEHjPtr4ZZX2zuQhshMkytvCjTLn6vpZSe5pFrkm9OOWpGODp4fWrj+2G3X7Hx1lv/ah9hFQPoHqAlI9+w6ZIKUBORQwuLlceHx+ZLhdu7m457PfM5xPvv/qKr37xc958/Q0f3r0nxChVVq4Hs1pRWoHohZimNAEL3hkGb9j3juOu48XdkZfPbrg57hiHAee9MHoxWrFpH7qOAQxCOkpR+6FBevJRZ6emtMq/Qkyt4stY6dF7Ly40RQxPQilEA6Uk1SIGKAnvLZRO10UhpsI8z2r+cdExgCKNoGa0WbpK+5uv+fDhA6++d89nP/gNnn/2Gd7vNglpdRtb76HAyWa9uy3zZIVua1WJIhrWy0U1ImlL2TQXq1JopvpdgS4XYi64lNtzuGJbj2abKD6pcGvvWL/edIcVmipZ5EhW7SL1Z55I00xNCk3bzHXV+b6jH0aGceBw3HNz3LHfjwx9p9m8kPeCWu0tS6TEjOkixnuKlZ7gE2JgLi2AmqLeh3rAb1tCaRN4Cxs7U+0Nb4N2m/zzZOOsB2xloFo1Vau3sfbyavUqh3bdn5VYZ9oZsd6ApwedqRafSt4JKfGTn/yUn/zk5/zr/+6/4zc/OfL2qy9588uv+F/+x/+Jn//tV2pZ6/DeiBtUTnTGrwQ8I/1ztwMzOoGB+45d5zgOI7u+Yzd4nt/sBJEaOl7cHHhxe+TusCPkxLWzkDrG0Uv/M+zIJbMfOx6uMyFELlNkMoXOZg6D01O90Hkx2ShZ5m9XB7glF1on3nuWJZHTlTAtnM8XaS3lTD8ODP2A9wJbxxyJc8AskXcfTkzX+QmaYQyijfeeL//4j/mDmPiX/+b/wnc/ucPmgqqjsEpiksR72wuVG7syh9e9WtdCGw5gjE6Wk1ZOM2fRyJ2dIFDRiMuZzTI20hUh/9W9ZDQpMbr3mp95yaocyMi4vqTOZkXXnSIplWDbArdZiz99/nYOtTNnE64b/+Lp49sD7vNn+M6z2+3Y7WVSjXUybi/FRWUiixB5aiWYCyDwK0bYqjLOrwbL2kNL5NwI2g1GLTVbrgeOHgQ51ze7Cn3WSuejYvN/o/j93/N9IimqkNgKea2PtWYxxmByIcbAdL2Kbdsyk3JsvcX5cuHt11/z5quveLz/wDRNpCdSnkr8EU9Tj8GrztU7Q9dZdp1jP3hudh23h4EXd0dePLvhcNgx9D3WS1UblBi1xNQuu1ivVZ1rbpVvWBbpRanhREyZkDQA6+GpkjglEAlUX+JMDGivN4rN3bIQY6BO6ZAxa0BJWsFnWJTcVDW8mg2HGIhJet/LIgHf9z3Dfs/eWUzXqSaUlujIX0vLmLfru9H1t99vLFiRE+BktKBwkI36QGSiUViZjEeh5izXro6sk9tfFDaqCeLTKtaKZRYoh6GOs9wKWIsiGLIX6tNWYlftN9cY+7TjC+CdpR96xt2O29sjz57dcLw50Pcq60o1yVoIMYsbUEwsUVyorIfSi0VrdQdKam6TcsakdUhDpmjfUOw1k0LHdVRZzhUJUgJTZg2E20TV1EoWwG6AK3E1EoOSel3Xa0S9Vpg1Qcj1ibaIwXoPPq5VQA7zN+8+8KN//yd8+rv/lH/229/FJXGX+tH/+kd88bNfCE/CWoZeiotlmli0fVb9okHQnd5ZdmPPzX7gZhjYe8vgHYO33B1HDruO/X7g9ubIbug5jB0OSU6OvYNoGR0wGCw75nnm8RLpNGHqjOHuOLIbOoJCw8ZYOu9wztB1nZyRSGIbU2ReEtdJNeshcZ3nFmhBrtsyL5wfzlJJFiOJeM64KoOhbJQoqkAhE5YJ53u++NM/4Q/HjvG//Vfc7TsZlpIzztg2BMOa0jqGFdnZZqTrOqi3WJJhStHJYkYd0iwmZ5KekQnahDKbCy4XbM6YbKixxxmlXelelqKtTkTLLZbI7PakpE6tzkuNYbKGTVaSH+t+r5Kb7ar7GE/79fXt/0bAdd4zDAP9MOI7JcmQSakjxYUwO9Iyk2IUeUoW9pxNmoF2UathhZOp/R05xQUakZ8Tl5KkB1CFoVaBe81qpUiQTSZ94m+Pn60ooIBZTSrW+1xa/2+lc29gsQaPwUc/2D5Rnz/MM6eHR67nM9bItA5yYjqfeXj7jrdff8P923cs8wxsRpRRqex2Y+gui9abwuAsh95zs+u5PQzcHUdub3Y8uz1weyPuMr4TskBMGRe1+5EhlkKxEla8sWRrWpUSHUSHjtVar97TRKfoFJDMEiJziExLZJoX4iKM5iVE5kVIUrH6p8o7lOfLKr3Sw9tVcw2t4EuhzbzMIXL68AHnPMfjDbvjEaxld3eLH3q5/BnNPjNCa9LfZMy6g8vGxMEYhSERBqNzGCd9T6PcVbmHmWJqVQqxGIHLihHY3agspWQJ3E8q3A3UhMR2SS4jaMBtWbSt8GdpG7pW59XR7Vefc/Pc+n473zEOI7v9yPF44Hh7y+3zO4ZhkGEBOUk7YZ6Z5pklBDGznxaWGNfglUtLuOr9TkkJhrrnakIcFQlJW8mfbsrGLq1oUN4ctBtgrt6Xj99bfXNbJUCt6mtgaPay5Sm69GTD13C7ORjqORBS5q/+8ie8fX/iv/l//Atuu8Tp/pHf/x/+J/7sP/8pl/OVfhyxzhNCZJ4nwrIIcdAL8c/oNektsjcHz8F7dqZwcPD8ZuS477k7DuzGnt1+aANHvDXEZcYUOHQOt/OEWAghUWJm6Ad2+57LdebxMuHPC90Mdr9jHDuckWDQOU/OhTkkwpK4LpHTdYJpJi2RHIIYnxRD5+Xi2iIM3lyEZCUJWFHnRsM4eMahY7/fSwshRJYlyFCCpHPPc6RMC67r+Okf/RG7/ch//Xu/x84JkmBLXqkxpq7Vyupdz85toG03UbNj4yrbXBjUxro2+EPWVmoeyraAzRkbNSY4U10ttPWTNBEvT4q+rNJCoxVu06HX/9YhN5o45JpsF1hdsXJ73WuvWAWuH7chN49vDbiHmyPWerxKTLyvAbcAkRQ7vPdE7wjzogYJhZIjiYRJTjfoGkzrgbJOU5BqN0WxKEsxKsmqMk23TiKuiZxrZdtu4Ob+1YPwCbK02eCy6Z/WrNtOrmReT8kc9afXm7J+P8hNmK9XTh8+cD2dpSfjHWkJzNPE/du3PLx7z+XxRM4J76pmVog5q3mDQF/OSLAdO8NhdNzte57d7Hh2s+fuZsftzZ7jsSIPXaPBG5v0TWRICVuqhlhIQuJNaijFkjTgluKVLKAsv9Ynku9POnhhnsUP+nyeeMwRYiTnwBJn8rKIHjFJJirvo7q11IUrn3PON/Y7ujgl4RBdYZxmzh/uef/Na8abo6y/UfSPuuJZd+iGdVc++usmicAIRIWz4s9sdfoSFlsqMSlpBq3VGwqpNRN30R2abGVjq+SqalqNKpIxUKzBIPfA1IBLbn15sMJjqJCsVnPrfM4adJGkoDxdryDWerv9wPEgLZ/dYc+4OzCMg45ckxZBt8z0ITDPM/56Becw8yKDMTTo1lnWMUaRv7RKQ/5M2oOPKaqbmFRMW/Z+lWPJpV/3Z10DTqvbrQFLtY7cOndVSMJsvmZ1qEHlfla+gPzK9bObZ9B7utn7xvDh/pFvvviG559/j9/57kt6F/iD//DH/PyvfsJ8nTjcHokhcblcCUEQG+stnesxBeISsMbQOcPOdtyMPce+49BbXhwGXt7K/jzsPJ1zMulHrVf7oRe/+SJGLzkVnPFMJJwpONNJQjstnM4zuRhuj3u+99lzhr6j95aUAkvMGOPIMQrBL2WmaeHhdOHD6cqbhyvvHiIpwGVZmJdIUhk9VlEnK1C57+VaeuvY7UaMkfd4frwQs3gslAbbJwmi1hDDTHqI/Pj3/4Bnrz7jd//R7+DV4MSQNePUITOlOkwpmajlVx8VNvpZa+t5Ly2f9oEhZWmL1MEwKSNKFCm/ICE+2ZItqr3txl1OPwdqfauIqSmV1yy/NyvRqxK5tlyCJ71f3Z9P9P16zpGf7te2b3/tZ4GuH6SP0VXBd4d3FY/PlDwQfU/oemInshBrLkxFYSqtkGJIot+NARfFoMHiWmCQ2Z0LYZkaRC0Zh75w45RZ6QVi0OBLPdyoecXTI6lsP379e99s8jVA1+9/0rstm6/VDV1/bxFXmOv5zId375guV4bdiHeW6Xrh9O49D+8/MF2vQlYwhq4XhybrhPnYZCsqGvfesesctzvH8+PI89sDz24P3N3subnZsz+MjONIN3Q456jyEVPp6bKnhJCVy+YamA0UaMjZgwY7750SJSpr2wgikZMQnuaZ6Tpw3vXsx47z2fN4nujPGVcSpCDThpK4TVUx+sooti3rbb2RmhxZq3e0UJZMmmce372jGwe6cWS8u8EPHcatPSCpvJSAty6ElT1bV8ZaUqlez0hP2losrsl+PCq0z1qTVVy0VsltUpZA91XgVzZD5o2RsYLNhUOnpkimra8lVxs6SykyqzabKmUTn/LGZ6jruli9ZooSlczQe477QfgVu5F+GPRgH3QUY8F1Eec7fAwY54UkVtSxKUUFBdYgH2PQxAuZo5vlPcaUGiQdc3UUyu2wNMY1/aNEuromWfuy2k9vY/isWft7Nei2mK1JkD6nsxp0nW0DENa0e01YTa1mn+xv+d2pFL768pc8vL/wD/7lP2dvE+/fvuH3/+3/wOP5xHjcsUzCwQgxSO+1c1Qfbqd71ZTCcRh4ftixd47bwfHyduTV3Y4XtzsO+0HlVgPj2MtoSt9RrVkpBZMXyBlvC7mIBt87j7GW3X7g7uZISknuZy+fFwVDr/asokzISVCMlCOP5wufnCa+c5n46s0HfvnuxNuHKw8UTtdZZ7+qSYQiLN57KaSsZ54X2edREUYjNpT74w39MFAQ50DnHPM8kWLi/PjAj/7f/y8++eQzXr28QUg5MlHKWC2qTG0xrifzetZuiKlFeD+SaK3o36pFLzgBiBoikzAkh843Vl6A1aq98n1qBVs96KV0A4WaLUJQFRTMQhEYu5Qkkr7q/FfjSwu4Wn5pwi7SOt0zbdTYrz6+fXhBhTzrVBrfqb+yUyg3kbuOqAG363oMSC92kVWfC7pZI92y4LwGXD1sxU9Ugm2Yr8Q4y5STIlmSNdJvs64XA3bbCcNSduPT6LctdeFpxGXzJbP+o/5Y65Pp12r/9uOgu60zWgda+6GX80mGxk+LyAiM53o6c//2Lef7+5Yde2fxXiZ5WCsuXrESy3LCOcPoPXd7z8ubkU+eH3h+d+Tu9sjxZs9eJTmu83KwtuqAtjisld9T9XorLG5oQ9E1gzPW4drrEbLIuiMEOsk5EZeZ5TByvI7cHkbO55GHx7MYnXuLt8B5Il8XgR1JFORA9dZqNmgwSuvIJav7lVLrrcXgMV5e6fXxJFXu7Q23rz6h3w30Yy9zdVGyj1HvaFMX/8cfeue1Eqq/T4KubGgH+ILKEIq4T+WCK1kIGaaSqPSAL0bITlo5FbUwpfJmW8CFOgtap9G2wFOKoWRHMbKpBVrPbY5oJdIVjR5inaqD5HMmW3H8Gscdu3GgH3p819P1owZcQTxSkvtpgsjbgrYcfMqUKGSxNpgkRVmLTgOx6ikpOqkpBEmcU2he1kBDJ6zzgG/uZqLFV6ezur90nVZSWkVTtjZ5NdjKYVi1kbpG1NWrkuIkaLP5KCtrXesVNECfrlc+vHvPl1+/5d/89g8oaeKPfv8PeP/2Lb7vWS6TuD1RGHuBbLGG424UstUScNZwu9txGDp2zvBi7/ns2Z5XdwPPb/cyCH4YdCxeh+86ueVWEAzfO1JIZNXdF+O4vTkyzwvTtIiRi/cseRHv467ncHtDLgVvLTlFrBVGv7WWGJPOJg+4ruO4GzieOg6DE5b06PnyjfSCH6eF6xLFoxmx7M0xM6eFGCdSEmMUSRYMw1AlRkemeWGZA9P5IpWus7IWU+HDl7/kz/797/PyX/0rHJVrUw/K0tAOUxQdbdWi3JtCJaJtjvN2HhtFMJDZt2aDNJUi7Z4Cwcp4xaLRvrT9ljA5t31oiqB+oEYlKwwDdTJS0baBVveJzfhHU+tEIXmWogiLkdVmStS3tXGf++jxrQG39vWsTo3pfEfnvZBdjIGSZIwath38KQnLtejBTh3ppHBV0MkWTivYnKMMPl9mlmkihomSg8BvxmJth/UZVwzGK6moyIWvYaZes6Kbf+0PPJVRsPn+j3OPX/nO8jT7WrWJaI9JF0JRAfnlwnS+MF2uTU9bbCZME9PpzHwVAwxnLJ0Xqr2xViBFnV1qcqJzsB89L+52fPLsyCfPbvjk2Q23t0eBkHd7CbZ9J4GR2tfWcGrBZiEkZe/USCMr+qGwJcoYB3VmEfahdeK5Wy0i6wGJEg5SHBiXmTAM7MaB/dizGzv2Y69/7+n7M95duL/MXBZZC8kYkaA4vZY2Y5KcuqYlCE4OT8TMwSCSpjBNXO5lVnA3dtw8v2PcjbReu0mUJDrrah/YpGSb9MhUaEirIda4q9NHCp3udmMkmPuS8CXhFGAyBf05TbQ04K6bWdZlGxa7ybK3r6UhMgqVFZXTFVbjk6zDFIqpUw+EXCXM/YTJIlfp+077g6LBdl6cxqy6wxljMUkP+y5rxdvj3CIWmQZpG8Q6BSwSo1bTTjWFpTRbzSXOLGEmBrHGtM5oi8DrlpPXbW2hmuauNq6FymDdtnBav89UXfVm/27umTBf64D71Rik2Xq259WDsaxXu2B49+GeX/7iG/zxGd95fsP711/xyy++5BIS0+mRlAreGYzxhLgwDB3juMMUcY4bnGM/DOycY4fh87uRz5/vebHvBEY+7tkfjuJB4D2uE5JpVkJE1bVbb3FuwOUsyZC2c2RNWYxJdP0R5wf2N0dFdeR7UowaFGWv2k4CWAyBbhiJ04XdOHJz3HHY9dztB25Hzy/fnfn6w5X355nzrFOG0iJXR/Wj7R5ScMaSlsTD9Mjrr95JIHMryuLqei0FXzp+/pd/ztf/5B/z3d/4vPEXxMp0tUEVFctGrlMqMXYr8FrP8YpMtH3rhJVdCX6mZBKFmAEjlWsyGYwk81K5it98yQnb2Mi6LoxRyVJNeGsiLOoRrBWNr93QczUpqBwX2eROkSunxCwjlf6vj7ffHnBTSlp1rFNknFa4Rk+cUgrZeWwpLcMedhGsIcSIMVq81x5QCKCGBgVISawiwywDz1OUyRmWotBJEVqM8TiTRQcphT8GQ9aNWsXS1IO11Czpqcdm28zt6NONWisYU4GwWtWugv7s6mEuJ4R1lhwyyzRzfjwxXS5CHqvi/xiJ80KYJtISMVkWcudkKESq0hx1Z+odHIeel7c7Pn15y2ef3PHi7oa7mwN7zZz7fsD3PdY7Ie5Q1+7mMDeAczh8Xdd6DyoBbIVZTe2PiIGoBlyrB5pmdgVKSvjYkZxMGuo7z9h3jLuBw37H8bgTWco40ncdzp7g/swp6ND4In7I1uZ2GBqtOq0SmZyOGhTDFY8vcm+Xy4UPX38jpJXeMQyqIdXAlEzaZMW0Db1W9bRAWv1N6iFfjLAEfP0epIOTS8bnSF8CPiecQk5SbdZDvDLl85MkUHPRin/owSEvJpdqvgEZu6nyoFbowvitPSwHTt9LtpTsyDI1HWvFDU6mz3iBPDFNjkVd/9bJvrEJaz3Wij2etamZTCRlYvsodoECp8vrSikRooyhm5eJOczERYJ113WYTg5SlPhWdF/abNp+bZuMGmSLMknXZtB6/da2QWtJ2K2Fp2kBeoWPDWDXwxmAKgMRtOr+wyN//uOf8o//xe/h0oWf/+ILvvjZ3zI9PEApInmjEFNg6D1D35MW4ZU45xj7gdF33AyO7z0f+e7zkWf7nmd3Bw7HPeP+iHVqPOMdrvMCR6dMNolSLM6p/Wgp5Ii2mCQJGUbP+bpgjMX3Pfubg0C5zqvzGBiv0poKu+o79c6TnccYQaukpeDou55nxx2fvrjwxTf3/OLNia8+nHmcIqdpJlZyngFyaszyGOrwes94GKXqdWK0kWLU4SmChISYefvNW/743/87nr36NxzHTpnKNeeqcqA1qDZJWVFGvtGvlCyuVEpZWhPVNfkXfopYxuacSFYQrkSiGGm5uKKe28plKVlmRHuT5XfVvbyBR0SiZ0ia6FZkpZ4XEl9rg9hoPJU9ampiWcDKYdH21sePbw+4IUhQS3XiTw1QdeKM9FKdnmylZCG4pFHM0FkaczHlotVvEEJJEvu2GCNhmZmniWWaSHHG5IQ1Bec8vhhlqiWs27wGfY2OBuTRqr32DRti1cePTZm7AlBrxl0DUyrrR0NKNtexlCx92g/3XE9nsh5YJUZihOV6Zb5cicsiFYFdjbVTTMRFRls5k9kPHS9u93ymwfbVy2fc3e7Z73b0Sr6wTnu21rX3Wt2vmhPTloii16FKLmoftwYno9q2OnWj9SuUHajlnrJy5YB0FrJC1r5zDF0nLlRdR+eFSOedb33kx3lhyUkHmmhVUq+1tTi8/k6D9x3DTmztKms2TBOPb94yDB3PXtzC7QHrFWUoUmHKhq3HT7sym9ut12SzIpQDr4Mrqr474UoiE3E54POMI2KyLjbW3rC4RksPqGqS2sZE7QsrJKwLZzWA0eqTTcBtsLghF/FzNtZrABc5U05aDWvg9ioPqTrHkkUK5HS0ZkUC0Gttm+e2mAfYLPadJWchLrrqClTNbWQaTFgWcQObJ5Z5lgSs8zLYwVpwiZKd6na1utmsQaNwn6lVBTUGm/XPGjor0oBCxJZNoBXnOGs2a4gVwqxVbYFmbmCMYQ4Ly7SQQuKHv/lDwvXKT/78r3jzxVfMV7E39IrQ9Z2n78cVuQCB7Y1lsIXP7no+u+15dbvn7u7Ibj/SD6PA+v0O6zuysbqeBAWQRE0IojFEWUV1Kao6wWBwnWHYjez2u3X/AcY6cefrOs0aZf1YI8HLIAe9HXckKxK3g/P0vSSw4ziw6z2fPLvl6w8nfvLFG77+AKdpIeSiLYuCdeIw2A8DznekENt4v37owRjOpxMPD49gXCMKzVPkZ3/5V/yDf/ozfud3f5u+dhWoDlOl7deCkAXXUa4yT93qdDSrpVMthI0agULB5oCJM0TxmzYpkUskqpDTib8gSRM5q3IfW6WoCF8iW6mYrZXkkCqHqmeinodW+8Ho2VpYeQpS3DgKXgOuehWWLH7Pf19IOcwzpWTC3BEG6Z0ZW42alWWLpyh/KZOxPslkEpfACekkY5UFmbA6YF1sB0W7ugbc1TzBWRCEw2JtJjv1YM4Zk4WcIpvYNFsv9AisR+mGV7aWeaYuhBUioG7cTSXcflLh5NrxFBiTlrXllLiexG/0ej6Rk8y1TKpvnU5nriexLCxFZs9kgKTTl5YJS+Ywel48O/D5q2d8/uo5L18+4+7uyH430g/SPzd+7dlWaLuRzz/KplqAMZL5Y0VAU9mcNaGsbz6371ur2wYH6KScot9iciWwOCheeisI3Oc7L5t73zMMjn5wmPcPvDtNAmGywtXNHNxafNcxjDt2hwOH2xt818shP8+EZeF8f8/uMBDPJ0y4a4xqhzIg1QVqfVRaUtXy1TVhGiJgtBdotXdOzpgUyXmhlAVXFnyesSUILJVqqNUqtailHaVBmnWJ1Qqsth8kpleziLTm7LVyaYFH1jw4jPFyd5N8PW8qYWMc4lkr9z4lJbaFQI+4fWHEXxZTfadViuZWg3dj1cwji5lJck6huCwyqKxziJeJeboQpivLtGhlOZL9dsZwomRNIGrvtkIwxuAMeo/WynQlP5V2wsouNqzSv4o81Z/R3bv1xWXTI//okQtcrxPffPWaw90znt0duD4+8pd/8qfM15lsDMfDnvn0yDD2eO+xGIEqS+Gw3zFYy87D9170fPfZwHde3vDy+TN2B/UnsDLBRs7FGm30LKkJaymUbMTty9SWm1FvEUvvPX7UlHG9HHWjY1wnz9m83Y2OWUQrMeFsFOPwfU8EevUX7rqOvu+5PU68uNvx8m7Pj3/6Fb94c+L+uhCKEh1LIS2RORXGveHu7lYZ1TL97Hy+cJkmaRvmDGXt5V8ez/zpj/6I7/3WD/GDxRnaeNCWGG0S1iabKxlTHKYiVSWpFruygA25REHF8kJJMylcKTFgGilRlC0ZyEZmf1mF8I0WTHUEgnWW7Ix4dhvpAAkpT883a8DKMJJs1zUlMUIsZ42tQdlRjJdAbsWT2ZZMsenjI7k9/o6AO0nA7QdCWISlXGQsmzF+Y9QM0RRcERNs47zAKkmyjWIsqUCMCUzA5dq/zW36kATcmRSlf6vtAlnILuG86gRdkgOyViTNiWezwZ68i7pqf83D1E398edW6LnqLtdpJuJqImuz6KzJM6f7e6bLpTnzhOtEmmcupxPX84UQFtWdKmSTsw6RFw/UZzc7Xj2/4bNPnvHqk2fc3d2wV8mPa+zhmvGqprGe7m131rewiaQt8K7BtB5xdfGvV1Ajaqs0SnufbK759rkNEnw777EjeB1Q0A8CQTkvzMyQE6drEV1woVHt62B05722JHphdDoPJpBSYp6upCSG6tf7e8LpGYNzuH4QoT2bQfR1o65XQ96pqSFYrldWAayQatQPNiVMCpg0Q5qwaYI4UfJCIjcoWYJtdWPSDNo8rdpakKiHbqkwWg24tYxbK7e1n+QE9jVSPWfaE8uazBljnTD6cySmBbdMzJNUP9bI+ETZO1YqeGNwOWuvvH7UPujKjk4hYCqjE8hJOBbLcmWZzsyXq1TQvsO6KMY2yrB32aGK2SfXhM21kdhr6lbT9ZShuPXfpm7ZFVFbV6dZK2Xdp3JOqgSkBrsNGlWM4eF05Rd/+xXWOm77wF/9+V9wfXgkpcw4jsRpwnmp/L0V9u88L4yjEKZsjnznds8PP7nhOy9vePH8jv3hiO861ZV7HUyvrOa6R+t/dR6s1Z4tTpQEqWgV75xcNUU+itGkSyfgYNeqq2iJ0aB6jN6viigID9f5Tn575zGMeOsYx5HDvLAfeo5Dz7Offc1Pvrrn/WVhSjKopd8NHA5Hun4Q6V6WhK4feobdjmNYyEWGjZxOj6q+SMzXiS9++nP+5mdf8Y9+97c4WpE7NRdBo8W5BttSe6okQUWMRwiJul6swLc5afOmAClQ4kxZJnKYZc9WWFp5Qc4W6tjAUhTCr3vOaDyyOoZPSXfZGawzWJPbepP9sVE5tHVsVgTQqoKGTA4CY4vXpEiLft3j765wgRBD09CCMBKd9zjNAlJex7nZqMHWJxloQNpUs6lpLnPJ5KhyoTZpJpBSwJqCdwJE1MXsUsSlQE6ObD3WOEyDrmiVLroU1zNq7d22gs3UzbD5ZNvQ+jOl8dzImWYQUExpGyMXsc2bponr5UJcFqk4kpAwpvOZ8+Mj03IV7W2nU5fUaMCUzOAsd4eBT5/f8J2Xt7x4duT2uGe3G+l7kRRIJVJhjI+D7VqotoceWma9CFpJVU1brUDK5sO0D1MFjmhWqjBohchaAK71sv4Kaw0eixk6CjsoAqcZhTFfv3/kw+nKFNRWTdmHLZCURFhmykle0TRNXM9nLtczKUV8Z3n31TfcHg/0zjE+73BG4VlkFKMxttafoH7dT9+l/M0UGRdZasBNEeK6mUu4ksMV0gwpkOvcMV1hoHpyTXYqpLQNslX+sgZcqYrF2GKFW9por2p84rL0+pSE30w96oHsEsaINOM6TWpwAWOxOOsow4i1vu3RQqEkg3MJ733jYhgNztXSMcVE1Go3GQPar4thZpmvzNcL83WS6U3FYG0gdZ4YRZ+frRNYWaHdkrPIeFr1WRdrJdRUDHhNYqxRmZzu0NqHrQHc6ucbyerJjt9uC9NQLess14sk9PvdwP2bb/jZX/2Ex4eTVIPOEsPCuB9w1pNT5nqdxZqwFPISePFix/dfHvn0xQ0vXjzjcNjjuh5nZXyp916CbRZpnjhx6bsw9ZUh8Lu+tLoXsUUCrHUrCmUtWE9FnQprAtyea4Xh2pSqBihYGhpnlNTqBqmMnXV03tN3PSUmeu/55f3Elx9OXBYx4Lherjw+XjDW4oce5xxd37d+aN937MZenZoK03wlx8j0+MBf/cf/yG/94Luwt5sXpdhjMlrRRvlAKkIwgjiBwLiKBqy4O4IyxQUTZggzZZ5JKSghKit2WIh1vTWylOxXbw3FZCKCkjjdd9aA9zK0wLT2hbTOxCtB/i0FHorg1JG0guZq2iiEPoqiU3/PgBtjkEWUVFtlXWMkOt+J1IOCTXLTY0lYF9vXXSqInkmqmhAjJoLIHyIpRJ3jubDMYpyQk8DJpRPpkVRJHh+8wF3OkZXcY9S3sx76GYUQ64YzPCFabOOr2XxsI1aDsloQqI5LVfMJ1caykklmrdBTjHTOUWLkerlyur/nfD6xhEWn/MgBnOXJ6JxlHAZe3h35zosbXj2/4fZmx7jr6TovbMSa4da+DTVnrqeLWUdHPXmU9j5bGdDev/nojZv23zX/aF6DbHz6xDWqwYc6GaRl8hqETWHoPM/vDnSdx1tHbz2ddapZnQh6MlSv3ZgidlmEdHY6EVNimmdm/Sgl4b3j9S+/5rCT8X7Hw25lRBqLeLBV4349tKs/a6H5Etf3pLYVcoikiAkzJUwwX0jLmRImTFyIObYEY/uxiunXiroFXGfbPa9kiqxBReQCRZMlJQQp1Ct7LFO8uE8+2bKFDRTuuFxm3r2fOAyRw41l2O1xzjJ0HUMnfXRna4gqpEp6dGuFa83KPM0pko36Y1dP5RhY5ollujJfr2KEbwxRfb9j5/F+Ifvaz1uHlDgsVmzBFImqa7LqMMs2Fun7e1qltghSpNp3VrzHKz9grfdKu6Z1NVrFZGOMLNOV0/u3/IN/8o+43r/HhIWwLLKvY2DcDYBM17pOM856TE7EKfD82PEbLw9899UtL1/csduNcg5atSd16zmEkXVWyTZZE4/VD1qTry36pFI80xLQullts8s1RgwehOuwRZpkr1bbwupvX72DJUiLhEwc37L0mjeJl3cOa9+z3+/45uHMN+8fOD9MLFlep7EW38uwBScNddVsL1IEqG9CP0hx8Ppv/oJf/PS3OPyTf8hgV2lW1vtVSiaV2Kwya18XolwHJFBSkw9QImohLxNlWShBzHZIMifc6lmUSyFRtP+uQVLROWdKK+a8kl4rwz1GsbB03ipvwJC0AjYRQZtUciZxRciIJWdV3CDE3bYOCjH/yqEM/B0Bt0Z07xxd19N3Yk/W+X7dzJUAYtMTJrP3vuk/q8+qLB6RwMQYVA4UZHbqEgiL9AWsNSLQLpnqRuOslvzO4aynuCisWrX2k8NO/qw5NZuKo0LFLdiIMroFHq3tGlTVTgE95CpxKtfKt2RiRPpbNVmIUSYNhSCV2cMjy/VKzrk5/5ScVR5U2I8dzw4Dr57f8PK5sJF34yA9pHpQt1enu7lVA5vbBM1HWDLqFYL7OKiWAm0I9ObRlkaD6WlSBulvrhaFRfsu9WOT4oD6pxpvmwcrWd6DXLOMsQ88XAXCEllYUdKOIiUKb8YYdbCCeHVPlyuvv3pN13n2x5H9cYS7oxKHHAUHufae1wpHTzElChtqBG6DMlKCqJt4vhLnM2E+k8MkVa/aja6JTg24Wv3nNZg0iLhlz7WnWe9U7SYXTNmMGGx7RxnxiJWpy2AzuCJMW1uKEMaMDiYIGXqD9z3DuGO/3+sAg15HNK5rObkskKkymp21OvqwyieyyH1qkIhBrSGvLNPENEmLyVqHLZLpu2VR8wRH1NZAzglXnK4PheVYt16tQ59ck3Y/aGYjNVEi1wpZIPxmhM/mupd6u9d7lJADcppnrucL0/XKcrrn2e47/H9/+jcyDSsXOm9JCZyTJLrKOkwp7HvLDz458MNP73j57IZhFKmPrKeKaqDnoNz3lNRpqR01Uqk1zEjvpTFl41hmmzFRS+lyBiPXsVS3sVowGtP2m1gTluZmVhSuLVHmHLdksaIqRc7Uruu4uTF8377Cdx1/+Ys3FI7ElMgPZ4iZJQn3Js+ZaUI5PEYCpZWkrCQhgXkd4P7w/sSP/td/z8vPP+OTZwcG2dXqfS2yHVOEvd2IaQakgSre7azKddkxWSeqLTNlkeo2zjMlSA+3UFs8ai9ZVqKvqShJEUKWMxJsO29xTh32jFirS3wpyhEx8jm9x/VWGRDirzrFtQJNJX45KWpRyU0fPb414DovM1Zl4PiB3W7POIx0XS+jq0zdFSsJQ1xhRDrk1O9Tqgj1aY2RFIIaXQghJiwzMURirH1ZyMmtAbcGXSfQTfYelzsqP7n2Mtbe0BpYbYWT2VS0dZuWSp1aJQctJreYVFpFuw40UEP/lAizHEpiSxnIFOIyM10uXC9XYgjU+b2lekrnjOssN4cDr17c8MnzG+5uD+z34ldtFEtcex9K/zFrwr+m8huQaYub6+Zujxah5WDYEsN+5furS0qrbtXgO0X9qBn1Nujmp79abgmdsxwPoyzSXEipYJylvHsgnq8sUVAOYxYlnNRfLVvNGkPvPaUIq/vd27cUkzne7jjejFgih+Me0/etMqiQuNF7LNnn2puuLMlSUhugncJMXCbifCFMZ5bpTAyLDt9QJ6YawKmw+noPttNPBF6vAXeVtNRExtQ1rbCWHGKawRdHLgi0mOU6WF17Djm8TXaqiUTGNfYd42HPeLxhtz8wjHsl8lh9eXIzfMl0USVXOq3JGqMMfCWcZJQdnshhIYWFeRIEJ4QIZIwVQ5Dak3ROq3PniU703NaqhK9s4ORcIc41cWxBpKgnc9YKTiP0r/jW6vWv80qNvr96P0ptAOoact5wOl+Y54WSIj7NXB/eMZ2vInPRQGCMvPmwRFIudEYO3k/uBn7w6Q2fvrxltxvwGmyp17XINC5rnZDqcsRYq+tlu0WLGrSwIusa/IpmzKLrr5/QdUWRdb1BUYrZkMx0j5YUMVlsRA01QVRuQhE2sJytqt3XX+G953jc8x2Fn//y5++wn7zAGMu704VrypxnsfOsfuQhKPkIo60hJUeVjO8858vEV3/zBX/1x3/O7r/8XezoZf2XtWQwmsiLqiNRWch1LjdGp0htHJ1yyuQwi7JjDuRZfKMpWXk9bbCfkP5SUolRIcfckBVTJKAu1uKdFJTWGby3yMC1grVZYOa0xp9sjVjuOnltRtdkNkbaq8aLkQ2atH3L41sD7jiODONO3Wx2DMNI1w3ikmSFxi5qiULODpfXsWtOq9KkmVjJ0rOtutNlXghqDB6DQIk5Fa3AgJxEH2mNTMvxlth5UuwloyqrTVd9GD3NbP2HHn6OdW6hfKlmyGsQrgHXoTRxai1S1/7GAAPVFWv/OcwLcV6I84xNiWUSTfE8z5Ix6+ZJKcpmKYXOOW73A588Exep/X4nPVtXSfS10a8PW0Qz1nbdJh02H93czXvf/MAac00NaO2q6XfkdoABiofppq3Tbso6anElUa29XDbXlgJYy9AZzH5kCZFpWVhSYoqJJRe4TBJ0k1S1stfWgc7VtagU1YPOC6eHB96+fsuzZzd0XjaNJF36jlpVWfvVpgXcmjGLzV3UdRdIYSHOV6nmrheW6yQBtw3kyA2p2QbaujbYXk5TA23lINT9UCsEVvtHEFcbDRpV7FVJIjXguhptjBFdJg5IeGfZ7XYc9gf2+wPDuMP7Hu/7xlVICjFao/1GraZb7xij77OaXygKtSwqB5LRilK5gbFFdckW64L4YrtFDDc+hqytU5Zu3uw8bY1opSPQp0LbTX9Zo+ZT/oDRxlENNm22diXloOYFyMGaUxYuyrJQrid6l/npX/41d3e3fLg/kXOh68UN6nqZNTkX84PbY89vvNzz+ctb9rsB77SnWp2uMFqdyZ/FKCFwU6rK0qgBTq5zC6IUStaepVZpxnpZpxXOBVDDmHZumUrJkl9gNCnOOqmrpECJoWlVm6RTK9vSksQVjRmHnk9fHJnmwN98fc/nnzyjH3oep4V+mjlfA0mLDdvJEIe+8wqgiNokpYAzhr6XwPjFn/8Zv/X9Z/QvbqRFZizWejB6v3QPolV5FbxKYbG2I4E2+SrOE1Gr26jFTh1KYIucV20UnzrQSeunsNXJO4O2AyzeZZU4WrosemzvpWLNGYWiRTppvOp7rZjllCJudykljNWkJyXJjz4+l/Xx7QPob4+M457dfs8wjnTNT1mF2Ahj17gCxVNyJFbiR4U8KoycIimuBKl5mgnzrFVEHX1V2kyDVCzJGmJwWkF2OpGoQgUrG7JRicyqJS1t25rGlqx4PXot6vdstM9YPUwwbY1i6zGt0E7RTCzFKAF3mlmmK+F6pXhHuIpWMcVAnZOaciZErUicYewdt4eB57cHbo57BiUmWGObtrRu2FQKplg5lFqFUM/3zRuq0Hn70De4DbbtuNPooBe80TE23ycBdb3eNbiu1YkcajXYWCMymOb7m0vzvbVWppHcHkeu4cA1ZhLSAztdJuYlNDN84Y9ob6mSaIpk1JIUWS4PZ15/9ZphEPKG3nak+lHJS5PF1B64vNTGFo6yJqW6FfOVZZpYrhPzJKiLVOU6HSetJihs3neFCOu1NaAOTBbvCp3P9M4KzG5Mex9mA/fWhE5s2eS5JJ31GoSyvjfXDhVn5Joe9jJ8fug7vOuaNMVYo5VRxXFkpqlXEwPX3JqKwv1BRjXGQFyCVLZLIMxBR/hJwK2MWud0MIlC/zEGfOzEj9l7cnIUpwhN6+O2GmdT6q2QMkaJYZtkpo17Yx0sXlnJVGcgvFy26mbS1rpjvlx4//UbGRh/umAdfHg4UTAM40hYFi7LhV5bE8TErrO8uun5/JMb7m739GrRWMckSC8e0Xtm9VlWglOOOs1G12HJtZAw+qeuxSyQcklJuDKmVuq6s4vuyry+m1IDi5X0sZ6tApuuHyWrMUSKmqxAdcar4VpcnwStstbQe8ur5wcerwuX91eOu5HLdeL58cjz5x0hZy7TLGhBVka7McQYuF6v0mIwhmUJWOt4/fUbfvnTv2XkM3a3eyHTWq9BXyVNOapZhZimWKPjP40mTkUJqymRtVgL88IyBZZZeEC1f22K8DHqsIJW/WOok7pqTDAGnHd4V4iu4Fyhy6jbmCV3onvunJxpriq9sgYTvY62iKwrZbkvVUEjM3wTv+7x7QH3eGQYdhpsezE1cCo3qM1hUjMOyM4rnKzBvf7ylJq8IMyzMpLnZvog83I3FRto1qILMLOyl8pTeNiwEktqhVta73PNZlbrt7p0aZtYCBh1Qg8yK6H+ukJjR9YIWE28U4ikWQ7qMM2EaQLvBCJfAjFH6pimOlHGekfXe/ZDx81h5OY4SvbsvVZl9Tpk/UMzPwEUBdYxlWW8yaA+Crjr46Pqq17jetiXynouPHm0Sm5TxT15zm3Q2UqrTEtmKpFFfjbjnWE3dtzsR54dM3Mo5FhqG4c5hqZNlPehFa7GfuOMyjYc83Xmw9t7jsc9d3c32n+RuFrd0KwS65qHMhq2ikrMYiLFhbhMEmznmfk6MV/FN1b6mXWgeq0Ai27mzWWith3WK+Q0cy6dwxTRCztX16S8t9YBMJvnqllz3ljQ1aTSQMlee3VJZiTvesaxpx/65jC0yvX0eNm0Wmp17TWhcRpwc4qCQGlVu8wyyi/Mi6gLciXEoZWYQ4ZfGFJGZrFWf2Al1dUkte1QvbdG917t6zeP3aLXVpGSCv1D9bPWFWVQnWXNASXoYsQYRFehEI1iZFkm3r/+hpIz8XrmagyXy0LnezXsn5W165VAZHh+8Hzn+Z5P7vaMvW9IQ50qU7L4n0uV5NVowbfEqWplcxQ/8QYXt3evATAVOaDMOiVHnn+TlDRcoBKojEC5LSnWhEUDcElRr31qiSq6B0vJyn6W7zWaAKaScdYy9pYXdyNvH2feP5xxzrMsgt6Zrud2v8Mc9+ScWMLCNC+knDTBijL2sUjf93S+8tc//Vte3EHOB/w44rpRNioOsqGQMK4mI1Yc1kqdniWJSsqCVMQQWKZJnfm0lRfiOnO9KKEQKViMInmVpNjai7o3XC5k73C54PUoyyLExSQ5a0WSu5lRVda1metz2tqiq/dJIPJv8b349oDbDyPdMOD7amxv2yxT1/xDpEjKtW9bpRCagZaskJ0OKA+L9G3jsmgFUS+GVqOKB1cCllciRvtQuMrZFRKzTay8yl4KK6HB1qDbalpd9po9K92GTs0dkkEOE0wLNHWgMUXmzIr/sXhAC8wxEZcFTyc9wSQzJHPF+RWOHL1lHDqO+4HDvmccO7pOZ8NqFWI0e5Keyyqil0PetsNE8VdqeVft0UQmk7WA0BPdgGl2gU8jsAJVrJF5E8prRDAb8F4hqQYn10y8BfdtQNZsUw9gb6H3lrGz7DrP2PeMfWSJmViyBl+xaTO6wKs9HEgVTYG0JObLzOXxwun+UTLU3uE60VJWDkFleVeWegtoSSrcBp3Ogfk6M10XCbhLJMZElX+1gQK5kqXWS9GmE9XD0aCIjaMOq1h/ZA2CIgOqBc1ahdTnqL+hjQ5TSL8OqbcWhqFTYxRJdiujvZmXoJClHgrWWLyvdpCKAKjrT4oy+3SeBEYOyzqQvt7rom2XdV2oW5b+rx5IrZ9ODX6FOmJvy7GQtyg3uVZfLdjWZK2IXtob9SPIcr1sXXs5i3QmKSxrniZX18dHPrx5LfKplHk4XTEoGSgFhk6q8qEbcaUwDjKQ4NNnR477keb/WwRyryYTpghagq0Wi5JcrKqCtZqvJiiyCOuVQcxjrFVeU8G4rPevlgYiqZT7aGqrcwUH9Lsq7A7CdSAJ2a9ZJBZFtgoY3Vs0pm3Ua1ZwBsbOc3foeXu6cFlkDnbImTQHuhBxvbBzL9cr58uVZQkVIZf3bavkc+H123vuP5woPjLGq/pL9xTTUcpqpOSco847RpNkIUvS9usyVc5PIMbEsggfKIWoPerNuD2znhn1HFrJjLLCsq0VtJxzxlq8TqPLiopVbKUGW/moxiPbI7MWSxL0s/n2Pu7fMZ6vF2G3irXrhnWq36tYu5ABNsPTa/WrzfwWcKNUtXFZnlykCjludYvOiTtK1/cydmyokHady1sDbtUwrhDq9n3WZf+EIqRVCaUy1wriFSL9IFeMyn9aUS1whDqgmCKGAHmZSfNMWnQWcIqUbKnDuoWlLb83K+TgnGG/67k9SGXbd76x5BoRQheJkH/0IK4Er7JmaE3HZ2q9sN5/QQZyq3ilj1l5fx8TprQqLZtFU5+/IOV+eUo6qov4yaJun68Xea12svaBjW5qb2SAd+8dfdfR+8iSonr6VvYiFFv7wTWbtNisPcclMl0mHh8esR6GXU8/SFLmrcN5maxiXe3l6juvkpckKEVYBJ6apsjlGgSuWnR+s7LwW+9W0d3t9ZMh7Iqa1B6UMrNzXjN16S0aqlVDrbxreKkeuW06Tv2zeQ7Xay4sVO8sw9DjO78iJGZdBzWdMpjNOqifMe1uViJfa5Es0lMXk/iaStWEZ02ExVPbMfQdQ+/pfCVibVpKurbWGbhr8lZqtl5qy8O2SNIwE2WeOiNJQ8ri5iPPnOsyI7fAZjV4S6I9TRfefPkVj/cPWAOXy8y8RKw1xLSw3w/M00zXVQP6zOCceCQfd3S+0z6e/gY10C+xYL1qLbNwD6w1bdKWKCX1XpQKkWolt4GW6yzfkqRiMsVgnSJZ+vNthRRZI87KZ1uCUgQKzVG0rc5A1ATR1jTPogEYOdSsk0HvFmyxpLAGK2/gdi+DD0IxTKlwOV+JuTAtEWzBedccppx35Bg0MUPzc0uKictl5us3Z/Z3A/fhTN/Nqlmu5jbq/+y8IFrGYUwEXEtio3J/5nlhVuTlepmZp8r9kVjSmOy6z2uxVRqkXrB5jTUaZ+V4y2bdo2q+IeDiGnCbokAOZMxGcsjmo97fv7e1o+gC9aDdnjA1gQW14KobyzzdWAprZJ0UVHW3dTJJMwSq0JceWLZqb7uuGfbXoOv8IL2AVr2YdXPLyUwt7PWkoFVe9bWW+trk9dXMyFUDR3khYvvVoA1ZTDZLgpBTFPvGZaYkGbaQSm5SlhjlwK6bjKJ9zM5zs99xe9iLBKjz6zWGFqBrgmbWy0uVN/3aj3ZAtf+0P9dbV1eFaR+12qhJxTb7rhk7psK8+gx6PcqToFtJHGjPqlaCigoUybalD1fobKGz4NXtSSD9WisJIpBKItvc7m/GNETEIJl8mBeu54lukB5bzgVrZaxcXb/NktSYltjklEmxBtzINM1cLjPX6yIwY1DykCIFTfpTK+62rNbqttTn150uiZclpoxPmZSE3e5K1YwL4iC3UA5ZcRaqt9X86m2Wd0kh472nDKp5d4I+rf37VcFb78taUK5EJEmIZF8GvRYyj1cD2DYZMOCdwXkrpJleVAzeyyQxpy2ninJV0GnlSKztBkkadC9qcbiiUmtCWHT9WNVB1u+RQ7T2estaeWpqUvfE5XRiuVzIIdB1npgic4yEWOe6SrXUdwMhRHoSh7Hn+XHkeBhUiqgaeCu/P0Vh6nuKaElSTXEskDDeKkQqs6jBQBIYviYSxvpWE1WkIwO4Tg75hsSUdk/BqLFG1SBrfhtlDxVjKFGSJ2vQoG1bwClASRmrSWxOGae9aTEdkfvTe9gPjsPgeZwS+6HnfJ2Zpqv4Luti6vqOoevBwnQtlBgJIVCK+ODnUggh8vWbE59//hzfO85hwZmA9YsgLK7TD21VWo9IoZJ6HRSVXkamOTBNgXkKGnhn6ZerkYzRNlwdDlETPXTPy5rMWKUc1kKlaNKTs7haZQfFGmQWtLSlrOp2mzxVmfRV+1uMPE/FaMqTg+Lp49sD7kYL2g4V1sO1dRwLrNtZq5CaCRQRY2cNQLnBU/XAt+0n0YtgdeJF1/d0/UCnQbdrk3I6WehufX2t4qLCWptgY4w6XMkGL2XNGEupw8GFWu5bqDbtaKskKUrBZU0mYiQtM2GZICecNYQiutx5ltFnUvkoG85INbcfB26Pe26OB3bjiHNdg6dan7SUdg6ZdiBrlVKb0dRv2EBV60X8ljta2o+1rGkroWnXsS4UA+oUhpEu1RpsKzlDD+66LoraJlaoT4XttV9uNOB6C96qE1leQF1njDXYbIVeXzJEod3XgQ/tcKAyFxPzHJiuAeM6crGAkJ1k8JHF+9oKWZ9HekKRuGjAnReuV/lYliD9yGpY8OTe0Ag9piYY+u6tRhbb0BGpHmPKhJTFeS1lsffLRloTphpf0FCbitZUWzlbBfj6uWYs4T3G+LYnWhJqVgh9rWfrbtCghj6PskVTTMQgifAWGt8OFRD5j9NRnV4HVSg07XzT+Fq3Bn4LK4QMmjCZdv3kOhmtGMxm/+q3VSVCEemZNWWdwqLnjtHMdEW+V4b79fGB6fwo/VYDS0iS6Gi1tyjxx/me5XLmuO94cei5O/R4Z6j9dFc5APq7iqmTfqBYr2ejeIVXaLso6rdW8zVz0pF11ul7sE8S6vopW8lVRXrCxpoNt4U1ETUGihK0XNe0sQIZ65o1BpMDxisJMQnhLKaJYmTQxZKlMnXW0Hu43Xe8fbwyesd+7LnOM0uchcRpHfM0U/oOmb0t7OoQ6jkf6PtBCGnXwNv3V77z+ZHee+ISWUKCJWJNbP771jq8SxQcuThJiFMhxERYAtMcmaeZ6ToxTcKTKSnpmElDhY3rWjfUNVURFpGTVU4Pih9QrKxBTBu2Y1id+dqAFz0zJQBrgWK0X49wGlyNiVBpOL/y+PYebi/VpJT79bPbemkLB+ke2WbkulG2GXXVYhkVhreQphvPYjbeup1+9IL9V49dWyeZbGu5XMF4eWWbTVsD0hpwt5VJVkhtnWlaA5D8TXtLmpFXCn4KgflyYVZLRzm0ZGzVsswt467yls4ZdoNnvxs47kcO+1FGm9nNpqrXU9MZeHIx10y3lTtrz7odYi0B0f+0qsisCVJda+26rD/zq22Hbw/eT/SP9W7Uf28XBSuQ6gzSx3Xy0dmCq1Uu2qE2ege0Sm4OUXXNUYjJEpPAwfO80M8B20UhXajzjjFKXvKrTK0S03KUgBtCIiwyH/Q6CWS1qCY8q3FLrephDbL175vL2N5lPQSzkolszDibsa4O+FbeSDH4utqMU5mNF4s5s5L9hOikFbBel5wSnR+ww56+Hxthajt0ov53m4/VvLSS+KiSp5TXA8saTEVyLFg9jBqvovN0vV97wV5ge6eEI9cS9YoMbT3gnp4bK1dS1niFnOU1f/QclaELG0007S40lrceojFmzg8PPLz7QJgX3G5QmHNiDgslZ5YlcLg5sCwzhsxhcDw/DuzHDm+9AgpZK9ZabORWaWPKOkUqJTAR4wy5SLAzXoaYW+ulV1uQhF8PbmNgc7i2pBYEmcEIr6Hl2Cp3k2CSdYtnVdSId70tDmO6RiTKUfyxbU3Wi/aeDWIqUoRVbb3DJOEGOAO9N4yd4xIWxt7Rd5YlWsIi6ou+61X2aBvcPY5jk0PGFHDWczmduc6Bw74npER2ThLjAiEV8hIxAazJeAupJGISDW5SlYAYsIjJ0LJE7d/qtKEsbGCjQXWzdARGNjTWrKl7QINnaQmetuWssM1LrX51SeUM2ao/snEyaUjv/+p66ERYmbVY+/tCyvvDLcY6vO/bZtpKGdo7exIQ6qekPgR1/4AWfJx14IxOIzGgZTl1IbpNsFWylLG+BVnhTKghgB6sm9KtHSx1c9dP1xhUA279bNVYSvCti3ujSS3SCJedLonBskxczidOD49cz5cmdYopqkEAzRzEe8fOO/a7jsN+ZD/2KuFQALXBlfLuWk2ylbRsA2ste9R/tX2OerzWg0qD+NNL0+4XH212DJsWQSUFmFoYyZLc3PqWtDRMYH2eeg8q+FH7H84aOmfpvWXoZBP33tDVBUxVdsiCz3oPstrbKV+GmJMMRF9EIjBPC7br1gO8yFi3qmhKlYukUHiKSSDpEIlBCBjTHEQGE4RtW+fKlicLSteDvk1rdBKPk75PXZ+67yRDtwWTMiYVqXBzxlTGfVGAy0gUts5jfQ1cOqbYKBfBCCFIIPtI1x3o97d0g+huZcCFebr+a4WuQbYNT2iSr9h661Z/Xy4G4y1EeY1ZKx+rfdta1Tq3NbtZCYzr79XEq2ll65Bg01oOTxIaUwORactSrv1H7YsM7WJvDkxKIcUCRAowLwuvv/6Gt2/eSbKZC3OIXK+yTykSOr21LNcrN4PnOHhuD0JmbL669TXkAlYGtaeykEvBGyMVrZOeo0MSFm892I5inRQZVmc4Z6mfaqJdSVYSsLSSyrLmUpHrLlD2yt+QFlTW7NhgbFZXqe31qNckKXq2OsJhhDhUctZLaOR4yfJehBvg2e88h9FyfxVUauw75iUyI+S6Ugree50CJgE7l0LXdRrYZU0tS+D+4co8FbpOSIQxFZlQZCElA0ne30JkSYkYaXa6WT0cFpWozXNkCVHh5Kzkd93vZT2HauJe9N7LFZFzqFn2Fj1jdO1tiVIpIy2gjIwvrUG6feh1q8UPFopZvRq2cXLz+NaAezjcgjFKPKmC9srCk8P51z1lJXnU3o2zEnQ678je68JJcqFJSp8uFGTcka0VrgbbCm0XjCzCnMXnNIoEpM6DNVrNbQNq+3vZHD5tRdZsWf6V8xb71+/Y/EwphUwmFSFjnB8fOT08cLlcZNrRMhNjEsjFiHOJtY7eO8Ze4OTd0DEMHV0nB9QaldZeaAuErZJdK9j6b2NkPmpl9D1ZZRrEq3/t06qMj+7ZWj22jKR+R+vBls3rbHdZnlkz5prarPB2ZpvW1JxAJCmGvhMJwq537HrPdY5M1rDUoLpJ4JrkoQjMJk5fmSUE5kV6rt0ccH2gkQuQ762ewK0aKXLwp6jG/LGOtROYK8RMiKkFXMqaWKwLSkEnI0FRiN9bFiMtM04ZSGBTwcaE9fZJ0DVZSBu2KDnDOoz18n2aoFiLIiuCFNVqdBh7jrfP6DpP149CJtSxmevazU8+ZJ2JVWfJSazubMF7S5cspcjc4xQhu4JR4o3RHlatZF37u5W+biusy3rvKzGtJuS6j5vCr1QG+ErnQCHsCv3XNSSs+6LLUe9BzVE1ScxaDVGyTOMKgfP9PSEEUk6EKLXhEhaGoWOaZnzndZB6YT94nh16DkNHtxm6sMLz9f7nlkjkGHG9RczsrRCRfCcsXOO1V1sPcumEFaBO/pIumG2VUi5FBz7oeq3X3rpNxWQUbs6rV3NWaDMpfbIYragd1lsZdh8XsePNSS0s6zYXUleKmojp69z1HfuhY+xm3LVw3I9MS2BJHTEpKRT1z6ZgoyVGGUrvvBRIRgfNXC6R+1Pi05c7bI6YEiRBUuWE9Ldlby8hiO1mrMlibpyLZYlSQeseFXc2QWNcQwe0JjFCOCwgpkE1AGuQTblgbWmBt2CIWZJjW42din5fQQu8ur81SKg6pFT4X7XUuRYqv+bx7QF3f3xywNumb1yrKIkPtc+z9gIrm67rHGPfaTYvH0s7TGs/JwlDTxdWhZOrc42x+qaKZOcpSQ/WWoGDq4CftkE3UGdRS/38NHCWmgkZNjXwyuGt2WCNgfU5TZQh1dfTidPDA+eHB6bLhWVZ5KDWHpU02CVB8a5O53Cq9VunVDRmchEijLyS5gb96x+bXlCrdPX1N9jEsgbLevFZk49NGqyfZ/1ePahXLKWlgyv8otW1qdVKq9DlwhotRQ3r4QqyRr01DM4ydp7j6LnueuYQmUNiiYaUjRiQm5o9mha6W9aYRdYhPfOZfl7ox0HGQ1bYzGiwTascxer1Egs2qXBSsaQi80/lQyrq2sOlXZt2oWh4QpG7VVdy1q9VXa6s+UJMupGjfrgCVmBB6c9lGR+tkFVlV6/5g3g554KSmjL7rmd/cycVprZ+nO6XFqxaoiVreh24kLAliRzOOfouk3P1uUKrNVmHSYPO6v61EiS3ZLRGGKt3qkBKmebvq7Kl0q6PaJxTrh7lek1NfX7ti9egp0hH0Xug/Dkoa681BUG8qvTw/sOjHpJCglnmRTSnzslovr4jLhFnBXW52XWMu25F8+xK5sxFAmzdglVnKz7ywsUoxuF8D64nJiOwsUKROSoaZ3W4uRHs1hjt/Bkj+v9ce8UVWZA9YNUsH6SlYMk6vQdNfItoRouV6rokmU6URCFgXSdrQs/ddiKomxpFglRxBmc7rEnsR8/YGYbecbks4vxnRUUSQ1DtsIxeddqeSznTaVIw50CwmceHE/cPC5++eo6xgZIz3jrIEDSQBVMgQUlOA2BRQp+Q+KoUKCYJwDHqAtC9Xiv2mqw3BaSpfCO5jkIFrRwdTYxT9Xm3SGslyT7UhBkjk6ySgxgLtXdr23Nq8ZS0xClFX9+vPr4dUt7t21bdboS6ISmF1I6a9b8t+3ci+WAsa4CpBZwy+jb1lGT0zuNdp0QMOSjXrCFB0qCYUoOzUmVnUm9SzejXoQn5o4Ar90kTBLvKnupBVTve23O2koDCPHN9PHE5PXK5nJkmsSeMSd7PVq8sFX6t7GRKhWvBFv09Qt5ak5Wy/uL227ePCn8oG3Wr1d18d9kuq7LWCoWiZWReq+lWxNaqNqm5eCXWbLK7GhQcYne2uc5Qmi7XZl0fxWwOfunxdV6q25tdTwhJbB/nxBwSISXC+lbRrKituwozGwNLnTa1iEGDT7LAilahaJZrKuFHteI4Ky6VRlELqnfrSnZamdi/5hbAxtWsJgX6UjfJ7bbStWoOIQE3acUn+0CStCxifGpIr6IOQQ8yIouJUXpXznnG3U64D87irG/SuhpsFTBrl7KS1xwqz/KSDMaUyblrK6cGwOqWU2ClxrbvWmHjdV9V2DcL0gNSSadEUjOSejtTLvp7IbV9IO+9nhfOqSSjapbrHs4FeWmCgggJTgg0zhqWaeLhwz3n05kwL5RSWGYhOA69J8bY0IJSBFYeOsvQe3qnCJ4iC8YYgeFrf4IM+noqHIy1GGXbqjRX3KdU12mQtkOFz4yrshR1/9Uk2EBrT6w9bNmcxqB9WbT3njS/1cKnyox0PqvJFkp1vRKXJ/Q5jEnkqLPFQcbcaXVtjcfGRO8dz48j85I4Lw88XgND57mGwNB7grKSrdow5byutWVZ2I07wBBj5OHDA6/fPPDppy+5uz3QZ0glUCJY75mWGZszMZaGvGTtTcvry4SYW2Vdk7RNRdRik61rOK+VbisUWP8srYKVM9ekBMFQ3EqSMiax/prSKt5ODzRXhBRrcqa4yisSv/qU/p4Bt+sGPTQ+go61osl1WO+mD1gzaWuMQMi9l4a9ZiCCu3t54XoYFSRTEA/LtarFCLVedJw62s8hb05+mWh+NwFXLvSqIUxZIOCcn/biniQRGnDXUWpaBazfLn/PYi+2XK5cTmeC2jfGGAjqB2yU+VuHf6OHw6o3bmh/u+0t2oHe6F99VEhVC78nV3ut7fWitC/X97w+h/5fv3N9jy2uKEFFGyjNSrOOGKtB3vruydKpJiKViGNSlEy7vRntk2vV0nWe3bAaSiwxMy0i2ZhCoMpGKpmmVtLr9RDLyxBFnzfMoh/1fdIqqmyuhQGFhYp1KveSFmXMhaBM4qhBItegt9nPT4KuabmNftK0e0dN3CpBRSVfGoOk0g1J1knT++XmTlS1wwYl2zQURKsHdXNKaibf9z2x9lirBt7UF18ZvjUQrhIsr/dA5tnW2cB6n0oWlKHm0qbeQT3ra0CugTUlYrQYm1p1UUo9tOp5sOqO60FXh4CkLJK6eh1Ae+ONiCXJUjXAr+tATEjktaUQW78vAZdp4vXXr5mmhayyJxBJTOc9yxL1vmTlBxh2nSSCmEqIUqSkBpJS+QUo7GsU/hfyo+1E0lKwOqSjUKXFRQOCDGGXtSwDzGUQvbz2tkt1fWWNrUlNNgR5slW7XclcqgrIWUmpOhxdEDOHIWGMp0nPSiGZQC4zKQSKyXjfyf0w8nXp43YYA89vBg7vHb0zzN6y243EfKXrHHnJLRHJpWihZNQONbEbR6JCvx/ev2cO3wO7x3cjaQGQAQPOm805aUROp5Vzpqg+P7V9XyfRNXkYKxJSWfa56JS2LQJq1qALmlzrOVCipN65ngO6zlIuZC8uWFW1Ug8En4uSMpHRo0YO+JxF0vXrHn+HLGj9Usssa87dTBLqocOaiSFVnXcWOiesOTI5WfFGTnrAJYRVKqsE76y4BSksKBloIkWjwVfJS/r3GgDqBa0MzqSHQMp6iOqmrgvZtJ+Rm9EOqlodlNKyx7K5ACUl0rywnM9M10kcXYps/Cp5cgozVCOQlOvcRyP9svX8ajdtq5OTpWCaNqx+vm44o99ryqYar3BGKVJxGYESyuZLa6Zcf0dZA2xFBmql0uQ8WuUpi7sFvEr2qrC2hAfZdDFRYqDICJFWp8nmLy0w+84zggYmCClzXQLXZeF0QfuVtN6I0eRoXYEaNGNWQfxCCAt9HNSQQKsDo0dYLpgExCgwUpZqKMSk/Vt1BkupreO/61HTtnbnaqDVBG6db7tKdHIxpASEjEiXahAWjW5t26C2cD7llpgVKslJvrdokO18J/KimjyySQZqxm9os0gbp8IZeu9InZehGsVrxeDJJZGyZYm1h17XzxoUsybCse3Nenh15FyUWyFngjOJZAq5SDAC24JtTXJiKlJl6f111uKdb3NbrSaxzWu8FE1i5GCTClQWfIyB6TJz/+FedaGbfNaKc5MEYrmWlEznhMTYeXXQUxOHpmk10koqVlQUK15gWtFQkMq1VrHbfK+ej6aW91bON3JZk0pv23Sq1jfWWbOVIW1KgaQJZR3+kGILItaIgUTJBeMNlCR8AXSrIvfQuw7jO+I8kZdFTXsyVm1PwdB1PWC4PWQ+uTtwPyXOy0kSAGPonMcMlpwjMUGIiRgTXS/QtXAjFvpuJJF4fDjx4WHi5acDhQmDkKuWHHSObKFzHcXBvAQMRa1Ua4BVZKPFobW10/ZlWT+/8QXSgmptD6wnp1HJpzCucoDsDDZrgpsSQ+cp2VKKqHUMQQ2dPKlLpCjubbJZXeOxpPzrz5BvDbgyyqm+tFYmtcNG3mf9/Po9lZHceYspDpMtJRuSt/SdJSSLjw6fhNFVr4rvLK5zzdmqaOCrtPg6nFoWeSWE1CpildcIHh8JKUv1okG3oHpGhaIrnXvtS9UKt8LSei+LZi8xkueFcBUD7xQ3VfTGVapCQdXNSCQNDm+tmMZXR631SmtwrTiWa9dkW9M9Kao0OFbWbavcbX0++cYnBDF9XxqKqAt1JSVterelQvC5HbS1ytbjXw48o706TTrE43ShxIAtGavaSYfKIKyoKOqMWIMcmjchcTcFzteZ990VZyHE+pu0XdCykrVyTymrtCeQQmrIhLU1yMn9qP7aMcvayDE23+CgvqwpprVye3Llt4nKes9+7aNl0uvGlgtu1gqmSHDIqaj5gBUZGUqGUzjKq4FCnbsqM1rz2ppX1ydJKgwt/9zsV5ktLTfLKLogQVfWYt85cvZsSVUZT0ipzQQ1tiJRQrCR5NIRYh0SzpM2TopJKwsJKqYkMEqQ0SQqFb0XOm81pEJMURMD5X/4Tv3bnc4tlTFvaDXYpE21faQBdJ4W7t/ds0wzOSztvceY6IderAqTaO8lSYbOq/f2BpGgSmc2qW3lnBm3omvWd6z0bCg5KonOUHIkG4dRJyVJmhEPZVsT3SrHsm3dlBQ16dTqNQVJKHSMX/VHjjqNrJSCVaY6RkxV2uptpDmd6ZwiqUSMl9F00Xcwz5S4UHTYujGCaPi+Z1cKnz0/8HBZuD9dOeXqYFVWkpR1mM6uMLMmNssSKBk670nLwod3H7hcv4u3QoCl2JaUCycE5pRonOq69Yzc76jXpBUiraLVhOYJ2FRRT9b7uEFjt2djTtLvNiRp0Zi6noyu97pHPCmJlreLiT46qXC92Moam8XUxvz/FXDrC/v4E/VG1rN/XRgVM3fWYpwRmUk25GRIHrrO0CVLTE42oCkyhNmIg41o+twGNknitqI9iZpJNv9ajMI4FbYT6GGJ8jHHNegWXRhuw7i21mGcMH3t5gYJNV+TilIoSyTPQQ0vgrJbk8pLNCAVGt0/JhnTlGLEW+lh951j6Do69b1dpT5gkI1irEp96tzK7RGv5+jaK0uN3tnmgObSqs4GfSj5qQ6TbwFFn2vdkPV56+LUBa+SqZSLQLAps0TpR8ZcCDFrL1UnzEzivuUtjJ3luOs47DyD73CahJESpciIq75z7AfP3WHgdB3Zjxf6yyzygAKgdqKYFuipgT9nnXCjMz9zEc1tL8PXJVBIUpbRimoJ4umtAzSyOqCVtMp1yna3r60p3eRrSvN0866BuX3OrK9VboXBJEkUchI9sAuG1KeW+KSUm861+pLXedC2MlcLkmyia66sUpqGs9bX0LJ+SdCcBe8tyVtytGTvKPVAIZOLZ4mJXs0/okpiatW1tnmEjWkyZJWZSHXklNAkh07R2bBxk5tHCrGgaycxLZk5RpktahyDt/RdxLtFp/hIcmGQNS7LtCaGqZFo5iUxnyfODyeR/4SAsUYhW4FmQxJmuncSFKxF/bclGVlnsmpLqJ591ss+QveY70TqgxgnlJAgzNpyAVwWbzUrkHqWm0WDIcyaqGCNrGE9Q3NefebzspCXOotYXpfmxPJUzolUq+8E3SmrvjSlpByTxDxPWskmXCkcBrGVdf1O/A0WT54nTNRh7lb067nzHPYdn9yOvD+NnJaFOUU67+ScXRZpA2oih6oCXGUGq1mLNZ6HD+8Iy0K3FxOjDrlnAvFD1GSUsvI1UyqqvdVKfBMo637UEwuoRZWpIMST3LhWvO38yzqHuf5CA0adw5zLJGv0bIiU5MkxEqxUy713xN7LiFAvRaTYVSrS8S1J+d8RcJ/2zZ4+FGIq1W8217qnZdKmSAApzpK9BNquswxZNqScpQWTJLBYb5scovahStbmuc5urFDrio5aJVWZxnoUmDAxhcgUInMS0/IMOrvTbxxyaoB3G1iap+87J/K0kKdFqo2UCRp4YqOnyy2vVU1svc+EM+JyNPQd49DJbEjnVBogq0C0ekoU27ib1KUkYnn9lx54UjjJ4Zr1kDU2t0BeWsSsqzexHRxf4ePqdVzlBTXWZCQLTdoCCDExh8y0JK5LlI0Ss0KyQQOuEJhKlpF0h13Hs5sdMe847jy7zuKrw5X2451z9H3HYcwcdyOHsWfsHNeQIGoiIMOSPto/VYqjVVcSWYNzjmHoJchmBbONFSRiWUhqmBGWQFrkAFrHeT2tbnnyz5Z5Pvl62/41OTHq92uyumaV9nU2z5BtIqldXIpWB78L+afrO5GVKSzvvaPr1gk/Qowy65rdoBX1zzaGUNdLG2up19FZg+/E1UcOft8qxaH32pIpUg3FQtPHGqNkKoOy7yilSIuIhC9VmiREtJyT9srrHs0sSnyR9RS5LomQsvQQjWfwjrFPDN6KR7MmHL4yp1rvPxFjEUvKKCiHjEkT69WiSFZSKDymQghJ74UgbM7KnFtjBCer95FSsM5rzxU1pYdiHW48UoxTK0Uh4BnVGZeSIGSs9+AcxkvfscroXNdRvRxzlqrTKswuQTJCScKfSJpMlgRR4GU2trZGPQ3wjkRiDoZpWjhfFx4ezzw+Xnnz7p4PjydClvmzcbowdo5Xd0ee7Tu+8+o5d7c7doedeMjHgA59FVcA3+H7ws1+5m4/cHfZcQ0ZbNSkQVozxv5q1WmNTv9JieIz02Xm4XLF3xyEs5l0AxlBO0SxVohBzlaBqbPs1Vg9k3NDjZpsq+6rbXBt2W5ua7Vu8ZJN8+auRWJNmiXpkcrWWUM0kKIVG9hORltaZ4idI8ZI30nC46PD+oxxXv2wHb/u8a0B91eCrVaVuWT9kIM65UhSXZ+I6FdvT5zBZ0txjtx5hrxWpcYVgWaiirzVpaYdGJI7NnZsNf6WzVH/1Am1RjdJzi245JSEVBMCs+pjra3ifQ26VevrMnUYdztki8qDYiJdF9K8NF1kolLJU3MkqodRKRL0S70WXrKh3dAxDr3CV3XYA01uVXt/KFmjditrVttY1GVTsVL72SJIsbZaMZpVI1ugNgyr0UF1GBKJiZDSqtYtKiNwSRJcpyVynQOXefunBNygwbgGvJoJOSOSsHnpWII4Od3d7Hh2HDgMnS5aqxVSwXeFfsjixjV07HvPaY5cVRuYs8oojG7iltWuRImk90IC7oDxvrnV5ALESJ02tG5QTeSslUrJbGJuzTxkRW72AQ35kE0sFYepvbLVTk0QHFMTg9IqXSGurNIX5xWOi4GuX9Sj2DU7yr7zpKFj6DpM1236iHKwFDZohZLcsiZYRY0u6p8gs0GtNXhrKV6N4vW9lVK0ryqWlCJRkTdd9beV1Jj1vbSDU6+3c8olaOCfHHwlF9VAZ5YgifG8SCIXSiFjMSYKczkXord4p/A2tIq/FCETxZBlLuqyQMkyAF37vvM0Y6wVV6Is7aigh3gV/gmHQw7GztepXWK+Q5Hq0fpOg94i2tpuxLqBuEQKDuc6spEDGQpGjUtyioLkLEFYzEqSKikI6aoe8gZyMuKaVKDkSArLZnh8vXxJ1ptRkVgsGOfJqXC+v/J4jbz+cOab1x94/faeyyIGFdO0UJB70nvH0DtO08wvvnrNzdjx7Gdf8Jufv+Q3f/g5u73Hjj3oMAJrHC5bvC/sxoFXzw9cQuQ0By5LxFjZa7lMujWMDHjQXnFN7HIuhCCzbO8/nHjx3d+glImQA9CTkwzOSDGL21sUxCKFpJ/Xa2tK23rG0pLwtkPrOVwqq7tIkabJUuOROEvOpsZtaqMNI/ehbvGklXpOhhwNKbk2pKMkR/aWGGXkal88rhTRrmcH5u/JUl4hG13gRbVzJZOL9AFSCqQUyDlIn6FIdlYZsFbhPOqmLrLIpVUplYtZClvJkjErSUCyjdyCmRyQRXuvpTp2UfRCkqW6cEaCNUUyxBgCixqI+5jxXabLHR1QVFtGsavhhUIMJmVyiMQlkEOEzoFzZCMi6RSrIxGt/5sKBJ3R6LWYk4DbM+ootTY2zhqM82JiUa3DgDboHHR2QCbbIpkJK4FIDp+VSFSaMcnKg9a8vQVaUtQRgqENEM/qkLWoZ+llDpynwON14fG6cLpI1nyZJODOi5hEJP3d1tAOO5kcU3se0ueZ59Aqi5QNYy8SHUohmp5iwTqp7HZjz34c6KeIWRRmywVjSyMhNbOPUrTHWHRohIwe6/oB13uiSk+SkqG893SdJ6dOArkxQkJJiUjdyaYFmM1WbpuwQs7FlBZwSlZ032SqtZsQmWy7V7n15GjVdCXk2GBx3rLEgF9CSwqFuW+JvRe5REH1tlu4aq1qq9FHydX2rk5TiTJEXkk41mSdwFPU/ctBt679pK2CqMmkdZXM5DbzdE1DZKwRUqAtul6tJAIyZUuumzWFoHva5SKSigJOoe5URH9tAaPtC0ImxYKzAoXbFaOXvR4TNsn+s8bTO4XNjSGmiEHGvFkj8o8Qpdqu71F+zjV9qffaxy2Ak6k2FOn/Ftfh+h7b9SL5UDWF9GV1LWnCXhGjdXyga+vHevGbtp3XitBq10d0vjksVM8B6SPbRmoy1hBi4hrgfF34cA388s0j37x75P3jlZAN0zwplO4oKbIEmdCz3+3AeMJ54jh2PP/kGb0pTMvEn/3NL3h7f+If/tZ3+ezlAdsbqGSlDNYJ0nC7G/js+YEPl5l35ytzEAKVd55cssjTuo6SsxqOSCEzDIOcjTHw+PYtOSRJHHIiLBPzvLBMYqkagiRj1ZQmpaRVKBJLSgGbnxgvPUW+StujpsL2pmihazX51FtlNsz3GqBBWyi6VU0hG9Eni2mIeCvkaImdw0dBzzLgCzhfcKU03sjHj28NuE3nVKqWNSslWypakcQspDCT4kxJAeICKUCO62xC5ETuVJQtbwLxm5V0H1uLIw2atZIWSntWsofBYPVwl96LBF1ZyslsxPfFEJMhRkN0sCzCcM5ZekdYi+9EiuGdl0EJOh+0INlzXnTWoo4SrMWzjHMqTeT9RK5SRFMYs8BB3ggMVt2mhtqb8w7jPcValgwhpCZlyu2wlPBb33v1ma0Zr63XwFTbxJUZS2PHVp0oWuVWmEpmwaa4EILAq9dp4fE8cf84cX+ZebgGTtfIea5BVti8IeYmGTEN6pPMb+g9Y9/ReacEMRkWXlLgcr5QcuJ0utL3PV3XMXSiuyZDJGFcz26347CfGc4zjkWgMJ1w4oxRUp0kEbUHFmNimmYxNVd5lvVevIozuJSkV6/X3nmPmz1xCTpTM+AqKhAT2eS1X6dyhRU+XtsOEmhzW3NkfV3oZjYKZtXqU4NiRQJqhW6NZNwpW1LMRJdwPgqU6i05dvL+nafvi8w3ZGVtV4Z51oM6J5nSlZQgJlO6AiXVD7VzRHtt3jVUJJdCnwt9SqKPNQZXg5qSrbz298RKUl2xVPctDP3NXrTSJxNrf1Hgd1bsPJ2FQmZOiRJRsowcWBUp6Z2sLa/a19YdywV8IvlMKeIO5bwhzAvz5aL3Sqe5aMupKColpKAqIZSWVVfnKHsvQdH3gCVMM9lY/Dhi+076tbjWxilGuCPWWUkSalKrz1tSWBGzulf7Dpe6VvkWI71P4RMEKRRUsiTIk1hJXpfMl+/P/O3rR96dr3z9/sRlTlyvk+pJjfZTjao94Nknn/LsxXMOu5E4T4TpSggLs/EUU/DDDtt1fPN4Jv/V35LDZ7x6dUvfd8QUBA3xFt9LknW37/n0buTrhwuXECkl4jpPUQ/ylBec8wrEZZZQoIiEzRiYL4883r/n8PyWYgyhZGZVCqRYGhm1ynuckbYkeIxOBzK1iGiBdkMDfYJA6TklEKjeBzG3KKwGGJQabNcCs0LSVfsfi44FjZboEtE5uiSyoJwNqRi6BP1oKCarBeffJ+Aqllt7fU1mkwI5L8QwE5eJFCZyDbhpweQFU5LQm0pu0GhRqZCCWtLfkXuBSSjjN5GjMCsrmcdareowzYTeOSPVo9PMx0AphuTUCQWLKRaTHSU5YhBJRsiaNBqD7zqGYWC/3zMOA33X4TRjTTEymyvTvBBz7Y9qjzZDSWWF7RTXkFwhK4ypU4iMaWzQvhO3KdeJ8UI2jpANlynweJllVFjQSkTlAdaiB43OwdTM3iATd7rqSdzLc/tuSwiTSre0alCCSE6iJ5bh60IeukwzD48Tr9+f+Ob9mXeniftr4hKyMEjb2jU46+g6S1d/b+/1vQkhrHdSMcihmxVtKIQw8+5yISVDth3dsOPZ3R23tweRA6RAtj3DbsfhENg/TnRuYjEbg5SNJhOFPlOSKU3X68RwuQqDPGc6o+xBBNLypTDkzBgT0zxzvU5iyXmdWOZZtGzBEa30y1q/SIXslDW41QO/StrIEngE9qraTdM8SapXd9FDXzZEWSFxUzEydH2pa5IV3gGl6DpKpFHISitMnbVCWqUMOdae5kIKCznMskfjIsGgjqWktnGKJmeQsxhhDH0vFbyx+CjsS6dEw2qwIcmeHvSbA9BZzQkoiKZYmcQYhixeukuUSrSUwhTB5STetchQh52zHCva0ck0IqvXzZnq8aP8fg3uOUW+xMKOiQABAABJREFU+eY15/NZ/Hevs+7TGrzkwLbGroz+LKTMzhklqjmVRFrikijW4YcOup45ZqaQWFKSPnSMalxRmdK1pWok2XboaEp5bZ6C67wgKDlJwPW9VtKydow15OpjHRau5zNLCLw/Lfz5F2/569cPvHk4cZ4Tth+YrjIsQKZFWbyXmeG9ddzuR573lps0sQ/ioDT7zEMofPhw5jQHMnpm5MyXb94QATcOvHzRtQTMWIP1MrTi6C2v7vY8f3vi7XkmJBlX13lLCZrwIa5Xtsh88CUGMjCUjsvjhTe//BrbjyKtTA5rB4Xis3xkSYxsjLVhIoFCm+yreiGvULKuhdWxrwZd+Vz1nkbVLU+1/aYpHGRf6K9kS0aU/+RK2S9J77kQxowzuM6thN61DvvfF3BTpZxrTyjmRMqJGAM5zsQwEZcrcblS4kzJQUZA5YhT/LoywiySrWZrcEU8WF0x+CL9UKz0bEwUphrVW7YIM7MOvhbvVqf2YnUCTBUFSRB3ChNQLCU7YnRM3onVljEUZxn6nv1+x83xyHEvo/KGvsNZyVyXZeFaIF+uxNrjM8hhk6VnJSOuWigQ2FAh95iV2G5oEFxtrmMdIRemZeY0Rd4/XPnwcFZWZWyylawicN8qXEU6lZbfOxh7y37suDkMHHY9Yx2M4J3YutnqdkOrcHPKMps4LIRl5nq58nC68vbDRQLuuzP3l8A5FElQ1F5OLCotvZeB4+PoGUYlgvUSbL1zeGNE+mOkCu8EEpDh3/PC+Ro4TxdwE9elsKTMfnR0JoPx9LsDN3eGu2vicSpgL8yxag0r/LMBkIrMzZyXwDTNQrDQ9kHXiy63VqcFSDnTLwt+nPCXqxgX+I4wzxgXwC4k6zAxYsxKxqqIT8nr3w1rq6bOay3KGBb5Tg3Sq3lIna1sNBWvwEUzRCmVhZvUDKyQgm06xzp4IdUAW1Scn3OrZFNYiBps0zKRwxXiLOiTuppVr+3aorAY1c878WfOReV0hmqg30woqlkMAknLbpXn0+6PTDuCp/pHZetG7dVZE8gFpmi4RIG5nLUch56Xtwee3e447Petpy1Vr9UkwaxnKoUwTzzcP9D3MjR+mWfmZW5nWYxhlX1Z3bF6MHbqimetp06MAUuxGTcMRBzvPky8ebjw+nHi/ePEu/sTD6eLckMEUXJW7CHvDiPPDoNMHhrET703kEymMwpxFvldJoPpwKjvQYmJOMu9C/OVMM98eLzwF1++52evH/nq3QPRdaRSWOaZkDP97ojvRkxO7Fzh+y8PfHoz8nzsOI4dx/3Ibr9rUjOMYVoiHx6vfHM/8bO3Z776AO/jjnc/OfGz93/FP/+dz/nh916w672gbDmzM4UYAnf5wPPbEf/2rMefaehdqWmqVvSxkr3UTDUsC6f373j1w3/AuH+JGyNjmJnmRRIinboWg5Abl8kxKyNLOAiOIpNvELpZY7tQE7xVGmeapXDt6dbiuO4/jRys2FQ7WTSpM0+QlZroidQvkyIsRqpG4zOuE1hZCsVffXx7wFXf0GqBlnISODkuxEWCbVhOpOVKTjOkiCkRVzJY1UJWaFPfVn3x1eow6oiyktH+ZAHjyNZTElg9vOo8087LoS/PIUHcKk1b/E5VOqHeqckbxs6y65zAPcVSup7jcc+zuzue3d5ysz+wH0YG32GsaCDnZcLHQujPxG7SvrJ4ndooTEiji6jeowKrU1HTqknm472VYfO6IK9T5Jv3j3z19p6370/c35+5TrLgUlIjjco8rocyqsnMYjjfW8O+t9wdB14+2/Pibs/zmz3mMMqcysqEVn2bGCdIAKlkhHmaOJ0vvLs/8+bDmXcPV85TJOaC945ezek7HT5Re7T9IIF22HX0fYfvdESbynfqffHaSjAUhl4ceWI583A6cXk8CzqynLm7GTkeNGHYHbjxO16VnmwH+vcf+PD4wLQsZDJRNZgW3UAKQ8r0nyC9oyjkqd3+QD8MFdED7b13IeDGGTdcMN2A6S7Y6xUzzcoWD6QlkIzMS83aTyhFejVV6iJk5I3DjcKXVbKzrYqbRKfUzVyvkdVRd+sQ+cpsBkVwEN1ujIklRlwQaVplEotxvw6SX2ZSkFnNabmSlwslSFLckKe2Z6AqAKoTlAwqyXiX8V7QKON0/VuP87ZVuEZfX3WwqkkIGoCdKbp3Pa6T4fQFSLFwdjMYS8yGKcF4lapz7Hs+fXbks09uubu7Zb/b47pOdK8UvMKVAEntHJewQK5ub47TwwMxRJyzMpEmBtqgcoS9Li5QykHQyl0KfQfOk51ljpZvvj7xxesHvnxzzzf3Z06hcLpcOF8mlpSYQyCmAuTmP3CzH+U9PDvy4vbAwRvu9p7jzjNEx24c6XxP5zsZx+iEi5AyIq1KkRxmwjRxvVy4v39gmS5Yl7m5u2GxPfPjhOtvcHSYGHjWZb7/fM93Dh13o+ewH9ntBg43B8b9TiR56PnhHTfJ8mwsfLbP/OCY+ebTHT/+6sqPf/aW96eJL+4Xbv74p3zv1S2fffJMUDpb2HWGYDsONzd0wwcIjrREYhLDk6xJqei4e1m72TRJpu8dyzxjnOe3fus3Ic0s88T1cmWeZ+ZJe7rLQpwDsw6wEUMho0llpJAastCaK5X30vb7mmlv+fxrz7d+/7YyXjk5tq7vSnDUNpGmY0+q3pgyLsmfXfZI2fH3CbhJHG0rJBNTIsVACFfCdCXOJ8J8IoYrOcqCtyS8geKM9CkRXdITPSmrK08NlhaBY22xgHgQG6fGGZQGqTpfjSOKfijLU5+nQn/JW3wSmKNzIkfqiwccZhi5Oe55fnPL89s7bnZ7dsNI77y+78TkHGUOXMeRMM04Lz1cEzMlJnG8asQmrXDL2tdtQ7KpfRU5yAqWKWTuzzNfv3nPF798w7v3Jy7niWWJ+hxCGIm5ehSvB7fRQO9NoTew6y2XyyBmEynikNmrJffKvvZNfiRC/0iu3sXXifPlyofHK+8eznw4XbnOgYQE26FTqNgber2O7fOjBN1u7DSRqIe3sIcksRJUouusTpPppQ81B7xLpHhhuSxMPrBzR8pwgzVCKhud5eamMC+ZmCIxzZSSmKrmuWanZf1TCBiBZRIzC7Jh6AfG3V7WoE4ESaXgYsT6AZxMdcHIMHdjPM5Ykl1IOCKWZGzri6Y2DjKTa19ou3VbGS4rvfyKL3Ytzmuw0h64zu311m7b9A0qleRCJxstC8YYmaqSErHpeiMhLCzz3BLiPJ8p81kq3BQwJkuLwlqKlXUpULKlBgxrjQ6arwFX1l1GA65z6g9ecSXUvlX+bimqHshaAVtBP7wXeBxDsOo2FzNDnxgWy6gkpLvDyKfP97x6ccfh5pZ+GOm8V3tIowFTq/tOdKDX64TvenJW3ek0E2LCOyM6eUUYrHPSz80Za7t2nV2VHnmPsZZUMg+PE3/8k6/5yS/fc7rK852nmSVlTucLlyUwzUHJcgbvBK2T2B+4nE98/drz4ubAy9s9r24HDjqR6Plx4LDfM/Qj4+Eox31MMrs2R6naSiQuE4+nE9ewMO57nncHvv7qzJuLJZY7dkvm0/3C9196Pj/03Ox7jrcH+v1OpGV9h+0cEemvorwaG4v4iveGgxvoe8P4OHHT7xjdp/zZVw9csydFz+Vt5ifv7+md5f7dGzWpkCa9Pbzkdkw8vP5GLSiFzzAMvSKClm7oCcsshKOcuU4RzIW3P/8Z5R/+Ni+evyCnyOV8ZpoXrtPMNF0l8F6vOL9a73rvxFLXOkGeVF1RqvzNrGdx5U/UM5i2n2rA3cDFVEKeJPNtXdfgamToipEa8snz1bO58W/0fYb4dN/Xx7cG3BADtYebs7BZ47KwLBeWyyPLdCLMZwm4aZHq1hSytRRvMaWjeYaqSXcurOYF7W2tfxexdSfmCEX6oLVnWYcvY2o+I0y3NYCrbMRW4obFWaHDO2vpHBjX4XcDx/2em8OBm/2Bw7Bj6LoNa9aQUsfQD+z2e2HL1fmL08JiphWazXnF7KGJ/1PKYOokFISF7DvmDKfzmW/en3j3/oHT6UpOiaFzdGqEnjIEDbhrBaOBJmdMERAlACbC45zoTpP0c3tL74Gc1DyhE9hK2X1ZoZppEteYtx9OfHi8cH+ZmVPCOMOoxCshPmlP3ApRzSHuUdWHPRdYcoYg1WaOiRwSppRWES+9o/cCCVlTZDTf4NiPjqFzHAfLzWA49jDaSApXcizkeaYkmVCy7wcZOF0CS4lPKkfpW0rv0k0zl8uF6/lCXAK2WDrbrw5CGnANnlIcORvyCCWLbtxjCMXoBCFLwpKsk6BvhZUfE0hfN1cZqjzKNuhKO8HUXVmjZ/v6JpgqWiNM2Wpa1H64lrjEmJinBWMdIWYu08K8RKn4FTJd5ivLNBGXC2G6UKZHynyihAlKwtlCco7OiRyO7ETjLNivkkU0k3cOlwVlohQNxqshh9XerwRc3aOgQbbg0bFptQ2k/dFSIFvpSXsnaIi3hk49dV8cO57d7Nnvd+zGkX7Q0YNGwTzlWIgKTdmnRQZlhFiYrhOXy5VcJCloSB20CrvvRJ5XVH5kDPR9h/OODFwvM3/451/zhz/+G5aQwBqMyQxDz2FwvLp7TgHmGMVUYgmczpNoiVMih4UYCpfFkOeJ6fLIcj1wGDyXQ0+8DvB8gZtbmYzWjRgvpMdkICZxbJtj4BIj2Q/87dcnfvzNmQ+TY3CR3/rE8jufDLwcYD84dscdw2HHcBhliAIwpYUwZZXmCWrWe8eut4wWeie8GmsKu9ER4sw/ftXx1WngYcn4wwtuP3vF5fWXdF7WSMiFfjiAH4khYUfD3Xc6Hr/5khhnDILauE6SF6lUHJms6ogZMHz9xZf87OffcPtPfptxEAmf63rGceQ69VwvHVfv1WlfzndnHd5YkgtKAnQbkqnO0VWymalIklmT2xorqgR0G3Brf9dYozFnrYSrs5fVQRRtX+vPyToyhJDxIeNclJber3l8a8Bdlpna58gpivB3nlimC/PlxDI9skxnYpjIKWBIOAvJq12c9jRrS6RsAu6vTPFp/S5hpVlrtQdrpUlW6drq6mJEma2TaozME20wnkEsw5xk2o3coeMCh579MHIYRnb9KO5PVvoUEpQEGu18x263p+QiloXzwhIjIWfx0lTTi1yqDlhu8JpLKOsTQ8ISiuW6JN7dn3n3/oHLZYJcZOakl99ffZ+DflQtZIypef1Sivb5BLbLwDUUHq8L+9PE4IXEsUuevk84X3Wb2jtTNvK7+wvfvDtxf56I2hft++rqY54QU+S6KqxpHTZmosmUEklBnXyiWDumOWByloDbOXa9Y+gsfSfVtjOwGzzp2LPvO57d9NwdPHejwbrIdVlI18B8mlkuYs/XWcfge+aQSSaT60Gv481AD96cWK4Tl9OJ5TJRYhaIG2WCKiyfjMEb6Fym7zM5Sta/pEKXZPOkYkVf6TwhBUIM6i9sqAG3VNlLC7qafJmtHQUqDVKTllzZ+7W3uYFlrbRbagsBazVJQOaBloViLEvMXOeFaRE4M0a1qZwnwnIlTGfi9UK5SsAlzkDGe7FMLD7jcyF3+vodkNGpSXKIGOuECKKOZtVowarFosiBKoxcxI6wJhrVA9sYrUwdoMFWIfBNniItAAq9d+yVh9D3A77r6boe79ZjKucCDoG4TQGtos6XC9M0s8wLOUmwT0EME4r2hp0x4p9sCt6KhWXOmb7zjLse4z0hZf7652/4jz/+a67TzGef3vLqkzu+/71P+eTVS7phwPcDMQtv4MP9I+/f3/Pl16/56qtvuL+/5zpFTCmSTBjhfJweAmXo6MrIwSeOo6fzHUM30O/2ehRXIp74CV9zwYy3/MF/+Fv+4l3idC1894Xl//AbB35459kPlmHssIOnG3twMIeZ6+OFh3PiMhXmIN26RU1GSk54V3h51/GdZz03vaW3BQd4D8cu8t27Pad3M6fHC8vyNeXynm7vue2Fy+L7QjKReY6k6Oi6PW44CoPYGGJKzCGKXjhLgmRVIklOxMVwvf/Amy9/wcMPvkt/OwqzOUO2WYbY73aCRrSpUHL/gjFE68jOkUMQkqm15CRIkNh2CsTf6lblD9i2Hjd7rGaZCiVvuj4NAVntb7MoEYwBHQYhBSCKshlCLNiYye7vqcO9zhONJJLEbzbOspmX65llusoGD5OOd4p4Kw4cKXUSXLXvKaQL0ZulYsTWLaNeqqppFUqkwF3tONJOaJvnunpsipcqsvkMDTGPStGubOiyhgwhNjinOkInUoDNjYBaHQu8NI47SoFgpV8TEF/TFMVmjLy5qHrQOGcZRgla/dBjXM8SCg+PM9OcOF8mcRNylpv9QO8sY69i+cJqoZiLsjmFLBO0b1fhgaKEGocwlmM2PF4T1i0y1zUW9sUyWpE7FAxLLHw4B968O/Pu/sz9ZWGJGatj2qwzTdtsKgxhzObaW0IqhFmzeGcIoMxYYT+XIH3CToli8wyjt4y99HtjEOZh56xKQwydN/Te4BxkV+hIlDizTGeul8AcRToQswYza+mczFredYpMLDKA3lOYLxeu1ysxRKH7V0YqRnluRnqXyFi7znfQddiuJ3aCXmSgOKmQQvTMygKtkHKVCzTyVF0E7b+6oTfBWHiVpsl8dZ+vfd1Sq0X9aQ1+FEFPCoI8JWBaAudpVr2iBtzpSpgupOsjeTqRro+U5UJJAZmUY8mdStDIgljIZmoEOxXj6cvSwAtgDcV6cvlIC42aajSIX0hsha0WUQ4rY0xzmqpDTEIshCD9M+87nJc9Y1wnlW3lItRrZbIQJa0YPxQs0Vju398TwsL58YFhNzIpDKzdckl6UmnuW/Myi0OUN+xGx3jYYbqO63Xh9flCdPDZDz7n+z/4Pnff/Q2m8YY/nQuP75NcnwIxDSxpT+heEj7/DfwnM4fTA4fLB+L9a6b3X2OXM5ZIZz3OJHISnsHlMjH4jnE3bt6To7BniZEy7siL5X/+d3/Bl+8z4XHiH33vyD//wQ3PR8PNUTgUvrNkU5immfcPgYczxNJRrGc83uDU6jmHAJcLMRjmkDm9Xnj/MPPDVyOfHDxjJ6Yf03Tlk92Rn5CZz1c4v+d3Puv5p7/xDOcMS4icLoGv3515uD5wjZYZw3w5t0THOIHll2VqsrrOGzrXid49R/Jy4f033/D+4cLtfhQilHX4Ttacc52cblKhCYs4iTeCzRBR2o8sTWrL1iDWoVmLuLUHK+dGdWczdZeZyrHQITeNYSwFYEbNLBoCVL0HVsSqUNraF0czQ24zRp8+vjXgTtPMOrFnkY/5SpzPIicJs/SMFglAKUkW00VPStLzyRicIAqtOpWAW3S4gCVmGtOywm/OamayqVylrjCaBBRKKiRTSMXgisCeYMS+LQnDNmbxcK2jANeKox52T3ts6wEhnq593wuJIUaCgZIiaREP3mqvKFWKBm3n8dYK2cg7dr3HdT3XJfH2/YldL1l133uG7tDmkor1phxKzWWnqBVdcoQlsszIdBa9GG06jR7GphQuSyY+LCyxEJIhGQ++0FmpCM5L4c3DzC9eP3I6X8WEwHupKDo51Bo8U/vGVgcPaK8vpsISFuYMiyksCvERk7hGFQmi3hd8SswGJgf7PjL2QmzJmjg00/wi2WLXSfvhukg/OiwT58vMaUlMSebWSr/MMwwDL+8OvLrbM3hLXALTEjjNiTBfmS5XlmUhJJF2GO3jCimtmvDLvRfyklRv3ntKJwhNcY7kxbHMBddY97WSjaDroNSVUOtWDbZPWyYUhWj1cCiaPUqP0bT1V9oBIv2yKt8prs7QNcwx8jDPAq8uymi9XkiXE+l6Il3PpGXGpAA54Kw4GhWV6mUKtogJhRjw1zzEqZOaHFoxQcJSksDuRnIVbMqtv2w0gZG1KxWG2OJlUoJYnZaMzvTNiSUELqFwWQpLLOAdxndgPZGOVBxS9zrd97RrLEebE3mddSILu8jITJJM7RrHjutZXJ2cd5giIxr7Xg5Oslxf7+Fw6On2O95cIj/6i6/40U/ec4pH3j6M/PTHj/DjH7N3hmfHgbvDwPO7PaOXBF0CixjHnK6JLx+vvLsmHsonpJffYW8Xbqe3mPk1+158vqNyDmIKyp4G4yXApATXAF++mfi3//OP+epUeHi88sNPB/7Z53tuehgOHbYv5BKYZsOH+4U394kpeZK1dLtCLpEwnZmWyPk6MQcZuCLAiehiH4Lhb7654D87cLcvdEakdj5dydcLFNi5if/qNz/j8xcjGFhCx2m03PUwusyPv3zH1x9ORJ1aVLWsUMghMHSO/+K3v8/v/Re/TUmZP/3rr/nzX3xDLIXp4ZHXb9/z4vkd+07kZs4amRdtLH5J65xn3VPWOZXKueb1kOPaYqrJnbSdsrZu7OajVhTbXlBVEBg9E+RcyKW0szbXsk29D9YquKh8ymAy2Gwxyeos5199fGvAnZutnmb8ShpJRQzNZePIwRVCJMYFY9ZZhcY4ivF4ZLEXIxs4gZKCamWrMw4b25Pm9FE9WWuvKGo/VwzrdVKFqyJ8ed0ypFug2CUVCQzFEBEf36IXK5XqmlVWyYd+PiuEUOeMAtJ/iGpBFkJzLzIYJQcNjLsdxlqCjGjBO5lIFGNhniO994zjIAQZU92xqpFIlAk5Kso2GohLQUSN2eKMW4OtMrNLLpQIMSSmJXGZAtclcpkz56VwGwr9EEjZcrosfPnmxNfvzsQY2e96Bucl2OhzVdJWylrBNRdBOSSmmDkvkSlmFiQxyDqswRdLb2WxGRtxDnoDozWkIelUHCipEmoqSUd+xnsJamPfMfROGc6ZmAIhFjEg6GSCzNh33OxGXt7sOQ4OkyOny8wv3594CAvLrMSLaZYDzQphRpKYTIh1JF+V6WxkLE6c0bCZZG3LZkVnrfI4Zb2KW40+x6ZP9GRTV3IXVetHc6pKWYJXMkZ7RSp1M0asT72j63pMN+C6nv8fZf/1ZNuWnXdiv+mW2Sbz2GvLowooAgQINASQ6mh2s00opJAi9KwH/W3SixQh6UkdCraCClHspmk0IQIgHAkUCmVuXXPucZm5zVprWj2MOdfepwzFyoh9T940O/dea845xvjGN75PuQ6lLcpazosXQfVlwp/PxNORPJ2I80IJUQh+JddgUyrqw4o02aJIToA3ZfXaBolFEeu8bMxU8w8FqaCMwO3CQr60HVKskqExyrWs61ch0KpA6nJNck4sIXEOhTlIIlWchc5SjCYi42JzENhem3IZ2yiQkyTaRYmCk59n5vNEOJ0lUZtmwhLxQQ5sQ6YQGUbDh89uuH+YOJwjOI0dDMFu+H/98af82fdf8uWbGaM0X3t/zzc/2PJ8Z3nvZsvTm5HtbsRtBtDS1BIUKskIVozCvwiJZY68uVv49PXEX78O/PC844fecnt+4Imd+OiRIXaasChOh8hn8wvm/JrjKfDl56/48Wdf8JOX95wYWXzk/b3iux/dsB0NZjRklQghMcfCqzee16dMwFC0CNW8+cmZwzmINCdy/6wzcs+QfvXuZsuj3YY3S+HztwtDNwoRsuvoQ2bsB3xMwnh2EJZFmOYK9psB5yxLjPz4S7BVP7wlsKUInO+s4de/+TH/+//NP+IrHzyhhJnf/JWP+O/++V/wZ5++RC0PvPnJj7j76APczVbWF6IMGJaFZVqYZ09YAsknyAprHLaDpBRJaeHcKEWJYS2oVuJqM/VQCpS5nJt1j67CMUpVvQIhZVxboSJoNKnN1qOonkDrfinIPtXGipayFV/gXyrgRtqLEJivIPZg5AQ2ko0nV1myJhFYap+vUK2KTMHVKgwtFW5ENmwsVfyCVoFKppFzISuhla8ZSd1uqYpNBC9/T0E93HWFralmxdU1KGWmWFiKIhktZAJtiChCToQk/QyFyH2lUggp1occqrG6i8Qq8Ri9fN68U5VW9Npye7Pn8ZMn5FJ4e3/HNE0ikJEcCoV1lu12w6PbDeNgKTHg/YJfFqY5McXE8TRLn6X2HFrmJDAYFKXrOV6B8tzGkxKlREL0THPk/lR4dTcx3J3Z7UactYRUmOaFt/dHjseJwRl2FZpViGB4qrT2mKV6kxGYNlssmrenEDkuniVmUl2RqmR0LlilCcoIhFWErdppGKu0p60C9KUewq6NGTkZ97LVEm7oLfvNyKP9luOSOIbEnGKl4yuc1gzWMBgYSmKvFV2nGTAcTzLTmZaF6XzidDrSpYS2Aoc2yD6mVK0bBQ4vlWXZgm7D1tuUaSoGm4SIplNCG5FaLEqJcL2seprV2uppSoOsLpWx9Jzl+qAUpFoRa5GQM0qjjEY7R7cZGHc7xptH9OMW2wkpZrjZ4+u6DEtYNWeTeD6QkzxvqRwHo+o+Q1TeOpXJKlc3qyzvoY3tFIGVo1JklcmqkChkMkThUOiiMFpm9YVTkKqMpFS4cglrolFaygwoTazJsA+in2y6HjqDHUeSMwQKSwhoL+0RVRNCcfjJq9BDyonzPAucOc+U6DFKQc74ZZFDsiSULrje8e1vfsRv//pX+eEnb/ijP/8RSy7ch45//K8/ISZN9onH+57f/42v8utff0yfF2zJbDeWfuswvWYOE8dpYZo95yVwnjzzJNc+V2u7sVM8Gh0fftPwn3x1x09edvzzv/F870vHKxw/eDvzJ5/dM7oDMb3AK0so4KeMP8+CTADZFbYEfv3rz7nZdGiriCkA0pZ7OAS+fIgU6zjNE1++PHCePScfSNpIgKyPUmFWpeB8ngm1O+W04fM7eLzrySkyRWFzT9OR0+zZ7B1hETN55zq6YYACQ0j4rLnd3dE9nElF1oK2Tiwa6Xn2aOTvfeMDyvktP/zTH3N/d2CKmQ/2lle3W14sC3effsoXX95L5VoSOgVyZduHeWY+TcQlcPFZbv3YqgymDdEEstYQJB6ZdPFtrjDd1aOtQ1Ur2ybEVGo8qi5YRRqaa8GrW1szo+v8bxvO1VpjrMV2HcNmZNgO2P6XrHCxrhY2GV0sZEsxhqJrRM8ZnSIqBOmlxFCrM4GCorIY1YHqMMpRtKldowpR5ZrNF3mu5miyWiblumErdNoEDlIqxFhISbIXq42wmnPt22apZqNSBF3IzqK0CObbYcT0A8pZspIstem3ZlpgqQdyyYSciFVY21f/VDnULgxlhRBd+q5jMwzkkjkdDb5kSCIHVkAOyaHn9mbLpjcs80xOnrlklpA4Tp6708J5CXI4XcloNfhCZjflujQhbl2qFWDVy/Uhcloic8jYg2F8mMWOLckhNs0LMUaM7mvP2cjCa8IMyBh4qqIKGUEyQirMUVyYpiB+w6hKcoN6qAvESGXOogrJaIqTqtE6h9MNgC3Sgx16+r5bx0Z0gb6TYf1H+y0PU+BuWphDkqoMcAp6XbAliZxozKK8RcCRRLVoWZgOR+wwMsSM7johQdXKPdckLyWR4qtNH6Ap/tQ+ubqaxdO6CtpbdHYyvpEqElODdZtnrTMDl5aFaoSMSkRcYS8QuTlF0QoQspI1FjsMDLsd+ydPuH36nM3uBtv1GG1J3Z5oHEaZ+l7EJDurmaIsSckaLzGjosKQ6r6SvVeyJmdNLhqDRdOjtFTP0gNSaFPnQnOU0cBcCYy5QE5CSqz3slwlLqUiGSKAcklc1gRECdnNdQrbjSI74Hq67YZ+GDC2IyuLj5lUIlrXGxFk5YRcwEe01UzTIvZ80xmVPGFe8D6u+sSFwna/4eNvfZ3Xr17zP/zRD3n7EDnMkDCYYijF0ynNNz5+xO9850O+8qRn28lEQNdZTKfxZeHw+sCXr4+8uY8sUZFVJeOZHqU6UIrz+cTy6oTNnqc7w0dPR77yRPPffKfjpp/54x8dOJ0Dszf0g1z/rMQtar6/w/uAdT3j/pZ8OvHRB1u2thY+K5KiiTHz9iwKaa/e3vPl6xPD7oaPPviQL1++FHU4A912K4jcNINWbHc7jg9nUkjcvTkzbnq2Q8dS1b9Caup2Vrr5Crp+4NHjJwybHq0gx4jRC7vB8vh2T/fyjiWLVWlJGaeFk/JsO8B84g//8C84nUU+9tGu55sfP+HD25FPf/yGw/KKz374Y/pNR0dAx4USF5G5DBG/LOSQqrStbtEPbWQ2W2uRa40rwbBgUiaVLIMu1UCEFd0U/kETdxS3u+pqhKrSnY34qFbuRIvdusijkeKMNnT9wLjbsr29YX97w/Zmx3iz+eUCrhvrL5SqHZITJXVY1xGsE1PlOm6Ra5ZQSsG6HtuPmHEL4xZcL8HbWnTtBUrVoGqPlioKXbVfQ5CNm3MdKE8CI5WrMRwlh56xlq7v6oxrzeAz0puiYBV0NbAqY3Cd0M7HcYPtejFVMJpcRy9SQQbllUDfufZavPcs54lQGZArm60e2KXA6XzC3sv4QokRp5o1VXXUydIfKUV6h8EvnM4T94eJt4cz98eFhzkyhSQ/S6gHs5zWqirstPERq+Q96pJRSfxcSxZyQC6FJUSWKE4pXR16z1WaU4Jp8/akEsfA1GqIOgImDiZSVzqtMX2dw1Ra2IBaAq5RMlLjVIXtSyZEBaow9I79duTJ7YanNxuMguPxTE6JcRzYbjYMwyDjEVo2gDUXBa3dpmd0lsnISI4l05EwOVC8Yj4nDknhTWaOET8vlKiIy8zx4QFcR8xgh5GspT/ZrqdspFyRggqtai1Sjar1Ci9QbGnQlLZondC6tk9Y6VCyuZsMaOsnIcpkjaFf6ow1lBXRybn2aLWRAGh6umHHZv+I3e1jdrePGbc7jLEoNGrcwrgj5ozve+aux3cdYRlRw4ieJ8Lck+aO4mdSXgQy05qiDUU5CRR2QHcjuhsxrkMbS1aS+JhcdahjpPhACYGoIhmR7yvVhlIlOWhLrPC1Fn1t6uy8/SkrTG0UaAvaCoPeWIrpsF1H5ypZytpqZC9nbJNnjDkRKyHGUVjOJ8LpxHQ6oMmcj2dSCIQkY1zaGGzXscyZz18chdNRvYiVLhgTGTT81t/5Ch8+3ZCmB+AZGBHugMhxiry9O/PFy4mHMyRtJWkHpmnmPC+c56lel4x1CpMixwXuHk584/0Nu43hu+91HE/wvU8XAoO0iFImLGeW04m4nFHdgB4Hoo+YGPjur3wbNb+tCbGqCbLi7AWRePHqwJf3nqcffMjHX/2I/X5HzIG3b+5Yppnzssj9UBrnLITAaC2bjRP0axjYD4YQEvdZWkC9lTWWfGTot3zw0Yc8fe8pBbG4jGXBmowzhk1nGfqecxQMpGRpNQxGU0Lg+5++ZugdSmnOqdDNorl/aw2WQvYnpi9+yPS1D8ldAT9BkLWWq+ua8Fuou5FK5CtQxTRUkqRTZTkLlTGUaC4CMuoyby61XG1VqlJVv5AxntzYyDUeAApddSLkECh10skohTOWzXbL/tENj5485snz5+wfP2az2zJstr9cwO2GTT2aKnxZEiX1JCe0eGO7qt9Z1YxcBwVsN2CHDWbYoPsR43qRzzNWsmd9se5qtOw2RxiDJ86L3FQfKDlI/7ixyJDMuI0L9H1PN/ZySFSykfSnIFd4rs5aVPKExRnL0HV0rhdvSmMEjpDcgqwVSWtSDVwpiSqTX0R68eI5quoBIIdNfnhg8YHOWlwl4eTaow6psMS0+sbqDKfTmfuHE6/vjrx6mDjMgSUlliTPGbN40MbVDUXs2jqt6YyShwZTMioJYUnXTeV8RKlqAFFkLKavLESF4eSr5m/t62mrax/XiMi816QiA9zkOjJQ53q3uWPbOWIdAdBadGNNDbhGKRFpSBatC5vNwO3NlieP9zy63ZKTZKt+9mzGkXG7oR97rKtuTVnMwYfOsqvZ96ZznK0nqoLVikFluhJRSRFC4ZQSp+JZQuQ8F0rqCMvC6eGBYnuKcXRKU4xUfUoZARDq+pM2Tx2CrZVmaXyDghDYkHGiBmmpqk7UtJUVZh0TWJtJ0qyVhaUqjlIus4Ki8XoZkVMoTFZkDMr0uH7DsLmRx7Cj60ZJBHJhM4z0uy1nH7Hayoyi1gTncL0jDj1+6YnLQPILxS+ULLwHZQ2677HDgBt7hmGk6zqMdTSmshAcxWpSxwjao0xEx1CdmSIpBCgiQ6pURtmC05e5W1c1xJ0VuUhjpBrRTXZUmxp4NWgRajFWAm772XVf1iRF2h6JnANxOuNIHE8PhHlCa433nlQyRiuCEpWraQ48/PAT5nlGb7fsPvyQcLgjHs6YUvjN3/gW3/zoCT/+wSd89P4zlLNVvMCTc2E6ez794sjrswJrOR4PvH594HSceDhNBGBaFnQvYhrD4Bh05MmuZxkGfHjgWx9u2DjFx7eO89TzYrKif+kzJs482STUaIkYphyZTpGNcfzar/0dXvzlv8GZ2nLIEJQiFMPrB8+LN2f6cYTkefvic85vNC5PfPBkgzY3xJQ5n2e0Muw2O5zRDKbw/GZkO3RrX73rFM4UBq3p1IjWJxSap49u2O03leWn1ocqIgN6u9uw24wcZg9FeBvOwE3nUKrgc2Y0Fj8vqJLxIfP2YULZAacVWmfS+YEUEsOjW2lllYsAhVGsiR1UpImLs1tOdX9pu66noiXoaqhcF2CtcmsMrkm0aETk+r28TrZU2krj8dbdLMWlVgrb9Wy3Gx4/vuXJs8c8evaMR8+eM+z29MMgo02/TMB1dfZNzo8CxZC1CHtr49AVP7fWYruBYZ4oIAG2G8D1kq3VYKtrhdvMk6lvqs35Nr/EqCxBWzQLoWhQbRRGyaC8s1KlDiP90OM6h1KGZkCfciWFaFUFXSXYtgPSGFMdSIQsZJq6T4P3kllFu0NMF/PjK8i1tNddD8qUCyl5QkyMfcd+GOicow1pxpzFl9d7Yu1LzdPM8SQ6ym/uzxyXSFRSfSjrUE6Rk2eJMylmUAmjFIsSt5VNZylOskSVssyVViUu+bfgUOw6w37Ts990aKO4O86Y04wxUk3KrLQRTWRrKSg6J31Yaw0xiMqWrYYRxSgGbSUo6IvkmUFhK/FHIYQ55zS7/Ybb2z2PH9+w321ZFs90WigZ+r5nGAaBlA21Hy06pL0rbHrLdnBsOseud6SUcVqzGyz70bIfOza9Q5XIMofa4xcUIfmF6XxGDWf6/R6bsqwHpWtP0FyG4VFiFVnrWUEhNFFlIuABXwqhFEE+1i0rAiuyqctKlGo66ZR2SGWqRdZasckOrkS1ItCrjMhV4Q0MRTu07TG2X0Xum6etNQbrLDZXScBcoJN5U2stqRP5zRRHYgjkGCDL2IYzmr4Tdnrf93SdQPraiDJcKjL7G4XRRVGGDg061DGdiFJR4GwdV5a8PLdk/l0nwbbvHV0no1fW1dEqraHCsUVVW0qlBbkyRqQg7WXet7RrJhtV2k/F8cXdG4wq3L18jdGau9d3otFsNLturCMaMrWdalVMSKQloM2ANoHbXcfv/vav84f/6g+53Y08erQVG7yUWHwmLJHDwfPiIRCU4e7uwIsv3pBz4fbxLaPV5GnBqCIezBTmkJj9zHQ4U95/QsqO8a3n2U3HTa/46q3jzSzjKyZ7HnczHz/ueb7ZcV4S33818WPbk1RPLD1KOXajI+gsXq5ofDqhtOVXv/kRY2eEs+IDJllclsS3U5puOzA8fULvLKOD/aaj01RyW4NZC84otoOlNxZjNuBGxn7h/Sc39IOTIJuR8cm6bp01jL1l2zmc1mir2HaG3WDZOs00eXY3N2yGnjhH0bnXiuOS6EqsZzacjhPLNDH2H5DIRK1J1oqJSwqUaiZCqoYbNXbkDAUhB5ZWwVYClKpSnaXmwK1/W9rWrQS8UomoMionxKnmRiaxrwln1D1bE3PrHON2YLffcHO7Z7ff0w0D1nVCGG6tpP/YgGtqc7mFxlIvlq70aqM01jj6fmDc3BCDWEihLVhHMQ6ssLaUMcIW1LoG3Ur+KYVcRP3IaodVlqiMDKXX4JyqMbM2WprSQ892u2W72Yo6TJ3Ry3WUJjWGbT342txmq0pMzcCNkarsEmxrBqXlgudM7d0utSrTK6mkwDrPV6gMveofapWCvpPrZ+qBkhM+LHi/EGNPQuGXwHSWoPtwnDj5RDYW1w9sNz2u60FNeB+J+HowVzciLa4qTbBDF+ntdk6CSD9reqPojObpfuT5ox2Pbzdoq9nenzCvDyxBwNWYRFt1GDuGqlkberHO2yyeuXrZSiUoVa0ylRhU72MjM0mOU+UKnWHY9Oxv99zc7tnf7BmGkdNxouse8EvEOUdXD3yjRVBEHKKEINM7y9iJKf3Sd5AznTPsB8ujXc+j/YbdZiDnwN1dweeCCYKKhBBI84yr90/p2q82FmMEvq64hgQVoyihCAehGGJOhKTxSuFLWQNuLCK8Uda9oYXEVveHjNc0BmSRIEuWoKvzOs6lZb+vMFcposaWiqhdCVP4EoBXh6AMzbfTaFHf0aZgnavTAdJXytmAs7U6EBk8Gddh1W/unKOzVgJhbRHl1qPKhaIzCY3IViBrux5sSokZSTZGAjllTfa6Kgvad46h70Rvu1a5qh58QlCRcyVVAqXsVVFGK4qmoyf9u+bi0qYRUiH4MyVIYmWM4XSYcEahXYexmtM8k6nOM8rhhpGCwZ8nGWXKmd/8jW/z+U++YJ493/rae9zsHZ0TApzWipDg7Slw8Iklel68usf0Pd/8xtd5/PwJP/nxJ5i3B/T5hLKK0+kk8LpSFNPx8s0B9/5jTj7zKBc2veYrz7b8xWf3LFnT65lvvHfLP/jtv8PXPnwPCvzLf/0X3P3pS97Ohi9enbDKsel7ok6c5sBxWig+8/7Nlt5pSo6ARW+oes6Wvuvouqo1UKVDZXJSBBqydtJOql63g1OMvaHTmpCE1Do4xUfvPcW5riZ1eUVsGqF17Du2fcdoZc/cbhw3m564TDx+tOf99x7Ta0UJgUNOpJJYYiKUhE8ScOM08fDyBfY732Kz2RCsIYWOFIUBnisLXEbwKps/FUjVmShpCFQCbsVktanjX9JG0WvR1V6/9B+F+Cq1kV6jaiNB1jZTPeMaH6G5sRkrSW8/DPTDgNKmqgw2ZtAvEXBbBdqivLihCOuxNaGxFrqO3NfRliwcrqI1RQvJqr1ZMXo2GPRFQKCOHRllVjlGe8U+M8aJ5yyid9r3Mnqz227YbjZYYy/JShHz95irb+863iP4fFsguh5SZn0Ndf61qNq6S7TRBb94/LzUi6hq9n8xF7hOYrLgXqI5fa33W19g09mkCnaI00SS50rS+9BKWN2DtQx9JzZVncOU6tuZZXFYVX1CjaEzCmthsIqxF9r+uZNZ4G3f8eR2x/vPHvH0yQ22s/TDgYLh7ngmVLUslKJzUhE1KNzV+dy5k1nrHCsrW0kfSRt1EbFvQbcGW+cc3dgzbkd2Nzu2+x3jdoMzjuCTJDxavIi7zmE7i66BLKck98iU6k4kPrtjbyFnxs5wsx14tB95/GjHfi8VXEpC5jJLQYUkMoxeZsVLrewa6Um0czXN0Lzka0tITUoiphAAT2EphVDvba7ynU1Hex0F0hpVYT9qIG/G1jUduWTLCFN4jdqsvyKtkSQG8ItPzEtgWQIhJlyu5IG26Knb0GgoFpwoq0nVqcSiskgwbAYDRjdhl4sAjF6DXFWaqkIDWWVJ5groLIHY1ERBa8hGV2/dZkunq+CJFTjZWnGS6mSUy1i7vmFpJ8khWacx1mDcJFulJcIqOiAsGOGUeC9zpcf7I6ZzxPMZazXDZk9IidM0473s0RTb5EMn+zwECganYNM5/vRP/h2PH++42W3Et9oVUsw4Y1EqcV4ip2nh4TThQ+T54yc8fnrL0yc7jm9GpsOJc5g5HiZKyVjt6HrLfrtdtb27bkMuha4z7O2WzgoLeLe1/Ff/xe/ze7//93FGEf3McHvL91//M/71DwM/+Mkr/v7XPyTNX/D0gz1DFxh6GLuFafIsIZGS6Fu7Ts6D3il6KzKIqhRIcs76VElFtpPpAGvoO8N+0zH2js5oSInXR4NfAk96ePp4j3U9xEIJS1W7g4bwWK0ZOzk3rLXsR4elMOy3vP/+c548eSRWqF2HnxbCkskhiQhLKSL4QyZMJ2KB/TAKG7l3tZ3nSN6JAUV1HiqpUHRaWfNtzK2hGEqbSipWYHKd7TVXRjesY38qFzIyn6ySFA1CmKqk1PpOVUv8FFVvW0niUhPhXNqMc41jTazlPz7glsrouswtqSqXZXTDzDSqWLLJ64xuKqwZhmDqeq2MJeA2M/lKSKnlvzIK7UQsvsFiruvreIkIoHd9x9D3DENP77qLR2w7rAC7jrFU/97COuKxZiaqvZ/12BLt5WqIQD34/bLIHGeUzIpciDESGsQMK8wl54dUoTElQopy42kzieqSZSlVm+6KwRh2ncOojDKG3hq2RjMahesMZtPjjVolAcWyrx5s1tJbTe86BgtjL6LevZU+72ZwPH605/l7T3n69BG273DjFnSH+fINL9/ci2hFYRWZ76oMpK3SjF3nCCHW+epMsyHTtrkCNb/eJsLvcH1HPw4M25Fht6UbB1FzyhIhFFVD1+jV1k+SJn2pnHWuAfyiREVW9M6wHTr2uw23t1v2uw0hBpbFc5wC3Tmi5qqvGkM19I5ocpUCrO1UTcOaUFpcZMhKmMxKE1F4amWbMiFJclSqWLoYWNRdUINtM4rIdUE22pUMGFzB163CbT9bWgBSVQov45fIdBZ5vWEzMe4X0cc2F4MDH9MF0an7xJiMVkZmGlWdvVSNUVnXYa32xfhDXwIdLeBW3dvKVFa5imSUdcMIgqR1JUMKstFcj0TFTSqAZqChjFmvd43p6/RBg4xLrTxKTVBlr7RESEQ7jBLS2XwWE5XT/T2bzcDr16/ZbjvQDpdgWjShuthI4lPvsbaIWEJgGKz4Ic8epxXbXrPtNc5kstZMlXexhMiyzIQQsdpw9+o1f+MnHp7fMJ8O9CrywZMdh4NmDoGb3ZYnNzvGzrEbO273PZ3NkE90ytINNyj1FqtOfP3j9/id3/lNbh7fUJaFSOaDZ4/43V//Kv/ui0/44Wev+K9/93fxf/sZT29vuL1JLKGwxMx5FvnG8xSZl7CSxUouxKRX/3ClxDxFGcNmHNg120AtgXkzdAy9GNbHJbAUg59nPnre8ehmK1ydLKbqqp55Igqh1oDbW9Ef77VhdIbNzZZnT3fsbjcM48hGJd581nG3LBhtSKqyrlVF6Yg4JyhmAVTR6Jww1pK7Dt+8nf1CJlTryCogUtG/okR9rCWAqk7CNFtJa9RVFdtIpoWskaRbJ3KUta1Ko1ZVjfA1DlK16SHU9XE8T6h+5LbfCvPeaqy9zqT/IwJuWlmUtVFdatxqJJFqJtBYsbpkeVS4i2paQJ1JbD6WpgY4qQCaohQi7qBqT1A3w2NJfbXWOCPZctcJDNa0UeVsa7J5iHlB1rWsl+/lmh+oFc+/FBbyFKo2yBuRq7qveI9fPDpnqQAQ28IYY2UPy++qqzIll1JdXBJOiddm7wyDE5jWWosx0PeO3djzaBdBGZYgMnTOWTYGepUZLPSjI1qkSoPq1mKE/qQVfaerEYAcqH7xOKsYrGyuR49uePz0MbfPnuC6HtdvKVh8SDwcz3i/VH9VqdZctVFzpdD1HX2dzW2OHKplcXUzmwrTG117iq6jG3q6YcCNA90woJ0YbIfZV1iqrBaNzoroRU4XuHW9D7VgtEYCL20DqJZxarEdU5bN2ItZeWdRylcx8ypynlM1ApeDd73xa7YrwbZoeWSl5EBAkagzqLW4vJCsKrjVrkdlGEt1Vg/5KhjRiCmtLVPq/HmNA7XPpC6bOkOOGT8vnI4n+nGg2/SgirRRjGJIEXJhjuK6I2xrsY5s5gMiaSdEs6ZoZqowiamjG6gmAlBh8CyBSVWelzEizNGS1Ww0GlNhXmG/SzBXK4veVHcsccmqEDKsqECL201qs6zwemWINpGBkqseedujuXLPMtP5KGYPecEZObw3mx6rLc6NPJxEhWt1dVIFUkHToexAioFNv2M5ndgMPb0VMpEtEWKW4OUDYQmU5Hl2M/Ls8S0pZY6nhWWJvHzxQEmB7dix3XY8323ph5HRGTSJzihu9hvGjWWeHxhGh1U9xzQSs8Zp+JWvf8yTJ49wWhFNh3UwDpHf/u7X+Zd/9pK/fT3zyed3PFM9vR3ZbcBHUaRDW1IWneTFZ6bZ40Mm+lhhU9ljxiq6rvpYd6Jv7oxwLqzWlUUsFUtQjpeHM/gjv/WNb7MdB2JK9RpWWLUhIEnOg7Hv6Iwh5Sh+MypxPs2kZcEVT58LWS082ndMR4N2hvsliue2FhnYVDQh5wpzy9ia1Q1FtZhgCMESNYRS/Y2J5JLkQal8HSM6QVmhUgJMRd0qqqklhrXpAZk6UXWOXPZ9SZe1LWVlWRPlC0qq8T5yOC3Y/ozpN9j+VNf5gO67Xy7gLqkycusLs0qvBuOtQtP1BZgaUG1lvQpppJYR9ZBZNYvrQdNGXgpKJOOo36v2fNZoyHKDrTZYIzCVq1CYBO92CLP+q2o/VlMw5QLrtWq01KCIyuvBKfFfrf2JJrgdgzAyHdQDVbUBzjXIX6p/ORhiyfgUGVJg46R6vR06bjc92xoQOqvY7zekGOicY79dVthQobAuY1Qg68zoRKy7ZOkJtD601gLtjr1ht3U4IxlX9HK9hjrLenO74+bRDZv9DaaTEZBYFIfTzMvXdzwkkaWbZs9+29P1ViQtYe2tydttFW6bSVU16JoKMZtVC9d2HbbvsV2HceIzHBYvoiE+yF2+sqQzlX35Mx+lVfRyoMdYCCHwcDihS8aojNa5imYoxsFVI3FJUGIIpBCFfJGFbCSx71KqlZaJ6cpSbiM9teprknCoGlSVXTPmJi2nFELMM9J7bIxaScBkQ6sqvVRKrYJT022Vn2szhaL1rbFADpHldObeGlJJnE4nNtuernO4zS2MQeQ8K1PQFCHPWVVWR6fWvpH7xZogNQ/p1pcqFYJryYz8jujIGqNJqd7vUmFK1VpNrGeBuU7CatBte67yHrnMIbdRwtqOo6FmqQq8KGRaso1YUYV35MmCX1BBZGVLihQl/rjvf3DLdr/nz/7qE7nnVSwmlzpDrBSGAUqRedPo2XeKJ6NlUBm8rwTHxHQO6FJ4/2YLJWOdE3u+rUhUWi396sFZ8QHPshZ9qKI8zuI0GIow7vtMSI7TvWMOkZuN41e/9dVKsLRoq6FonAt89P5Tfu/XnvLD//EL/qe/+BH/67/7lOObiY8ePaXvJOCWOqKWkrScck2cV4u5Wl0YK0YTYtjRECkxEuk6i0LaW5lMcgN/9ZPPuO0Sv/7dr8s8+hIp1ZpUtqWcnam2WJyB3aYHLJ2Dw/2RfFxwDgZXMLcbMIr9Tc/hYaCUxOEUOC9BxjCzZ55m0b6+QkRXdCMriq3z41rJzK2qqoCUC6dCG5xjnTMvVoxN5Gy62F+2qZtcCsUkaSdlRVJJwpaRomB1warJSxslFN6EuPJMZ49xE9gDSYmxSPCe0P+SLOUlhdpnDOQkfbfeSnXpzMXY/NqNhxa83iklK6Sh6hhG3Xy5iLyjogkAVN3kOqKji8B0ugX6tolrxdyC/XV1qWtFjpYgXk9eWA+/C/6syoVirmqsldcm84W5zhZSiqj+KDmk5Pu1f9teQ6WlZtV6vJGcZbPtnOFm6NiPMuLSVWUlo7bCDNwMPJ4W5ipjto4elUzWhWIUqZgKodS/WSFBZw3b0bHfOhSFw2mmFJmD2wwd++3Ifr9hs9/Rb3ZVHnAgZXj0cOTR7Z7gF2LteS1hg7KGru/q+Jap90PTFJKaA5NSzYe4zVjW2Unboescpa6C/6WIKMIyiX2colRTe115MTUAtpnVuj6ozFepnATmDSFyWE7EZRaoWGX2O5lp7Du3eqcK+z2RQu39VIRA6Ur6gtYVYS2l9YUM1oKqqb1HqVg1ppT6yFI1r1rQMkdbaMmk6AiLOEIGjPSMNCI6kfIacBUXmNcaJyzdquuZfeD8cMAHz3k6sd1vGIaO7aPn6FvPyYvggCoFW6DXSiQhdTP+uATQxmNYoW1aoC1rlSm/cklImltuS7CaGxeFizxnS8DqvWrVcNWorEFXDsXcyH+lqfs0BawWcKnwLyLwonRFnxDzdIMElsq8TjFyvD+SkzhW3d7seP7Vr3M8/au6R8vK1ZAXkKDElZvyeDfC4Q1PN5p9bxh6Q28tYwf7rWK/9UJqPMk+2XeWR72pnIra9tCQo/T+UsyoBMoa+q5jux3Yjroy+BPj5jEvPzlTouf5oxu++dEzNEJiUtpCzChtGJ3jd3/ja/zrv37Nj7+856V/j48PE18tPcNG16oT0DKlIWS6yhOhrMewCPjXM6712ytC6Kyqs++JHMXi8OUh8+++/yN+7xtP+PDDx5XIKM9d57PWc7IuFowRrgUUeme4z4p5WnjxeaIz8JG/YTv2KzlTLBOrYp+Wue3kz5QccEbjY6VCKBEeinW6I7Qpj6qDXv0MJCnTGudAYapYRalxRZ7fVdKY1nJtStFSpWclWgwpyfmeW8yonJl3Au4lpjW/8VIU8xRAn1giHM+BzaZnM/ySATdEEaJY/EzwAaOlVzh2HZu+RztVDZ25ZCRarVqVa/ClVQms8HErZlpcLqrNhLYKuWXfNdBVc+pWqV4/ruLtqh60Plf9vBGpcgtYtPNVxmdU7U1kBCopUSjoRkkv2RqFrpBKrr28d96AaokF7RhDU43iNXRa1JGsVtKb7p304yrDLcyeZZ6Z51kqwSDGyjk3+E3GNEqturQRKNVZy2awbAdDjJHjeRZfWK3YjQP77YZxs6EfRkw3gO3RJjNsIzeP9jx//pgYFu7uD5xOM8fzwhIy40ZVf9BqNaIqltAWnoZmcyXVbdMQdShT7QC1ppHtcijEZeF8ODKfzygKXS8BV0RVigirXJkZlCoorpCs3FlDsoZYVbFiSEzTzPFwwqrCdrvBWbvOeypEEjPltI50CdNdXqNsHF1BjSzBtuKuymp0MthkyFk2jrWyRgxgkYCrS14DLoo6mtACbhKoPkZK0hA1Kovmc0mX1yQHowQoaxphUHyMixZRlkwheY8/Sw82x8zpNKGmmaNPeB/RpTAIgwrT2VX1q1Xw10nqZc9I2XmVh9I8hkuWBKglJiu7um2eUkN240W03litnMs7BxRXwbaaIhQRtrns2Su0gau9dLWnVEk4NH6eoCSSn1nOMypnwuwZR8fto1vsdl9bPgJRNywjF1EGMzlinSOlwnYzsDjL1lk2fcc4uvp6FSllOq1qgmU4TZ4QZByqJdmkVlGZKsojbmGb3cCTRxse7R29jeQA0xSYcfzw09eYEvn21z7gyX5Pjhk6h9Y9ySSUkrnqD5/c8Pd/9Smfvn7BH/71S37zv/wmyReGrXj3NhKj9GetrPmUKotd2gANWhApWNk7Aq9KEpVzJnrh0szK8sd//SXa3/Of/+5voHXGz54kaiESfCvjXbyIK6kNBHnUCpVF8lSFiD8E3r4ybJym3AgJbbMZmOeFoQvSMsiIKUz0xCK8hAKELMp5S7WdjF7Up3KT2l0ZxgptNU65K17wtQ5yLQwUVF68rIucoEoFCzye6rXLNB/20nTIFXXCU1Wxp6o4Z8RhyxhNyYFlPuNj5HCS+/PzPn6xlnKIxBiYppl5Fhp95xxx3FQc29CED0yt9pq1nVrfbX2R9TnV5e2uXytQ9XpFUrFBW1IxX2C+NuKTEdg41YxZXz1PrgpKqRGmspBdQkqE0vSI5XXYCn01PN5oUUjKKZNjRGfxdM19h9VADLVJH4VxLBgGqLyeCgKvsyovWTKqRMixZoigtcF2IpFmrKPrI2WIBL8wLxNhWUSzObRDWZZP0SIWIAvMVEaoZnBCkDqdJ3m/IWKMYTsM7Pei4mS6rjqxOIrKmK5jd7PnvfefEsPCeZo5HM8cjjMPx4V+GOl6IcEI2aWxbGuV1IgYus601mxPWQfGXiELEpBKCvhp4vjwwHQ64axmHDqcVRJokU0njIW8HvgNwpY+uEWVQqyjKKYUSipM55nBGcZxU91+HNbWERcqoaj2hiTBkZnWUrhKylQFpgwmJ0o2OKxUpFbhor2qtpUE3CxKXy3gvpMIllJ7yJEcZY6QFEQFpwQZZ2hJgL4i82mNUbbO9taRMq3FikwpMBaVNTnC7CNxOnOYIsEHLOJVTNehS4fWfR2Zu8pIrwKnatGsBjcJeBeYW617VCrDy48rQSSUXnlnF/nGulbb56ohwHXCuVBn1qv5SZVIvYaY5XU2SBmUkvRctOQyuRiC9ziVuT8dyTFVVvLC0yc7do8eobRmt+l4+1Cr7DZRoOSMiGHG9j2ns5ihb6xmay29kzEm2usxCrMzDLZnt4mcpsjp7JlmIemlJIQbYwydFXexYezYbjq2g9j+ja6gS2BRhRA0r0+aF1++Yjda/u63v4IlQ1ZS3VrZQ1WzjJ7Mb3/rOX/+g7f81YuJ/+lv3/IrX3+fPhs6q1dESOWM0VIZ285d2LRwqexpHJwM1YCxCUykXJhT4vWp8K//7b/j73/nKd/49geyhn0gRzkXkxdilgTutoaFyKZKobcGlohDoQZHWBb85GWPdga3MYxjx7JI31v8lGsvNSbOy8IxBIG4faz+zjPZL9Vw/io4UlAGjJVrJ2jlpcXXWoBiy9cq1Va5ttefavJ7CbY5pZr8N5ZyGw9qwbvykrSt3AeznoOq6t7H4DkuV/vuPybgQllHV/wisHLwQaS72igBCuP0SgCpSWXNfFk3obp6znc/5AII/FZZoGulWAUErgCuVDNy6kKSLdiIL9dVba4L6eJm5CubrWX7UVWHmsqqxMjCJSUIotzkjAXnZAFUNm97F+8WCpf31Q6gC7sy1oVSDy4tilcNQsR1qC7RhQ43dES/XHqPufVqavJhqnaokYTBKiXmCzlznmZCrXY647jZ77jd7+mHEW1FJ7do6dcY1zFutzx5+hg/z9zdHfE+siyB12+PWCvjOs456XPpytJrC+vqobURpmrNsqlMXYoSmCpElvPM+XhiOhwJy8I4bBnHDmtlhjDXLLekVCvcugly1ao2ir4TpKF0lhylj26IVTpT7qsxViQiravZfVqDt0Kq9nEzoF1Xmb3VLCMnkoEUFckqktPEaHHB1g0o927tU1JWaLmJdbTWPjVgXQdcUoQUUVkelFaFUHvx+gLF0tyJ6ty30iI1WoT5r123QvWLD0zTmXk6o5XCW0dKYyUnAb1DKTDKiCfwuk7rvVTXh0KtdmslnJtBxhp8yzrOpJWu6e51EG6H3SX5oFClNEvlLFV3sBp0Y5LJhmZK32Q3W//8MvuIkMGQvqHyM6PR3N89MPlZZtWTmLpvbm45+cRu7IThmtLat6t8PVJMaOuZkuHkM+89uWHTi3Kds6beQyjImKIzid4ZdkPHvO2JCZZQe+cIK7Vzwup31YhDHjBYSP5MjoWoNH/6Ny8I04Fvf+t9vv7BE1TOkmDV8047sdgr2mK7nq+8/4T/7O8848Xbz/hn//Yzvv2Vx/zD3/66ECi1urjaFEGJdDt/6ulUNRzWe9jwxVWoH8jWcQ6Jf/MXP6GcXvFf/m//S2xv8YuXc6iOwbUKtwUqQUBkbt4pcFZxu7vBL4FCx/FOOABxidgiwcY6aTftdjKrHUNCk9A5Ms0zpxjROZDDTPJn0jJRguj069pqa8YBurOYqj1gG/NetXaGuVS3XMkTk2vLUJL7lGI13RAS4qqtXte9pbSuiCCi5oK4yg4QRzilzSrZGhGo+ud9/MKA26AiYe0Woo+EIq4NK9Ufvb5BXYPMWgG1XVdx5LYB69nFalRQS/mUE7FKEba5u3qZ2vZdDYZlxYimgFZp/ZqMqpaVlFVylo0dxdM05LzCVqZePOdk7tQgs6BityIVrjVabMNSINUb0MhKMs+oq69peecwg9qfyqlqF6eaYV9cLlaGqAFVdaFNZ8lDXw0S6ghKubxm3rnWYlxQcpWejJlljvg5sL0dub3dc/Poln7cSsBtwRCNLh39uIGbW57Mng+OEyUXHg5nvnz5VqC0zlUIqGo2W4WqYiTaWjACe1J7uGodAWuVsKKUyDJ5DvcHjvcH5mlGq8zQW8axw1lNSSINmOMlyywlX8GMcrAMXVU5U1Lh5Zgp0aMQ9SNtHcVKwHXWoZUR6KseFkYrut6x2Q0Y160JWakBNye3GkDk6n7V7gGFCztf1XnUq/7OCleuUHhe56tzTSRUqr3D1T9XnsuYup5qQqMb/kqFtyvbvyhBG5Ste28zcAyew3TieLhHUdj3W3IUxy5JWHokbDiMshhd6kRBZWmuMjztUF5b2Zcgq2oftyJMkvy1sf4KG7+z169YFRVqb8Tw2gyU+9v2f87VE1vm6Cm59rzr2aNYZydFy1axNYbjqwdev3iFMoZpOqFQOGMY+g0//uIzchYBG21YUZRc2c+UTAozuXT8zSdf8p2/9zHbm40kmFatARcMxmpMzBiTcD7gjCBaufYOdSX5yNkgxDoRbdF0DjSRKRhCMbz18Jf//of0pvB3v/0xN4NF5VL9hKmO6kpGl4zFdiNjivzud7/C2/uJf/z//ZT/8z/5czqn+f3f/IjRyrx9Kz6ovAW9agqwVrqltFNY0AkxodGkrJlL5AcvZ/70z/6C/9Xf/zU++Pg5MQT8cqlmqQpupVRXqNYvLhmlCtZqRqf5+jc/xlnD8eGMDgEfBAqOIdI5i3UDm3NAORjHnoCvZgmZlCNL8Ci/QDhDnCAHjAVbRX4MElShkhmpXJyVDKauYlJdO3UtC1wse7IJaaQqDNOmGXKsyfEKJwsLX1H5Cc2PtyKwBWgeAQApK1KKDDn+3Lj6CwOubtVUnTlKdeYoxIyYyVOrtnp4dHW/Se1dGcaXGNRiUqmN7GYMkCv0u4oJtJK4IDdZi1BEqaddvt7IV+WzYkUxWdV4UqHETAqVcVyh69Zkt0aTi0E5J8EbICaBAGvApVhSloFrpRW2c9jeoWdTX2q7BrVCQNVxErEKbP6pYrxQX2utIqD2YJQBazHFrofQxT2p0U1UO+JkIZUMKbAsidlHzmfPsgSaZGJTd3L9KD3LZiKvCso4jOsZxi37m4X3nj8lhYD3kdd3B+7uDrzZjVitSaFns+nouh7byWZVRcaSBPKvjikILtnEPciJ5TxzvDtw//aB6TyhlYxDDYOj72X2ONVAuwbbfOnfrkQtrXDaMHSihV2yJoVC8IYUZfZXG0upcLKrpBDqGm2bpe8c23HE9T1NDJ98CQCXfm+uG1GG7BtjUZZeJdzVf9u9b1KfpcjvtecStx3JrhvJSsaTZA1Y08Z0VD1I2pYqq9i/MtX4o85SKq24K5rlPONjZJ4ngd86T9wGUgpktpcqlAYdS7ApRkbnVqZy2/QCFdXAWycHWu8XrqDlK+Sq/XJpUniNa1H/5opoXirmy36RDLykeh7U+yXfrgIoWqDdkiuPQSs2KbOczhwfJjb7nuPDxDgMPH//Q/p+ZD4eK+ksr/e/5cOrqlyJFDQ/efGGY/iY8WYnhURNNuR8MeQMxjkMhqQdvdJrQgOtOJA1a7QWT1krWucgcH/IjmOM/Mn3X/L2zWu++v6e7379PdEvtwPKdDSyJjljqkKfdh3JOzad5h/+zrd4ePvAv/zr1/wf/h9/xP1x4h/+7jd5su/pVNUruzpbBfW/0ONWFGJNjA0pK05x4Xufv+Uf/5N/xd/9xmN+9x/8XTCaZV7WfmbJ1Y40V7g1N590ETo1NXHcdJpHj3Z05mM++/QLcpg4HhKdNVV3wDAOA5sxoEKka8IrRlBScoC4oKMXJb1+i9vpVeTHWVOnNMwVaVYWoRD3WFnzDWW8jAHVnjPI3myFXg2+F8i6ejrXSlpRFYKVWvejtvay+Ota16aer/U+pl824BptiFpmQ3OG2JxYiidU+DH4KGpFOQMjqquzR1r+o2t21TLcJqCYK4ycU14tnchlbcCrfLUhSxGHBlUqlCWwFloOvawukDIt0K6VS1nZxrna6oUs/aOipUcJTgyUtbzOUseBcm6evJYS5aAyxjBsRvpxxJzmlhOsEHMjvIri1SU5yFlg7VSVUlQ7wEu+EMq0FrZhS0quqiXWKlp6MCpnDImiEsdz4v7hxN39keAjnevYbnfsbm8Y9zts36+CDGsZUsVGtHEMw8jtzQ1x8cyTJFQxRl69fIufZ043G25vNmy3G8ZxpN8UukEyTaVMvWgFqMzBmMQ8+jwxPTzw8PYNx4c7Sgrc3GzZbHs24yAkm1zqwq8ksUaiKGUNFtSWgVZKRjC6HoohOViMInglDlXWUYzFOVdJITVrK6zkrsF1bKtaWUvqLkG+XW+QXmFZv96Y7pf+JqKORFnjx+X3L0E7p1LHUd7JBCWg1f5Sg5NbwK0xr7YmrvrkdWxI1Xmf8/FMOBxIScbJztPMPM0sy8SyX4g5kauxQKyz1CkmgT3XMYkrtn+FIlul1FR2UmkJB2gu16FVTuthDnVcqNTKuQXiS2Rud7QF4taLUqXSTWuwLVlmsnOmyqYamvSlMzIudHhzICwRdTOQU2a42XLz5Bk+wXw80HduRYdEKyDLwZ4EM7PWEmPkME382+99xne/+Z5o6RZQqqULlX2Owg7D+h5yovpxX96nBnFG0mCMvKcYIosvHJbEp/eJP/zjf09v4Ve/8QFP9j26pHU/ihJIHY4yVddaa5xxaDS7XvGPfu87sEz88Y9f8n/5fx75qx99wX/1e9/h1775Pjcbi1NC5pNbouqrklEaYdEbsrHkAktRvDhM/I9//Df88R/8Eb/23oZ/9A9/G9cZmZRIK4VVkv8Ku6aU6plWZ79LlXNVSkaMNDx6/gQ99Izbgc9+9AkpBAyGoevYbDfYh5leqRpAhcwYleKjjz7g61/5mDSdsEXg8VWxrOrEayWsaL0iM2VtZzRRixZ31uq+cmFoyEqpiX5OxJhWWFkKyLQmPg1W1rWNYu2VKhsNiBLtZlPvlxDzauvo53z8Yki5EheKEreUEBPLvBB8pBzPnE4T8+JZYqisXTmYknN0pc5mZlXHPuoFqH2rldSUZKQhVyq9vrpYqm70ojIqNzgrX8zZS4OUWTGT62BLqhVuqhq9MYrHYhJj+qQK1hoMHZ3OojZSFDl4IS4Fj9NOnr9WncYahu2WYTuj3x5WFvTKUkaq51gg5KbO3Iga8rwCXUTJnWpVQNWmlUZ8u5nlcrjV7Iyc642U5CLlzDR53r49cvdwBjS73cj+Zs9mtxXRiSYxVqoyllDC1/PfaMN23JAf3eJ9pFB48/aB82lins4s54nlPLHfz+z2O7ZLZNgm3JDQfQLrZNGVIqzcJTKfzpzu7zk93HM+3hPDzHYz8OjRXizBnF7hr1bRt56tnMGtLCrroa5qcJJFb8lW1wJb0Q8ivp8rauGs9LnXAFkhJ2cMg3Vs+35lMsu1uErwruJEWQNqeifgrj/Au8iNbGaR/aSwEqNaxS67Pq8VZFN60rrNtesLqam+5wtbv35u5Ip8GaqyW+2FxpTJQVoK8+yZfSKkQMyRkOQxdz1j7xhcI92plRktCcClihdEJl9aP7kxVCWCXqrcC/Ki1OW6XO7b1eUqSMuoUCdsoQ58V2u1Jt0oezHlerVq5SL7IKJ15PjmTlTEKoFmtxvRmy1LzizTIiiHAp+E+NZQiNYecAa248DpGPmLH73kr77/BU+e7OmckHl0rYxky1yIoEoZsi50zraD8jIahoyKETMxR5ZQOPvCm3Pkf/g332c6PvDV5zt+7WvPGTuRrm2M+fW1GS1M/64Df5b75DpSDDx/vOF/8V/8Nh/82d/yx9/7lD//87/mL//2J3zr4/f4e7/6EX/nWx/w/PGW/Xakt6ZWeKKeltEsS+bN4YFPvrzj3/3Np3z/ez/BnO74vV95xv/s93+DzdhJsZEaIlPh1yRchJxybbvUmXzKhThHEdtQo/HzwsPdgfN5YaljazGXqliX6IYOfxYnrloioqzlvae3/NpX3yPOEzGEFRIW7Xt1CaQV5bveh7S1VpPBxmOQ+fvL3l3PnIZqVVEfSZDTinhdkuTG3bi0T1sS1tbGqhCjKnidBBX4eR//wYDbniCVzOw9p9OJ6TwTU8F0juM8cfYzPnhC8ky7jVQQVTvVNhxdSUdGF9mYpYjmcQwiY0cuqKwuc5K5BiIl7NJSR4JkZEhmQUultaPX6d4LHNkg6pQEIg6R4j2pGsjHGIkUsjWYIqblYr+micuMn88s00ROAjH5eZYFoA3DZsMwntHG0OztSjt4ankSYsIbSKvIgchEztOZ82lis+lFt9i2uWJdq05p9gsrmEvArQ1/coQkxgpLiJwPM2/vTtzdT3if2ey2PHn8mCdPbhn6XqAVgSdqBq0RibwkfpNBDiyjRYT86eMbjNEMXcfbN3fM54nlvPA6iMzg6Tix2Z4YxiNdHTWSnmKt8KLopMZlYZnOZD/hdGYYHbtdz3bT0feGljULJb/COznVHjzvtipqdXsh+EjPyFhFbzq6wdE5Ma9PsWWw70KI18Qpo0W1zFrx9V37dS3IX1d67ZBvAZOLoEUb3C7lwhlolnsX0+p8FbTzO++ptRWag0oTy79UnFehrJFg6gEK4Fy/ZtpipCFs0xgTwZ/wSxSkwQeWXWCJniVumWPP4Dp6Z+iMxtlCZ4uMvq1/U6Dx2BjFtToujb1JrZ5UGzyqAa19pyFO9SulVrQq5zWRXllmuZFY0opy0J6/6HqPpOrMWnxmlSo8nGd8zpwPMzf7HZvNhvH2Med55oMP3+dwONB9/3PmHNb71EggSis5+HXm5vnIly89//gP/5YPPnjCr377A0nGdEWsVqBNfl+VjGlQlpzskmg3gmQbW8vgs+Z+SfzzP/kxf/GXf8OTreO7X3nG85tRzkRnEZssJVBt600aDdZSKkoj7ZAOqwPjsy03/+nf5eMPnvFnf/k9fvjqgR99/wf81Q8+YdzuePr8Kc+fPWbfKXqJgEw+8nBeeLg7cXh9R54nnnXwnecbfvN//l2++q2PGba9iLGkmtA3Jn2DjoskXW1tlDrG11Iuqew1ORd+8oNPePnlG+bzQvSgTYfpBop2LD5grLDvfYyrtoE1msdDx74z6G6zJv9tznkdSbtugdQz8vJPufr3KkG+2sPFmPVMuH6sCXW+VLcrGkVLevUKzTdiH43XUINwSyxLTj83rv5ilrJq/UgJjrNfOJzPnA4nqWiN4RQWznFhiQtznDnOW3bjwKbv6TuH1RarRPDcojBKzAta9Gyk4EJjOGrUOmZTBxOUAt2IQ+1ArNNCleG56iK3C3kdbKME27yIJ2iuQgi5ZIrV+OJZSmShYI2RYHs6cj4dsZMEzOA9IQSU1vSjQMrKVPgg1zpWaXQplYmZCKGQipXKvGRSkCB0Oh4Zxw6ttxjXr8ISFH15v60nKnhIDQSpHhhiwn04nnn98p5Xrw6cjgtKGR4/ecxHH73H4ye3OKMhRgpeqjglhKncgl3wZD+Tw0yOAUVhv9swDj37ceBm7Hnz+o43r95yvD8yHc8cH070fUffd7hOPIkbSxB16WsqCkYXeqsYtj394KqVojCHmxBGI1+0HmoLuLlWnas4SUu4slRzqoqgDJ3DuA5rZEQlVbWupjHcGOsxyYhbqqNZClXZwHpNFsgtSNSgtgb4ggg9XMPcvBNwL4QpLhv9etOvaMW6tdaA26pdSSxahcs7AZe66esyk//XQp5qhgPt2qUkzNCcZnLIhDngHwXmfWDeRnZdz9ANDF1PZyx91c8WyE4sGJVqTGtZayGKmbtcHxl9UyWvycmqwlO1yIV3IJn2ei243NeSipC7QrrwK4Lcs0KpCY/YNEIWEwalyRjIihgz59NMWCLHNPFrv/4d+t2WoDWvX7zAWc3Tx3u0NSifqoDGpfzOFR1aFs/Xnu/56Mmev/z+gf/jP/4T/nf/zW/wm7/xVayVeXIFMpdfEYvWlBaxFlVRtXQ50IGMISh48DP/nz/6If/9H/wZW6f47e98hb/369/k0c2mFhRKgljyYqigNKr0gqhEX6vLIM9oNM50mM4xDIH+1z7idj/w+C//hi/vjzzMM0t8YHo58fnrF3ypNSorXOforeLGwUeD4fFXe957+h7PP3jGo6e39LsNpnc1YfaodUSm7ssQVlZvbqzeXM1bWjKZC50TreI3n7/gJ9//BGct3/rGx3TbjUyIhEBSlrdvT4zbgdMSCDXZTxnG3orlaIzoqsUN5bJXGt9grXLrNmyVLJf9+7PBttRCQzhBrfhQuqBXJKf9rLkE21JWLgNrjPoFAVddTesUEUv65QLu5bgRI26QWVYNVAJHpuBD4HQ+ow3E6FnmgXkcGLue3lpRWClgi14DbvUMWjNlVRWN1jMOVS3OKlRbceM2a94CrjECCTZ8oWRWYYqULibZKQicTIioFNE5SUM8ZUooRA1LnYecphPLdGI5HfH1a62Ctc6BEoFttF4ri7WHVZeCMC9lrjhRMFbTdwarYTnPPNyfKvnF0PeirSzrqwFxl4svRMBcVZM8YZ45HY68evGGly/e8HB3AmC/GXny+IYnj/ZseyfM6umMMgG0qye1mC2XLM8V/UIKMoaUS1mNBEZn2PWOxRkeKJTgibGgSqTEhTDJCETfOVHNqu5BqCqYbk11InGi8zw6rNNAkvyhbeCYru5X7a2XhuZc4Ns2GlJquaE1uE7T9RbjROShVOUmHyKzD/iQKlNcemnRR6nSroJhe+4mor+uvrqRWlVZS+CfCbiSzQrr9x12fLm81nc2/+UPVHjsSj+5VvKoSwC+Drhr+K3Lo42ztWmCNUFFmPpkkfo8Hc6kkFgmz7wPTNuRzeDZdB296+iNFYs+IwpyTrdKogXdOt5UCTIo6Y8LMSXTPGsrR60iWu8mx3KZJNimKlzgQ8T7iA+BGMQCMuVMY+KrqggkLFNqC0aSdz8v+MWTY2b3eM+T589IuvCjH/yAn/zwx/yj//Q/YX8z0P2///DqeksfV4Kc+B37AKfjmf/lf/ZtvvHBxL/4sy/5P/2z7/NfHRL/4De/wgfP9jLvqmtVrloP/3Lv26pQxtAIkyEXvnhz4J/8i7/kv/+DP6dTmd/4xof8/f/ku7z3/JbOCsoXFk9RB0gysqRg5cGU+Ug8PxBOB0IIUul1DtcPWNtRUuHpky2Pn9yS0Ox6UZsbrGLsLPux52YzsNkMbLYbur6T4Dv22KFHO4NxVtp0aaltuAodxyjBIyVyqGzefBFyCfkyP900FFwvpKbXL17htOb9D5/xje98jZtnz/n88y/47NMveXNYIEaGzcBPvryjICYpKRS0yiznMyk1yV2uAtxlX15Ox5rAXh2XqkHzDQ26bNXKBK/3UQv7uJQi7YKiyaohUHr9uTXMlnpvVFvQ+rKw19ZrE5qpAbdcvbCrj18YcCuQuSo/4Sx2MzB0thpWO6lIaw9qWRZUThQfSIunDAPFdaJf2Xo3WWPQWEzVR3ZY45qpEOV6ho+WOVz+BVYkS6AIJVlzhQBbdZuS2MnF6OuGlsOdUtmqCpwqlEr4ySEwIwvnfDri54kwi0yiNoZuGKRP2A+k0jbXhTigrhrVLWSWIl6zMUM/OJ4+3jP2PTFEHu4OwmROiu1OsdkY0AVVsy+l8po5tkDrl4XpeOLh/o6712/58vOX3L2+Ryt4/PiG589ueHozsuk1OgfiORIBrawkNG1hlMqcjgG/eBYvOs4pJawT1mAKgXA6gT/TERitZGxdr4VQUjyuKHoSo+7oXBHjAiu6rdZqjDPYTiOEPtErrWfUFUsw1vGZVPVgy6Vaa+OvK7ZbA5TRlencoa0mkcilKh0pxRJFBGGqwgSpFEIM+OAlAauHhVbN/aRmsBc67brZaIe9UrWq+amEiBpY6+D/utHqpl/JRdcYd32GNpTf9MVbUJCNXa4OjAvLdN2YSFATfXO51rrKnhZtpTqpI/u5KEJInI9nUkwsi2ez2YiqUi8eyJ3ROO3WKteodtRU9nAUUZLGAG3zjsZUQRRlpBpVdTymJTFcWgENxRAFu4SPkRAC3kvA9SFKdU4lkjkra8dZoJDJki5Gj3a9KMflxNd+5etsdlvuDw98/uknfPHDTym//zvsb5/X4BTQSiopERASVCqEgMFxmhPFL/ynv/01Pnh2w3//J1/wj//oc/7tp2d+/9fe57e+/R7Pb3s6c0ElUJo6fl35dJmiDSHD68PEH//lj/ln/+rP+fyzF2xM4qMne37vt77De09u6LsOYiBME3OKuGUiu9MaxLV1oAp5mQinA+f7B2KM2L6n34xkJYTFFAJ5mSkZbvYbwug4n8+SvNbr3jnNZrBsBkM3ujqjrtAlSEspSwuraRHHlFYLPrFxLuQU1qQ4xqbyJK2LUnvDucLsOcv5+tFX3+f9D59wc7MlzRMPL17jjwGtNJvtjqws3//01dqE0FpRtOgsWOuojZJ3eAKqrv1yYeC23Xe9tViJBOXy/2r9ycveXb+qs+SRqyoX7wTcqx3IJYO8/P8VvW79WxI1f8kKt2UKSmu0dXTjyOb2BoDNOOBsB0l6sCkFcvaUJCM4SWuytuSiSS31zVCyQmcRbHDK4kwmuYw2NWgh86sSdK8CbuvMt/esKxmrzr21r5daHsmcVZCAuyyrIw6KWhVYGTeqkCOVURhDYJomlnkRH9yccc7iuq7q21ohFChoMnWlXDwT1xteX2vMwhC11nG7Hxm6jsNJzALm01RnReW5uq5DWbPeKGnwC2M4Lp5lmjgdDrx9/ZY3r97w5uVrpsOR3W5k7DT70eJUIk5nlhhZpqX2RgxG25WcUyrk7UNgmhfOk+c8V8m6aiigUiJVSTWrC5tBKlTbWciJHDNOQ681o02ShPViOi+WbAZlq2UdeR0yb1lpIyu0udeU6thUEyZohuRX+6Zdk5Kv+qm5wpDKoIwjA7MPHM4zk5fZ6ZIzwQfmuXINojDr2+aRquwaMr6MvFxvmaZQdv3/pVz9W79XqAlgRT6oWbR8790tf5W6r1X8uoZ+5m+335EnWokcxq6az9kgnrja1LG8KlSiRHEq+sRUZpJPhKHD9x2h78RKD41TF85Fg8zEt7cKHEB1mKmohjI0/8/LmEa5gpnVZR/XBDUmIUv6GCTgzqKs5qvAgql72uUMRdTFmhOTIFIzSYlgvzKax08fyb44nPn8ky8ZFNz0jrtZLNwa2WptVxUhXqaUCTqzhIj3HhUXfvWrj3iyH/jzv33LX/7kLf/tvzzyT//sC7754SO+9fEjPn5+w6ObkX6w0gtLieQDh/PCZ2/O/Lvvf8pffe9THr58zS6d+JWbjnG34/HNjuc3A04lynTm9HDi9HDA6ML+doPtRD5UG+nbSlJchPPxcGRehATWOWnNaGVYzgtv3h6Zl8DjnUNrw7ETU5I5RO6nBWdFlEPG70vdu81K8tKDz3WeJqVErgG3ILB7I5ymmKoaYK6CNKWOv1FNJQrLHPjoKx+x2Qzsb7YoFC9evOJ8nLjZjGxvdyhr+KO//AE//vINZujIpWCck3hiLeiC05qUytVmuRRi60x3w5saqnTZVbT24wVmvigcrp+rctm3V7ls3V6XzwqX/dvmpdXlh0v7mXfOqZ9Oyy8f/0EDeq0U1jqGccM+Z/quQ6MYug6rjASEEIlhIWeHVTA6y8Z1DFZ6REq1ICqN66yqR23OxJwgeRpRKFP7PO0Ct+C1Pq5Q19b3qhm50dK4t1oO+egX5mVmmRZRysoF6xzWdWhrLhe5MTBTJKSA94l5EX/JXGdnXZDsDpPW8Yq12Q4XXJ+WjcmNiUngJYFONNuxwzmLDzUYn08c/MJ0OIi0W6vaa7DNUXqPYZHgN5/PnI9H5uMZFRcGJ0o2OnmW44G76HnQmmURpqpWSrwqe0fvpIIB+dvneeHuOHE4zRzPMoOrtajFWHUROzDWsO2sSKgZ6b+J6VVZWa2KNv92EU6gLu1SZJZVfqv2VZOwYCVbrtVtyasQ+WUCrgUiIXrFWFgWgTZD8BLctcXaDoXBx8x5DhymhTnInGXOhWVeOJ/PTPPMEjw+irtMQcZems9nXVZrpfnTG+c6DjbyVNvs65q9+hz4uf+uoblu5va1dx+NgnT1y1fbuiiZPzbOVZcmC0WhrEFlmVsFOQxbcqqQvl5JkJZMUhJ0SIVcgiSlVaSlKMSi8ArO14glptVibm6Mra4zldilmxCB9Ny1iG5frkluko7S4hEx+pkU4tp3N0iVlPqOGHqGocgcZ6fJRFT0qKEnpEI/9ux3W37848+Zzp43r9/wW9/+mKcff43P/v3fEkKkzf42tnrr5WYEgVpiJoRE9B4y3Hbwn/+9r/IPfqvjJ1/c81c/eskn3/sef/YnEboNehzQRlNCELnXEPDTTFoCfTjz3Ea+86Rnf/MMrwxTSOw7Qzo+8OBPHCfPm4eJ4zTRm8Ljg2O/G1dbzFQUEVh84jwn7t5OvLif+OzNgZwi799seLqx9EYzxcLNoxse7yyazOg6OgPHsyeGwGnxDIuI+mujKDmtc6+tFVG0XmVzU/DE4GsR0rSnmzBRFkJ5LoIc0fgWdcwmIwI7ReRcwzyTlePuMIHrefr0hn7oeDt5/uLHL5hjZlMDngZKTLw5TpwWj3IOqHPu6qrCbYfN1ScrP+AdfPnSwrnmVbT9un6daxStnViXn6FUnOdq/62wdesJt5dytYmbwMbP+/jFLGUQlwXXsRlGcXQYRkwBpw2mSL802kTsHCVHrILBWUZn6Y1kzpTGokwUK/1DnYpkhznhQyEkCEl6nq0v2iz+1BUTeb24VwCB0TBYzbY3bHqRVrMKSl7An4l+IfhEwaA7K6pOnVvhQVWgpCg/RyAmhQ+JxcvBrowhBJFMzMXjcxIYLKXLJtZXoINqYJwiZUVMiKQk0PeG7bYjxsLpFDidFsJ05pxF8k7g1lYB1qHsEIje116rr8ziyMYW7NAx9AYVPdPhgekgFnbz4pl9xBkjtmBjzzh0dE70r0PMHM8Lbw8n7o8L5znggxi0i9m79GiHvmM79AyDw5hCIZBCIeKgiAxn08C2uaAb81TlWt3kqzaB9Hsam/YyzhIvaAFVXlxBqS5OrRDM9aBukHSIkb7rENtJTciK47RwnGZOsyhvGSOD6PM8cz6dOZ3OnOeZ3i+rzrJOeTVl16ZKK9ZAq9pMIw2pugp4Px1wyyWjXgNuufrZ9Xevc+gi7GR0JYjV9bP2j1jPkPUMaMmM1oyuo3cDruvoh46sC5YecZ2uM905Qg51rlZEVsSu7FKdOqPpi8JRhNOQI1MSm0lfIfhckgT0krHaXmwZ1UUEZd1T6nKgy34oK6ScUiLHgKnqW5pMKIWQRG0spoRWirQ4lBsofoSxx8SC22+BEw9v38rzFFlHU8785IefoErhK1//gO2Tp3z2xR9U9jer3Z/wvC49+wLMIXNapOU0hxmKZmMCt5uep99+n9/51Y85PRw53h14/faBlw9nDoeDaJ3HgsqJ4allsB2j22O1IpTClDJ5Cjx2HYNVHB8O+Oj59PWJH7w88Haa+fDZll95fsMSk4wJaZk9Xnzi7hB4cbfwxd3MZ2+PLClys+vpnGYJmsEaPnj/KR88GRmdVKpDLoxDx3H03D2IM9fxPNNZVQ1YXIXoLSRJpiTYCrcj1ikOVJXJrbKmQp7LZK2rxkCuieIFA8pKsbnZsL3dieiHMtANfOO3foeHF5+zKdKm+uzlA3/74i3X4SilRFoCr16/4fX0NZY50ds63lfRTCFNtcTp8ruXCrNcAuAV3My6P6/2a7587UKuuuzdtddbkBhQCyhVEau2H1uC3ZDgRvZrMe/nffxi4QulsVqsqkLXCQtUGXQpOKUxRVFsJtlMTIacI0bBYOugshYyC6WgcsQphTNUHVpx5Fl84DgHpmXh7jBxmoPMRTUo2xixxtMyStJUqWKUGUSji6ibbHseuS23dmTrxJknkOmjiMw/pMyS1JrJlVX6qxJOslDzzeJBm8pilGxGmNSZ4CMxZeYQmc4TKbQ5qxYVLje33fNUiiQSqS2UIrOPWkHU6KxYNHgv5gqNOCSZfqGoTLaFrKBogeejUuSsajIkeqROMgxiTHhfE4KYKMUwL7JoYgwiVVmkVzMtgXleyDlJz9U6jAFn9Up4GgaxF+s7i9aZnC3JarxS4txRMj4klAq1Ci3YYjHFVHcltVZwuR660nvjoqWbrmUclYyA1cByQTigKXJZq6WnPo4MncVqTQiZu8ORF6/ueftwYvaRXKTfWEom+IXpdOZ0OHJ8OOL6gS4XnM2reYCtsGyTzrzmCL+rOXz9cdl87f9WmIvLBl+/+dNPU5MzXftVur3Pn/Ojl819ycytsXTWMfQDcZnJGgazp7cdRptL8laVdHKMOG3onGPsLIMzbDrHk97yyMGghOG8BM/hvPBm8tydJk6hkpuqg1hTe5Fsv92jOtrW9m5tYWgE+ShVrMCVwkBhUIXBaQZn8UXzEBdiznRGY3QmlcCygC8JWxbcdsOTYcf93UL2C0YVIoVTiGAcb1++4dnO8fFXv0JWmi8+/4LoI9ZZUoxyPVITNEGSP10IOfP6QbxYW9sr98LiFweews2m53Yz8NF7j+QaeL+2RkKIdd8tTOeJs/ccThP+4Bks3Gwsfec4L4EpBN68PXC4PxFL4c3dmUE5ctbc7hAjg5iZ5sSLtxOHOfPBky3vP9oIGkjCao3rOp49uuGDxxt2G+nNtjlSbS4jZUetmM5n7pnE/CElSnGUktYEBKq4SCnEGClVx7whQK3lVZQWWLlpIFDWo0/WfGbsNbv9AMGT0Tz71q+z+fg3mB6+5NM/+Cc83B/5H/7Nv+ft8Yy2gmgqo0khM58XfvTX3+PR+1/ho9sd+16mEARlFUceGc9pe+eSxF7Dua1KVVdV7rp/CjRxm+tg21pU18FXFgO0t3ndUMpXFe6FW9GMQuRvro5yP/XxiwNuHXvpjGGwlhITSlXfTSX+pChDthmTIKVqcmyNqG6opsOcscowOMWuN4xW0VHIMXKeJ6zyHI8Tx+MbXt+f8V4ul3FODMyrTF+p5I0QqlNHjPSdouwtj+2eW+34YBzZd+LmEpzhZDoGU4XRq3tDKsjMb1XwMVZ8fVAaO3ixpFMCI0hWVYNUjEQvvrHn07nCVaoS1uRm5HazapBoGXis8HmKiWxktnAcNL3tiKMheC0szZBWyzJJOipjNCdyCATvmc5nFr8AYiIhZCVbK0K1woHG1UFsDUuMhGq+kNPlNZWSGXthPrq+F3TAGbG5q1Z3rjPVuDmRUyB6+TueKhBSocCUJCPucsJli7H65wRcYW3LfLmoH+WU6yzzFRz0cyKOCF9YumFg++gx2/0eZxTFL5zme754fcePP3/F6/sjPlWRjzXgeubzmePDgYe7B0w/MhRF14twvBim16qvaYHX31832xVs9O5L+9lgfEGgys98bSURXf22Kq1rcjUm9FO/twbw+t9UEr3R9M7R2w7verQxjN2GbTfIngVCBh/qWEaM6AJjZ9n2jtEZbnrLR7cDz3aOwUqADN5zPBy5u5t58cbx6uHAy5i4q2hL9E1n+rLmi5b1pqoST7PrtI0XYB0brbnpDR/fdjzbb7i52dM5y7RkXr8+cp6P9J2i7xXKWLI2LLlQbMc49tx0gbf+VImKcvXevL3j0x/8BB8iz588Ybu/4fvf+1t+9IOf1DaSJYZ46SeX2uYgo5Qho3h19Jwmz2CNVH4hUKylRC/VsTFVG6AIsUw5GcfKGdtb4rKg8aQIs0/Mi6ekyM2m42bbYRTMPjNPC5te8fX3N5jOMm56Qki8PpxAbxmdJOHHkPE58/TRwLObEWd1VfQTrscw9Oyr56rrbJXfldlWgYvrelFQSuJ4OmO1EAVRWVCPeq5prcjURD+JuH+TOVVaSHe5BqBcEP0CCRDrOZeK6LqLJGMWJLHbknNmPr5lePSM++L4v/+zP+Lf/OAFUSmcUqTcfJEUyxR49ckX/MWf/CkPHz3ng6c79psNXdfRddISs5UY2HZOKzhXcZumgnS1Ry8tn6vtWANrrtXsJSkul/XRYmmhCqBcl9VtIqGeMSDQZOWCNNnin/fxH4CUmz7lRXou19NBDodW2gsfMpeLZun6QpS4OHRWses0t4NhYxUdiRwygzIQNfeuYPNCnI+cz4mUFcZ29EOPvQq4IXi898zTQk4Bs7W4zZa93vDYZp64wtZmVC4kBR2GnC3HJbGUwqJlUSmTsMhok3ZWDjnElAGl1hlOW40GQKTc5sVzfDhwPh6lwi3I4azUqkrTqjGlLlVuTAkfBAa1GlRVTrFO4bSm03Z1qcl1hrFV4UpBSYnoDd4UyBaU9EOtlYBrTB1XchbXW4aU6zzwRT4zhCRBvbKBxbXI0A892/2WcbsRHVjn5DmtvZpxLgIhp0CwVUw/K+nlZC8VPDKo3gKrybL5m9exBF0qVMVayed0UQ9bK8mWOVfi1Dq/2Q6ArPFJ40NkOk588frAJy/e8tnLex5Onlwu9lwUSQT8vHA+njjcH+h2e3Qvhg5aswqYZEBXOBy4EJnqel6h5fa99cW++9nPC7jrz6wB9/LT1yM/1wH3uqq9hNpLxTxaw2gNznb01qGLZXCOoevY9h1GS0vDh8iyaEKw6FIYO8N+cGw7w6Ox49ntyJObLX3nUKWQc2R/s+fm5sR+M7B/ZTGvFH5ZeJNmlhhX5r+oEsmaxmi0kYSVIu0e4yyOjh7N81vH737zKR9/9IztbouzjpIy5+OZrcu8OUS0SgzbkWF/Qz+MZCRp0Frk/T4fel6+fEMuhSVEPv/sC158+jn7zcDzp7f024H/6//tv+XzL17RjQMxysjbpX+rLpezFFzneJgCxzlhB8lWg/f0zpGWGaUN2UrA1e1krCQhUJQYyHGRcZoQmM4Ty+Kl2tv2DM7KdTAZpXdshw4fPEWD6TQhO+ZQmLwnBA0ZYkw8u91ws+sZR4cxhr4AaIa+YxwcQ+/oqu8xiLqRkCvEc3sYWvAYKSXzcJIqN+dUhWLkfMmmESkh1tnbGKuJvSlktLRetKlStbWFphWltmxalRuDJ/kFVUf1zq8/JR6OLJun/Hd/+Sn/8kcvWXJG2TrlUdtLRmtpny0LumjevH5Lmo5sRmmVjP3AMIi2QyOvNpMItaKVsoH0NeenFT80ghTr59CSxStUsv1/qaSwuofV1TYuP/V5acVR4zo0QugvLXxR2UkFhIrdUnGKwA6wwhghiK6yVgqVHcoCBrS1OGvYOMWu1+w6xagyOiRKCThT0KPjsLE8HjRvbeGcAvOSySZhSkZZJzc2Z1LwpMUT5xlypN9ueTZ2vLfteeQU2xLpYmXOKRi0YmsN+95yzgLv+uQpUYzI0eItq1CoJAGjkXlyyqiuskCrYLmfZ46HA6fjiRADqHerETk0mz6uBOOMzEMu3jMvXjxUbRbGdu1vaQXaSmVJaaxO6cuUXPC1Go0VcgZkOLxz2E4qXBFevzC9xZZQrBXnyUsPJhRiKaDEvL4fejbbDdvdls1uSz8MdL3DdK46QilWPeccZTDeVEWVmvkWpYg+iBNTqFBUKbhiMLkphOVLwC2QkwjRN5Y3baVpaI5DTW0rlyL9stU9SRHygXxYOM8zd3f3fPHqLZ+8eMvL+4kp1YSmtQtQ5CRM5ek0cToc2U0zm5xrRabW61ZqoqZqdXBV1NKYxOtc4CWqXu79O//30/uppsw1Mb1uRK3PV7dZpqzZ+/o31LvPrbWmt+Lo5Ky4V5li1laQeOsqUJmsZOQi5+qslSBHhTbQ6UxfZ6p75yqBUKMHhzN7eufoOkMqifM8ifhNg1JDrEIWqU4ziIcwSFWgrcaiQCc+2MBvfLzj6199yv5mR+cc5ExKBWvBdYneFjJiEDKOPW4cMa5nXgLdMFZEZsM8f84wdBwPhbdfvMGqwrPHGzaPbvmjf/Pv+eu//oGwX5UmxOoapMGoRNIKTRvrSyitWDIc5sRoFEYVvA94PWFyBmPIQVUt6SsYs4jylYztLYTlzPl84nCcsDpzsxvYjOJb21nHAGx3MNezIMYg1oRGs0m1fVJ77NaIznXVZEMpxTgO9L1jHDv6TtAna209FyOxJFRR8lAKow1DZ1B0QOEhJ96eJlJJbDcdLkn7xFgxopEiQ2QyU6pJZ0kI/iT3H13PNQNZK5LSUjGjOBxn3r413G4f2Gy3mHSmLIVTvOef/um/4F/803/NIhktqLJWoyln+mHE+0xZFj54+oz33nuOSTOKVO+bQiXIsVQlgUqcrAYcTSthneFek+MWw+Seta6qonVFriHk1v65YEpr8C3tt3464Kp3kmtdxzlbi+znffzCgJuqwcCKc2eBInLVO41V6itXv9kUkmiyCmoBrg7Hd7UnaDWDLnQ5QwrkGHA6o63ittPcdoqNLugUSXOkmEqKcJGslcilBZnxzfOMUhmXe3ZOc9MZtga6EtHVoFtphTOaTkNvNJ3JqCjKOdgs+YSpcJFSEFTdhE0eUNirpn4/xyhs18OR6SSwlr26udcQYSuFSl3IISWWEFl8wClQOYu5taoCHpX9KSMctdIrMkkTi5BJlkUCdohJ+pNGY5yj64R5LdCyW91LUhT/4rklDEVIDjqCUprOdWzGns24YewGetvRWYcz3UXFCNZMsSA7TWkr6k5dwsUkgRPk3iDkl1ZIlFJAX0hEwqmSHnSu0Pd1QGtBrX1eiiKmwuzl/QsGtpD1wlIU96eJ12/veX1/4s3xzHnJYG1FRcwqmgDSU/HzwnyaCMsCV3DWur/aJ+vmg9Zn/VkY+ar2VD/9/bYHyzufrz/TAu9P9Ybbdlfry7hk2euhUH/FAp3WdFoEZaSldbEam6vkYIoCqS+zJ/iAAqLRxFlTRsPGRsLSk2MPTiqFjIh5KJ0ZesXjxxum84a3h4HP3lhhqqc28y5V0aqFrERKUWuFKpaCYtCFr2w3fPC4Z3RKSDxKEneIqLxUJau8Zh9KiYRniKEaBYgeubWSkI7bEfNaMZ3OOGv46IOn9Jst//x//GMOpxnTu4rmRPGdblVukydQCmUMqcCSCnfnwG1n6FTGE5hrL90agypi0aYpVB5o3dtybf3imRchKMUU2Gw69puOoRP1JdeJVKN2DhsT3WKI3hGKsOWVsTKloDTWCOJWYSCsc3R9x9D3OCezz13f02xQKYVkBE2KIYIPKAWuM2jTXVTTSubuLvEwe4qCcXAy3ZH1OtoGtc3TEs4VeZJ1qLWwnZUxZG3WoBJS5vXBo+YFg+b2ZqIbDiSz4UcvZ/7FH/w5D8eDnLeItoE1llLnebU2aJVIy8zpzQve/+636NRG1nOR00O8uKv+wtU2bG00rs7hBjrLmVMLlLpv2mtuqJvcTJGoXMHnd4JlI0KV9Sy4MKKvfrBCy0ordNHrPv3pj18YcGO6Gn2pmpopBtHJrTq8pVoa5VygaIp16CK92yyvgZg0BVm0Kmd0juTkIS3oorAl0ZFwZGxJomo0e7Q2hJIhGtBVvcmL3ZVfZiFW+I7iF4gBXRKmFDQyL1aKQmWDqMPV0ZRcyEX6Ns0xR5kqNqku/R3JVlSF0gXmST7ip5npdCYsMnLTdR2rd2sN0hctX6H3h2gIMROCwLqLAnIktyy2bgiBkOu8YC1nBAoOLNPEfJ6Y55mQEspIP9N1HV3fr0IkLTkoGSIJVQwUi1IOq0c2YyJnBUr6lV31jnXFoJZMSp48SyYrC6hAEwihzjBmqWq0Eji7q2MnsXrbqsoyj/XAkIq9Zph15KmUViWzZiiqoQUt2NaNFGLmMAfujxPnaWYJkdhYybPn4bxwXgI+FopqYioVdqoHUstUhfG9kEJY5wEE0qb24lv/q+5EBVWmpr3Qq2142dRcfb5+fa2ELj9VroOs+gU78up5ytXzvAN90d5WWR22yIXoF5agyPOyEhZzzDUgeFKUis5pw6wVfjEMJvH+ruNmtDUBVDIalCMlBVQOWJXZbSxjJz3uJmeZKsFmTQwy6ysvWkOVWFW2YJSn5CCtmyLrQpSNFmJchIyUIrmOEopqWMBHT+ccKUyV/CRjQ67vKGjmZeb501sePdrzN9/7Ied5kfWTCj6I12rOaU0C2yfNetR7z/3xxJf3Z55vHNqCUZEFMKqQtZHRqiKyx9nq9b0Gv9R20cIye07nmb4z7HY9m20vY5RGY10HRvS+lVUo3WOdocfVqlJDMXJjU0SBuF6NlnEccTLXg1IyplfQsq+SJA65yPmhrJUsPagqfiI97KFATh1x03M4ZQ5TQGtN1xkRBELM6FULZkojvVwJsFCqEmBFn2qPXkE9UxWf3i/8+as3fPLyzJPHNwQ0P3xz4N9/+opzkllqYwwoI0JHyqxIHFrhuo7gI4dXX3K8f+Cj5zcodB1Hqi1LIwYfkqhf/LLX/bRWtKpWr+1saZr3qp5Ded1I1+IstEpW/ZwEWcL+VUV19W+BJm8ql09IxT/v4xcG3FDl3FKV4CsxkLzHTxNhnkjLQvGhVgoijqFcISlDQEllRqazhY2FDZroCqmIwa8YjyMya7X/kbwne+kDZKWJJBmuVVJxx5CELewXis4sy8xpOnOezsx+JIw9VqtamUuPMUQIIeKDELswbeHUYKtNhRDVer0FplBrAEsxivnAWeZ6Y4x0zmGclusUwooEtLEmnSEUxRISS8iEWCTo6oIuCrKIClitRJRdKZQyl2oqFWLMeB+Yz5MIcvggtoLOoa3FWIe1nTyMRSGbMKdCjlCSjGz01uFGIKtLv7SpLBVEsMQHPF5YxKUd8EW0R9uMvAJV9ZwLIm5flHhBWnl60a5GqvBUWAkHLdtcYZjS9keDdS9z1ShpR+VcWGLmYY58+TDx6v6B43mR8bGi8Lngk2wEo+06k9qSD22MEM4qYzBFmf3MMb4TiNvr0aagc6u0uS6/f6aCbd/6/1fZ/jTIfD2z/dNB9xeH4Ev1u1a6Ve4zp4SKCWIkLhMxzqio0LkhU1U2M9XkAliUwjpNSJaHXvP24cx+tJSS6Zz0tUmyT3MOlORJfpbRuRCqrJ9A/NfJxKU6r1wJMpTE4jX3p5lpmmsiX6AkUvT4eeJ8nliWhViyiOBbqZpiFsMQU3HMYbBopTifZxFricKLyBQ++/wVh9PM4iP90HM+Tet+NMbIaF7lpOTcKjhDjJFFab58mPjKoy39xmC1VF5TyUStpbKlVpJJdmiOUoCkLBDx/XEi5sSjzchuO9IPTvTGu17G02oioowVHYBmslJAKUcTVVBIsq4Qrenzw0zwR85TkOBghOhpjaHrWzEiZh7GGWKovdyUKKpUvoYIYIxjj/eBKUZOc8A5OQPXUZl6jWR9mrrmZL+6SpJqMoayoap6WCo8TIHvvbznb14dUPZzmie4MSIbqrVCVd9nSa51RTig5IRzTl7b3T3nhwfKs1sJenVqoFT/YblM1ygY6/ndgm0sdceuUFGFg2sgXs1J6vdyvd7v7Mea9LX1revf+pnitv5O6+Gqcvn85338woA7e19HCoQdG5dAXoIEWR9ElzjmqiAElEgumqgEaihRE4KB6FF+Rm8sw9ZhbJGqTisSmTlGDvPC3fHEw+nMNE9iFKA0WiVCkndR8sXTM1Yh9dO08OXbI492A7e7DcPQMzorGy0VfC4clszDEjl5RVB9JQuJOo+qCitUSKL5HgpRTIb7VSn4ZeF0PHI+nfBBoE1rDM5Ycs74Qs2g1h0jVW+EOUQhTKVqsF0rkxgrsUmzyulpdVGYLqlIRbx4pkUqu1Qy1ggz0TpbVbYUJZY6siFJSfSVIBVbj6JNe9bIqdS6mVpGl3KVQPShzl1KJpiKMBeFKKElsyRVq0TpNRpd268Si1fJwmZrdj2fJuhoqYv0yo5OXwKRXCMhd/mYOPvIwxx5ew4czl5GtppYOAptrEgcWrM+rKnwuqThUIpICM4zwQdWY/IaLFYrPaNrf0lfJQI/tYFWuFm9+/9X+xB+OvDyMxCy+kX/U9798rvPUn8kZ0luUpRWT/SEeaLMCZYCUXSOdU08bL3XKSdCTsSsmEvizfHIl/eGjSuUFEXC1MjEgaqCMHFZOBxPvHmYOE3LKvYuRfRF2GJVl6pyhxTpn84x8/mbic9e3nF7e4NGxDOy95zOZ86HM6fzIv3MTgvJzQe01XT9gLLS5jBa4WPm7v4ABentF6my0BplLKHKDxYKIcZKDrLVBIDaJiprtUOBxXs+efmG97Y9+/6xcEsQKdhUSlXYEs31lbORq850iRwnCbjD2DNuR1wvJEZlDLqSk0ptGemiIES8D4Sg8CETlhMKU/uvEL24c50PCz5G0AUfs7gJ1TEsrTU5RfH4VRltCtZK9dlZkXQ0FYIdB0dCY7qex08M/cnxcHdkniP7nXBBGskS6sjP1RJXRoiSxtb2Wg18cm6Aj4Xj5AVmplTugBJ9bmulE9RM45GZYGsNOciaSTmzHwZyPHM+PHB390BI4peb6yhh8w1PNclvQVNMFKiJMhfoV7VKl59Vp6LNYV+C5brPrrPaegGuc+/1t9a9XAPt+jev4vzP+fjFAXeRGc0UAnHxhHkhLwHlEzYrTDEX04BSkGQlEllE1EJrilZMp8LkIN0M9HqHHh0jUmGmlHjwgdeniS8fTrw+TkwhIGqgGZ8bg/OiLVxUASOH+SkkPr8/02+P7G5vsJstj0Zdq9zCKUbeTJE3c+QQDKnrJTusDFytWyXTaOUy0mS1pTTmW0rM04nD4YHzWZySVJ3btMai1KW6pcITIpTe2MkyyO6jjPyUqsKToiAHWglLz5oaqJBHTkLemBbP4oOM8WiN6Sxd32GsliSkekymWAiL+KH6OVSiUbla7NLXXP12FWSlwRr0MFC0xqfC3EQ9lLQVQtUgbhllTIkleBFCMDL20XVWiDdO0ZtC5zS2BlFj6jXOhazKz6zDxnhsvaY12BZRAvIxMofEFGSW2peLmpEEFIHXTWNWW6kA2px1LgalpBcUvcdPs4jGp/TOxmlBV7aQQMvqGm76JT5+OtC2P9M25c/7+OngW1rudvW1a7nnVApLNegIPhBmT5o9askoDzoXXGX2dsaKCH/O+AhLLEwpEUPk7nDmhVH0KhFjYDs4+ur8Isbwiek88+X9mReHmSlGStU8FkGaIglNPZApEHMbmVNgDLHAZwfPv/3hHUpZvvXhzNb1lFQ4LhNvDgvnkNhuHaZIn9XoKiWqDbmoOoZUWJYF7z0pyYCKVortpudrX/2QP/jjvyaiUMaRkhf43HWExeNjqmxlgaTRSgJzFq7Kw2nirz5/zftPdmw7J+TEqpqkSzUhoawkHjmwM7OPvHp7IpXMZifBVqnLfcoFaUkFcSea5sjpuDDNkWUJLEvEz1H6qYgpRQypqjll2WO9IxtdiU0ZPwdyKoRqRxlTrN7IUYigWmafrdM8utnw7PGGYbA0G8ibR3socDqc2W6RqQQl7OiYEvWuQuWxoGQfCz9EXaDkJHP4U8jMIdSfM1hr5Yy0dpXrVEpVIiM4Z9HKoFTzWs50nWXRiuV05u2rL/Eh1AmRihw2OEmp2uqUtl9LdrOSoNwKqFRf/7p3rqva9cuXYbuy/mA9x1slfHmKuo9b5XsR/VjPEXX5vV864MbltBIu/LyQZg9eht5NuRB8Wn91JVGU6jmrVBPzYzbiNNNbRYwdoyno5PFh4f448eU58BAVQVv0MDC4+t51PXKuDzCloDLxdGdZTMfboPnsnDAHz0NUDNWr9hwjDz7zEGFBhDR6K9WhNaZS6uWSaVQNfOJ6kY1GFbEPm6eJ8/nE4qW6NVem3RKvf6okqbewFCERnRbP3XFiP1g6O2A6LcSTKEo7RitSEllKo+TVpFhYQmD2niVl0Tup7N1cighcLDMlKbIvpJDlEQspVNhOaYztMLZDdwOqE83VNhuLUqi+w+13KGdR00Q+HtE14OqUEPUBqQhTzsRp4rzIiFSM4k3ad4bN0LEdLNtBgzZ0na19G+mhFFXEnLtqmKq2vrmqIuuVSzXYxpiq6lcQ6b8CQt6CpkJmam+oQcjtc6NFMYoK2UtFFPHzLFXu4kkxY7omP1r7/CUjCHRNBJqQ8ZrilvbC391UVwl2fRvv7tSf8/FOT/fqx8v1J0rx7hxgpXYUseLLMZGXRD4nOEdKyOgifdrBaca+E5UxqyElzouQk+YkqEv0hZf3Z2yJhGViOziRUqz2aDlmDmfPJ69nXp08qR56Tf0NVSBXLWqlV99hFHU0y5JRzAl+eBdYvv+WL+9m3tuP7PqOAjwsAVThhg6lHdp2wnhGWMAxTaAUoWTmeakz8K0vW/ja1z5g8pk3dyeBMrPAqdZ0xJBEprUSQFV9XSgjnIVcUFrUy+6XxN988Zb39+9jlAiMNllDiZqixma1qslc5s3DxOE88/jJDcN2rMeTkP3KHCnnxOHuzPHgmRex7VxCIDfCpdEydmSkgvMhSbAwlfSkpF1CFHN75To2+0cUY3DbLcvsid7z6vMvOd/dc54Xab9FEZYZugeePdnw4fM9z5/tgILRhnHoOR9ncpLzTDsLSuRsVy3wIihUa7EJXqrX4icnOd8OJ88SItaKKEfTxS+5VAEUXXMvea6uc8Ilqb1U4QdFOXdDYLp/w+FwwJqbOraXrxDEmsiVJLK59TrqFjSrpKmgAOoCQuW6ha6D7/qfBik31I9LXG7g1vUW5JIQv7tzqc/1bsi6/viFAVfPB2JMzKeF89lDLHQFTKngpLko4uSU0SXiq52TL5mAIinJxH3ty6EUd5Ol1xKAl2XmeJx4ecoEu2G4Ndit0MyvZbbW0ZFVMu7qcHWGNPa8CoZ479lNhaH2G0Iu+KKYcRTbY10vfRVrJWNrCU0lSbVg23WWpCEn6fnN88S8LKScpAKuGVyDh6+TAoHY2p0Vyv3DtPD563usLjgDettjkOxOF8lmrVYUU8XiyeRY8FGuaSwiMK6ViHpPUyDFuoGXRFgyJCWEIedEdq+3uGGg2+zotjv63Z5usxFZy2olVgrovqd/dIMZOpZpZj4eJfNXEmDD4gneC2lsnnm4uye8fMX05g2nu3uWeaGzsA0RHy0pdxjTsxlq5Wk1MlqfUVqUjBqc3bx6VmUnVdnLOZOSSFD6qnIUorDjja6vW9Veu6rrQVvpx19Z1qki/fKmVCa9+JnlfGaezoTgsb30e3PJJGKd+U3v7KHr/kyD41d4qUGMV7DVu7hyex7FT2/Ry8792a+r9b/Xf23dzuRcCD4SJk88TqTTTDxHTMoi/t8bxt6w23RsdltcdYGK+YwJYiSe67X3qfD6YWGZFzad9PtE7s8wx8JhTry9P7GUjFGaZKy0D7Ss4VQFcdY+pNZAruNqUhUZY0hK83JKnF96PrlP3PaW7eiwVnE7itBFoormhyg8hxwpWmbMwyJWkgUFOROWSGc1Q9fxJ3/xA07nRQ7hknHOkaJUlSmnq+RGgkCseui6SccqRUiJ73/2ml//ylPGrbrYRVYmNlmCcDEGoxLTEnn7MImm8+0OY5AedxJC2Tydub87cToshAhLENtOYx3DOGCNptcOm4u4qFmD6hXdRtNvNvT7PcPtntvnzzl+8ZJwmHnvu9/kyTe/xcPLL5gejgQU482GH//bv+KLH/+Eu9dv8Q5evHzD6eHM3XHmxfEtLx48v17g4+d7gXudox86ESephiNRJ4wVY4hW8JSSUUqEdVYXjVpYShuqcHeciTkLESpl2detHVNXrLUG5xxKFcZ+YAmFQuuzl8oRyaRQmA733L/+kpvdiLJSYCQuo3Iy90+VwpTI12boi66mHqq2Xdr+pVbAV1v1p7epbN3LV9/ZsWsx3SpZ3iVWXUXkekz83I9fGHCdPxKXiD9OnA4eXTTYjk4LLKArNLP26nKpUn0V5lKKpBSlVlRzhLs5MmUxJy85EXximuFMj9s/5masso6t4mh9tXooaSVVagu4qjbjtdZ4C2+jYirQ1YysKAPaopzD9gNdP9B3ncghVtH11eqSCsNU9jAUkQQ8T8zTmcV7me2rkLPAFvlCL/+pm1cqpFqA0xJ4cX/GaNEpLrmw6xSdLphSEMkeub3Sn9CV6VuqSlURkD0l5lAoqQg0NXn8kiApnOvZ34zstnvcONKNA/1uJwIC+xv621u67Q7TV/OGam1ouo7+do8Zevy8sBxPxBhqpSezlnHxwk4/nqDrWUJmmkULGi/QXi6KVNp8rVpVvIwVREIXoM7tXdK/FlYuPRmZha4z01E0rBcvoyEgkqNidC7XSRmZQTa6MZNtJWbIptCNpl/nRZP3LPPEfD7jl5l+s8F1ZrXVKyXKQZvSpb+8PpAe3Gra8FO7Sl0C4jsZ7nVmTHnn934qcebyDNffbVVjQweUTJL6mXA84o9H0nmh6niilcNpkU7cjI7tZsBYQ1g0s48o7yssmNDa0nUOpxVRSRuGEOV+ElmSqCehdZ2draIp2VSiTZb5aJCgK4w7gfary08zPXBa9pbSWsa6EvglsymGsdNMIaHOM646bBnXoaxDW4fPIukYFo+62iPKWL73t5/x+RevmBdf5+r1agUYkmgGXKo0AE2Ifr2vpvIBlnlhiZEffPnA+7/yBL0qWl3OCF15DCHBeQ6gNTf7HdYajsdZSH1Rs3i4v5//f5T9Z5dlWZIdiO2j7n3KRURmZVZWVnW1YPc0RA+4hjNcnPlA/nN+4FokZrCIBUEADaBRqlOGcvHEFUcZP5jZufd5RlSjX5Wnh7s/ccU5JrZt24ZhSkjEiryAweHmgL7fYHOzxy/+9JfYb7eIzyNuvvgcN7/4gmdOx8SjBT+7x+HLr7DbH/Dh3/0nxOcRv/rf/gX84YDbL3+G7//2P8Mf9ti/uoUZM8ww4m5/i/6rz9H/l7+Df/Mex+MFFGdYazCMXJcOIcBmYNN36DcW/XbLspCibLf0q1KDSkn61mG53JUL26a5EJ4uY5uPa7BkwEZLPk1joIp2QI9xGho8S1XbywxKzZiHAQ/v3uPVl7/A3WEraCErba1LT6kwxC8i2YwCkpDiSBBB2Yv8WUymbQ4YuCb7aapkfrpPsbomkvzDyHMI2qvbRq784zPcbb4gx4R6HjA8TTDGA5sNKATAW17YQi4iWFS2qKiZhSOKtahSU+v6Hv2mg9sEjlggG5U6eFOw6ys2i3iLLPAls21pv8CInNG4JhjND+47dLIpGMZwsJ7hkq7rsN1ssel6bEKHTqTCuCYA6GC8lrWnjHm84Hy5YBgGpJSkHioKLSRyjbW8wBEJMHzhrezWlAnPY4IxAyqAMUZ8fb/DZ/sgo+FYG9oWhpiszkOVoCHmjEuMGKMwl1PFMCaMscAah91mh9eHWxy+/hqff/Uldrc3CNsdut0e3f6AsNvD73Zw2y1LWTomttRSmVCz3bI6jOvgjQdiREkJlCKcyTDkYasFQsEubHHbbVC2e/jbhLkPCJ6w6Vm+cxOAnargeKdqZyBTsV7WmvQtZKkldK4VyLliTgVTKpgTT2labxYljijBzckQdd/uvVGgCLYSqmEh9pIy0jRjHCZM44TtITEMZoz0+tmWhVeqXAyS91ISFtsdbneyLeI1bfNBN+2LwEKXyMso+sV2RnsD+fvSYbCYgJIzpssF8+mIdLkgjyNqnLmu5QwMQkNteunfRCR4wxrkRhjM1ljsNh0OG4/gBF2pOmAc6Aqh7yvmssG2cJtaEmGYWsvCbiWupwIQeJ/vJTtahwBCZ7RswhOpnLPog8UmOGx7D+N6FHSgIlNsqoUpgNOxegJXb7oej49nVGJi4e/+/g0u0wQXmMXMIwBrU1XTPk4iZiyrlgA7GA48a81ioIHf/vCAf/HLe2x6VlQjJ72oFa01RYVvdtsO212HnAvePpzx7mnGOFScLhlPU8SYMw59wFef3eNP/+xX+PpPfomQDV5//XN89c//KTAlzB+O+MX//D8i3N2ipBnTw3tMxxN2X32Bzd0r5ir81a8wvXnEdh/ggkG422P4+gvY4NFve9z96gv8vP4VbLfBTBXfffcGr44XfL3f49Xe47PXW7y+72Eoou8t8liw7QN2ux7BB8ylNLU8Y9DGkOqitZb3bRFnWwthihnHMePxeOH77tku17q8jzUGwUtvsdR4nffIJctzuM1sniI6z9rVx+MZH969xa8LJxowaES8ktlIq6NswYHYECv2vxruwLCiwcxnoTQq7sFfC2GYFUylNdjmjpSfs96qpC4X7V3F9X4aT8Yfcbh9HjHGiHI5YXgaQMaD9gl104NCgO07Hr/n1NQwYaItdDFgXddht9thu9uy0xWyT60V3VWys1wURnk1i9C/mfZvjXCVoAQIfEBLm4KVJnIn46i8d9j0PbZdz+o5jmHlKpFJJYbFDBEr4KSEYRhxOp0xjZP0MLrmRJdJNx+fCgFAGtM5kJgS4eEcMeeClAu2weN226PzcmbE7TjCCZP+PQOXmDxxmSKezxFjLJgij6FLBdhtNtjc9uju73H39S/w+Z/9GrtX9/CbLXy/het62NDBSragzedUmfENgGHsQqiVHTwRi4fUSKBMQCqwqcKnik0BboyH63sc7m9QaYMQDLrewZkKZxLr4XYqDUmoZaHUk2I3CtNKBN0WL0HYyTyrdEqJtYBLZVhLnZsF3w/LZKngWHXJCTFE7xMMQ00QqLqmgiTzhWdhLHebwj2C4lSrZQeyEKnE0RMBVqBJqw7W6OGvMty1s8UqUb12pvqnn2zo1XNfOmd1vjFlnE4XnJ4vGI8XlGnkvnhrQZWdCgkEaokZxzVH1JIbUQUAgnfYbDscDj36TkgqVXvLBTYUxEHlOFPWkWy1HaoVeJ3lYPnILQi999j2HYI1cDXD1QJnCJ1laT7vLIIP6Dcs3+e7rkGEaktInOM0jhyE1oT5MnD5OBMSRc7Au4BpnFnnWFi3tVY4D4TgkBIfbykyG9t6VOK6rLEWoe9BRHj/dMSbp4jPfrEBbJapOqbJoXrLxCA4hymx0EvMBc9Dxg/vL3h8SniOGeeaYa3BPvT4s1/9KX7+5Rf46le/wvSb77ALG7z6xS9gSkL+7B6Hz+7ggwVtt9j0P8f50GN3x78DGZiffYYuOARnmGgZLDa7HYAMZytsAPrbPZ7fH3F+POKzPuDLr7/AwRbc3PW4ueuR0ggTNjyIZprR9wG73VZImtI+JxB6g0zNYpUrxNnKUIwpFTyeZlzmmWdp9z1y5kDMCcLH14sDVS88izRHDp6sZdRJ5n53gVs0p8uA54cHPD894+Z2zwggkWSohJq1LEXM7LZCtHNGxoquRSzYVpCQRQ2n37JXF9IUwHbaiGqVMZqpatYqLpXo2o6p3Vr52eW3P318egB9nlHmGdP5gvPzCZkMUpyQd1tgu0MgoPhOjNNCnNJh4iCOdkLosN1usdtx247z9oXxRYPrqJ26pPly5o31dWWRViaK1EjUhv04664drrPoPE/X8Z5HYVlaXmtECMEIlJ1LxjTNmKYZMeUFryeWQMuFBwIszNZVdrI+NrGSBIZAzlPG85BwiRWp8kJw1rWRcCB2Fs57wAGZHIYMmEuSHJ4DDW/5+vShx2azQb/bIux3nMlutrBdL5OPCJRZShOFhFhOqDL8nSo3oQOGhyvEBMQIO0eYyFkuzTPqMAKXEd0csaUC1znsQg+4Dr7jmqE1FYYSvKvoA+tn1MoELh6aQJJxLPd1cbjibInHGc65MDkrSh1byBxMHEMTJvGeFbaCoCkcTCxZDcRw8BzejJwS0jwLcWoWrd3KsDOtoeDlmMzy408xYPPiu/7YzknXzSpTpfU6AXRAe1s/y8T5jz6MYacxjTPG4wXT8YQ8T0AtsN6jFobgecQjD83IRMyIFVZ7pQqdONV3PTa7HXa7njM5IbOoUEkuVRzuooF9Xb8S2UO9DMQxf/AW2z6gDxIIEcFLlhuchXcqLuPQCb/Cew8YJhRW4haZmrk331qHPnjUmaVdDXg9VUPou4A0M2Eo+I6FPkoGUNF1LG9onRPmvgC81rBkIIBu02O/32O4DIgx4XfvnvFXX+3RO8eiIuBMhm0aa473mx7H8yy/t9hsNthuM57OZ/hK8IklNcN2i1//zf+AL7/+Aq+/+hku1sHvtggesMHDhz2CKQjWwjhOZGrN6IJIoxbmjtTthklNmVucpvOI4eE9+t7g6dsfcXn3DFwiwtMzfu4K/N6i3++wv92g1glh0wGWUOeJBUQ2AaHvUOIMkt70knK7/3KR2C5Xrq1n9sk8OS1WfDhekEsBjBXd9tzQIGsNgnM8TEV01UuumHOUkiTLrhqIUlZlEZ2SE84PH/DuzQ/44qsv0XmLVDJnjzLZCKjwzrRjhIzuY3YyM6OV/MX3hz0iz7U3bWM3bJKNUlvX7Hokd12hVZpeWZGrBYx0D6i6FDV5zY89Pt0WNM0yR/SCy/MRU6mYpwvKtIe/q9gYj+h7OMNswizaqqpQZQQ+6LoOfb/BdrPDdsMKK6a1E60yAW0ZaZDbcsBrJZAmVKAXSax0VUFxiVa1T9bJ3E/neJCCs45ZhvoGpfCXKCOp486FyTpzTE2/mLNVhhlT5lp1VdKLhdRhabmLchbL1CHe7KmwnNxcgQrpHyWgFq6BWMvkLW89jAfmarEdMnaJEDrWIs4zH8dmt8XtboNtF2CJkOeENEwosUDrKTBWBL8di0hpfTZncOcB19l06D1yhs8FJiVQjKBpRBoG1MsAP17QU4IPTB4xnYXfePjeMcOaMiwyDGWW/qwsL5jzKhizDCk22TlZyHwruTY0yQDtcVaGMkEHZhhxtk5as4LngQvWmkVQn5ZmdZ3eUUtGyjzlKE0j8jyjlsxBgRWm7coZNj5T838vPK3RDX+9ubQ2taAzuljbE5bPkL8RXvz5ZYBszNWnp5hwfD7h/PyM6XLmcXKSzebouR9bhGLGKcKgCglP9mgpsM7xOM3g0PUdwmbDpDSGA0S8hGcd+6qzmnV2sZw6aRuEgGsSoDjHKEdwq0zW8d7rvIO3fN7eOXjPrTRqqIxxsJ7bdiwZVGdQnUUcBtzc3TYFOAsRK7EOMWbEFOF8h1mYzCQqaN4xSSp4j2maeH0AKIn3r/cWh5sDsCJc/fh8xnHK+PnOcTBhACLV6rUIocdmYwAbMM4zXK346tUNXt3c4eevJjw+jngYJnx3PgHbgNP4hC/sPbpdgP0fvuZztBneOmYkIzMZ1ToYw+hAkQlfcZwRhwnjaQJKRE0Z03HAm//4O+D5hMNNh60h4DygzBO2nlv2ws0NwjbA2cTZpnXcV03srLrOMZdGWv9q5gAFgg5YzzA8ipE2PemxNgZzyjiNGe+ejsilwoi0JPNc2Nk6CY6dWzTNYSpiSQAIzgGlGMBamWZW4cGQcI4RD29/xBwjnN/BWst7mM0HKiwycctjU+uTvcd1VoKxHOUWsQfsr5v3QK1LgscoVqO7Nl/0cp9ePcRX8T6oAOwq0MZHH590uA+nEU/HCZfziHEYMKaMHB1cKdi6gJ3bIPgeRj4kJ46eU0ooqNyzJj1ZXdcxWannIehWbgQnlbWdUDM+L7JZNUDL8GDNREiwfWGWkhA6nJVZocxk1jF3Fla+mwarohRQKZwF1syZnzjTlDWAkAMiauQdZS4qVZ1v1Drd0QK8TFwSh1uqQYVFKkDMlRvSnYMnINUi14EdtPceNjjcVIO7KQGwvEDJgmKFqQah67Hb9tjDIMwJ5jxwT7QP0igPwFqQ84BbCUHUCls5XFWHayrBiMM1MQNxRp0m1HFEHQfUeQTSBGcybDCwvYfrPdwmwPeOWd9FpDBzldpYbf19RUbxWWCV4craJZlYIv2F45xxmWYMMUn9Vp0tR8ZW7nHwoU10qqJ8Q6vWBhYSX+qSRpjK02XArHONFen4SRlD7uaLY13+IMfefk8vvi0B2FVtlj7y73/gsUTd/JLT8YS3P7zD8emINI4wJYn4CBOOSuJsfo4z8y2IMEwRU0yIkaFlb3ueUe2MwPNOWmZkX8qxNUET3Wtav5LrpoxQNVbOKlHGofOOB9x3QdpphGEO7j8P3gsECC5rgAUTSiXR3DawLqDECL89YLvXzyEY5QVUnnlsDPfrTjHJYAJg23cAEULXSX9rbrXcUpnRvdttkaaZRfvBhM2n84AfnyK+ut2DcRPTWM0qfuF9h8OBg/mYIrpgQWTx+e0eb78/43ic8bP9HhfKePN3v8feAvvtDnAV7z884ma/g3cdyLAiXJpmFDhMc8Gbb79DHM58Ly8Thu8/IB8nhFpBieeJ33UWYb+VCVwRNieQKTDbAH/Yodt6gIQcBgtQZcEaSzCBbSOPPJWgs6o6GAceVRa2EVvNqAN3nxynjDdPFxwvY1M/5XYsrtnCaE2XbVkpRaYCcTKz23QyBYr36zxN8LsNnPNcrosJl6cHXC4X5gB1AWWW0pCxwr9h5jGXMYwI8agD5l1jLNr5GKw0lSWJsuJLdA+b1dbULav51lK3XVob9QsSpEqB+SdBuD4+6XC/e3fG6TzjeB4RpTWEssVkHaZ+wiUMcD5wr5VjEYt5jqygIqQMY9HmjLrg2qxcvSAcKS9MMt6/V7G+/N+0ui9Z0WFtZBw2AKp6ZK2T9h6OrrXpuhkOvUCCaVOpLDMpzjbnxMSQlDkrUwgUogOsDpf45jdWajtc04yikYXgrBNyGVjk3XnkQphTQanS4gTDE32k37UQwVuLPnQ4kMVndxWbLqAW8OYpbAAtArwL2BRCfxoR7DP8LsJ3XLNlCUsP6wsrxnDkAkAcGGQCiBFYXmQeyzghjwPScEGaRi4nlBkFGeQqbOCGfL/hLxecRHkVVLkdSnuNswQuRdajqnnBmBYASOyDlCumWDBMCechYpwzMpEMCFA1MEYrvLAfreFsqFZRN1L8VmpNKrdZwWsozjPG0wnT5YKas7S0KJQq5CndRFg0ns2S6qI5UrnZhCV6bjD08haChGCRyMTyupdOd+2PXz70bw8fHvDwwxuMpzMoRRhhmQKAdR26PmGaI/opwliGUi/DjGmOmKcJlQq8KULQM6IsJpOrpOXN0MLUtDAwZJrDlSWEhk+Qa87XWSOZK8Ovmz7AtzZCWsg50p5WVEzBAFQJqepAlIzeO5SSUCuw3W7wh7/7DWxwqMSTZIgIKbLsnyGDOSUwKaai7wO++upzvH33xGskJegsXbYLYD10mY5jjcWcZ4aancXv3j7jn/7qFjc9BxdZdI4LkcD1FSF0MDsLFx2KWm5y8D+/wcY63HoPC4t92cC9GfF2+k+Y5jO2Nwf852//n7CmQ9jucP/Vz4DqMT4NoJLhPGHTW5hcYC4Jr5OFF8sfvUfue1AwSGlGmkfUPCLVCLMJ6PZbuGAAJPAkF65TE7E6GUphVSoQ4jghjhFJ1Lmafr44rFpkAo6hVu6ZUsHzOeLt8wW5VjjnOfskQhcCBybSC++955q5JkhSbuDhFuzwamXmMwGS7TLbfnx6wOnxAV988TmvKWsaiYqRmAUhg9gFJpraBZkyLNqhdWcIxKxIGYiVtZrvMWi8IIWS18GuJoT6b36ebVDYPxQ/f9Lh/vj+wq0fIwtZ1JSRi0H0EfM0Y+onBN+BKrVocZonzHFGcQZeySbOwHimhxurzch8Ck2fUtJ5ze7WkHLLGTUKVqO3yv2NnLQxpkFU3N7DGS07WHmfxbPz4AWpcVXRjs6itxtTYrZ1XYIBfXxMsm+BKiS7lXSMyV2LhCEfp0MqhDFmxMzFTmsBWyyLmRPXzQIRgjXY9AH3N1ts+4BaOQozZHgqU7ZAcvC1IAwDO/x5gun7hTDlOxY2d9qHpixqcSRWtF5LYZGLaUIZLkjjgDgOyHFGrhHVFJAjmGBgewfXB+5j9RJECfpQSQbLlyrkMoaS+a7adq+MUXQContNmFLFMGecxojTGDFFhpG0AV+NuXNMXmG1mdrE7zkD5t5fUvhTvYOce5ojxvMF03lAniNrAju+yaowhatEdMWqXlzpT9ZCG/W1Wi+6L5f67E/32seUqV6ur5ciGZfjGafHJ9ScYCoL0qjyjnUzpikgDDO6bYT1ASlljPPEPeXThAjVW0ZztnwZTOMKUNs4q2BDnSPRkg3oXiVq2YazCvvzmquFAx7Nbi0McmZ8UEkufI5W9mOB9RYxZ1BOCJuA89MRp/MJKfPYSWsMt8wJAWeek9RtgeAMfvHlZ7i7ucG3371F3295XRIh6PoxXtTruBqUSxbtXmaB//B8xtME3N0HhN6AZrCuPFjWseTMM6jDMsSEDHdHbAKwCQHHDyNorth6g0PK2B4nvAob9CkAYwHVEeZ5hP1wQrfdYxt6OG9gM1CHClgLF3pUDxQTWN8AFWkcUC4RpUTM84BKGXbToTts4BwBlMFko8KZl6msja2lByiprMCaCmsrsvJwam19rRDUoEB06StwngueLhkPxwuMdc0+BpnSBRCCCHlwPF1lCAyTWhVipkIy63xhlVNgfoIxwHi+4PjhHWr+c16TjTjNzHIyC2m2rVFCC8Q4OJclbLlUARKejqxhkv3V9qZ4U/U16rg1zaUGWUsiKE7fYEkgzJrg/eLxSYf7+DwjxYR5Zup/E7nPPP82xoR5nsVIsbrLGGdMcQR1Hl1Z2mUUcIUeoPy2MZLl5+Wsl8MloBlyhW7X5lOvFdcnl1mgjaW6vpCkLQyQrIohpiJ1PBLnO8eEOHP7g7YWwC5tKEQkr6k//YylMLAIOqz+DMNR2pQqLlPGlCpH+ALnWclyc+Yo3wceX7XdcKuNSsWBwHNNI1AtweQCSiPypQAxgLoe1PXw3YZnCovDJQh8IhmM9sHVWlFzQpEhEilOKDmhVpbahKswDmwMOhbW8NL6Yw2AWhgpkGEXLbNsJBtcOXmNBvmS8bi3mJh9fR5nnIYZ5zFhzgWEhVzjrbT/uK5BVZVIjrU0g8nxHjsgI4ae1ZE4G56GEePlgnkckWPklhJrG78AWDZiu4tm+S57G1dQtPrU6+islT9a6WHBoeQpLx3uIit35WxXWyPGiHEc2vQdKrUFkToJxwWPsOlBkCH0KWKOTBirxsEkanKRhZgQlSsxa7clD7pvsRqatDoPMO9ADVWDjAWBALiVxFhqEL8Rg+yt5awQkoGUImpkLDrhNDB3DrWy4pvre6RpBIiFNaYURaaxNIELC8J22+HrX3yB3/3uBxjjUPJKejAEGMMzrmFYIKfUIuxq7tfsNhucpojf/HDE13evm1Y3jyllDgdg4IPjdjrrUGqBkVZDYwxubx22fcB0TMBc4S3PczUlo4wTus0Gftsj9Bv0hx1cvwWcg/Ee1bBGeMlcm045s5zneeCvcUSKjDjZrUd/2KLrLbyvi4PV7KxW7u+VsgNEJrLkjEoFBZX752ttATMrSlWALAoBSXpuYyE8DQVvns+YUoZ1HrnUdr+5fsvBGgtTCDM8s+6zloM0orXShsd8jyKsZplhPc+4HM8Y58wIigpXSK3V6V5pKxSylipDzrq3pLRmrBGfLyWCNjVIszZZiy2rXbI60v2nTyNNW6RdzGo6YQBDK+HH68cnHe75FEX4oHI2JcdFleez5sQOl0TpI5XEGzpPsKZjB9YMyuo4sTC9VglBOxM1YpATvNK4bVZgnUJoG4hkPitCBQBxsNQUpaguhqnmcs3kTFmCjIg5yuzZqt5NN6sXRyLqNatAYQkl0I5Xhft5MbNjrZUwxoLzlDDEjFgI26AKWpIZZia8eJ8QOmb7dYEZf5VIgldCMRWZKgh8HhQjavYocUL1PaibULwHvOdarkRhtMrwq8wcLTmhSqZYKHNG5w1MEJRC+q9t5+E6FmY3kmFSyfI+nGXwkAnpg5QFoIIpLaCsAIGZjzFXjFGE4C8zTmPEKPVbY704W8lMPI/gAxRKLig1S1Sr044ASN1qcfR8/rUUEb+4cC/rNCJselbCsdLkv3osbhdiTOW3Cgmvn9mc7nLe635yS9rpff3Kj+svf9zZshOriPMEECE4g5qJ7wOJUhsMlxKCZyY2gJQTUknIOcLYwAGvXC8u76zaJNoYNH4w/4Sa012FJWKWFtTKGdbYNnI/AEIu0u4mr8m5SsbCOmStF9twySE4IbFZFmmJ4wTfb3B6PuH4+MwQPRkhcvE9RuXWPusMPv/ZPXa7Hd4/njlAFjvQCRNaMznjLArV1sKSU8Rm2+PmsMc4Rfz2xwf8T3/+Gve9gfUOjqrwPaqgYxlZ4GmG3QltRkhn4QOzseORuwRIBG+pElICqq0oNiNPFc4wp8MG2aswqPOMdJmYwDklTJcB0zhjzhFh32F7t0G3ceg6grNlyexonc7wdCdo26RwOEoRVC8mtoGSpVs5nwoea5cKMOeKORec54L3zxFvni4stZqFrctmAK6zcD4AKM3Zq7M14OzVed/6t1XfOQu5klv+DGoqrHR4esI4zTjs7wCQ7HMuXamQiU4UUq4OmcbSgQZQACf76lO0xejlWtbAsspP9crtmuaa1MkvW5sa6Qo/2d3L44+ylKlWpolXJVCwo6tyEeM8MxRLhFwzYkmIJcGLmpKexDp7VZq2Mbq11+4Wy6ZfZYrLODN5nrYlGFYessa2iTvOyDB3vQj6MnG27avILE9tA4o6d3bAPE2Y58gi/kAjf4UQZLNmRKN4Pa2Mxcfp4FQJVW6hgUEuwBArjiOPtbrEgm0w8MaKzCD3IM45w8h8z42zCF4EAeTylMzzaU2u0ncpPXI5wZYZOU9IidWX4Pi1mroQ9Howe7fK9WCDXUEWLKDgnXwZWM8OmIXMFVph40M5oZYk2rPL5lUWt8ZK1i61PKqim5wrZoGSny8zns4TzlNCzHzVnDWLRrJT0t2CNBQhfOjGR6u3akBkmhOCvCbFiGkYcDmdMJ4v2B4O6C1P11lg6NWiA66c7U9d5vL0qwCTFjemNVBZjvqWyzr5hNN98fbtuRr4gSzXIYkz+WQTbJxgRgvrDWrJgOPWilx1qDcTZNokJ7CRYShRPYYeg3hZ0nj3GrFgzVzTPl8RJpXtC45rrpzhcglDhXO8JXhjhPDG9d1aFfogjfIBAB8+POLdj28xxQTjA4/qFMep05+sHO8XP/sZnk4js5VRwWqvvBZizNx3L3B2oco2xAA2OOy3GyldWPz49IxvP4y4/cUWxvD0I7K8dnNlG8gCQAJvVmIoto39JISNRXAd0ikiTRFEDpQAzDNo8BLonWF84GREyJJUZYbzPGOKBWNMPERh22H32R7bfUDfe1hL0iYFJj5aknvG36so4vENVJurgwNkzwuxsE3GEpg0Z0KMPEhmThXPQ8aPTwMuc+RgBwWwDs5o8M33ywpZoRQO3L13KJknj1ljhCNTEAIjA6VUqPYy7+uCzgDnpw9I44iu+xwlRQ6YYQDjuBQnDheCqjCDmRXvVCt/iVV1f7GzprLyPbSIVzQka4mel5GEq81L4q3bc+V5Wp/+2OPTwwtSlCguN6hheWNuH0kEJMupeakVqWZkVFgptC91TDT4UE/iOrO9PheFOXl9MJSrKavW41jjU2FGu2I/LuSoqw9aQcpUxEGVIvS7ijRNGE9nDOczpnFESpEXgWGt0V40mK21jZF5ld+Y5Vj1HPR4ddQcSRScc8VYCUdb8TTMeB4Stt7i0LPDNVVn2ma+ZpaPgXWSeQqTlWC1OgPjwdEzEchUhsoqwWTAZg0GrHq8ZVOp4pAaekFtDFtNGG8X9SVnRYxeVZwgmS3JtUwiqsAQL0ewfGGYPIZVDZ/vB7cMscjFNBecx4Sny4ynCw+V17FczkhLl84vFpSjKjrRnMhy46vci4aKrDJ7VELNCWmacDkeMRxPuH19D4NbUZBiLeDVcr3yu1f//tjD/NTZ6mJ86VM1Yl7W9vJvJfxp0Hh9CAStthLxtXASjKnByilhniY2JNZxPZ0Ky+1Zp0gb6yYbruxVBj0lQxACiQYuCrFrJE8cOCtPQh2usWwMO8lSrTHwPkigyA6WM0Jq7WioSp+sXEOtwiAVZzGOE96/eYenx2ce9OEtr3HURsoJks13HSuOffvjhyWAJCOtJfx8rTUrycuIXfPWIc0J3jj0XY/T8Yx/+9sf8Kef/zm2YoOstciGp3HlnBFKgrN9uz4lF2Z+O9GCJoILHv1dj7LJKHNBHjPSLAzhAhjneaU4Cxsc0Dl0WxbiqA6gGwdPAX0XsNkGFprxVpwtNeUv743MD7YwQgalkpnQqIhFFSUpYaJraQCQjNMx23rOFTkT6+HnivOU8XjK+HAeZZAIr+5NCO3a1lJY4MJapMg97k5G9KWceY1KdltqhasMAa+RTLUbVIHz8wkf3r7Bn/zpL2VjSeAn71MMaxJYrPlAuuV57S47x7Q9R23jGQlG5MK0eGP9Ggmv/2hAbFtN+SNPa49PD6CPc3NQqEUG60IMJY/yKoqBw7TBwOvhAqYZR3mQOgW982jhR8PDQVeGB2Y5qYVWBWGqOiZACISsPYEvjROfR0HNy9DuWrK0A2XW170MuBxPGM4X7uPLLA4A6en13rPmahUHIzdsfawfzW5paU0BIBkJF/0vM/B8ifhwnLB1Bt4EbMNy9LVWxJpgrUEXOjifecam8RLRgclojjPP1qRtGC7mjJfkWnPEyeUBJ7CI0ZSgkaeMky9v2xxMY5X8ZcTpKmrAxowZ3gWkUn96nxWOMep0zcpZsxBGzBXjXHAaE56HmeHkKSFmfrWV669TgayVdp+aF2i/8nAEkOzH9foSh9/gfQm+ai6IEmRdTkfM48TzRa3S35f1J+/yU6dLa+DKtN8tkTGuNmmLkl+s71UgjdWBr67hyyxXITF1FljGl1krpQPIvVH0g0kxmSpQgeA3MLYDWcdOVshSFRyDWtlL1A5yHfOv95hprRZ6MhZA530Tw1B0wXkHD4BKRnCc5RRJq63lQe/cTs8BFgDEcUQpBc9Pz3jz/Rs8PR0RU0K3CSLcohBthQ9cyur7HsM443w8w1grDtBeZ8KGiV1BRHByZSfME3ASUszYbJns9/s3D/jD+y/wl19smiG3zjZC4DRylhv8RtpYqwg68HUqOaFSRAg9wt7BbyzszsLVABczYsxIlZq0LQUmmZaOOwuc1Rnelp2sszCOUCkDWW65J3hjWRBHGLxkwJC+SLlqPFpXlrQIuZWM4cEfzsjEpcoyjgTExKpSz3PB2/OIIeaWDW/6Hl3ouf1stc9zLlznNoTe8+B7rq06sM51YkGRLPVfxzYxzpFFf3Lm2cap4OnNG4zDjP0ucCtXE7gGjJEpX85xIGFE98CKLWiZ60LMNVqfhvq0l7uL7YTaKmbPX7neJW3WxHIVFrOf/7jX/aMOV5KgpT6xiuCSSbBUhezjYb1DsJzJ+K5rC6SNaqLFmb40IkTLZtb2H/0814gs1HosOTp16LxvDvcKgmoQHsnrirBmub+WJLut6myHAcPxiPPzM8bzBSmlq6zeSR9trRUxRsQUF/il3YCfOt6lhWIxwByx82umZPB0iXj7NKCzQOcIzngImgsiZitHkzCOEyRNRN+LAwT/ynkDbsrnjBdVGtSZCcPHp2xpCYh0oLzqU8O8yGat5SxXZkzqmTZZQ2WVK3Lw8ktXsTppLI4XxIToLC1AnNlOeDrPOI4ZQ6rIkpFw3dYu2SpW6lXSB83wuCIf6wBP94CFTqkwkGCEKkqMmM5nXI4njJcL4szw/drZtnWq/nbld9fwclsHbSG//OXywxqeMvTCR6+eXwnMZrLUnJoegwZxVjY7EWct5PS6W8A4yYNJAhEpIxCXChB4MIBxfI91CJpeOLUjJMpX10GsBMBkUIhhY+MMakrcslErMhE6z8puKuYCqqjeAblwvY0q+i4wlFwKUGXKkLGillVxOV/wh9/+AW++/xHDZYRxVrgknBk5ANUY5JiQidfOaUiA9QhOh8+TZNtoBC2SWc21EkjE+A2AVMQlOdZPH3PG/+/37/D1q6+xMQAUjrYqTci8h8CRA0oqgOP+ds7CuYwx18womXNwgVGrsPXwySJVAoyF9Z4DXQJgmEi57Buez2tqEd4KxBYKkgBpm3PqcD3IEJAFZVOn0EojzBhmdalVpme5Nh4LIRZgyhXnqeDDKeJ5ziLtyVlmFzp2noAIHQVY4xDLxLbacT01pSi8C8A4RluIACoV/bYDVeHQpASdxKa8mtPDWwynI/abz7k1SJwc8yJZPha1MDJl2RaaKi2HqwB/3dKmtl3r/5WulvY6Dha+wPVmJpjmX1jw4sUb/GMdbs1JogI1yEq7Jh52LBGwdR6uC3AhgCSy8F0HF8IK/lsdiJ78VbAvVYV6LVzAkoMkmRQ1UoQ2zAfn4I0VKGr1pSQreQ20VlmVJJRBOSPHiDzxuLbxcmlwclOQwhruU/WpKKoovMCrRDPqfBqzVbKDtTJWO3cw5JlKxWlKePc8onfAvjMIFtgEA0dLFplLwRS5n9JJK4xzTvcOy6YRGGp2BJAF1/UEmpd02FgnBCT9t3w3VtPlxekayWQVfhZoXNhnbQEvC3H595VEoqwbdbh6S1KuGCMztY9DxPN5xnGIuESe0wqzCJ0r61aDrir9wgSl96uO9urxAmxowhWKNxEhp4xpGDCezpiGETFGNnhX6x5tw+Jja/nF5/zkENYoCP30+VfB2trpmmXt0MvPBmRN82BuJ9efI3Nh1UtgBO9ALnDvqDheWIOw8fAb1tbmjECui+GAgKwRaHmRzdNghOTzFXi2kParxDDrhIqdsMmdZ3RI+QUGhtsmjIPzOjiAgZZaAWsDDBmUHDEOHPz+3X/6W3z/+29wPp4wxxndbgOquWmZWyk3XC4zkwhTQcwFn31+j2GaYZJtl9hYJ8xsGZJRaiP3cY84t90YGMQo2RYRfvvjA759/wp/+nkPU6uw3g2ocA24aFua9NsXAutHOwkyMvcA51RgqzpRRgL7XjJ/yQ4hLXaVCu9n2UPaAmMlsTHECAeX27hubsQRWAuQd6iGrU0FwdTCfT2EVtM1hqFvbTOsErilAqRKmBLhOBe8O814f5owpSzHB/gQWNEsZ4TgEERrQSFj1rF2mKeZ11HljooipbJSuIa76QJKzEiZEFNuto0A5JQwnM54enjE7d2d9A8b0T0WdEcUypwEQ0RGUC4pSwrqw+xjsRUrSJn3KonaHTVhHmUrN1vX3PDijhtNikiu/7XfePn4pMOVuJmzqBWc1IJ3Eavutju4wDNWefZvZWe7rrfpMTUoY7EqiqdzL5Y4RapNJYknxED8bm2iB51MhpFAjTcRqSHiSL3yymptPyDZULUiTTPiNCJNE6bhglFm3nL/bWmQAk8TYQhdladIslsnRJGK2oyqkrmWfuLF+S7WCi1DnFLG46Wic8Cu45GBr/Yeu7DAM6TOwbBIuDVA6DrWDjYW1kGIJ0t9GSDu0wWBYLndwHkYG+Ccl9GGrtXbjGGJOXXEi2PgDYuqTEcJgJi1xe8vKASTw2j5H63BSEBJNDExS/s4RjydZzxeuI59mbNkF6InbVW4RCJeqeu3Or5kuVXqP+aFc2ssW1rQCpLdQOANH6eZWyzm2AQ6DBkYx/VFuzJ0xqzPZgkWlx2z+tfKsbdnqcO/ip9X77V6boOoP1qmwKp0orAgJPPl86oyls6I0hoVNii5Vm6B6gO81Oh5bXAJgawDrGtZkJ6C7rN2YVe1qlyZAc1IkJH2ED5HY6y07ADeAkQFRlpriBjJ2nYBcZ5RUsJm0yPNM6ZxwNP79/juD7/Hh7cfcHx+Ri4RsDxWbjrNyCLR2AWPQrrfLLPW04TD6wOc7JVSooZ9LZuplREkvm7cjWFdgDUeJetQdCYyneaIf/PbN/j88DX2gVrNmde9QcoFPkYZvsD2LNcKG3gutcGyf4i4xxhWxssZzZidCKMYoJqGLqElLqaJxrBIiaIrHDA5UV0CFXFYJHuZ0SzQwiiGBEm5Vsmu2XGXQkjVYK6EMROexoR354gfjyPmorNrWenJWYuYOLvlDhHOtqOI0OiaLrXIOi0wZDEME3IuDRta9AAM5jmi7zeMqhAhpojhfMHbt+/x1a//VAYtAMYyz8U6FsQgTQolYCxg4tJSU+WbXmFlAFh9sTeN+BC1XvwHzjVaprg8r+1T+cFK4C/e8mWngz4+7XDV4bQ4Vj7SSLYUAvxmi263h/VePpwL8dYHWOuhhBWtJ1wZDKAZZa3FtQyXhPyzei5rtBJD2FhaDJp6CKlBZ+YuSAhBjYm7XACt380D97LFyP2Jc5qRWm16qQdzLTVKTy4v2tZPagBUuyheyQmSZIFXQREYil1+5iks51oRLhGbbpRN1MPuPTqeNciLthQgRszOrpRQupb9ccRMoCpOwpqV0XaAC6w85QKcDzA2iDH2EgGyQ4Y4YIlwgMrN81yfLRLQcL8t37/SnAKtM9AmOrE4k0oq28iZ/fNlwtNlxvMl4TInTLmiVKDJYYqzBWTQNOpi/LHcb70nSwM7X3+t665rnno7yGhrTcQ8Togz9537rkirgu5H3ch2eb0GgKtyAS8Fs9qULbJaFn3L9a/d7YsEd3HWa+euf9I1XIlresI+NmKMVVyFwOgF7z++GjzhB7Dg9QDR12aUw7XX2+ZseecrMlB1QxJbGM4u2AjrmgPY6Y8xCzGrtCDXO2COCTkmbELXgkllT+dacXr/AfF8xvD8iA/vf8Tj4wnfffM9YpwQOo++67h/tyz7rRTigMFamSBUkWLEbrvBzc0Ol7ePUJ+ibWK1EoohcPfbwnBuWZExsNYzMU/W7t/9+IRff36Df/b1HsGQzK7ma5NldKF1cs0ra7JXELrNBqH3yHFaAlaIGpNnO0kCJzPpxwGVoXpjLU/5EjRK16MlJYdSc75eSi+1sDiHKRnVsM0EEahkwBikwmStlApiyo2dXo1DpsoaAaniwznj/Tnhh8cLZpGJU8n+4D1PBiISmFxh4Ip5jtxyJSiNtcxQLlThqqp0FRHJYAKfDwG5JplGVWDBqlZlLpjnGefHB4bgnUNazcYlYrTQ0AIxOxlmUIj7uVvgKGmQhaibqXOltoHZ1hCJ5OPieFtGfOVs20sA0n2mf/l4J+4nHe56h6+Znlx/CMyY9QHGemiTNIGjMdVkbfNqm91ophKSfEFh8IVYww6GN776JVpBaBXFGGQIs3Ldo0u0EKIqO/8mtycEIu0fnsYJKUZ25Jbrs3NMPBlIsqWFaMMXv1BtPcQKUVpxatzfatpNal/qfNuDn681OSIgE+EcC96dOApnnmiPm41D77XewhFUSokJJs7AeMuMTyszYCXKY+PnoDVbWA8TAozrYLwoT7nQHKwxbnG4wogi6dUDZaBkELGwBffbyrVu5AXONHValJJJFllFFfPgmu1xzHgeIp4vEaeBe5HnTMh1tZA1oMHCXtT2kObgrxzewjFoMo1km6N5Gc0C4PYuQTjmYUASMQIXOrl/rL4EA1i7+qyVI+QNuARRrTVCPmdZ8erATPP6tN5kWCtJXWe1uo4A7Q9Ee74GrlbgSyO9yi74RQEMS5sID/cAB8ROZ0rL9rZCwtIsy0ACHv7ZWQBkecqWHkflfWFghKhXMafIBtkYGLDUooXBPCaUlGGMRSQRSSn1KgCucQblCeP5jHc/fsA3f/8DMip+/vXP8cP3PyJsNtDh8mo7iCBSshY1C9knJoS+w1dffYEf33xgUQ2rzHU+fq7fkZCLDE/kkbXmhKhXK6GkggrCWDP+1X/7AZ/d/Am+POjEMWojNaugadY5GQfHWR0BMF3PU4tqAaSaysckQY51gA9Sv2Xn6g0Hws53wqfwYthlH4giEyQoMpK6W8e2j4yBzYRiC89mKUxWypkDnFSyoB76VTHMFZe54MMl4t054c3zBamyZGOcdCa447bEygpSIXCZCgCSaBsQIH22DimXJVAkRhUaH4gyCgyItLzAZLpgHUCJFQzHEc/vfsT5+Rnu/g7WemRKsjmEaCdiLRClPuNWGb04VA7m2bYZWloWCbVlrVe8nNW+w9Vv9Z9mEREyfL4kyKVej5ePT4/nq8RpujysYWfqnIcPHS8CI8OGpe8SlskYpkGVphk3Tc/lGnEssEphDcRggOu2HD2QGP1VJkyExLtlgfogG0kyQSZGZa4zGV4gILDC0MTOdppnUCnogueRZpUQY2S9Vc2q1WASLS2Bq5uiBtVaMSkS/a9rbxoc6c1Snd5mLC1v2FiAh0vklgUqMFRRb3rc7gL6oM6EZdZijKxKJdG0NwHOMnGNWcxKjJIM1gWY0PGX72BdJxlOWByu9LUphshTkQqMOtyaQTZxkEMFyHxO2svXNJOlYT3L1B5uGeOm9jkpQSriaYg4jwlDLOxsgZXQwpJhgZj0Jpd7gY1pub6QQI3rkEIQqRU8LtBAm9yvnDJI9L9HTJcB4/mM+XLBZr8T/oFpKEU1rvWHfnQfyXr+mASjPpagdfWzboblSfqP9VsDxHIJKp3Ylt/q5RyE2ab404UgI9EkCyAsBsaYpS/bLcGaVSKdZRa7igmoW9fSkiXielmVNMt1jDaVinFg9SslutV5RkkVPsiM5LBBTgVUE6NUltteigWcJUzThKd3b/Dmmx9xOl7w6tU9Pvv5F+iDxe9++w26rsPl+SjsVgNYNmGlyAKynG1Oc8Tz0xH7ww28c9I6JvNxJftQwplmL0XkSBkOX2yAdRYlJhhn8MPTBf/qv73D//2vv8BdL/C0ELU0sfEEGM/O1pJHzgVd3yMEL0aEIV8lB9m2T1nwwrrQ0AljeP/ywrPNlhBVIOtEJANoPzyo9daamlGEA6J7MuWMOSbMMSPmKsNCeKjKmCpOQ8bjEPHukvD+MqMYg03fIWZp94OB8156mYUZ7RwIBO88z7pVFMTyNS1ZpzJ55JSYWhA6lBS5+8JqkM99uLlkdN6LHTAoOWMeRnz37Vvc3N1x66dh1rkVNStYaWMDmr2osnc4GNEukRdblzTDvd7HjcksSnb8ZEEopWGNJNBUO6VtbSDTcoOXj0873JXjgGZRTXiAi9q1MItMlgC3kpg1UYo+9fZXMBo7LdMyACN9f83h5spaoSTRCSCbRzIxkqyDSIw+Kyah8rFZyzcviVhHjAIbE/eC5cyj+NIc+XxoyR70GBe4QIEJWi62QDyl6ZMs2RBInr26qaadq2QQAo2OiTdKkL1biGn5t1uP3lsEB6lJ80gt55VR7EACP/M9CrBap3UyxMCLw3UBEIdrrEDIxkEYI9A80NbKKU9dNYRTaedEVbNZnqqUcuZB8bKxi35JnWyOBcOccBoSZ7ZTxpQK4mqweVkvl1Wg1oYKyMKm9vuPrK92nSX4Wd2LtcMFQUgsnOWO5zPG0xnb21t0+12rSavDk/B5lbxeZ9kGi7P9icLUai3pqWlQx5a3pce6RPDS6Rp5gQi7aYDd6mOMCMlYyiDDJIxFrQy1rVW/tNdSxWKskOOsiMg0dKmtUSy1bGJJblNl9J1hicZaCsac0XmG6uZhhAEJacqBckUUstE8RYxDBExFSQXx/Iw0J0yXEefnEyyAz3/xK7z+inD/+h6f/+xz/Kv/9/8H8xSxIYNxjBLg8zSinLgmnMsi8JBLwdPDI3a7PTabjjWZ1aZZEVioYjQNZ1ilFAn0dZQmB2udd43MWYnwt9+8x/2uw//063vsPCRxqKDI660DX1cYC2cI5AyTlxzXuEGOkwTVFLaeB8FI2yGM7EsyUgbyIsjvpEzGQ3yN89BsmSpndaozlubIus+V12zOCXOaMY4ThnHmiUpgyDVmQkyFM9tzxPtLxOMQUcHTlpLIqxYyjZtDxPC0CtF4z304ugdKrQjW88i+1l/LazB0Ht57pDizk05VBh0wMlpSQZLaOmAxzxmn0wnPD+8R01+0pMUa5itYJ/AwGZC1Qp5a7UFiUwaodZaHtW2UZ+PbYIVqwrShKborSVJaA7RgVlEGYGk7LGorXzw+6XB1UFzLIFaMUYVuEyJM0YI/M5YNAkcr4JS9SHZBLwyJYurtV1aNhhgBJQBUHSllWc+3vYXU7xJDUlS02Z+z1JJEiIFIhAx4cWm/qPMclcU4YzyfMV0GXqQCh5iV0g47mOUL0Pfk5xkjUF81MlhgZSjbCZKmK3KTDLS3tbXWAJhzwYeBRLu0IhZCKj3utwG73sJbA4OCnJlFydmJh3WVoT4jJJgQ4CSbNZ6d7pLVBs4MnGsQZ1uEa4jECtLB3mlVD2cZyJwi17/niHlOSDHJ7zXjrYilYI4FlznhLI72MmWMuSILK7KAg4taBSxTY2+W7dFYgs3pLshI+3vlYEZP5prItpQslCcA8PnllDCeLzgfjzi8fgXUe3mf1caDWd3Xnzp6RmxWxCysstjVv9c5LN8uCTJXf1wHoz99LOvHWYuksKJmo0p6g7Dka0Wm0nRzCVUQAeHjWCzZreHYXetcWts0RK3HXdWU+Dl6O7g1xqixjwkgwnbHHAPvPYhkclQhAA77/QHWW8zDAEcVpV6wuelw/7Mv2b7kjM12A1ML/v43v8Hvf/M7BO+Q5hnzOKGgoA8dOyGn49l07fLezIJ27XZbnM5Dyw5LrTwuk61mC4YrkQhEeChhsSRxlNYiSwY2p4x//ZsfsQsOf/XlHr1DQ0CqwJiBCNbx5JyYM2xMMMYy8iBIEq02XRWH6YKSG6305Ur/PIzsQbT7j/Xa0cVTmOCZY0RJETlGLplMA4ZxRIwJw8TkxAqDmKl1CzwNGU9TwsOFg/ltx8hfJrR+Xe5VFptPhOBleKExXAMuy97ihKYw+gRxjpbHavI8ZgkirEMuM/pNz+gHyQAD0iCXcLlc8PDjdzgez/js9Q2jbOJVaiVRumI7WgotI/qIQFZ6sLFKHszK7hmjOetyLwBuqdJbIEk7RN70KigmVlDDlcv4R5KmmGahDfW6mcXXV4EJCoFMEmdrYcGtQS4FvumqUvWxTESXmkHbyCqP5oyOtGPJOiJxgs6peWXIk0gMtdDyZQZoFm1gKpmhNFXEpAW2cMYjlYQ4TRjOZ8zjiCLju/TA1LAqXbxJSqLda8Wd2nms675qRDWbuSrCr+7X+nWlApdUWFCd2CHlIudYA7bBovMWZDjCZPhYGMfOc+bRMrqlt3aBM3lhGS58rM4QHES0NVSBmkE1gUpi6cY0o8QZeZ4Q44R5nnhy1DTzCEeZsJRT4YkuqWBKGeOccR553N4QC2LmTUyatULE61cZIczyt/Xg+RerRxzXksW2W8InceW0jKyr1s8rz6u1Yh5GXI5HzMMAapmOxrKrN24PJYRpQLI8TT/r4/CyBg7NjC6Bw8unro7+5Z8YRhS2OBnAgV2gEElSzswGNjz2sdQsQi5co7VO15xtwa0ejwM15Tb9bCPGS1mZWhMW8j8L1VsgVWGuo8I632bgEnFmA2OQLFALYRhGTMMEVGB3dwtDBA/ubbW7DVKMePPd9/iP//4/IMeI4B2ejxfUmll21AqzupAMnGfFJc7sLU7nCZfzgLu7Gzw+PqPMRSBg4qlXxrSs38p5kQRZpRC84/tfRD2pCrxgjMFpTvg/fvMjvP0Kf/KqQzBgByJtRpsNQGbmgCN47kP1TtqOlNXr2EhLmqT98Hq3raAqKLl1bfDC5VKNkXXPZEnmTPC4vgk5zcgxIs0ThssF0zTiMo4Y54SYuPVuzoS5EIa54DhkPI0JxznCe4tNx3OMsyFMpqL1IRm+3pWAzgcuAzoHguURrsKVMYZrxsZybVwRpy54PqVauLWz6zFOjDq+enWPsU4oZFCpIFgLSGkzx4jj43ucz2ccbrawRiQ0Ddv2lDOcszBGJWWXQSSGVE0Lq359YaXrAm+Ik15kaQ+S/cRJGC12QPZ+C5yh2a/u/39kDbeRM8ySOnOGx5NJqsmQzj42dgbA7OFSRLGEcNhim1hbt21UQMOzZRNDna3okUI1kfmLZIFCIu52QjCimmSacWzsVqGoAywpphmu9imiZJTENdw4jZguF6R5auw+Ahobj0jrlGLclO74IoZQlmg1K1xfbwVRcybr+qM+SG4sCxiLAyjA8yQTQnJBzBnjocOrXY+brcjAIYvDnSUjt80xMRyoHHM9FgnTqmboth0AZ4wicmEgRb8M5ATkiBpn5DghTwPm8YJpHDAOF4zjhFlgetVHTalgjhmjwMiXiXVgp8gZTpUAi4klWJHjdLEu93MN0f7Eea2c7JVzXcG9y6+YmOKcg6UFWdD7lubI/biXASWxCs51WYGa8zXL4uW/1OWz9Lj/2MN87B+rl+i++Ni7LFk7Zx119bNG7KVm1DmxAwIHzVXrl12Q/dKiQNmfeuzST7gKcKzuUxgYYraxMZ53VK1wEOZpziKsURB8aHVcaxwIvDbGUeuOHl2/Q9/1QoqvyHNEmhNcCIjzhLfff4/HNz/i6fEJXReQckVMCRUsEJNLZWUlcK2w7StTUclhSoSn4xmvXr/G7e0B5bEiFh12UVsWX6p2JSzEH+88pjlxJm+ZDd4bVlTiIekJz1PG//7ffkT98y/x1S0L/3hrMaeCOVfExGxb7/k6xDhzUOE8ci2smWwCQ8RJrnEQmLO8WAGiQU1UQLmi5NRq7ZUINUWROk0cDE8T4jhgGgdcLhdcxhGXKSJlQc9SxZQIl1gwJsLzmDDMCZ2z2G54lrg1VtIutk2Fh3Gz5K3YcR4uIqxgUeArpSB0AUSELvQoubQFagxrlTtnmUQGwxKQtcD7gH5DmE+X9v6KIIIqLsdnvPvxLT774mfoO4dqtPDI8HMsFRbEk4JIyz9cGmqcDCzZaRVHvDhK04JJJfuSbk9jpG1ooS0ayIjZZXPiI5H51eOPZrhL1ZK/mABTYESbs4Ihg5y5OE/WwsYexQGb8aZljE0goRmlldMFQzfaw8ctPysJQD0aWmWX65+JDSZDzpLFeK4xcx2KHREkQqy1IE8jpjSznu44IY4jckwggYOuMiaiq/rN1TWiVa1WIlQrLUJLxEPXz5c73o5fMx0JFvjPvEhGaeCPKSPmgjkJDAugVB4qLhJCLQrjkyRWmOKZW7ClwBQlZXiOUnWEAKExCQyzWPhdKkfNVJJAUxPSNGKWTTyOF4zDiHGaEGNiebpckHLFnDLGuWCYGT4e5oKkA+iNMGGNaYGcXTkoXROL4+I60U/qoliySHW2ZvX1sYddXeMG8RrWns1JZuQOQ6t/wTlo3KQD5N3irjSWku8SSMnnfOwYrhyxaf/B1a+vnPVqB6yCDj1nbVPTcWiVKnJOEuDVpaSjBsRamGplzCaP2mw9uzLerwU9MGAxhdV1NZAAb5kGpGGIqZYzGcxwZhlk753j8lPiIfH7fQ9UgnN+CbTA4wbzlNFvd0hxxvHDB8TLCc/PR+RcsN30OJ1ObKytb5mp82z0VHVskSFl4Zfn4xmH/QGHwx6n04Xtl2VZRibnG1haSDKtDY2YfOSEjAkAXecRU4J3nKGBgA+XiH/5mzf4m19+hl/eBfReRDES7wVrDJwXQf7ogE0BhcDJRC2wpcCHnntgUwTAx6DwK0GyMmJUg4P+AgMLkpm8jH4xr6OkiGEYMAxnjOOIYZpwGWech4lHMJL2wUv7z5xxngsyVWx7h77jISnOepQK5JhBfDo8cclSW/vrJEJrxbWyM3WWZ6TzFDJeZ51nJaoi1xWAkFyZjRxjhnc8inScATLc+salGod5HDEeP2AaIzb9TvRB2AYaAMFxK1EWMpWpihgw+3nZbOr8l6B5tQHbb9QWtf2nKIQa8Yb6LHuYVvOiP/b4B0lT6rVb/kAKXxTEFDGniBQjcs2A8/BlB7/tme1bF+JVc960ZA46VcSZxeHq5taEXJ0fEbcFQSGBSm3IOVWOTq0zwty9zjas8y0rLTmhzEBJCWmakeaZjz+nZVB6XTvc1dQbuSHrVifNkowx8I5VioiI55OKbt+Se/3U4ZIx7XwbxCrJbi0cuZ9jASHJYiLMueLV1uN2E7AXSj9D62htFjXzXFrKGc7HRTLOOsDw9ZB2Y6iQhNWinjjtWkX+MknUPI6YxgHDMGAcR4zTjGmOiNLTN+eMOTFBaowF01x43q94Wp0y5IS9XmBgigQYWBY65NqwmIX9yeLVxV2F9HKVXeomIayuu74QS1Ajr7Eyv7SkhHkYMV9GxHFCjgmuA7N5mzO1qJZE7nRx2Ovvbf/gp5ksQaEts/q1OLkXTnr9fmtoek3M0hGRJIFkStqOQG3NFmLUxnkLawJQC+I0COyo052q9LNXGDgJ4mR26+q6qpygKkesofTOO5QkyEXr4y6YZ9Zh3/QdH1cWkoslydJZL3caJ5ABw5/HJ8ynE5wB3r15h812gxQLhmFkp+9caz1xznFXAji4tt5IEM8B9jBGPD094ebmBvv9DjnnNrVJ7YPzHLwScauYY48qqlFCLIqZSYac7jDZpyR0IeBpjPjXf3iH45d3+PVnW2w9b/0hVsTCrYR3uWLTb3hrpYjgg2iEs+qd9Ux0pGxkb1ZuBJAlUkuWdj8Z21e0zZGh9FQyawqkmTPaYcJlnvF8njDOnBAx/5QwpYIpcvvPlPk69p1DFyw672TuLxBLkdq4ZX2CSui8RbEES06yQV4dVaBkospCI0KqNMZI8KCCKIwWlFIRQi8Ot0qwyK1e2+0G52GGlx5lKORfKsaH70HzE0wJrCFPMinMWthqWsZZKjXxFSJI74uuVwOZ3Km3UzJlsUTGNJRNbYhVAofuSyNELd23IgbTtvMnwv4/0hZUJANZZahA2+i5FIzjiGm8YI4zX9QuoLeszantPGrQdeSZpuFLVmtl2o9mtbQa5F2boy218uxFfd+6TIsBLcxL7StUKJjZgFy30yy15IQ4jZjHETHOQvTJTUmq1qWJudHG5SIuvcVKSiEYy3KTKkDBilnaB7bKcFcGVHu21NERx6y8eOVmW8usw1QJJ1Fhionnxk77HumGldpSIaTCkz2yOMjSd/x9nuFD4Dm2yjAXQ6IOVyH/2gp3GtAw+SnGhHmemOE4jRiGCcM0Y54iprRktlPKmGLBLBs6ixFTcXJtP2EYTJYIlsRKKyLqbHmjUtvUVzC8OmXRim7M7xeOav34ySYwgOp0Z2GwT8OAaRgQpwm9rEuSDFf3KsjCkWn9qlraaP3CUOdq2gcparXyty82HP3E6bbD/JizJcktBSorUg6BdAm0yUhGjoaAXDJyJeA84uZ8QowXzPkWOXPrVlVSoLSqWemfJaCNVFyCGRV+FFNVFWngbJErEqJ1rEgBwGMCU0GKBTlJtkacbcdZShfTBSgZzw+PmOYZrz7/DO/efEChjNB1qCnBVgJELKJMmf8t3RNy0TirzBXH8wAXOnR9j81mg2Gc+MjNsjuVpZ2posSELngEu9R4i2VBfmMYUvXe81D4UuCcw5gK/vb7R7w7jfjzL25x2ztYqhizwMul4nbPmWi0QB8CuiCDCZznjF/sB6E2Jm7DbkR9LxOQYhVYF5IZJswptQRoignHy4zny4QxZhahELJXTIS5MKScSuUau2PEIjiL4NguT5ntfyHT7IuGtawCR6uSgeFkpRQoJyCVmdvoDAdpwXv0fd84Ms6xWMY0zwtvhgrmKaHvbuAsJw/OG4TguWMjVTx8eMLTwzO2+1v0m8BiGpbRAmUVV4jjrcSylW3vaHBPS2lQ1nM1tPiftueIkQjeQq3MusbTZHu1va0Jw6cefxRSvkq19fcCZaUUkaIq9MwoVOCsQSdzL1voAKyclkYLWrNlZ+uwniIj/9EsUMQUSs6oqcgUDrUji+iFtdIiI5ubJ3bwJXFOWIu5chQ4DpjPZ4wXhhBZ0jGKs6WfXi/D0ITWtRp8oBe3VmA1q1VrGbVgNVHp5fXFAjFjZSf0fORzyVhUKkhFFbkySiVQ4VaGmCumTcY+BsyxYivTR7Yxo+8SR9Lew4elpctYViLSOsfKRmmqzUFOKUiJ+/bGacYwcUvBeeTMdo4MeefCm5cdLVP6c2Hjy62ePOXEOTUoEFk+zcLUobXw8MW6WdaOBoDtuxDiWq26ZezXDncpkOjS1NCWP6zmijQnzOOE8TIgjiNC38F6pwcj/X2yhu3ymWSW92zlCJIAQhc1oW14vc9toPt6XZA+8dOb9qolSc5NmcQ8QFw+w3IwwKmARP6lANOE0+mCu+GCGEcWQChVlKQAQ1VgVv483Zv63WlXglng1poZMq6VRQSsdUw8snycKRcYowGfRTCE4CpqdcgxwxGwgcFUuOUnzQM+vH+E2/QwxmIYJ1F2MsLgrSzSTzIRSzMU4vtRAWmh4Sx3s53x6v6eZV2lR1wJlLlk6dXlSTRMMqsIMC1RoFzgfW2fb6xB8IGRPVnLBIvvHk54OE/41ee3+NnNBhsHjDliKgY/S4Q5FuyCRedmbDrPU5WEBc3kotrsFWQZGFlvZIyUApjUxQlCxZy4r3YupUmmPp+n1gqVBcFIpbbaMgzgvfBnnEUXDM/7dso0ZvNayTSuQFPXA1+3LgQYdbaZNQ988IK8LAGisQabTYftdotxHCUAM4hzZFIsxBkKomYNEJxtIwArOMipBByfnvD8+AGvvvw5+k0H5/iaeK9IE++3Wgnec7sawMExd9+J1GVzNi2MAHBNzmS0cpXUYvX9hZPljJjaLz5OmfpjSlNYGSmFlWXxsSFOrFFMVaJ36e3zHj74llWKZdDDag36zlkePGCuowRaf1ZlqbCaC3LkzK3m2uZ5ckuDNu3bRd7NavZGDV6qtSLHGdP5jPH5iOF4xHA64nI+YxgG7s0t0voiUT1J0GCNUUzixQUScy/pizPMOqTAZjVBHL+BTIhZDClnKRWWbOut1AcTivi5JM6eDEeYcyYYVBjiiUXTnDHuPMZNwb4v2G885jlj7nPb0MEvwuLWeTHI9mrRmPVqIogyVMGcEqY54TJNuIwRl4ln1U6RoaCUK1Km5nSzbE5nLIxjfVfneFM3+LYSM6SJkQvd0OpALJYa7hqpZcY7fzUJ0FaiWJzQepIT5H2uWOZGRUPA17ZqRp2ZRHc+YR7usL3Zw5qN1InEuQnzl6o6stV10zYqdUJtsuzypGWjm5+spxZ8rXf5H3momIWWIVoETrQgMatAXltPDIAYZ0wTt43kElErj7ojgQoJDkQLk3kdCPLHcSbDfaCOp+OIzncpFcGDiTPVgKggyL03BPTBQ6cWTRML33OL2Yw0Zxwfj4jTjPdPR/S7Lc7HM6Y5ousDUEUrulZY4xndtjrpZgmM2XwylF4rYRgm9N2A3XaLFGMTkPGdR8wCuwcPogwiJ+s/tdVIBOSUm13LuWCz3WAYRmYMW3YQ3gecp4i/+/4DPhy2+Px2h/t9h7kwQ//1oeB+57EPFtvkEKyDM0AXArougDUI2PbpMHaW8WT7mqQ2rPtTuR1zIRzHxANA5sT7sCz7ak4FUaQYjZFWMCOZrefZxTrq0Rj+PKoGMWXMMTekivOLir4P4iRZ07iUAh8CE9I4O+B+WHDr0KYLXDvPGRyYJRhV9hKEsumhA+j7HqdhRLBeynwVIQSUmPD8/i1y/EuUssO2D5jrzMQox0IkHJwwAuIsD8KohVo5SZvfjFEnvGS2q2gfGi8b8QMOSwJAS29QIxUu+sWf3r+fznDNkhOsc13CQsiAtW1KEIxBt9ths9+j320R+mVEn/b5OXW2bWi8ac5KYa917biWCtJ6ZMrcRF0KPHzT2W0yh2LgG7PV2CWDIKDmjHkccHl+xuX4LL23Z4zDBdM4IaYkvVrq9DlSZpssmUwzvCtLKT9XqWs7mdELCs2pGmIneX1LV+e7er+lJohWHzDyHyJWHJoLwURmhOZckWpFzCT104ypc9h2CZvOoQ8enTjc4EX2T4yxweLQ1suDSETMS8E0R4xzwmWacZZ/j6kgJt5ouTDiwcIV1ByA97YJmjtnWltZJYbMCUpMocV5ro7h2lEtdfzF6S6O7WrdvoSR19Eq1OHKGQtc3L5qRZpnDKczpssFJb3ijSiGgyotTk1ru+sLSB8/huvj08++jo7Xd0CT/D/+PtTWDgcpK0xKjJY6y6rCIUIo8sEj+AAig9yQE1riYrJoabtKrBlAdFD5eZK5CmAnAbBDCB3iPIAgk3KcQU68RqwFyFieDyvwdSUmTMU5YhxHnJ+fkecJHx6eMU4R+5sbPJ2fUVHhvUWKBSUTci3wxiCn0ght1rrlXuj5g1nCwXc4Pp9wf3eL7XYLwOD56bm1H6bE9VjrDLxx3NJIBGuoXcMi/ftcEuFxn0GgZWN4gMGmCwiOkEvGhxNDus9Dj/v9Bne7gEsseBo63G0D9p3DzhtsvIV3CX3gnmVo76rG54aD8FIqsgRZOVfmc6SKy1RwmgqGpC0/i5551kxYiZFAEzzxzoqN4GBcg4ZCAAzLZ8bCk+GsKJCREJKcNHJXRSBrhSegJJ2Vy+vaOyvv7TBNEyNapcrUH05IGJZW5IjZ3JtNh+P5gpQyvNgQgB378fEB4zBidzhgWz2CZQa1Hlst7JtSKQ1NraK/7Bzb6sZ+VjRI7cXKRturrS1rXbaFQtLNAWvSZMyVLXj5+COQsn7JBBjd4IxvwIXA1PGuR2eYyLA57HF4/QqHuzts9zt0fQcfeB4mX3gZ5i5kKc1kQKsakH5VnnfJQ+Nra0firJUajKySfk3YoEGFOraPB3DnacZ4OuP89IjL8zPGywWztLTMkSfFqHRku8SVM0zn0IrxlVaCv6trVUrBHGcEHxB8QBe6lsSwMU9t81xdZ3UcLzKyZjDk5imKALAoSao8zcPKyqgVSKlgCg5DsOiDxSaow7VMiHDsdL06XbtkLMvxMEKWM3Er0hwxxoQxRq4dp8IZbWMlSlYpcAorFxkEZ+C9kZYIvjdCtly+aIGS185w5Q/bmtCJQIuz/cTC1Q2h1w8Kha4cOGnWixZh6wenOWI4nTCeLyLzubo3JA5XHD+L5a/Uxa5DsY86zFpVQMKuAqn/roT26sH3aWHm6uxPLUNo1quEPoAzUOcDdocDdocb9JstggtwZK/2IVVi58pKKjCrTL2daQWApdeRJ/9Agkt2CKUQus7DGIsU5wYd1sqG2jtGcnLkrgfrPOJ4wRwTfnz7HlqbHscBEIOnCBiMEcKUtDs5hyJ73RjA+SBTvzhQyzkjxYyn5yPu7w549equ9Y5bcK025cy9/6hwltWlrEN7fwL3+wKA9x3mxBKE3jlUEEIXEHNG3wVgXhKTaUr4kAvOg8PNrseQKp6HiI232HUeh95h33n0PqPzSRS8JFCVMwcZ5MrKbLnyzF4OsguGKWPOC1KUC9d4o7CDdV8ao+U8MOrlDUKw0h/NyIbyYnJmF9N4GI4HDdRVsGsh6JCQQWpVaL2KfeZsswuB7xkYLjdgsqLuDiZUcXK26XukFNF11NTOYICu6xDnBN8FjMcjjg/v8cVXXyBX1nn2xvEghlqgk0eM5Xp4oSVLraTdK2yviNZdElqWgKzrFcLWOk64lCcR8YIiyas/bZj48Q87XEIzeOxrNXMJCMaApG4aug6b/R6HV/e4efUah5sDtrsN+j6gC4GbnD33dzm7cra60cXJNuivVp5bWwp7ANnIUMdqXzpcdVCLxTVG2Hw5Yx5HGTL/hMvxGdM4cP15TiLYsNRv9S2W23Bt6NoFUjhcDIPKhDH70COE0BxFUU1OJVI12y/QMqRG/NIjG7N67nI8Fcw4nEuFjdyiUIpFzAVTNOgcsw77kNB7FssIjlmInXcS2Zv2mbqcqma3uWLOPId3SpkJF5lh43K1rpaMXIlvzlneyFK3tdo8XpeMnmu3BCWeX1/XpW977WzXpKSXd6bd/xfXbxF30HofR9QsukqtlsebE0gxYjyyzGOeozhW146FxOlWV1mrmxRRWd2gF491Tdnq/bQczCl83JjW/52ed3m+XrR2EZb3kYBPyTi+67A97HF49Rq3r1/j/u4eN7sdtiEgWCbtNVUpaGWcQCoQoPtvNTLTaPACZf5SUyODaM06Z0GhFyfLcofVASlmTFOC8wEhBMzDe1Au+PD+Ec/HC0LvMU+TaOZC6q3cI281M0UWgpGByWrouRc/pcxJegWmiXvVpyli6ibc3d3g/vUdHt4/wkkmVAvXcGslrmXKIPm2NAlIhQUbvLNImbsDvOd2oXYdwA4ipQTAoOs8vAGoVJzPI+Ic0QWPjefschtYunUj+3TTiZ002gpE4khZqCIVca6CEOiYwSzfo0jEEtXWeuVEgMRaJkap7eSZ43zvdQIPQ686SYjPr2bOdIvsHy4bLeZJchwYw7KPvBRtg69zZiItVYLz/BnzHOUYDUIXUHLFRDNKSdhsOmz6gAIWszBqM3LFdJnw8MO3mP/izxEcQH4D7wI8LFKKvObFuapClhNSLS2mF1oyXOzrClGUn6lqyYaDzKtB81h1oKyy3bZYPvL4o+P5IDBb66U1BtZ7nqcZAmwX4LoA3wV0XY/N4YD97Q1u7u9we3+Lm8Me++0G202HXjJiJ5mt1RMXg9rEJUoFifPi9pbCIgS0ZCxMuHIrh7sYGI042GESqBSkaeTa7fmE4XTi1oNhwDzHNv82ywIGINN3jDjUJRNqDGnwYgIguIOBDv7OhUUqgryP9745XJJFDUK7UbT2NOvRE6ub2rCltjbUzfOGTyKUYS1ghEyVCyEVg5gtZs8ZZ2hMREEZRGhEP59IW3gFiipcI2IGdEEmWpytaUfHmvHgDe2sgXeGYWxpAzDGiHOtbS3WF85WAzxd8IquENRJX61OqBl8GWShvcc1a1lJYNwm5hBEpEQZ9845uFJYAON8wXS+II0Takyw3XKyS72XxxIaZXZraYRkbZAe+8uj5l+wjOCypttdoIWJ/EcfuiwaomOuzlkDCGcMrHfwXYf93R0Or+9xePUa969e4bNX97i9OeCw22Hb9ZKtiXGCMpOXIMZAHOzL3xsVFWHYsXLUBpXIA/G6IDKIMcNZFkyIU4Qh7sk8Pj7hzfc/4N37R7x98x4pJvTbHvM0tzF1RKwJTEDr7+XPYK/akANrWg1bg5xSK7y1QLU4ngZsNyfsdxvkuxs8PTzDW4aRiQDrHUplIhWT0BwHxcrRkHvonUcuGcF5UW0TAf9SsJEEQzkhITgWDZHlklPCJWVMs8HoLYK16AMHw16cbXDMU9G1UYmnV60DgEJMoMqFbU+VzcRmiYMbA8lshY3cCYeGuR1euiTYmeTEhMcskKkB21cylaFacA91KQWbTc9OeUVkbGNB5Si13KS7u01hKgVZZukSCN47pJk7RQCW3A2dx3yJsIbQd5CSQkKMCU/v3uL48IA+fIlNL6QtKEKKpS3JCct+RfDLFQ0ZNGA2NLeRVZCV4fbiZAHiiVAqAEZtmwMSlmq2u86IFfl5+fi0w63qsCqqk1mi1sKHDt12g263RdhtELYbhM0Gfb/Bdr/D/nDA/vaA27sb3Nzssd9uses79N4zQQDLSCs+Tra6VCtIekdZG7m0HltI2u9EelBnpSosqhnu8uCbT5UVpebhjOHEJKnxcsY4DJgnhpNTlOxW5mtC2dNS7wSBhSdW8KJdQXaL42EaXKWKmCIqVQTPRAHnFj1ZADK5ZDGo6lD4HBazzFGj4QbutZMBZ5IWCosI7G+IF4zM5kU1XO8ig1AMvOU2AC8zSq2xzSAC4gDBEXPR/mOJovWTrVkWExPq5JgNjyjzzjAJQ+rGHC2zQcyGAEhkXtXpLnAygCvnqZvnY66nqSBpPfpFZqhGRjd/pYqa+bNCx7U6AhM1SubZvjZbGDJwbsR0GTEPI/LMkoLGLjUnFrpnWTn+BHt1XEbCfVN/2hamEbZZB1gvzmvdd/uph1ndByPcCEV+nIzds45LP/12i8P9PXavXuH29T1u725xd3uLu8MB+90Gu+0Gfd9xuccLAtXgeKyIUstQA6tZJZZgyzvPe7QSgAJSmUeGyeCMIDqVQCUx4z5nXIYBx8f3ePPD93j/7h1Ow5lF16giCUwYgmNGfC3S/mOVHCxqdirJymuiNNZ0Wdi0OcMaNuxv3r7HL3/1C7x6dY+aOJAep5EzOhe4B9UxaajTQN9b1EzImVBrlH7SglwznLdImUlVILTMsKSMmCp2XeDaKQhdcAhOBXW4TlxrwTRlJO+a6l4Q+NU52+ZbEDhjr3UJ6GT78iB20kAJQoqSfWCA4Ay64IVXoboFRgIS3vyqB85vyba1pGXyF4ClTVOCkbYvJClRlrxqKC/qTAQv9yJGbsFUDo5zDtkJqll5pu7u5gBgQq6VO1AKX/eSE05Pz3j3ww84vP4CB2NBOQoR1zS5TOXQ6FAI5xWBYLvCR65T56hdR6576OhEtAxAwwbodVebtfovAeLY/5EOlyrPvikocGXRYXVdQLffYntzg81hj36/w2a3xWazw3a3xX6/w/6ww36/x363wabrsdEmbxg4zcBlE6JyFopcQLkAOfO/G4wstQzhdRNEMEMdrTLoVwaK4egkme0Rl6dHXB4fcHl+xnBmOcIkgg3cP1abiIC3LDnWCUW+iiqPRnBGmamaTTfnaBrxt1QdX8eN+QDgreOrbSBq/RBdT71L66u/ZLqaTXAopucqhyAw0fq1S9O2QreStZIEbBUgVLi2MBaolCAZp0aKEgDoolMl6wa5rA5ftbBDy245m9ba7RXkv3r9yyxwOX981NmuHfPypSZGl/4q65Pj10y5FlHWtuu/8VqkUpFrRpykH/d8QRwG+D7Abno4sijtegniocrmZnXcZqm9XSEYWGXctL7hS3T835PhrmHTCmFdW4HvHfdbe+/Rb3psb25x85od7eHuDjd3t7i72WO/22G73TL61HViiCWI1VukZSRdb+J0nRpUo4EDtZ5ua1ikwBnAgAfBq6QmLAseTMMAA0KaJ1xOJzx/eMTf/+73+PDuHY7PJ8Rc0XcdZ0ElA4Yzx2HgOrB+vjEcMGWpvcIQjDw3RpY6NABC1wGZCZcw7CSmOeLtuw/46qsvcbjdI+UEY4EpJnGCdilzocI5z0GZMRIMMGJiDTOW97sd8mUJpJU97b3n3ttc0PVergurZe16AwcuTfD+XoJDK2GcNcs+bYMWJINVoRxq+wsNMfEin65a1tYygck5RhrU/IIkcOMYUdjL/Hvur9dzal3y7RxTSlfa5HJT2r7UMo76nyqGiMBTuioRvNR4jcDKOl2oVIMudDBQYhZnsFwqiIjThA8//oCv/uKfAOYOxglT2jFZqggjmwwEreTrp7r/pDK+pBmwZKvqn9S2i3DRdamK2pbngEL2weo5n9q/n54WVIqI4BNKtTzg11nYPqDb7bC9vcH+9gbbwx7b3Q7b3Q673Rb73Q673Qa7foO+CwxTWQcHKzCyLh5iTL9UHmyeee4qSoGpRbK3BbKrMDCZFzILXJjFMIgh4NCSUEtm+cbnJ1weH3B6/wHH9+9wfnzCcD5jGieUnFlovxQU4nm+JFHpbrNF3/cwsqhsjOI4FufTDCfUSFrpb+UadK4FNVXYrEbMIHgvcsUGkVgWs8VNZJZdA7TsodUPUFGhTEP+PB32YFv2oapdUo+TDatKXgwXoklo6q3Q/kuQznTUnb4YNr3Qsg4l0paNKJlVa573LHyuUBU/B01lqs10XV1DNIh1aWchXc3ytW4D0uNaO921U19/WbkvVXr+NIplZrs4Ksn2Sy2IKbEs3vGE8XTB5nBAv+PJSsWIBmslmWVMEjzKJRSDrOtiHQUzUYOvqwYaS2683rCryPkTma61lks78pnWujbRq+t67A973Ny/wu3nr3H72Svc3t4y4rTbYbvdYLvpl8BSWsecXcaeNWssTteQ9gxzJqRLQm0Z3yMtPxXu4azcE9x1QYwmT5lKcUaOM57evcPb79/gm7//Ds9PT7BEmIaRodqUYMnx5CEQC+ar2hLMwhYvRbgIhN53PDyBCCVzxGusQd/LBDNpZ7TOoVSLYZjx44/vsNtusNlsOKB2DnOMIGI1qxA8NHwFCP2mwzzNgMDURluSDFptExJoFaronAMV7v2lPnDA4HjMXk4E3zl0nYMzJAxE2xi5hrSnWvuBsfq37k/T1poBJyDeMtoEg6U8YNU5S+lHSgfcI11boA3DYhdFbVhd2uysGgcJDCtxTzAPZBDHpOubqph4bjUz4JZRJSnmkls/t/dBghw+b+sctz8l5gFoNhm8Z9WvuWAcI57evUEcjpiGW7y632KOka+dLUgUWQq+FPSdtkCyUaG8iHjAyHQgXsSowLK+ZLsSAdYsZNmWAOk2EZesvzAr2/ry8ek+XGu43YcLc7Cdg9926HZbbG722N3cYH97i8PNHrv9HjtxuLttj23Xo/cBwTmp2YrB0URANmattcHHkCyXHW5dDKYzgHNQTpuVTbxseF5ZWq8tKWIeBlyen3B8/x7P79/j+PABx4dHXE5HzCLbpxh/rszK0+K4d44jfomwU0oSwmhIo84Ci6+kdcO0ARkecpBrgaECco5lHy33D8Pjiu23trWtZ5S0v1SjVoOlxovl95rRGtNmlrbJS3y40gJgREXGSJSN5thaLbWVqNX5r873OgXnTWDZ7Flx/l6yWufYuRtjZNISL2LveEoQq0fVj/qS5XfmKvVdt8GwXTBXr7nKoLH6uwRthgyqKQJjL0Qp7x1q9U3ur5aKBIN5nHA+HjEcT7j97HXrK87WoY0Ga0xlrFrwFid73bq0BFJskHlDtOvNr7i+zs3xYXmtGDtWWQq8K8Theu+x2+1wuL3B3evXuP/Za9x/9hq393c47Dmj3fQ9ui6Io2XGulfRFrPus10FOhpYYeV8oWI1Ru4HFOuEJUh2ydloLdzSl1JCihHT5YTHN2/x3e++wfffvcFlnOGCQywzjDeosQhTdSf9tiySEFOGD6zFy6NAl/1iJcO1BkycEUNoiF+7zLrl3wfH84KHy4RpGHE4HDBNEcYZ3NzcyBSsick9MQlKXtAFkjVDTUmpgpXKQgiYJs7Ca+X52CQZXMmJj78LKKWKyhTBUEFOQOg9nDecbBhw37xdRkky90cCWLLc9khMcMTKDvB+5HVjJLgmIvC4bH6OMpV1f3hwqx7XQcFE09WabYgXUavT55Lbumi8hEqtXTOXAmtU1IKTDesDpjki5yJ6y8wnCcE19a5+s8E4DLAWiNMkCoJOrlkHYyIMCkqaMZxOePjxOxzuXuFwu0MnkpLWO/QwKDQzuSxzqYsIMLUsNsZYSV54L6uETjXLdWpcGygCJYEJqZ1Z723+73JFfvr4pMN1QRRQfIDfbdDf7LG9vcHu7ga7mxvsbrhWe7g54LDfY7fbYrvpsek69D7AG1aQ0kX+0tlS4VptESYyldJk3uT0WrsPLIuze2jUbFtGZ620/xDPwp2ORxwfPuD5/Xs8vn2L50d2tJfzGdMwyEQTNiRKoc+S+Xi7GCBDhBgji/Mn7huEOtVWw1Sjyt8tlr9XGNYJJm43KJVbDZzUhgMFGJg2wlCdyAInrqDqdhGxfG7LOM1yXKvf2ZVjddZwL5sxHP1akkyTa+Ik9VTtiyQs52dgrlqS9Perg5Ds1rTWL81gjRHJS1hWLrKETAXWLsIUCucqvNuyaCiEtQSLy3e6/j2t/ypGWI6eVsu/yH0uQrZhAZbAYvTSQsFzOMEO9/mE8/MROUZpd2KGPpFI60FGIaqj12hX1vDicA0g/ZzrjdgcdcsaNF9ZPxYwa+2OlahUARjrELqA/X6P+1f3uPvsNe4/e4VXr+9x9+oVbg57bDZcpw0hXPVjq+FrOua63K4OVD5f6nsGIh1AYrDEyKrDRckwhhGtkpPIZiZ2YpcT3n7zHb757d/j6fER8xQB43C5jIhxBKhimCYxbozrOGsxzhFa/y1UcXM4MFFpGKEz1hqBqh07wXnOkivxDFxeOBXeB0a4sjB6cea1LkjbfrfBpu8QUwTA7GRjWVWp6wJiTI2BD4G1O+8XBEhg4pIJ3U3PM4kLM4dBDL17UcUzlGFr5f2iQTMRqKp+shGVLRKny5kqayGYVh4BIBk2tX1kwIIQQVTwmkQpKeqEhcSmdsyicVqULGqNbQQyJofZln1fDR/hpd7aqKzh0keTlYV+BpfmuETgkNIMGI8QOlzqwPVxEf+wqrO8som1EsbLiO9//wd8/id/jeNxwuevt6i1wBqLYiyCD1wrjjPcpuOEQoZvsNlhF+uEFAcliK3Ej+QDUTTw1YAeHNyxW5D3W5bdatdePz7pcDeHA9eC+g79YY/9K2U43uFwf4vD7Q1ubngKx2G/w36z5czQe3jJthRqk93K32uRTFQIUprdUoUWgoxBI0MxC1mGRYMzTQMwHC03rxBHPNPpiOd37/Dhxzd4ePsWj+/f43w6Ic4T5jm2mZmaJdSqog2SqVltH6nIqWKaRozTiJh5wxqRBVubxZ+YRwM4aLGdUFBYoL1kOOfQ206YekvtI5PAX6sMjRSaeJm9tQ80LbthH7/UMdeZLwMERtjDFt6yqHcQ9SetAamzrWUhSV07O3neaiHpomOijpFNKMZXzscKgccRAFMR18OhX1zAxeliMfL0ketslt+oW10/d8mOGaLTzaVj2XjCSmEI1jmQ5+HoRdAOWwlxmnB+PuL09Ix5GEGFx4eF4FCpSM/2i8EJsq6UlEFtyONyv9bbUDc0iZEyq3NWw/3RhFfWhHWeWyr6HvubA+5e3eP+89d49dkrvHp1j7vbGxxubrDb8d4MwTcHy2t9qdkZrGq1LQjic1H2p4ZI2jJkUQES0o3AxSXNQCnwwQI5grJBHEacnp5xenrCD3/4Fm9/eIPLecA4ZwyRnV4uvMeoZKSUEbwVEQWZSFb4s0phMosFQFaOsIKHSqizg+4ZDvqzCFSs71Hfd0hZGLaVEGNqHQV1nHDrPLfu9B36LmKYJqRUUYkzJC9TbIC1IlSBM8wc5gUgkG2M2PQdz5SuVYy+leDXolaDLnBbUIOZqMIZB1We4lhBVPMkaCDS/U+opIxsfrnXcpPlwNdYDvj1NV0QxawqPfVybyv3vvA6FttoLZfxSuJgskhmzzKQwvAXxTjNaC2E/Cfs8pQKbOFP8cHz1jQV2/1O7LiMm5ROglwrClVBNBJPm6pAsB4ZTKwqKeP5/VsMj29RX+8wXgibPsgareicQe4cYmYI3liBjkVdzltGJ6oEU1AHKhuxrvec7uTWJoSWnBCo7d91rvuxxycd7v2XX8B6j9BL797dHW5fveK60N291IQO4mw32KiztY43buVD1pomXWW2PCxeW4B0gTVzbkWm0VsY72E9Lw5TGWKpKfN7VHbYcRownJ5xenzA49t3eHjzDs+PDzgfT5jnqWVvujmq6D0XNcB1mWuYS8Y0jaiVMM0TYl4UqJaLf/24qheuMk0dCUUkghmVB4MT0Iye916eo6IALyQLDZOVyEpWrgXXlcdf+pCX30EXgJEarrPscB1JNmpaNsrZvrCTZYrLqqzbvle6zr+YOS56yStDrnWc5nCNZbWaSgoOLG/ariFaZKgZ7uLK9BprFqjvv0YF9E3Z0dlqGolNPqFF7KqV68QgGMtawxzNF1QQ5okh5ePTEy7HE+I0wQVp9dD7sILUrjaawt9Ebd1c4+e0HJOuK/21Wf0MyAg8RRqWz7Heod/26Ldb7A4H7G9vcf/6Hq9e3zdnuz8w8tT3vThbJ2tUe6aXUr3W/I18JgxJO56QF1u9gaQ8YNp9Rq0oMaKmGZRmoCR0m4CaJocQ6UcAAIX+SURBVJwvRzw/PuHNtz/izQ9vcT6duP92nHEeI1KugGXOSCqJbYK04OSUGxpB4JY6Arj/XxSKNLiByCvmzEbbOc/3Up7Ht4CZ2wwHF/QdSxFm4rnSRTLzOGec7IhtF7Dd9PDO4P7+Dk+PR8wzdyx462At33lnOENkpTkh8VGVINQhpYRtF6ASs/CGxWVKRbDgUkUpCBsHB0bXauEFwbwHvi8VRtSvILXjVS2S6mLnLeCsOF1mXTEL2bBGMXcKFDixPdzTKyIVngcxWMOsbJel7ivJT5HpQkY6EChnWGPQdT3GaWzBDmBaHT8VlvaMMcJLKSPOEcZannMcixoY5uDUKvVdRmBIiXHSZjhOswRKFXG44M3v/yt+/We/wBxZ/8B6wBonARD3bmdDCL6DolLB6341oJLFXopUEmcyS/AJpU0t6AE1W7gK+ddZ2Ef8BPBHHO7PfvlLOO/QbXps9nvsDnscbm9xc3OLm9tbHPYHbprfbrAJHTonYgoNmpLYmITwU3lUHCWRaixFIChqG5nDbAs4x7NInVtgZc3wakVJEXEY23zW8/EJT+/f4/H9Ozx/eMDx6chzTSO38jiRFuMOFeL3kP5LVmdZIt8oM3wrVcyqr0x6UdkUkjSIAwtR4oq8I/dSISBjDVBVzYbfHyFc1c5UGIPHAS4VFIV9vdjADIGr2k1VOHZdx1lSVK21cauTRfC8Eb03TV4T0BYgvvy69jVQ5zqsETFxCHxuWubcREiMtkwpi9yKoowBT27S1ajuXB5GILF10KB/0jW8jiheBjirh7agVGNY21otgED/SgKpTaVJ1ZIkKJSsNcUZl9OpZWaX4xld3yNseyg7NpYs3lHgKPVY9cWO03Wnze1/JAJeXPHLPXv9um63weuff4bd/oD94YDD4YD7+1vc3d/j9vYG+/0e2+2GnW0XFma/Biq0rGjNWA1oEbhYfynfoNaWFYvKI1+rNCOnCXkeES8nOIpIY8HDjyc8vH/C47sPeH464TKMSJk1fU+XqZHuLEwjMRoyqKXAusATcHKGg+VJPVDFo4rgHQDbemxrKVyXbE5IOhwkCCylwHoLVz2o8mcFH7hOKk6JJ8zwuQ6XATn51p4TDHB7e8DTE/dfEyD9qJnXfhWDLMEMSASCJCDNOWETPNf/jeH5tbVg4wLzPIrBHAtut5z9ecO9uJVqY44TjDgDCea0N7lyjVL+AmAZhQnhvDihLTsrkLSxkhWrcwMMOeRYYZDR5Bt5U0lOJAkUcesUjBBYYUSr2K2OAZIpgxMuJ6+z3JqUYoQhoMwsfOI8CwVNZeZWLteLzfBS5iGQwPC6P6qUMJ7evce3377HX/7zv0asBRtjYYgZy4UsLEozic56WJkPXaQ8aR0HXrVU6c3V1iHmoMAsYbUQ1NGQ1iWibtfpiln+4vFph/v113DOoes7bHZbbHdb7PZ77Pd7HPZ77Lc77HrObIPUg6wQpLQ+oFmMqkZVgZFJnG07qBaZWRivjpbrXaVkFhvPPO9xHieMpxOG4xHT5YThfMbl9Izj4xPOz88YLgM3y+fcagneB54FW3naDk/OKMt0IDlUrukWEJUmYlFppYFMbGDICEQBdbpKIFlugMJXzRHKTayV9VC1Md9Z2/7NowYhbOR1brbAVqZyH6iMLG1/16+rh2YvRmOYBfr1KwIVgesk1RpYreOu6rlKqlK9bqJF+FxrOarmpEGHOlx1ksLtgOSwy7Ga5divz/jjC/aqBYGW2JOXHcn7Lc6Y9bp/4kL43eW+mJ9cPIaZ5nnEcD7h+PiE0+MTdocdug1ni6UGIGltR/IMOWwNApaVs35nkmj65V802/004aINNSDC7maHz7/6GW4ONzxgfbfjQPjmgN1uh82mRwhcs2UWthFRgvZmLQAy7XqzwVClKe7lln0qmuAcO2jAZ3n4QZwxDUecHz7g/OE9xvOA58cTHh6OuAwzYk7IKWEaI6bEAy+UcGMtpLWvsuYugEoe3gfEeWrBTakFfdfL9C3OfGJaAs9UMrx3i/6InEMIHs46TFWY/cYgg+v51hRmROfM+7OSTOoxSIlh5mEa8er2FmlOMAC2fc9tJ9KmYnRf6to0HDCoHO2mYxifxBlTAWJK6CzPc61U0XsPAxnEXiwTqogZvp33QK3yHiKYYjiILWILnNO9YESUj+DA7Vk+GJbXdQxFe2tRqCBVg1wWwlmFAWUS/QDX1gnJWr1i3FtudzJWBs0LAx1gEQkG+VjkSBGIJDoGTdcb3HtbqcCRX9ASSTBABlSZ7c7oEyHNEWG3kyCZ2zWHIcJ9+IC3336DX//VX8Nah9vQoeSZ4W447DdbTCmiDR4xBtZ6BMuZdZR+YENlsQ+ykDgZW2BkknO6JkryN6f/EL/3sccnHe4XP/+KyT1dwGbTod9ssNv00rsn/bVBnS3ToxrxDUt2RBJ9FpnTSkU37rpex60UxjrY4AUqAksyThPGywXj+YzhfMLl+IzjwwNOjw8YzifMwyACFgwxgwAnCioqsWhFMSbOkSPLnJBS5o0jEbGKMFiiBp2SkcyKrXOL7pqhNwuxSxnKL2FSljU0oBXjkIiJO3yXmKSltU9juJ5dmkrPdWbD+SHBrpWNVs/Qz3TagmM5a221VqvQL8NOS4YozEvZINWIwyXT+nhb4AqGir3VObcSDa/gdGuWn1VxSMlcRiNqfYp+XaW4y7VcBxTt6so9oHrNCVRn64Tg1JrvVY9b39OsxUVp+d6ga24tmUauPz6//4Cbu1vsbw7oui36roe1TgZzM2vZgpn9uheqMB/p6nPM6livfrx6fMrp6uNwc8BXX/8cN7sDDtsddjvuFNhst+ikXmudKn3x/eb3pSaiUMWBNtlWgINlPYIq3IrKMNwq4eHrnwvSNOF8fMLp/Ru8+cPf48Ob9xiGCQQWwketmKaEObI+by6F5ws5C1CBsQw5brY9SxeOs5R7qImNlFp5lqqon9neY44JVFX0hNdSqQvrW88N4KlFSfgi3BOsesEZfdchFQ+AyY2G2LnlDFjrMY0RFz+iCwEqQ9j3HeZ5hjGBWxpX/b26v629Xr/BWZSc0XsHqhlEBcF3CEGhSweA5eI2fYApnPUCVUZEkkgTshIUjKrYFcnADCrxjF9mh0u3gOc2I+sACw44auH9Mae81C4lu7PWIpXY1iXD1+yQjWTMTjJZ5o2U1Xo2MiDAtsDQGivkIr4WORXMmFvW3W+2yEmSMtE8cE7gYNu1NVuJkGOC2Zm2T3Xi0/k44Ie/+8/48Nf/DJ998TmodKyJXQqqBAdeOlEg835rrfDWA6bAOy1X8HVmbgo1NIHXPC3Bh1wXbY9bdhZ7iUoaPv/08UmH+9nnn8NaixA8+j6g7zpmIPcdNiGg8wHe+qaLrHsUIDFsMh5KarZVVKSoCltQmXVGjt4aGMfeodSKNEdMlwvOxyNOjw84Pz9hOB5xOT7j9PyE0/EZcRx52L0x8KFD32/hpEagzEsrlj0LW1JHC8aceBO+yLLZGTDDDtIztoao1Om29hMIsUtvDi3Gkk/NYG2ptN+tVr5brZ5mXXsuacgKjlpqu5WffmjWaPSYnF1ltdqzq453UZ/hGh4fnzUA2SXDLeJw1dmaqg5J1LPkfdWxNx+5DkBAy9QlrB2sQtBrss4fOz+09+NLuZQsXmaLS2Chk10WVR8ALfs1WFqB2hDsJunDmzzGGaenZzy+/4C7z17j7rPX6DZbBCEsGRBi5tcYtfyW71WV7Owar1gHEevj/odc7PXjcDjgqy9+ht1mh22/wWa7xWbDzlbnMmvAowHwksXSiqm/qEkBaMbRAOJotYar2Q5fXw6gI57fv8fbv/8Dvv/Nb/H+zQeQtTjc7kHG4nwccTrPSHkliE8W22Bws3PwIeDd0wlkgClVXC6TkNGAYZjgJdOBtej7Dc6nM0LXIRdCTAXB21ZCscIAN9aCamGRHae9n3J/rJXarwwmkLawvuuYsQyevrXpLbqOez5rNRxA7CCZH1+I7WaDmDJQOCB1q2EUVj7HWSvqS0YctW8tVc5L9gcHHwxs4XnBTK7s0HUOpjo4cC+pBpjWBbjCxCwCw8owFrkywxnOoPOMPMGI2pPlhMNYi5KKKPZZ7HoesFIBIBHmie0jK1E5qHqPc9xHa21ts3VjKrCFz7GsdqcRaU2iCis9yFof5jp5Qd95OO9hRBBF9yYPk4iwxjFU79g9OYATMoPG5+GKntR2XcLx4QHv/vC3+OXX/xum8YKbmx0AoCZg4yxyjiJIw/ev83yPYuLs1lm2d2oCCpTFbaE9xbpP7drOS4DHdgasM6624COPTzrc25s990nJnMYueNZD9h6d8/BGG+XX2RcJbFuaoy0KJatRI81sltTGOO5xhGHyRBwnPD8wAerp/XucHj5gOB2R57nNzUQFNts9wm2HzXaH3eEG3W67MAbFIJCQjOZ5xihC6CzVmNnhsiQMnOFeWR2ywIOqpaYn2Sz0gqrdNwucrJnc2uMaowtXHITAGdr2A0jELy1DXow4IwZopC410RocNKgUL4223nzJcL1Ax14yS2U8AitHL0HBaoFoDdfJd1sNTKkoPFlcoudF81UTW4bN9BiX4yF1xrLYF7hXBCdMlV7Zn2aynAXyb6w1rTzasjBVghEHqrKbbS6ylAmykKFMCxDYxdRcGjmnbSwj6AZY8/Z8POLx3Xu8+tnn+OznX2Cz36HrArx1ICGerNvGjGNwqYAFAa5G562yno9mvFd/+QgkLdfvsNlie3ePvu/hQ4fQbdB1PU9dEdRFc39eb9rPuWT6DTqGIDlyra0eoxBTjBw3ARyMloLxfMbTuzf44be/x+//2+9wOZ9a29HpMuE4ZEwzX1MIbHnYd3h1v8NXP3+Nw2GL//qbb7FLVUY+jo0vkVJCsUDYbeCcR5pnpJRgnMX9Z6/x7sf3mOfITlbqy1YaS7lNkH/X9QH9psc4TMilSIDF/ffOe+6OAJr8qrUZVNBgUgM+7jllhMxErJITrLPoXI/geFydMqcJfK7OOeRUJcmrcC5IgGlBxNB3zRXwFiUlhH6LzTYgzRPmlHE6T7i77ZloVStCcJLAcHTknKCHkoXlQjCmyB6QLoTgxD4xM5lH2BHshoU8SlVx/wKZ0YDeW+TqsIGBm6VmLvaU0UOS7BDSS8s9xrlUeOe571j2qgZ1PHHJoes6MQ0sBuKDh3EOcZ4By2hMjLn1Xat9IKro+26BtAU94ABAYHVkwAK/+y//Gf/j//I/g2yHSg6HwxaXcUQqBbutxRQjJxWVwRsr9flSuXPCQJyulqE0qIdrhEXdyzBcgliiWbPYJVKk5aePTzrcw24rrLWAIIpRLHrveHA8riG5tdxdzZnbBIRNXEsFSl319rU0i2EKGRxfckYcJ5wen/D+hx/x4zff4PHtW1yenpDjzEpGIaDrNtgdOmx3e+xl1Nj+9g5h06GUinkaWS/5cuaxYFRhEjev58xTMFISSUdS6rz0WVqP4AKbS5PEYIkJXDmRdTa59M62JzfjpVFSq2Xr67TOV3nKh7McxXqnma4TEpWQqTTDVWfSjmLJVqGHKc9hWNWynqrV3y9Zptb1Wu118ZKtblsrYIU5wLR6A+e5LqWycZqJq6NGi6WkLknqM3mNqPLNsrGk11Cft77KRp2PXMsX63Sp4V7XbfWcmiKO9EWuuQZUK3JKSDEx+7KxjlvgipILhvMJTw8PeH54wOn5iO1hD2cPCIFRHngsKlbS8kFGERwjQZuEWC2AahdltaLW57787mPYxqbr4MwBzgdY57lf3rlWT2+GQPelIk4yN7RWqUESk4UYDrcQbId1ZuX1mmgaMIw8TQM+/PA9vv273+C3//m3OI8XbLc9nHU4ngYcB17l22AROnZ6d5+9wp/9xa/x5S++hDXAN7//Bp//PGFzuODbH97ATjO0L1IZqVUUj9gxVPSbDl//4gu8e/Me0xxl7jTaGiNU6S6ssB7Yb3sYIiQZoA5DV4abwDB0Ssy05eleDCnXuSKXjC4EVGvEwWQWYNBJPs6i7zZw3mIcY3N4BhC2suxxWpyIcw5GsmsezG6QYkTvtzyjuETEUhBjQWf5fobgYGSqj86fRnO2fB919jR/JMOt3jsEJ+RFIVjBcHnIVe4tJsuBB48kZJZyFKKV7mNGvBgBcpYHO7C9sy0A7LqAaYxQc2CgA+WFVFWF8EQyGdyymhSsQ425lX68zJl20mKYUoIRLWRO2kpLGLyzqDW37PjtD+/xH/7d3+Kv/y//N1RpUdhteqSSMRcPgkEpabFBxqKCGtlO31dLYCpqcrU/NaCA2FJJnppC30f28vrxSYe72/ZwUifQmY+LsTKwq5S6sWvrktWWohCyTPvRuycHTRI9GseRWM2c2R4fn/Dw5g3e//ADHt68wfnpEXma4L3D7nDAzd09drc32O4P2Gx3wsLs4UJAoYrpcsFpnDGcLpjOZ6R5RC0Z0zjgdDphmCbEnNtGllOADkZOEilyPUiVZDQjx5LOmWVDLdZ0IRYsj1UmuvYmq2RYlWSyqOHAe6jEoZFaiLaY6DuqKb4iB63RBlkIRuA2neKjbFXWFF3qr8oyhjhJkvtaKmAKALCCkLEKUTNkvfK3vAglQtT2IO7t5WNd53btUmpW+JGMXcvU+gon17613Mi15k2/iKV4u0DoJIGF15YYy2QOJbbEOCOmuIxPlA/Ua11qwTSNOB+PeH54xPOHB+wOewTfMSJhLbx1rWWDW0+ESSkbREAo4QmY1XKRPJZW2bGGF1cp/vWDiNB5j95tYBxDc1aOpTnbtsyoZWE8KDwvg0EEKmZBFLPUG+SeOg3zNGAsFdM44uHtG3z7X/4Ov//dt0DnEWqPcYq4XCJSNei9x6vXB3z+1c9w99lnuP/8M/zi17/G7rBHjBFvf/gRu9t7fOE7fPN//Gs8PB5xPA5sWKUnktvyYgtsc8rY7XrUGBGCxzhMKHXDiAZKC2SKODzvLDb9BsfzAK1bWwMYCWh1xi0kKEulMBEIP0UWtGZZS5U4hpr9cga43W8xzxG5sPwgw/CsjreUngCUim7jpAOSdQCC79BZAMRQr+96AFVm2hYE61hZyxiBtA3PodVA1BhxaryqnKiHaXXDB7YlBGHoy/0u0m+rGUDn7BWhiVb5hXMOqVQhQVqp3fIzqYJVy4LHPCVIpQzaE8zjDsHzxksFUNF1W/gQ0PUbPB/PoEroN1sMNCIEjxRTG+BQrQWVLJrUBBJJSKpis6TDIMUZORf8t//w7/D1X/5T/OzVFjkmACwoEgxQnMFcGGm0wiBv4xelhARQaydkTo9aUd17bLg4rlGbKxdR4mdFpz72+KTD3fY9rNGB8a7ValeJVMtqq8DIRSFkafupwnBsGZ1sZpJdRJKZUSXkOWI4nvD47h3eibO9PD+hxIiuD7i5vcPnP/85Xn/5c9y+fo39zS1HhLWy9u0w4HIe8OHDIx4+POByfEYczkjTgBxnzNOE0/mM8zgh5iKFbduChlIqIrLAJ7VdXc1OGQrFytnKNlLvRAtUtzwk0FjBiK1VqsG7Cv/J0AOgRaRGYJVqqPVjts2r2bM4ESUjXTmkQoDlma0MWdulpuvAUzpWRCplSwOa3bIjs0ZHlPHnWhkbxvVfYliXliaT62CEuPbLNxw/Fe1faiWL416dZ1vAQBMeEWi0SjsTZC3CLlm7tqdpSxTAZA8l4qQ5ohCriWUVRNHVvbqFLIISMQ0XHJ+e8PDuPXY3N9hu9+j7DVxwsJD2NQnQnFVFKXbAlWRwfeVj5PJpXY5blpCuqfUVMteH0x7WWgQbAOMA0VFe2sIkiCNaBoqnhFwySpFsXkgkSvSCs0t3gBhlNj4GTvroS5xxenrA2+++x7ffv4XtezjncLlMSJHP9f6wwa//4pf4i3/2T/D5L34B321gQ2ABg1wwjjPuXr3Gw+Mz/tW//P/im2++x+k8cp+2rC1KfI9jjOi70JycNRbbvsdht8M4zEiZ4VlbMkgyKhjAekbCYirImQVLYpkFfTAMDQu0rgMfxon79b2UA7z3SCW39VUqwREr0xmBVY0Fcq7ovUMfAmLmenXvPZRRo/wJL8PkLSysSDgGzwSpw8bzFCHLSnC58vQyGCOCOx4+SF3Y81pjh2vggxMCJgdGinZ5z+GS86xjrcNgrGEhEPkRxlR0He/tGgsmqbe3gB5sJ7z3KJEFY7znVh0ul7GKVfAO+/0W6fnc1qvC0Vy7JVRTmpZ7Hxwuw8xJmQGqBJyqad6FIPZl4VmACDmxLGRRhMgIamS5Ze3dd9/jze//K37x5T3ctgdICKoSiJP3IAnAiXgEIkFsfqlwVkYeisCFcgp4a17b4CZ6oe519cdPZbmfdLh96BqO7u0yiosNIBtK0g1dywsIuWA9O1YzsZYdqmMAi1+XlDBeLrg8P+P5wwc8f3iP8/EZKc7ouoD7V6/w+osv8PlXX+HVz77A4e4em+0W1jrklFCOJ4zzjOPzEZfTCXEapRE/IceI4TJgGAZcxpF7a6tmIgBaW5AMOK4VDCfzBeOeOs22VhdRjVtzcB+5iLQyfLSMalsYzVh+Fie5nrWqf7Pg2a3Qdox2i/kYmGum+skv79M6ozRt8y/Od+VwzaISZYnVe0ytKIVaMAyIjTcG2s8GQByplRqO1rnF6QMNwru6OO1SCqu3rRa0Rbs+A13ECgm264d1trz8bFf3kIyV+RAMt02VDaxCyeysePORCpWD72upFXGecTke8fT+A27v73H/+jX2N4c2SIMNRb1CCjRrrUQ801UPuLJqUjVrZ7v8W5bX1c/LhUE7J2etkGbMEtQCS2lH1J94SEdi4mBR0RlGnixByjvcIWCckw4CQU9ALFdWC8bhgsd3b/H9Nz9iIoMQOjy9f0QcIvrg8cUXd/jLv/kn+Mt/8TfY3t5LWULaWKoOR8/49ptv8S//X/8Sv/ndt8Jo5r2QapYOg4A4R9b3rQQfPHLioRJPT894fX+HD49Hnsna94iRlYes5fF58JxZTdOMOCcengBm9hMxPGyMRUoFzrGeu7EWpRb44FEKOzabeOZu1/HcV0W95pjQdx1n3jnBOYP9botRRn3GnBCsbTKDOjIxODbg3hhuV5KRfikB210HR4T91iMmi5iYv5ENwQm/IKgSFq3WidWhHdz200RwrMwbIlaKqlUydOma4NnQy96oqbayiLH8XizZ6lhiURA3qox06N+qMMOD97jZOwzDJLVSsQOyLmstsoMtUsmIMSHGgmmO8H1AGkdhiDP3hM/Bo5aEEjNUB5qoSisXj0Bl/gtfa08F8zjhm7/7r/jTv/rn2O42CMYi5srlhwr0vUHKVbJktW1sN513LIjDpgxFroGRdL+JXbywZQtBilb97f9Ih9sJGUTrO/J+0DSkaruP0OtLTosucqWFANOyHWWymoXFWbiGFocRl+MJp6cnnB45O00zC4cf7u7w5a/+BF/84ivcvnqN7eEA3/XcN1Y5c5hzxtPzEQ8fPiDNE3oL+E2HhAKUjOEy8DD1Urlv0vAGsOCabhX27RV5SDIeNd5XNk8MaRPlaM5SM7blPdTZqtrV9TVZslV+utYbC3cI6BK1BrYsmifsFEyTv2QiENqX+MzlljcPLL2I0j/r1Ola2zbUEkVgNdeRYExtvWcaM6k8IAckRsQpbLtudUW4aUgAVvA4rdyoufInV9dIjcuaiFHrov61tGa1RcrvYS1sXd8PtHruwjzUGpco56gIiARR2gZWSsF4GXB8fMTx8RHj5YKc7uGDZ4ENsxzDVT+yWaYLFSp8neRkDUQC5GpvLjXkl1t2vYkZFWBjqYX59bjFqkFwySiFSUcxJpSUWreAqdwGB5kyRHbhCWjflgH3leZ5xvHxEe9//ICHh0ec54zLmGByxrZz+JNff4n/8//6f8Wv/9nfwG+2SIkDXkNcN08p4v2HR/z7f/Pv8R//7X/AH/7wHaZ5hnFOAnR2MLA89zoEL33xBN8x636aMv7wzRs4H5qDYU1jh5wZdkzEn9mFDlPkFsDgLXbbLdKcUEpu91SD5Sxygt67pjpnjEEIPeI8AWAlNaLKgwhqxTjN2O827V6F4LDfbnA8jzzsXsb18PB1zgStc+icgUcVYpfBnCuCzZgmwrb3sM6gNxbeqHCHgbM8Q7dUQi1LOyKvqyqZIASvk77aUgAqjCyUAu2bBwCSAB0EFDJSBlxQPbVnLJbjkRKziJ20IuTCECoLaMg+N4ZHs55HxJRhLE/9yTkLCc80KL8UwjQnWM9kMMoJJgCd9P9qq6Huc2MdkCIPiSgiaCFBiJbHUs4gUaJ6evcDnt5/jy8+O8AGtlHBe+RMjUhJxAF2cNxWpa1WwTueuQzLe1Yw8ip2nbAE/FfcHaz8BF3/fv349PACuzQ/L7WoP+5sa5b5eWpI1XhDjKJkA5CTTjkhTTOG8xnn4xPOz0+4nI4so+cctjc3+OzLL/HF11/jsy+/RL/bMbvQ2DYQOZeCOc44n844HY9wtWATuIaRgPacmKssfnYUyh4GdOIGGmmIXpg7hfrUGf2kHisLtdVN1VHou9HiZNZ0cXVwFouRVCPPTdpLZ7Mx4Ik2uL7B+tV6bxUufnHMayhE702DcFpauDofjX6JRI6Y2asauy0s51UwZph4A0ILLvRvev4tOX2JCqwDEGjkvjhcWmE2rT4sPbh66Mvz1aGv2ZL870JLPZPfy8EZD1XL4s/SXFuPiNf8PI44Pz3j9MjrdBzuGeKT8sua0GWMaWMQOcuj1X4SKU1gJSay9CqqN2ibVjLYq01sNAuwUFPLa602Z5tzQkzsbHNMKHNCyWm5NyR1No3Ka10yJuKZttbwWhwvJxw/fMDD2/coJSFOE+IYse07/OpXX+B/+X/8r/jyT/9PMF2HAmZ7plzQ9T1Kzvj2m+/wb/71v8e/+7f/EW/f8HsYy4QhvTDGGpRSZci8EXShoFZeUzlXxFhhLAsUpFpBFLHtenjnhfRmNFZGLRlAhbFA5y3SxAY8aF0z1XavDAFd12EYRrYPlZ2c8zvkKANPNLiVjD2lIkEyZ4C7TY9xnBEz25xgrKjaVWmtM9zH6tmwV6pIqWIGoXdcMx8s4bDr0W0C/v/t/emzJVl23Qf+zuTu974pIjKzKqsykZgJgKKo7pZJZjLrNuu/uiXrFigRJCQAIthko0mABVRlVY4xvulO7n4mfdj7uN+XlQkKMomfwqtevoh4993r49l7r732WpasfV6vKJa0ebJqGTiVPZJkwUA1mohIkKpAikLmskqoS1muZ3AqM1kqxQrJq0apehs8X2tbAxS5qVCtWbgcudQFfk6pkHPk8nLD8TSKsFCU84+iMQYwSoqKSX7mneg6d8E3/I9lrIFmnddsDyXhmNKskwUqA8r63DgLp/2eb37xM37js08xbPDBqpyjI6ZEcB5jdOyxaJGyfIZ4l7dKt00cNOZ+bu2h9jwKlKc1hWByTQjl+7YfDLhLVXsGH6PVRVUhi9QIUotU41kFx1lGrlWtdU48da1dmG7zPDEe9hweHtg/3jMe9lAyN89u+OgnP+Wjn37Cs48+or+6kpPCSoIR83hRopqnkWkc8bVgimWaJh4fdzw+PrI7HJnmmVKrBiSnc2pNFOC8qqlnC59Zvq0B6gy2PQusS+bTTnhbJDXonZOe0GMw50Ql/cxS1t5ek3g8izUa7uuiEmhoLN826C7qUfZMWKL9XluMJHNURp5hgZGXxOJswa/L56zvggbqRsBaNtPGnljgNyFqtGrxLJlod3tdGYFrZafVoV0zyqrjWI1MVmslJZmprsJJfHLHllLIZGWBt7EfgXbPA25zXCmq33t2qpfRoXZt5nnmsNuzu7/n4faOq5sbQteJMP6aKYApZxVvq6Dr0pMSOUL1GlW2aTEss671bB90B9aE6UmiZ89K4Xa+pT9bSqKoz3SZI3mO1JyUMa6MWSNBNVh1kzHogiGJlNdRiel04vH2lofbB045Ybwnqun7jz+45r/6b/5LfvzZb1K9Z46zEH5KJXQ9YPjl57/iz/7kT/mbv/45b+8eBYLHi3iEtcrJEhhfqk0hAQk7X3S4nZXZ2NMUKSUJ/J8rqYoDlfeO0zgJUa5Kq0D6jbKYe9Vkz3Obv/XLs2aNJSNjL8MwCPzpxIax73p5Zqa8vK844Uji5oMnxkQXkmgWdEHbUjrqxtkIWsngVoTJFJmTx4iqlauO2VvmOdIHS9871aEXz9rgLSHI1MGSVOh366Riy3OSe6RUvAPvuwUNKoDxFt+Ctnqcy5pa9DrIcyXPAwvHoxoDWUdy2kiRCmfMKTHOEYuM12y3PY/7kwRDLWTE41jcj2Iq5DTJtekDV9uNQN6KOuVZXaKSsMuzOqqFTlS3ohqNtMBmncWo+pdzlf3uyM9/9nN++nv/mN/7R7+LLYjjl3dif6hzVcEaqgbXoEVFbguAkfGynHWO3mg/moZMaWFpVgTufPH4/vr27wm4LVysC17rC63kqNzExZsusv5eG1pZqkYrkJBVgoKwEOUGTHFiPB057B447XfkFAldx/MPPuSnn33Gix9/zHB1jVVopJYi3o5GpL9kBGjPNB6Zx5FUMnE2HI9H7u7v2e33jKP4IhqDOvWIKAJGvpdaZPHOeQm87bS1+U4JbGfyhZxXwz9wglviUVfoef2RBkonZLTG9D4ffDkn1LRAdH6FzupB2bdG0NDKZNHObZWuaQFXoJIWAIypS59HfGrNWRV6lrG1Qrjtu2njNyxQZEU9cKnLuWzV5QK9f+d9jVZZrf9Lg8u/gwZYHZQPPmCMYZxGgTvLeUXa0AKBwXIj82nGfv7ZwDIi07SVq+79itCsVzcr233/+MjDu3dcP7sRg4BhUKhYKkOjB+WM0/6uIhnGYGRpJxlDNuKIIvfIer7zesGfPsx6bG0mnFIE4m+3jD6jSf1nc8rkJL05iopD2MYeFRTLWaMVlNVKV7kAqs1bciHGxGF/ZLc/Yr3ncJoY58TVMPCH//j3+eR3fw+6LdUY5jlSDXT9Bip8/eVX/Mkf/3P+w1//HfvDiDUVHwKH4xFjRTDC2YqxnmmaoUqy6L2n5EqeZ0AUnIw1XGwHpilK781aUk5Mc2Lbd3jvSTkBOsZjhX8SQpBqtCWBisBgjNjuOYvJ6qBjPbv9gWIlKXSlyDlxXlrdOqZTCvpvAm9OMWOTavMaRUqo+K7T4CDBMabMbCrBOryp2FKwOu7ovQg5xCi9c0dWboXRBFlELZKO6Ymaljg1kQvVaNWrCbcQrJBAXCTZKlGqSguLtGMtkhw4L8IbbYyJKvsdvOguJyUUteS7mUyErmeOkSEESorcXF8yTVEmVEwjYKr8S60EH4hxxjvP5cVWxzUjfd9xPI2iAOYHsVXMSa6pWn067zG5LM/3oulQtI2SIjHB47t3/Opv/44/+MPfxzsgR8k4NA5I4ef0fUWxyzkZixPSFKRiMN5TkdHNqqTWqutRoQnvaGtoQdb4we2HDeiXUN3IUWUZ+Vkr20wTkG5V0IIiG2iqO0YDbWNSllwWe76aGwv0SJwmgg8M2wtuPviA6xcfsLm6woYgC5YSRNosW5xGdrfvuH/zmv3dLYfdAyWJ5uk4TpyOR1KMiuG75URZLM0Mvmk2G5sgRq2a83ew+UYgWbcl3H0Hxz//nSd//s5Llsr0SRjVmti0OlZLnic1z1oHtvdpkLBt+7ooSa2B9skXAj+1oJqzVKDCdF4KJqo2bM3Z3q2JwirT2NKPVt22hU2MIdaeZAN/z9GCBc02Kld5vo/nZ0vnW4P3DJsB70R/tcl1romhhk0NwqXtQz0Dn5+8vcKwtZBL0vu99YfXfWjvGePM6XBQvsEdL168oFxfUzRxa9VM2+emcrdUu07PX8mkYrBFlLwWYp2erKIAwJrScbYvUhEuD3iRV+Vl/EefU4WSS5Ln0zWkyaxEua7zMo6i0KGpKoXn13nwlFUoBlno7h/3OGP55Kcf8tkf/D61G7QilLGU0PWUCl/+6kv+2X/33/Ozn31OLAVs5eLiguP+ADXjnSF4Ga2ptZCdzGZ6a+i7jjJHkhXjgk3XiVG9E1IVJAnWTvqVc4wiv9iuec5KwBKRiXkWwljoZVbf2KpJ2MqSTzEJJGqMzsxLJSitnkKtgog16No6cSdKKVPHmRCCCmawoDC1CiQs870dpST14JbWj7eVWhKYHus7ao5M84ypjt53eC9oXJrjGmD02aWRnExd2hOLgpwmB9koCal45lRwJlNyEjclI6xfjEDNTXiiFNG1Zlbyq8BgOCNzv077vwaB4ocu4JywnzsfMLWKQUFqLO/1eTRWpDaTVq/TNNP1A1gj5zIXLRws4ySFElXYxN45VdBzuj6WpTftfCNwCfM8TifefvlL7m/v+PDFjUpa5kXW0VlDbRrKpqpCma6dQDVViG8g+JlBzSLU8KYUAZiq1potOTbyLBq+f/vhgHtWjWQNkNKrjauXraZt310eW7VjvhNsrVWrpXkWg4F5Fs/FKIIXOWeGzQXPP/iQq2cv6C9ksF8CQzmrLis5RU67R+5fv+L+1Uv297eMh0dhN5YqbMyUxZu3C3pTFW34ywMhGVNg8d3FYEzShzZrdbEGCDjLJc7+rYW/BosuwbbtbwvI+v3JxagNMq7rTawvMus/n0Hdzeh+nW1eQ97636WywiBzoI3MZJaAWqo8MFZhRL2HngSkllS0b22WdZmBW5IDLbrqOtPctKNBg7GcJIEMdQzJ2qKGCcriNWf3kP7BLJ8tPdcudHRdIKnqD2mFqsuy/3IQZclV9MxYMMVi7FrlgsBZpcHM+gRVs3Rr1uSkZMZxFBehu3umw4EcE1ahKUng6lLRVmeg6gwwBusETbAFbDEadCWrFlhtfaaKLWcmEk+3xXLSFIWlBQrMRe77lBJ5FoIURYgmcu0UKbAikh+8lwUmy8SAt6hBvcDVOc7M48gYJ/zQsX/zlpwKH15f8If/5A+4/uBDMeNQ4lHoeqz1fPXFV/zx/+v/wy9+8UtCF5jGI5tNL6jUPMlCrsIO3srYiIzorDPczlo674lxpus7fNdzHCcVe1iFFVqlWmvlcnshkHYSU3drOyoSQLvOMwwbHvcHYlSikBElJFsc05zo4yxSj5q0zDEqx6IsI2W5KKxc5TqXimg7AzFK0ubUvrCUjNXkxXmLqwGvKENTagsOas7MU2EIGkpy5ngcoXRcbDusq5QcKVlQrBgLAtJZqvZmUU6D9N7lQXKt+qtK5KpW1vOaqMZTqxUWexSGcEPxgpeR0Drn9dm3WnRYSTpkPbaYnLi+vpb+6jji8HSdY46OmPKS1BiE6JpiBGuFXGUAM2MMnE7iICUtR0FqjFnlOFsboBG6DEXlO0U7/3gcsRa2F+Loddo/8vUvP+f68h+z7b0UFSDjpFRtvWSB45GCoGSziH5kwBuzjPQ1xXQUVTL6zNZlJFDWCWM4E3l6uv1gwM0lL5CcyN8Jw7FVtrVkzDm9UqNE1cWuzd22cYwCUMR4fjqNjPsD0+m4fo0nDbgbEbe4vMKHTjLnM9ZzzYnjeOL4+MDt65c8vnvHabfDlEwXHJRm5bXas5mWqRRY6EN6Ytr8KQ5qkIUtLwzW8p3De1qhtZthfdEKNctfz6OWfmxtbLe1GpN/Xyu0sxf/OgS6/LzdIvVsfzQoLdVuCzNncC5SuT69H+xyXA0WPg+0S81m9aFTij727HjPCvHzfnU9O7blvNhVcMMZizPCHjeaHcqbn/V9YWE6grYiEsv8odFzlYvSh0xRpSe7fO5yPK3aPD/8KnDRInCie17bPadv0EiAKUYOuz37h0fGw5E0TtIH1Ao36zWxypKuyoxaK902M1swpqxJhzEK2TXzCkPRc/Ek6OpJLSWjvBxiFtKKtGmiMHKVjdwUqFrl4KxUCsGrWEatTPNMTYnN5RYfAkWFaw77A4+7vUwB3L7j3d0jfXD87u99xu/84T+iu7gAnWWUQO54++6Of/bf/Q988auvxNLu9pbQB5zzPDw8qsJbxRurgVfOszNGbd90dEfPv7PiKfvs2Y30aqeZ4+Ek10dRlpQkOYop0fcdYxbUo+SiesoyHxu8NLxyyRgHRsuU0HniLI49IrIg1y9qTxljca55V5eF1NUUmEoV8mVrpwQjEwBU6ZV656RtkpMifRA6y9XQ05kiz0CM1GKoRhIwZ2AaR0pJgkRY6e8a91QUpyRdH6ta8TmBh4335CzFR4XGpNT9Fhi3FLugOTKrL0HMJHlhexqcMeANKVu9F+tipZhn2ASPv+x5rIV5TnRhwNuZWKI+zo3bIuszFeac6LrAPE3Khk7gLMGJKYicV2351DZvLEpkACGEZa7aOXU5MpYUC5XM3e09X/zil3z2O79DcBu6zknc0vWr6JoJZuVX1EqhtX/qgtpZrVLKsi6ahi0DZUH4stGF8gdw5R8MuClH6fVFwcXzLBVuI0dR1oV+LfvaomYWlmk1yuzKkvmlaRaRit2e6SgOQMf9gWkcgUq/Gbi4uqQf+qXEaxT2WhLzeOD29WvevvyW+zdv2N/fQ85cbDf0fSDOE3OMxHkmzZmYssz/5bIGgScFp9LvnSNUqW5SdlQrC3g7cZU2e9qqu9ZbMZgmC7bAiSz9U/Rm/m7QrJp1tsjY+uSNYt+2hcilc82mJTToqI5RSMyYpScncJLUtKoxs1SwOQvMZGvj9tkFpmma0OfnaDmOuvZLlpEoqxd9gT7X/6FHvv6kxeMG4T11LkIhUhkur1ot66jVeQ5SCqeTEDLmeV7IT1L8l+WBOA+qEjDXirxSMcXoIsAyW1gUBm6nv+i+nqt+WWXIj8cT+8ed3scnbXdAcaqDbdR+LGtPj6JMtvV92ndXColCNkaYwhUNuksqveiQr0G3IjaSAvNFzdZjnJmVjWwqZxaKduEuWPVhXoQLgujJTqNR5SqLtx1jPHEcR/aHPbd397x8eUucMj/56Bn/+X/5f+XixUdYHyRRUSLS4+OeP/2T/5mf/+xv+fgnH/Hw7q1yPyo1zzIDXIoKp5gFFfAIkbILok/stPI1FLbXl5xOI+PpxHZ7qXctnE4CObbKp+j1z1XWHIskQVKlVFwQtGzYbNgfDpRchDzZYGUjs7iSEFlqiXLOTSWoob3zHqpdGPztDpOlSqQHxU/bsO23lBShFKz2IK3xlJwwnWdOMh7TDYo2dI7gJLgZA8FUnKkLmlQx0rvWvq/vrLBs0UmC2to8MlctCWNdk8bm1KbnQ/xyVWYyGMaYtaUkgVEfJdDCy4ZunVtu2ahBq1LDJx9/hAFevXpLnATijzrvWxRxlCRa2ceIdKq1MMeZmCJDt11Ijc454pyUPFqX8bp5jhhTCaGT86L9YWvUGtGp9Wo1vPryC+7vD1wMA5SEt8KC917VwwBbLXghDUryoY4/xlAUtjYYUOJmbe0vrXKNIoVJf6ea8qTwOt9+OOBGDbhzJJ352DbFkidv2P6o5ZU5W7hRbLRU6QHP8yxf08Q4jozHI9MsD04Inn6zYXN5RegFFmjZRcmJ6Xhgd3/L/dvX3L1+xf7+nnmcsMawHVofYFje+2Qn6jiTS9TdM9oof3oyZHe1p1KkV9DMltsKtwS67whEnL+JqWtAWcgtP7DVWleiDZwFKVZ2sr7fUiG2Ku87p960Y1sCmbAFndHZXLNWue23lj+ZtT8q/dhWjWo4/k5S1arTBcXAUG0V9v95UtfK7RbUluRG3uqcAWmb6MPyu5VFu65V+VnguTlDjeK/2YwflvNJlYpyuTTr+1az3rOt713bSW6/XyrGrZX0Uh1bQUksGnBLUdvII8fdntNuJ4EtBAxSnVQj4x/OK8qDWfZBzoEEaGsrxViMzWpi/uTygim6KhhNAFfEQMZNBEpOKRJTYhpn6VWZ1YTcWbckiI3U4xVKttYJe5fIlDK5VPrNBYbK4XAg1szbN6/ZHw4cx5mLLvBf/NM/5MNPPhEJUidVat8NTNPMv/yXf8G/+rN/xYcfPSOOJ+7vH5jnGestOcqIjPGwHQZyTLjg2F5dsns8YFwgOIH2ffDklAnOsh2EZJOSyGwOfY8zsOkDU8ycpoj3gWkaBVJVycZcCyZDnGeFKSvzacJ3PdYYkiayTu97sfFUpaEkOsoeHa2xLamTe8J5L4mwwqKLLKI+K8JTiQRngYx1VgKK9vBjTAQX2B1naoThxYX0tN2Z9GrJDJ1Tz+qK80ESAp0lT7Hgu04TfUHlnLZsZP0qktB50RygFIE/i5KfjLQzMJVUxDlHFAJl5KkobGyN3KNT6yNj9HiEdVxKZbfbyyiN9+RaiUXGgoyRgNjUqc4LD2uF8JWLjIPhHKNq5huk1zvPUdaNtp4YVOqyrbXCOG6QfY1lKdIMhdP+kduXX/HjD6/xoGInsk9WfYpLMVClLVRb0SRLF83gpQkftdGfTNVlygjSW6pK3Wpx0R7W72w/GHDjFEWJIyZybM3zcrYq0tIByQItywPd5OGqHrh1VlnJ2ier6wBy1r8HH+iHDZvLS4bLC8IwLNALJTEfD9y9ecXdm9fs7m4pccYC3ltqsbjghQilVlIpFSozqei8FW1+E13oTCs4lt7redVVrFzIZjewkIX0YViC0hlkWdu50H9bq1W5IarCUMuivyyq8N2ltgXbpVe3fE598rrvLtCGphAjYx3eyuiBt6tLztLLMyvBoh0fsOwz9QxiNQ2y1urMtupLoTWjWSZVpmKaO83ygGmwaX3bNipjV+JUC8j6cYsqjkhmivelqOCsLMXW42y92mrrWV6hlXI5q7nteWWiqc4ZbC/zli3W1+XaL8lFVdk3oszlPj7y+HBP6AIXlxeiUJMSpWbpTTqn58tiqixwi1AH6FxjXQwejAGb9ZxIi1GDbjPzbtU/C3oTo1hNxnFSFSCp1owmCO2E2gUadXjvSFmYy8f9idffvqbzjvDiOVCZppE5zmLYcDyyP4zkAp/+xkf8wT/9z+ivLjHOihBB6Cil8ud//m/4i3/5F1xdbbCm8qsvvyEn6as55xhPEUxlu9liq2HOM9fPrynVcYoZivjGYkSWMGVBHsZxZtMPzCkzjpE0J4aho9bC9dUFuRzIZVbWcl4gx6LYqbGSWMQkbkC1FDrvoEobgfN7wsh9Y3Q2tcS4EpeiJHg+SKtqVg/unBuzneUZAYH8feeXURJBGeQeqKUS54itmdkYDqeJ4XqjIy5J3Gy8VOhDH0R4wxhcsZAiwRus17l3DFSrz5yS31RHod3p1gDOCodFmUEly3O7BFEr/ehUxATCIGYqqRYycl+bJhCjcGp77eu3j+z/9b8DI+5KuVSmmBW5yrC0scwim5hzoaLQu64z0zSKZKh1NDeeRR8auXb2DMYV3kdLzgXtsV72PZfE6XTi21/+nM9+8zPC9QZn6nK9hASnuX01VNees7a+sZiQiEa7ogxG2kHNya0ZGJiqyXYVntn3bT9c4Y6zLJhJdT3P8PxzqPDsLpXqR+dba64LDOtVu3RSE2hjtRqwRvWDDV0/sL24XEwJQteBMQJFjCcOD/fcvX7N7euXxHGUXojCJ2sosrpXllwtMVXpbSkJpjl4rDmDMlJVyNvqQ+GtpTqLKj7AkumsYtnnjNg15NX1QV+e3TVwLcHrDI45HzFasrgKTfHovM/7JEC3B9u0z21BrcHLas2nAdcZYRY703qw5snozVlcPfsQTQTaxyxwsopS2BV2qkVGTmQsplWNLOepVeEt6DtbdXbw/BjWbflc3Z8GGZ5X55y9/5LktOCluaYEp7IgBbZoX7eeX7HvbLW9OUvZbhTBoKIKZ4V5nNTU4JbtZmAzCMQlSWUhOZn/LMZKFctKMDOiHrCcV2dkNMo2MGFdvVmjbxuxkoVnHEfRC45CkKJWPb+OtaUhn+msWKIF79UwxCJ4pCdOEeaZm5tnmDqzfxAZyNPpxDSNTLNI8X304pr/x//z/87zn3wiAhdFCFbg+Ot//zP+/F/8GV3ncKbyxedfkuJE8LIoz1MkpYK1njwXMT83lcuLC3a7E0nJOWjFmaZZWyCwO4xsNxuqsthjTtip0vcd8zjRBU8B4hy1lyvcjFJV71dHj0S6USphQ2Uz9JymWaU9wZgqTNkYCX7te1uDkPRikv4t0gOX+dC6iPIbawjOi5iH3itNi54Kpogs4XYzQM44I2OA1lTmCLtTxBnDxjuClKTkYkgFnJfAak3FhCAjQdZinWeOSUe5Am1Ux3mn7lB6LxsjM8DWUWLWWk3WLh88uSY9ZyBqdCxEu1RhnqWC7kIgJRkVs9aqyUKi0nH/eKCiJghGuA6NNWxKxSCuP1CkUFKEwRhJyKSa1WreWUpKut6Kf26DmZ3O0YpylgTvEDwpiZRmq6gphvE48vrbl7x6+Y6L7W9oMBboP9eyFBwUyLnpiRugacs3KrIUGO1xLNZgjdPpAhnDlO+FTIGavm9l+eGAW1VFhVKfBI3vXaa0Gmm9TTCrBKBWVlF9buM4YZ0j9D0VwzRFUql0w4aLqxu2l9f0GnDTPDMe9uxu3/Hw9g27u1vGw5GovbsYE1Ft94TQYER5apo5nCbJtLJoj8IKj7b50KwylMUVvA+yKC0epxKMKY15ZliD0Tpucp7I1LOvtZRdg+lCslre6GkKY42QZNZRmu85z08qzbOq9CzIn8/HNoWjBXI2opTT/HSXfW/94fPgv3w3Z5+9BoEWLPTkLr3s83PRXrLurwZbW5Yqb9nnFgfbzdP22yoLujztED+F2uVTpV/Ocv6WmVzAGO2Z1+85t+1Yz4/t7PifJABaVOSU2D08cvv2HZeXl1xfX4viURbCT/WRMnvSuQLJepWwtqEna0JjcbrQ5TXBac8SlaxMyJwTp+OJFAW6tFRxL9J5d6PQcatmg3eEIGMmxjniFDG+w1vLbrcTY3IMQ7chDB2Pj/eUJCNXx8MR7xz/9X/9n/OjTz+V8YgiftFd5/niV1/zz//4XzCNI52vvPzyNTlOeCtSfDFXSm4OXMoPqYWuszze7zgcJjqdAW0m8Y19MMVEqZaQItZYtr1njlnF/Bs/pDD0HafjaTG7B7WDM+bJmJ/RJLJp8PYhMM1iZCHGBXkpLnJO+DNEpfmv5pLo+gBHhSatocm6eufF4aYk5VKIkYDTqjl4x6Zz1Gx04VfGvKmMp5kpWDoX6KyR+dAqScc4Z1HIKhWs6MCLClMhqJe4XNuwpHXO2UVlTWbVVfxRk+8mmeSDowDHmKhGE/RaCUEKp7aWdF1QM4nMnBLeWKIRaN7kyGboqdYwTknuV+8XaUzrwFpPE7tpz2hWXeb2VHglyVZloC8ITRWY3rQkVbA0FfvxqrOwiuJURAyEWni8e+SbL37Fb/7WJ9IzNiq809DKon64thUtSmqsGvuMFF6tmpaEWaYOatUZXBDSZilYU5Z1+bvbD48F5awfokn+2e+3MNNIyk0q0VohG8i/6c+MkaZ7jEynkXme2F5eEXpZhGbtCwybSy6ub9hsLwhdJ4PZ88T+/o7XX3/Fw7u3zKdRbPzmyDjOjHFmjlEqF80i55jkZ5MIl4uiSlvYGwFImK5NCNy5zKAsRCfYC5JdlmXRbidAchBpvFftHciY+lNosr24nbZVQP87TGTWxdZYg62idnJehbUXLfGAFdo+JwetAbBV0u1LEybW/rM5e13rr645Qgs6+l0D8VpVn321z24/o9JmfNsP1mQM7S83m8eVZWzPPmfZJ9OqYrkehaILW6tyn9Kx1rMgpBB5tusC/chnVH3gz0L3emKXyh+MXL/lXLX7Z03ccs7sHh/FYOPZDR9++CEhBHXIKpCyVJ6083J+QR11qXpZznXbRwm0WWe1IYmFBbbKPTumzDjOS3C3IYhzkQbdZqfpnVQ73ntZsJ2QvozzXF5d8PjuDWUceX7Z85NPP2VzdUEukemViMm/e/OO+/sd/+SPfpd//E/+CNeJmlSphq7b8NWX3/I//L//mMfbW4JJ3H7zmhpnvKtcXG34+vUtfb9hHicNEAbnDZ3rsM6y35+WOWJnBLb1zupiLEQwsdOWfvWm8/Qb8aA9jomaC3OcudpuGbYb0m7PHGcuNwNtlDDlokmz1fcRRmsuRSUWPfM8M81ZGNE1Yl1HViu4koX05JuFXZXxnwWxqPkJzgWyJkp/UJ93nfUsGPG/BvouMHSecRwhR0zwTDHz4nogdJ6gz7iYMxiK2v95L+/tgqX5PHchiBmG3lPOm4WsZowQyGJM8sQZCbDiDQsxJ2IqxCwKcULEywTnhTSkPtgSF+XzvJORH+8sKQvjuE6V65tnVCzzNC29cYwwf43+binoCJOcs1Iqc0yAtBbneSJ4h7cyH26sXdQNvW8Mf2FshxDEcKNNm7Qedq0agyrTeOTdy6+4fXfHJx+/wJNA++BtHNE4KwVDtko5VwMLH5YiyVhEOGMpKuXL6p+dk6Ig20oy/8AKdw0WS/r99I7SEymztmZxTWmncYECs7iWtL6Kc56gA+rWBYx1hH7g4uaay+sbur6T0Z/Tkfu3b7h99ZL7t2847HaYKjdESpVxjpwmCbiSwRpt9svsXFS5SUNVGLU54hiFkeX1Mq60zowau/Zy7XK88geZkcxaHScJ1lbEDSQQf3dR5df6hUtsWIKlWbKypxUnTwPCsitKYcf8WpBax4POq9In8WQJjE+g7OW/7YUtyKw6y0uFf1Zho/vR9nfZ1yf7YJb7wTkZk5EMkCfBq2Xly0iROX/P9RQYWgHcSE96XmUvqFrltvi2oAVnydCT2/jX7mmW/V3OabtmS2YjP085cTwc8N7xeHfPYbej68SKzlkni0pK2s9Zr30tlRoqrjqMwupWe7xy+oSZvJ53g83CYpaxjLpo+XorkKBT6cJVurQxkS0++GVBMpqAha5jOj6KvGMZ+dHHv8VWe9C7uwf2D/fsd498/dW3fPrJT/m//Bd/yOZiK2RKK76t797d8pd/9hfcvnlLCPD6y5eUOOMdfPSjF3zz5l4g0SQSgEPoEIRJ9iFOwmAdOr/cOlMqC+GklowxVbx69bk7TYXn15eY4LGxkqaEs07FeKTKmWLk+vJSf9+oPWRVVrTKjcokoMK+lmgcc5pwtogdqfc0cZEueLmewSzSk9Mk5LS6PJMiNeu9ogx6PCkngrMkChehwy6ymFKjdd4wXG2YRiPTIMDD4QRmi+kCW9/hXJDepDWETnSVY4yq2idksAYF11LIBUoWl6ViVHgfhYxVS7nxSyrSg81F2m/tWemDZ8oGk9G1sBCLEPQwEvCNNXhjscnov8Fu90hj+pdS2Wx7pnEWvXpNgo3RsS/ltNRcJAkLnUq1KovZWypZGeGoypxboONcRUHOWr+0EwRuVua0upqVnLm/u+PbL7/g44+e0XceSsSUNbGXe85SSVREHtg2QREV3Gha1RIn7Lqg2uYcbVTJrai95a9vf4/S1LK2fO+/VV0YzBmEhVmJCs3fNKUoDENVeemNoet7Si50oWfYbLHAzfMXXF1f4Z1hOux49/oVL7/8kod3b1XyMYmzSIVURVs0xsw8J+ZZvDFFQLtKdVPLGmyNDpk7j8FSasKYRMPkBQaXMR2jQunnx96qzcLq/JPbw0Z7zcoutg3rbz9gXfDb31svdDEP0Bt/gUm/A3kuFboG6Baw2t+XytXaJWAamQ5QMwMjUKg1Z+M964Iuq3tjkQkWtr53i5vmye+thdtaZ7bouIhbyEqwBNeWLcvHKIvWqNrLejaXB6H9vX1bAqoVCBpjZHaxjQ/VtYUgLPC1mm1/bsSvSqWQ9XtdIKS1ojdL8G4oQTWSCVQjVVdKEbuz3N8/cH97R9/3XF1fYzsruEcplJgWmKzmRhwMEAK2npFQbJuHbomIIgE6p5mywFW5VLXQA+elsnVnM7a+Cc04hwvt756uE0/aOWbqPBKnkbffvuTq8hmbq2thgc8nfvXzvyOfhA38ySef8E/+6Lf54KMP5RQYabt8/eXX/Pu/+mv+7m9+RkqJh7tbqBXvDR98cMVhnLjbHfE+UKtRTV8hY5ai1nDOLabvwRturq94/e6BORaS9vaGYRD1OCMVeqVIQN1umUKlMorLTxIFpYpwMg6nE1cXF+SchEmtqkIxTjSjCmeF+d4PG05TpFJFsWqzkVu7VqxXl6C+E3cjJfmllGhioA1laoSt4D01x4Xo01otpRRSTGx6TxcM1orspguO7XagFlX1KoYpZYa+I9VK33m8VlYiQV7YbAf5fGMhyQSIU7ZfI0RWXWdW7oM4+HTWUC3S4y6JRNMslvu/D5ZYLLH1X5MoT43jrGOTEphF4lJgdBGpaPC9sIh7b7l5/oxXr9+waqqX5dnTwl+eYanASHlazmVoRVstahgv1e3Qd3Les+o81yLtlZwIQUiKMYrCmg0yVfB4/8hXn/+K3/3936e/2egYX6XWvBAWKSsaCtpfVj9xihSWpupjWqsQ03S9beS1UgWpqSb/euDk79NSPi+qnv5H5o+UsGSdFSUVhVuK9kWpLBegVbdd3+GUSJLJbLdbnr/4AGcM18+kup3HE+Nhx+2rl9y9ecXxcbdmktUQiwxWT3NimiLTPOlXXIbf2+iK1aoqhKAC85amU9HW56W3qg+EWY69nq/zNI3bZnzeKvmFSXwOJbeA1sLFedVrzks3FuCgURi+L9h+97qsFSHr11LUruXsecUrwfcMhjasVfZZVbqQqew6e2o4fy+Wed0nsGw7T8tBrdVt23FjWBIg6SUbnRFts27re+m07HoNNNhKIJbg7KzFIezvnMVJRM6fLjD1fM9YoOS1+tfEoO3zD21aUa4VbkuMJOBO08h+t+Ph/oHLqyu22+36biW3FwtMqr8nCVbBBU91HpwTtrKS9STZETapNYZiLcYUjAZdr+o6XZCxD6cMfeedBjK3vMZYjw8ejGGep0Ug4bg7YK3n2Y9/RDd05Bz58vNf4krhN37vt/n8F5/zf/unf8THP/0xxjmOY+TZ8ytevXrDv/lf/jW/+A8/53F3YDyJaE2wlX7b8XiKvHq7U0ECgS/nKA46tWZlLYvQv7OGTefFDrTIU+C8TDWIebwlVWEYD16cnXKpjMcTvQvLONak2tFU0WquwGkcBXI0UKthjjPWWXqdS26o1jxHDIbOB2al5+Yclz6cc47D8aTrnVO40q73Ei3RlgCeo7CgG0pWSsZ2MtKTq2gvo4YKwra1y+d7Z6g1McXCOEtV6lLBB4sx0t/PKZNVn92FjlomfPC0+dmm1mSdI85RrSEr1guBLNeW+AqDuxoRXAneC/c3F0UOLMFXXAKbWRC/WqU3j5FnMDhLSpK0yrMhCCJG2iGbfiBmsUoUx6JmYq/SlLrfGBkFyimRStXkJehcrhRx0xzxThCdBu2W0uwopU24GXq6rqNpQsdUGI8jb1+/5psvv+Rm+zv0XttOi3hTXRA2AxpIhSWPtgKNjg02t6RapaATtS8pVkqBasTI/vu2/3iFe74WtUVaIWR79oBjoCaRVMs5U5zAMMI48+CUet6yMAw3Nzdi5FyrsMymkbvbN9y/fcPu/l7IIMaIo0POzNPMOM0cjiOn04nT6cQ4jcQUF49GoyMRzspMWx8Cm82Ac8Jim9JETFEh4Yo1blnmxSS5LtmoDLJXhWmaoxBgzKJQtczjNphGF+jzYFeKDpzLU0mr5ZYAbxuT5zzQavl49vdW0T4JjoYlq69w5q0qwa7q662TL9Pmba1dMn1jLd/tgZyzkRt7u1Xu8r5Fq8nGml3iiiYfa/VtrVVWpcGYQpOPc2fjSwt7Wu06Ft6UYZnvXWB7nQ90ztMr9DfFSqyVVFYJTFRpagGcW2Jk0ArR0OT6an2aVKyvL2Acak2/nNtmnVirZOPjOIoQxn7Ps2c3Us02VMDpsRTx/sxVzp1IywWcLxACxrsFLsd+93qv5zMXu0DGxtkFRm5fIQgk2ipb2wKfSrNebjeiYBRnfvKTH3FxEZiPj+wfd8TDkT/8o/+Mw7hn0w98/KMPCcOG02nksDvwdz/7nH//7/6GLz//msfdnpojhkLwhtB3JBz740i/HcSiL+ZFI1fs1CSgbobA0HeK8gj5aRonOdfeMZ5GUq7EwwlrYeh7xmlmCI5Nt8E7Q8qRPnhSlbnZdr2GrqMUoy2nmRfPn5PSkVKR0Q4d7Ykpk2tlPo1M8ywM4b5XEo8Ez2EzYI1jt9+DPgeNCU8VYlJOrKzwKoHHOc9G22OSsBVyNuITHDq0ahHuixMXm1qFuGWsJ5dMLDDOEUMlZkvXGTwCZxqdVY2TVHJe1aVqrao1rc+gczgMVEvBSistZ0lO9L1KlUkR6wwuSyrf9T1lTnI97CxM8KNUwO05iiljg5VKtwZilsIqFbP0PftNxwv3jG9fvhPpWGMW+VdoxYyhZkgxSYHUbUgxkWJebP2yCmgknRX3pVJLptv0SpSTpCcX0c0eeiEIii609MEPj498+fnn/M5vfUrv+mWFbAXT+twXTGlTA6rBvCyrihTWejZ6p0iCFjZUi/tOa7FtPxxwz0tcwIgyNjgjvSLvJbN2dhEuN2ruXHLBIPKKTYFGFhw9wxqAttsNF9sNOSWm05Hd4cC7V6949/olaZ41GIkcV0qF02nicDpx1IA7TpMwDLOO9Vgdf/CO4Bydt3RdJyQsYxdW85yiyp2t8GcppRFYFZlsTDo1ha+NsKNiAUoSo732e0/hWXUIK7ONdczFVLBPDIubOEJdAnO7HOsIDUuF1u7a9hAs49bnl097D22sqalEtSzYfCfYchZs16rXYo1bbotShcbUgqyIU6wuPev91oJuqxbs0k/3zomFmKtYVzDFYMwCGvMUYji7NrViqmSRQ2i3sB5/rIs0p+Sw5+dheQMJupoMnH3SkwSh6Ny5QYPnE6f4s0BeCvM0cdjvOez2jOPINkYCQdotKCpQxSiClsBpRl5DE5MJT9Isg11QiObNKedTBAZc6906v8yKOq9JsNURByPjYUazomEQU/Xj7sDz59fYEjncTYTOAZmPf/wjcs0cjxM//fQ3qDmyf3jgm6++4d/827/m1au37HZ7Hh/2GFMZeofrBvywZZojh/3INIu92zhH1fgVBmclE7xlGDqGzVbORypibIBo6U5RINnGNramst1sVNlOnGusjvikLP9WVDsZpM+56TsOx0ns4DrHNJ7w3qrXriALNkYMYlhQqkrY1oo1jZTlOanr0nbb4Z1nat6y1omYh5KCmhavd0Lus3qP5pTYDj3kJKJBxpASxGiZvWEIDoKMa9VU8QZckCTROdF29sbJLKiFmiPFdlAzFasyrdB8vMGIHoGuIrJcWVmLVWvZFCEtFifKSDWr0pcHmwp97yjWkKPBe0dtY1ZBjWeq9G674GUuep7puiDGDcvonjwr8xw57g988ulPub/fkQ4ia0kSZTRv7cL0jTmBgVJkLGqzGTgeT8SUuNhsiFF69dUqymgEBrfGMk8RjCRDOYq7UK1BSHgl04Ue5xLTeOD1t9/y+tUbrn77M5wimRZISrDENBQtS1Fh1l6/rA9WFLsQlMAqLG4UsasKeLjvCiPp9h+tcJfNGsmqvCjTLF+NiIRUat452YGWgSssuODkIExdK9BfzonTYc/j7TtuX7/m8e6W424vtHDnVF0mMk4zp2nkNE6M08gUZxEsx0CTIUNgtT4EOoWRJcuRUYRpnpkaoaqIlN8qH1jBO4G07GqrZvThKfpAgsEsBslnP2vKVDTBCxZbviVgCc6+9ICLmjcX00I9S6j5tQL37KK3fiJ1DcntAWvBf+3NrtuyH1ZF4luf9Ulw1SNrN1j7Pc7f7ymM3M5hcxopzclmqRqtQiyq9eotvlgC0FWDTxUXZXFdj7suRX873Ba8Wy/cwNKLq7WjiVIkZZ83DeN1JOSsp6tBWRJxrfaV7WitlWq0NDeWlgL9+lYRFvRpHNntd+x2jxwOBy4uL5f3aqjHdytt085TbiYP8uUqOK+QfG2KZYI0NKarVLheWzRugZO9a3KZVkcdmuavJMBxFlGF7eUFpIjFcvnskjiPPHv2gm4QpbZN19ENA19//i1/9qd/xuuXb4g4jseRh/sdtYDvHH6zwRjL4+OB4/HEOM0UlTdMKQOr6I1xlaHv6cPAPMkcbnAy3hFjxncd0xx1flT63b53bIaew3EUWB/oup7TOGKdwMti6yc/887pyGAEU3A2LJMKKUVyyfShE5WylJQNbDmNEyC6AN57ur5nniaMcRyPR9ET0ITVOUN2RseIDFHhV2sNwQfVLsikbDB2ELJYSeScRKEpZ8ZZAu4QrFiSKiTcd07xowpehH2SsruXXrSBKWb6vsN4s/rqtrVWx4EMco+ZCiF4bJZk1FSYFWZOxVCqHEvfCdJQrWFMoyScOtY5F+nnTkqastaKYcA4k8aJoQ+E4JnmKIEZeRZPx5E4nnjx/JLH3eEM9dP+txYhKVVtSwSZ8cXQ9z3jNJJqYbsdMDT1qVk1uAXlSnHUCndd72wrHqr0gJ01pFh4uH3gF3/7d3z6059wMXgEaSgiiFGfCjuVXAFlG7u2DiblwxgoVsrgWpegWzEYW7Hfu1r8vX64Z38wRkg03mp126DkFVJtfQ0fOqwrGojtQhRp1YVpvayciNOJ0168RW9fveLh9pbj407mbAsUEtMcOR4F8ok6EhRjWiBk5wOunVRj6Lyn7wZ6hRSoVSj/08RpnJhjouRWmRXRXVW6mqXibaBpZ7YFsy2qgu9bUWdpFWUuq2RZC7hNWxmzSIXJ++i5bAxBraKqetCaBkEaI8H6OxetLv+RP7TA2BbkWldhhOV90CBeBVZ6Mhp0VtG2ivlJ31bfZ/3EswDfoOQl2GZlgJZV2ck0iF9ITdWAqxbvLaHKyIHompYFkrOGFaauq4qUZJ96NCr72KpoY61YsynMNc9p+eH5GaxUsTbToJsb1O/AGK+jbUpiq6jpdGkfe5YDrbBarZBqZpwm9vsDu92O/eOOy8vLhTtAXfv7y3trsC1Koqq1rHsb2iUx0qOmnhHd5N4UdEDO5fl355rvtCZRiPNMCJ40ygjexcUGWwtphufPnpHTRC0D26srqIXb1285HY/84i9/wf/3L/6SXC2u23D77Stu73dYKsPQ028vqFR2jztOY+Q0TdQqi2VWj+yqiaF3ls1m4GLYCJPbiqyfiOgnri4HUi6Mh5O0dmqhHzybPogiExVMYegcfe/JWfqNtcA0TRL8aqXzPafDSMqJPni6IH3SWkVasZSKt1kIZCYyzzMu9PLclky1ljjPDEPH1eUl8zwxjlJ1t3VhWeu8ZxxFejDnBKgsrDGYmiWJUzLk0Al5LCcha+Yi+u7HcYbqqaghejH4IEFUSKLSQzTOi3m8zs82pbHgxdNVCp4VAZP71mK9jIEVY8BZSqxMcxJlsgy5KvPZOZgypznjkR70FCVJq6UqBG2WZNXSDNlFY+E0zkLucnaVW83yWXd3D3zw4gXfhneCSsgQsZyPKg5VXfCLVKstavXXD3SlY56ll9s1E462pjrx+y2akRtkFlny5cy23xDVcMd7j0mF0+HAN19+zauXL/m93/ktSpoWmJvSUp1VNawiSoUtwTdAbvPBrceLAVvOWj/1qbHP2fb3++E2vNq2ylZhZHUZaQzbxuKtC3Sp/aa26GmFSxFrpmk+cTrs2D8+sLt/x/3bt9y9ecNxtyepJ2MuykaOor8co5CiirL1hAyxkl6qBtxhGNgMvZKkZCRB+ifNJkovWC0i81Zbb1ZYzYsAmVH4M1sNWK1iO1u+a6XUvI4UncG9TdaQUp5UKue9U2HeadLyHUcZMWx46nbTbgKz1sFPoeO29+dV9Rl8/ISR3I52eUY1mLVqViNMq3DbwTVItx1fq25z0VvV6OdJxiHuG1UVoEqlkrFVNI9tAatzuU1T2Rohc7QgVatkmg0+a8fZrnlMOmRvVmGNVf/VnP9Su2rrMbTTp3O6y/UClZP8/pr2HN1opLmYxMVqfziy2+242l8xDANd14lzS7t52vU8C8K1yEO9JjrrZ0DF4TlXpTKIPnbnjcwrKklqmYdf7DAluIvUpyPWIr24GOk2HSZb4ulAqbDZXmKN4e7dLbv9gX/+z/5H3r55x82zG+7udnzxy684nE4MfaDvA/32UvpiuwPTKON51qxJRPMoFvlOgQi32432m43a2BlKivzowyu6LvDtq3ei6JQjXfVcbrdiVGFkHtfZQtDF1HnLPCdhievC2CsLO5eMt7DddDjnxJQ8SqDzaqY+Klu6VjhNewwV7x1dkBGj0/GENQqdTmvSPKekhKnGGi5LxZZSwm8GkiodXWw6Ic0BOVUhUgVPSZFqRYt5jEkMD6j4wZOr5WbbM4+zmjh4vJO57oqsGakkDTCFahzYoPyT9fmwPohPLwZjvah2qVWe3BfygHonDOBcpR/trUC1wVk2fUCerkmIX9q79M4JilRERtRk4TEQK5u+F/TOGFItzClxPI68eFG4eXbJ23ePMrubWGaa55LZ9D1DCOJ0lZLYELpE8I5xihxOI2wGchbSrHMOV+F4EEu/ELxocFsvBV0RIpYPgfk0grP0vWOaIo9393zxyy/49NNP6L1dpxPq+dqtKlRN090oiqhPpjiCsTyLJdsFgarLKvPr298PKWvp/KSyPYPdrFHN5KJm42q7VbPAxUup3aqVnBhPBx7ubrl7+5r7t6+5f/eWx4d7jvsDcY6aEUvmI8o00hO0Ws4ZA13XMTjHecZvAe/EuLoPAYDY7NuM6KJ2zcll0T/NaodWVyEJlVQDHclQ9tlaTa3kiErWhVkX6VZZ6q0v1WubF9WsWM+bd17F8FvPcF3gpdJzCjmWJaExRhr1fpkT1ozbLADCGhyNWwKu82ZdgGnZnATHWhC4BLMiGUYeSGM4g5vb8l81ISoLhJwbhKuogHGtV22WwCw2YUUYkllm1VZ11QY7V5wG6GzWwNcESloNaDWo51IZZ1FD8sYtPVdr7WIpuAC1laWyXVIMJT0I4tJaBu1CsiZA1Qg8rcdPZTn25fhKZlY5xN1ux+5xx2Z7Qb/ZrHOwLeCeZeQCfRdqSqSJpXIv7bPaGTIs8LQxRhmkQb1r9asFXCuVrrErqYMq84ypZvqhI04jwVpigYvLK5z33L19xb/7t/8//sWf/DmP+wMXF1u++vIl3758S8qVfisjSGFzwWnK3N/ekeYZ6VsWFdYXWLZWsEYVrvqeYaPJhxUbNnlkMpdXPQbD27f3lCIOPRVp6zzu9lgr86Q1CzPbOgl2OYl61TxHaQFpNT9n0WvebDo2ClHXKhWT1ftzmmZySWw2g6BNGSqF4DplnTqOp1Hn9qVvHLyHzhAPmZhktra1kzpV7zKICMe270TWsBS6IdAZGLzV5dpih56SZqY5crnpSCWJUITJGAq7XaIfdJ7bGDad3NtTLEwm0/c9JUvxUmrGhbM1qUjwsF6OQ9ZnWYt9cJjqmY+TXB+071mabKN4xpoU6YrjMCeizs9674SoVEXbuMSs686KyKWYmW3C+UBKk7Q/SuU4zjw87nn+7Jrd7iSKTEbcobz3UukC5CoSjqmNaM1CALNGkckjnXfkHJe1P84R62WGfZpm+qGX5FnJU1Vns6mV0DlyKoynE19+8TWvX7/m05/+mIXx2ZArFSupNFROpgtYX4ZFHcEqFH0eheex4JzfG1J/OOBancV0diFI2VbZtsVDl6DSgq32oqoyyJrhQc1ZTOdPI7uHe969esXtazEieLi743g8irB4RfunVqsmWXYM6ElYK8kmTmEWRrI+3N4LczBnahLNTLGkkn6IsUhvxaxVTaUKW9Y1qcSqle8ZJKnBrTmvsFyYp4FSXwpG5nbR16BJRyMuWe3JWO0fmsIizN3ep7WW2/s72jyxjoFo9fq0zG34p3yZtvDq35djbu9b5WGxCIQqGccKTbXMTs/KeiNqYKxVVFZoVbl1GCPjBy3YlVIwqVAaeYOVqd3Oh1S5BdeQEvngdWC+XYuzCl0Cn85EL2QHlbRDbv61Ml4v5lnSuhxn1nt1qUCRgCwKYfUsqWo98qcMaHHtSZzGkf1uz+5xx+X1DRfXkb70uOWjzjJfDbq0BChlsonLPq1PWFP5aTPjLJCm1x7ukgQvzHmzBNsGK08xsem79oDjhw3WesZpohxP/MWf/gX/4p//KcPlFZeXF/zq51+wO0ToHddXA1AJwyWnMfNwf0ccJ0qObDaenCXtzTrz2fmgIhCevu8ZhoHgnPTqohCcgndYJABiLdaIOb2xdunDbgapjkcqV9sBqxMLJRfRLDYyB9paTSCw4uV2yzzLMdcqyZm18rpxnuh7YfI6a8kmqwNRVdu2gu2cBJB5VvW5wnazYYqJU50kKdbEoCEu3jmVfhTN5GbXhwbnvg/SUzVgfI9FINerIVBqYo5Jz1/l2nkuLzyUwpyhCz0bp4ldLDgjx1mUwFlyJuZMHyzWewXW9Dk1FhfE3ScmTSy1CCm1MgwdxlhiykxR0BYfZB2+eziQagUjwUqSU/FYLkVg8GykdRZTYZziCuXXRCmGaS487o58en3JBy+uubs/ME1yX3vVVphjwiL7FLwX6dAobarLywtyrozjiVqt9nwjoQtUIypV3nlKqUzjSL/pl1an6CYUUq4EHS2aY+T1y9f88ue/4kcfvqBvMzxnlW7TSxdClfhWl1LwC/JWheJomu6BEsAUBWgJ+ne3Hw64XohDEmwVSv5OsG1wWilFB89X94RaoKREGifGw4H94wMP93fcv3vHw7u3PN7dsX985HgQJlpFq+gmEWkrJmdSTiJqkbPCGFD1YbRWzIq7EMTWyjSYUQbMY4zqi1iXRrfyCqkGCo6gUn+dEk6sZim5FGIualPFQjDyTqDqquSgkteAuyx6rrGX1ypIrn4hxpmFpNN6nIq/WmvxrJR5V58iotYYOu/ovSVYMSZoalOtilsND1iCLk+CrS7mqr5jEJZlC7QCm5gniz36e8vnUJfM1lqLM6rEYszCjJXK3ui5rJSaIGn1qr1eTNNCPTNaKEKoE2jVUlxV0khVGMysWaa19MFp5VbUFs8sSj8lFxVNb8dy9oezHKWRsNo/t/lLYX1X8c5l7V9Jdl3X39Vzkhe28oH9bs/pdJR2SM4yzmHaTqwX9Tzoi+Z5oZBIZ8V25emD2s57IwW2CvdJD74KYabtY8qVYdgwDB3T6cjm4kYJJzPv3t3zr/70X/JX//qvePbiBbVWfvG3v2R/nMA7rq96vLd0m0uO+5GHhwdVccpcbDzb7ZY37x6QkZOC84G+73Fe+urWiIj8MQuS0A+e7dBzfTGQU+TuNEI1jOMJjPA7DNL3pGRqydxcb9n0PRgrcKuei81mwzRKsJU2SuXmagsVDsdRj1+EN8ZxZpzHpVNGewYUaihG1bucJLN9EMH8TT9Qcubh4ZHOd/jtlnE6Ebzj4mIgp8x0OgGOYdgIw5qKwzGWiOkdnevo+45gikoSdvRB5ATnaeb6cst2kCmAWA2HMeFs5LKXedMxzrJGBUfRITWrIg8GSZZjMZhU6YW6IjryRmwdRXNeOA7AAp8bJy3CpF61GGHsPuwn7nYnDqfIYYp0w2YpXHLO+E7d1EqR/vQcKdaRSmWKiW3fiQGBcjoOx4mHhz3Pbi6Yp8jxeCIjs9lUhGhGpWqxZI1Z5B1bBSppT2Gz3RCjoJetL95c6CRxrmwuBowS1HLOBOOW9dpWw2m/44vPf8Xv/aPf4eOPXgBNCEefZiPFUq5FEThZHXODoNp9sz6Usn5YuYbV/AMrXBvWnlD7elrZNnZqXgaTm1duLYU8z0ynI4fHHbu7W27fvubdm9fc391y3O0Yj6fFQaRgMN6B8yK+beVGrDr7OkcJurlK9tXM2L211E5goOIdycgpaWIbc9J5M114DKrCAjgs1aMzVGvAFSNhNa2Phdj6Hs6Jwbs6VWQ9zoVxqudlrVBbFVienq+kELdzOk5lqNpIbYvlMqhiDEJ0korKOUtwIhbhre57q3BNE85YgyQKvWKNJhi60LCybhuEr8Z6tFGptq1UgXZjNZwVqWZRCrxC2NLUsWsCUCrUTDXiRJLb/WqErYwBn7K4k1goTgOYaezjdbZ2hedZqkyvgSY1hbEGi1e0mv7fstW1ZXCGMKzEsEIpGnCV8GYbQeXs1JRSmGPkeDyyP+w5HI+M48gmbelrr8SfsyTKrJ+5/KVVurr6LZX9mT9wE12QpM2u7YWze29BhbTKDSEsSJO1nn7Ycjod+ebLr/gf/9v/lnEa+ex3P+PbL77ll59/zf40YYfAB88uZM7SdcRZtKNLEoZx31mGoefh8aQi/IZ+09P1g8zMP+6FRBMCm+3A9fVAMLC92AosHBP7/UiKytavQmIryogduoAzApsPw4aSRWgERKVuzhDntLCUjYWucwxdz+3DXmY3raULHacpSfWMELicEUnJGBNDkO/GiipTwWJyoe/FsWzS+dBqHNM80/W9jCohyFrVQiMm6a1aY8lpJhkwBTEXoBKnic1FD85gKPTe01mH3QQhtM3gt708Y86RgalUBqQPm7IMhxpvMFWC5OVFkNZDFpW9aiAmheEx5FgoJS0tH4x4NAfrdLxRX1cNNVWs79idTnz7ds9pypRU+ODmgrkYDkYITka1bEMIyqGQFpIzhqL39xwTm76nMlEypFS5fzgydB0ffficcUrcPT6Sk4z3eWcWjo41avun4c8HT87C58kls724EJtLjKqnJVKODCGosEjRfrVdYkHfBUX5JGlIOfLm9Ru++NWXPLu51l6uqE6VBh8v443CK2n9W4O2tIxyiJaCpmKdxILKP1BpSohRZlGLsfpQtwWjjbY0dY8WaMmFOJ44PD6yu78TQtTb19y+eyvw8eGwqFFVjAzmW2Xh6fxjTUlOYozMc2SOiTlLEFyqP2MwTqBI9GbPSargVhXntFa3EnfWakf6ulopGjSIiUdjzoWYkw7qy+v8Gc28lnXeFNYg2yqjVtW2PqfRhbEtKvIekn2u1WnVzyvL/klWVzXYylhCsxg0GmwX28BWNbcvt5KznDtTd1qELfREWOkslSrPuYZ3CVyscHKLEBUJko3nJeizaEieS50VGtwbmVNminIdS5EZw16DZyqFKVq1lRMWc8NZBdaX3lFGeqmlJVxIMhMVJpKK21JZE7WnxIUVFm9bY4nrgbJwFs4QHPm/nIUWcKsR1ad2tqq+e0Vg5XGcOByO7Pd79scD23jJFhUZKOWJgcW5zlV7rzYqlGNaqnkpqAvQUUUlXa+pVryq1GP05lkurwo9WOA4jtRcuH72nP3jHX/5P/0Z/8v/9OdcXF3w4vkNt29u+fnPviSWynDZ8/GPn5FSpriBEguPd3fkGKklc3HRc7kdeHO7I86V0PUyjlIru/sdaY6EYNheXfHjD5/Td6KT66xhnibmmBmPJxHyd5Y5ie+ss4beiCiGMwVvPdYFrO2YpwOmFi42HYdZiEmnw0gpCe/FQefm8pLDcWQcI9Y6nAtMc2KcJjCFzjm6TtpPziICHTHJXLcTVm/OFWMcFWH4TvO0rImhU+ZwNdSiUxNZ/WdrIc0RamG7CWooLzDzaUrYWuiDo/MOWzPOVPqho3MGu+0psWCMw3aBWiLTnNgMA9V6jK6x1gaMsUtCNkeRzA3Oy8yzQU1SrJAGEbs+bKViGYLeabWKj3EV39oxJh52I69vH3n7dk9OBkrhpx9e8fzFM/7um1uCNWy7DucDxzHijejGtrErCbpCdp1ixtjIZrtlOolxxTxnHnZHfvLjF3zw/JLj8chxHEWQxDvlBMkxeBW0mVPCnEZ9tkXJSVjrVlS0zrgs1nlqyYv4BbXp6bMIFYGKaGCI88TLr1/yW7/9m3z47EqRDnR9Pn/uNOlV57mmMQGGRNSAa5cCQ0ad/oGQsg9+IWecB9vzhSqpYokE0ELNiTTOHB8fuXv9ijcvv+Xd65fcvnnN7vGR02mU+b3QCAEyU6gzDwKDpCSswjkS51mcLErVilP8KAUOkZGLECQxEPFq6WPEnBaKOWhF6BxeF3U5CA201i3m7LLIZ5KSqsRJwuiohVvIYbmcMZNpVdGZzFjNC9Rea8VhtI9aVWlrhSNTzjIUXxtJTBMEa1q9qezd9m8aoVmZyMYpUcmuQbdJ/a2Bt0kpqliJaXR6eXCL7if6nmer9xoNzv5uDAK1VgkbtBtaZmw0U8zEVESCc4rap6+ETkYsKgLhnULUgNtE7lDIVvZrzk6TAiHTJcNyncaoaIGVEQGT5WcNVq8tmuk1b9fr/D4GFnZzy16hiurV2T2/ws6NsS7X8QnqU4Q4czqd2O937HY7rp8/k1xdK9OqmfZSqTcs26xBl4pUujJcrcejIgR4qFnEMOyyg+vlUcKbXmTxazVIf6vf8O71a/7nP/5jXr56w0cff8Tu9h1//id/w8PuhPOeZ897Pv3kA6zzfHN7pKbK3Zt3TKcRayvPLgeurq/49tU75klEOEo15DlKQI4ZbxPPri747CcvGPqe24cDd497uk7mKaPKvYahk6qmVoJxMmfqmiCCIWaBSFOcMEjSWY3D+8p0nJnmWRJRb7m5uSLlyv44YY0l+I5pFnEGasIbQ985Li+3erHq0uvruyBkIH1+jYHdfk9MQvbcbAI3l+JSlKKIeuQkTOyUEoYqo2lyMbBYvA/M80jvHKdxwtNxGmfcppNqWg0JnO+E6Wqkz5sQ1vA4jyJP2V3IlEgRxDEMg8whG0d1jqhEBWO8MpiLFkmGbrtVEpJZiqi02BUacpaKeLcbefnqlt0pUVJl2zn+4Pd+k5/8+DmHU+TrNw+MnWcwDowTT9wiKEvV5yoXsTwsukCMU1x8mOcp6vhg5fFhz2bT8/z6glIkGa9Uus4zTREqpCjMbauEKYzMODv1IbZ6LEWJsbUUmWfue2lJGCuuVkVHp5BEvz3HYqmYuH33ltt377i53Eh8MG2W1lCyPo3tgTRmSYaLVrCyDlhA1fQkIq9x5jvbD1e4CtUtEoZyWAtMmlUYoKhtUp5m5sOJ4+Mju7s77t685vH2HQ+3t+weH5imGecD1gXpCavDgkhryTE1eLr1X+eUiDmLrqbOu0rNpS4ozsmDWcTFQkQtMrFkmmC3JAoil2Y14JaiLFnTyDpydE2JJhfOeoFaEaKQ8FlVXzmDIE2DIFW1Ri9UY1Hrc3i2mQUpaN667T2tNbjaxlye1pe0xRloQurtwufSgrZWpq36dXKMztiFaW6MjOy06q31nq1Cw03btL1/q/kkKTmbKW6wilNTc4WOU8lyLVJeHnARY0CrCxH7aLPTzU6uMZGdlb5tyY5OZHZJVTLzQiVl6VE1mTcf7JIYOb1LaokCQ9eWK9SzI3magba/t7rY6PlrSdV3XrxckfM+TmVFfmKMHA4ndvs9p9NJxe7R86T7kL+nz2PaDJ9WwaWqvcL6waVkjC5ui8AFLfCvKbnAyiuTfB5H3n71DX/1r/4S2wU++PAFX//iV/z8Z19wmjKuCzx7NvDZb/yIi03Pf/jVawyet6/ecDodsRaeX19yc33J16/umOYq2rfFSb+0JnpT6TvD1dUlv/vZx0xz5fMvX7E/zfTDhpwEQfKdmHefTjPH47SYj3tr6TuH95aLi43oZBfY706YHNluO8qcyAXGk/Rjhy5webWlYnnY7QAIfcc0ZaY4YUzGWdgMgcvtgPeBaZpwxrLbHzCmcrndUgucTtIfFglYIVx6B50XTYHxJJKDx2kCIzrB3gVijAJtF88QVOdXK57ikM9MiZQ7UoXOWELoFkZ5sAbTC4xejWWKlmJgSkKsGvoOnKMale/sN0oWdJLIpUzoAuo8R67ynFnnMSFgSlGruSp12Sx31fE08/bNPe/u9sxTocbCRy+u+PQ3fszz5xtC34OZeHFzwXGcqTi6riOmwnwYxanKOeYkmtBZ24rBOaYo4zzPr6/oB8t0mjjsJ+KcuHnm6DZb+pPI8goZVUipMoLkSPpdUBzLOI0M/aCQdVSilPR7hZWPysV2WGsYJ1k3BI7OdH2LIa2YKZyOI29eveazT35C13diUJOlNywCRyDmgm3lWKc8BD3QtqWRWfGi7cXyvyfgAivDVftZAhOWpaqlFHKcmQ57Hm/vuH/9VtWiHplOR+33WLaXl2yvb3C+I86JuWWIRQTNi87KtkW06Ra3z8sqDmCtZLnBCVlGoF1IuZB0nlEyFOlReOfpQqDzXmBoNElA/BeNaWMdhZTOBDWsF5gOs+xbg6mbT26LSIL+1mW0Z6ma2phGy4zqCiVijI7lSMWcS10gao26Wm22Pi8KI1VsbRC2xLi6wKsVnwwxK4FIP2exZrNWraZ0RMi0hVpv1gY5W9HoZYGeq86NqvqSClsIh0naAdY57dOKXN8cE9M8kxQW9dYRnFiLtXGLBn92Qdi21qYlCRB95Upxlc5pIlGMsi/l9hfIPlMcYMUNJgSRmYvzrGx5WYG+jzVYlyq+/V2rzrq+1pw9ao2N3Xrj51KbbXSriRDknBlVf/hwODFOkWFbCJ0XMhiNi3AW/M/KcXO+T0r8i3p/uZjoTMb6ZjlpF+Z82+dSsio2SUo1HU+8/MXnfPvlF9x88JzHd7f8+7/6D7x+9UD1ns2l5dnzC3704Q0Xm4Fv3j4QE5z2j8zjEW8Nz59d8hs/+YhffPGK4ynTdQMxV/bHIyVObHtD7z3bbcdvf/Yp85z55ts7TqkQ+g3GKJHJGtLpxDRFTRTL0jLZXvRKCDRMY+KgDjXBWTpn2Ww29H3Hq9e3lFLYDh3bzUAp8O5+Jy0qYxjHpFBwxlIYes/z6y1W5f68C5xOI7lkNkNPKRL8Tyqwk2tj4mZskMpomiJzjIuwS8miFZ2TqA8Z7R+6jdU1pjL0g64FAvuf5kQIno1vS69XfoJU5N3Q452Fw5HZWFxwjLOAl1eXG4qxzMXgQ4e3SVtSRvkvlt47Ysya/MkEAlpYVNSGLxWOc+bwuOf29oE5yZjV4D0/+vEzPvroOcMg0qG5iFvRpu9UC97SBU8fZAyq7zsy0CUhS+W83Mh4I3PL++OJy+0GYw1zyhgL43hku70UJTRjlmmEfugYx0nU6IyjHwJFTWlyqczzxGboNR6o6lNtSIUlpUwInZgdGCsjaqZoXzdJPzg1kZ5KnGdev3rL425PF55JTKOhRa2hJsXHuR5gQ8+qnFSKLWAKMvxVG1nm17YfJk1pTbz2s1gCT1FZtJKVHLXfs7+7Z393x2H3wHg8ME+jsM28Z3NxSbfdcP3iA6zv2O8OlMc9lZkS28iMLnDad2zEG86DQZUFJgSPV8KODNlrZdrWPA0yzjfYOUh/yAh7U3qnOrrUKvbzgN3GKoxdAmjU5ntzuMCcVZc8rVTRLKf5klLXyvO8VkR/r0HJy9aupFl7hLk0KUP0uzJ8XZu1rMvn5KI3mDIESxVik2l91rNZatOCh95GS2946cc2WKVAMQIjG71eCiEb5xRDMcs4V2rnwgq0Zo26+7QArqeiYghBWgPeeXJpGqkiV+c9orecC/EMfzVauYq+taIOXrJt0e5eoTOTzZIMVYWD9RIu1eZ5gKtlZSA2MQr03mlB9xzZAFYIWy+j9KxmTscTx+OJ0zixTVJFBifSkWS7JlkNa2jw1fk+VYHJQXrZNQukaoImxawkOHl9C+SWWhOHhzu+/Ju/5u7dHbVWfvFv//+8/Podp5jZXPVc3mzZXvR8/KMPgcIX37xlf0qYWjns9oTgeX59wYcvnvH6zT2HU6IfZERmfzhS80xvK5vg6YeOjz/+iHnKfPPynXrnSnCLc1xQMio4bzGlEDqpZq+vLtleDOwfDhz3e6Yo/bGu89xcXbANwn6/vd+Tkqi+geE4zpymiO86UhJIeU4zlIxwMQ03lxs65zmMM77rmU4jx3Gk6wLWOo7jzHGaxF9b+6UOaeeUIojKOE006VKM+BBbb7gcLkhzIqeZLliGzhOAFGc618noUM6Y4KhWFuTDONN1HnecsduevhOHizTN9JeSVBiMuLMaJ2hfTFxcbfE+4IOTPqux1CyQvECZjr5zC4fCOCN8ACtzsscxkmPi7u6R02kmJ6hz4cWLa55/cMPNzQVU4Z1UG5D6o9B3nuAcxYhW9fE00Qf5HGutJJIHSy0ZF0RJqtlFxpgYp5nQBabTCAROp8h2U7m6vKBQ2D3uqUX0B2qRdd06R0kyzpVVLQ3jiXHm8uqa4/GAtY5S4lL0pJSoCElQtBYqXdfRlOqMdTQ1KbFTjNzfP/LmzTtuLoUgKKip8lWe9Hxa58fovWfXp7RATm2UVBew79n+owF3gUlzJue0jP/kmCXYHg7s391y/+aNiIQHx+ZyS86RmGaGyws2ztNfXLC9uqZiiLGoKXHB5Kzak7LvzeovlQJqsiwOHjp/aCBoQG2D5ymvC7yISDcHFbGOEkeaVY2oWifamakSsxjYt4qwzVO1ucrmcZlyfiLO/XR0ZBVCkJ9ZVW2RG67N4xVduNuSupJ72jk3SzBt72EUepeZZCUXOWEZ9t6Leg0t5ypYW6HKONUczdobtSrfpj0YcWe2C/FKqlghbbVi0KAltAEZMCsYl5fbrb0HRslMikTkIgQhFzq8h9bTNaCjKkV71XLsXXBKJrHELMQ1kaB0OCsMZuvAfgfGb/0jWOftMNKrCkCXE2DUJCCL00he2YMtkDZouEH/bQa7EZGWAGuNiJU3ecyW/VQUKRGTA2GEC/FunGeOp5H9aWSrQcEFSSiF7HUmbNIygbOye+kPK4xVo47f6fxnrVCNenfqvi8weM7cffuSb37+t8zzyPHhjl/+9c95eJzpNh3Pfrzh8npL6AI+dDhrOJ4ik7q0POwexd/22RWf/OQjHnYTD8fM5uKC3VEClqXiTOVyG/jwxQ0Xl1dYHH/3+VcUK9Dn4263SD364BiCVxUnSUZlZtcx7o/Eo/jVDn3H5cXA5uKC6iSR2+9OMt2g435iBi+qSt2w4TRG9vsTc4rUKsMzxsh7Be85zpGCYRpHUo74TsaXdvsjqRSaqYbVasZ7Q8ni++qdJeUiFnPOshlkJniaIsfDJDC5NfTeaUXYczocJahWsAjSdXl5RefguH/EebDbgTBVord4A0XlIkMXpJ3lRYBCZHpE6FOkUg0FQfFymrEps7nYLj1943TuNmYwUr2Pp5ndYWI8jpwOM+Np4vJy4NPf/Ak3z68IXtnvLgiaZqxIMcbMMAhM66g8u75gfzyyGTpuD0cub24wJkpLJyHJireUJDPHpRRijPRdYBh6Ss50YWC/P7DdXkCp4merylggspWkrHr80ke/uNyCtYynkZKrBltR/CranjEaF/Iy/iQGNtMsawGKylmj6GnJTGPi5etbPvuNT7QwsxSLBt3CsjIsv96SbU38Nd1dPtfQBmN+bfuPmhe0yrbN2uaUpWk8R+JxZNztOTw8cHh4oALD1SWu84zTiJ0nhhAYLq7oNhtcJ3Cy8yK6bpbFTpWQzFpVWx8xVuawNpsNXi2nKAVSpuaki7saIusi4xRydq1vacHqZKlVSjjWYL2Fqgt8WZWSmrhjznKTzEpFz2dw8LJQfw9UbEybLfULdFuKWSqlJrF5DkG3AOL0a1nc7SqdmbVnUI26w6gLUq8PqqVIFWQqTt1LFqi8gC+I9qxRYpPCxm2OFyo0a7y27uvxtJsYLKYKaGJgCbZL8lDafhrNws/kP4uMjVWdq5QIU3Rh0wrXW8ysyYj1wlyvDuuijgkvmcBSobfKshQhURXNZJ33dF2PMasnc608uVZob6ihFaaV7g0yt+s1BVZCRSNYtXvAsCRiFZnhK6y93HGeOY4TR1XC8c7hjdGZ5Ub8QEvZ9tQtd9pZ0JX7v1bNwEtBpdHW49KZxThHHm/f8fJXn/P49g1f//Irdnc7wPLhj6756JMPubjcUmrh3f2e3f7I5dBxGieuLi95eNhRc+bFzRU//fFzKoaXb+8pGFJMHI4nvDV4Y9h2PT/58Qu2F5e8effI3cOeWi1xSuwP93KcpdB3lo3O4BosKUXiXEh1xprAdui0d2/IWdaEGDOn3azqUOpEptBpBxid/dzvjkwxkquoNTkrRuB9cFxtNyRVOks5r5Z4KfO4E9cjqsxcBm/xVljgXRCm8uV2YJwzzln6oSPnyuk0yYhibrPZiMC+lc8OznDICW8DQY1CKJXpdMIOPVfXN1xsPVgYU8aeTlB7ShCkraXQoRouho7TGMk48KKiV0ul7ztKTmRjcV2vPV6paIN1WG9IY+R4mjjsj0zHKCTG40znLD/+rY/50U8/ouv9gvyVBYVT/kKS89AFx9B55nnCO/ns61q53R2ZxlG8mTtLTKqlbES5ypiyJEAiZDIoIapgTcAg7//s6orb+3vxwO0C0zhSEUJatw2YIuIeQ9+LP/H+SC5JEhrXvI3lGRXQS7WsFdUrVdqFTZSHKmxijCFTuN9H3j0c2AwdVmrbJ0pajRTR+DytRWet1YkOxZiqqOrlf6jSVOtnlQZParAt+pXmiel4YDzsSTGKgba1GO+pWRb2aj2u39BdXBK6Xvuxs2ToDZrOMipivcyZNCgUYwihwwfPsNngvWhlphjJNSm8mwSO1Ryjjc20oGstkuXWQtVGeG1MXz1pAnt4JUPJzV5KEQ/PlKQX9t10pWU6Z/9kjQXVjW1ye5a1H2idXQapS20WdrK4iE1VM2dXyHZZRBUWLgWDKMvQDZjNFrcV1qSvBVsLhoSzlT44hl4MvrsgECumMagRqLpdL7sMAgEZCmfVnrKvW9A0VQRJ2ijNiqcqFC0kKEeDrxvkUuV6m6THrQEYIwW0ExUznCfXiVSF+EC/AZepaaTGSjV5CUaSpK3GEkV78HOMhK7DO0fXd7poz0tyd248XqVc1gepwbKtLSD3/tPWigZm01jncl4KIrlZNCEQ67iM1yQkpSKjKXNkjBkfihDZnIjLGIskIzqkvCZ2bWhI8e+2GFZlZea0XidZhaXCzYnd4wPffPErbr/5mruvX2Jj5uZmQ7jcMGy2XF1vsd6xP06cxrgkaOj3/e7IzfUlz643TNnw6uUt05zIwGmKcq+WSOcLH334nKurS97eH7jfH4X0M41LcPNWeqiXFz196JjnGeOgc47Lm44huCVBtsbig6MfBu4f9tzv9oxjFCEQI22Cgsy2Gu8kmTlOxCxQojVlqZx9MFxuO7y3TLEleiL1F2PkNI54b+k7z+kkvWLvxeHM2srQe1GiKrLwyjEVfYZo/SsMheAMfbAU5XlYZ9heDDgrRg/zLEjNsOkYTxNpNsTZczE4hucXdBdbXNCJA2MY5yj+tN6wCYOsnTiisnEsGZLRtk4FZ0kL8mcYY2aao1b9M/MEoeu5vHQMH79ge7lls+2Xkc9cCjWVxQYyt+TEi1QnFLo+EOeJobdcbAdyFf2CeZ5J2j4s5bSM7XlvtAWmloZxZug7NpuOeZpltjkXnClcXfRMU89pigQfiFZaid4JsnaxHYhxpuZE3w2czEnae6XiuqA8ALdcjzWxNpqTSoEjvJ8GT1tSzkzzzHg88erNPT/58FJ0mUsjQEkstAt3Y9Wkb4YrxZglXgrqmvSc/fr2wwFXd3gVtsi6KGSZk51n5tOR6SSZSNhsqFbmv1JJpCpB1/iA6waZ14oTcYra71B2cxLKdzOCl2pEoAUfAl3f0XXC5kuqIpJ1YRWWnmQ1Xtm4y8wtIoVoUa1U04Rl1kFlrMxuuVKlR5uzGGY3+HgJ5utaJ2tfIy3JIlhaEFY/0macgJJ6ZGGW3gHNeqxV5MYtTGm7fEYjcslN0tRajLNU66hdD5st5uJCq4eEqwlfE8FVNp1jMwQ6de+wxi7KWC3INL/UxVS+wZAafFtfUG5Yu0A9VDBu7cPJwavQt2u9TWVHGqNsdklarFbYRanzaL+jWpnnq9aSqyFWQ+c6TL/F2EydCtUmCipwQOtXaVuhihAJKSsqIa2IEKQ/p211QMgjCxehnHGS9ZpKcr6iD/BdPsPK/l6COIZihPleOSP56cpciroYxcSUEn0WEpB1srg3UZolEdOH1yDEj7XqbTPKaGZeIUchUlmZC40xMh6PvPriKx6+/Ia03xGGgAuWqxc3XD2/JpfC4TASnGV/HBlH6T1aa7i83HD/6g7rDRcbefYeDhOJSn/Rc3+/o+TI0AlJ6Opqy2ef/ZQ3dwdO6uJ1Oo1gIXSOzlmCg8uN4/JiI8ziNsdaM8E6LrYdfReYpogFQu/ptgPXViwAHw8ju93EcZzFXaYTfsdplLGgWLO2FDK999QCzsPN9Za+CwsCVql4L6MnKUe6zi+ol7VV5QjF4CF4S9c5MVGZxbUs6wJccmIYOmIU9nMXPJsuyJrTOTaDwzoIXcc8jWyHQu/hattJr3a7ZR5n5ljI1fPm3SPznHjx4gY39JiaISUsTlWqxY82zpkxRlzf44MlYsB4THCIGJqIZcSUmKdInCZKLlxdbeg/ENMJp2OUVtcnYxrapu08Iyp8uZRlDDGpjKYxEDrP1eXAfsrc7w8MvSdOiePpyGa7EWMKJcp6J1WyAGECt07ziDGVYTMwTSPhcqvmHJGLCzF+MBa22w3TNCr5z2KCZxiUgGakwpbENjFY9ePNia7rmeZZn11JVEWNTRN/7d0aCz505FqxMXI6Hnj5+pZ3Hz/jk4+ulntC7pvWv23JOYvGAVbiTQVF92TNWLkZ/xsD7nnQXfWR25csojlLlVkB6/06cN0WUlmFl5GRWgUSqmX9ahh8W4Cbcw+gVYBfmL5ZM4mFoFSKVKxnkOwiCqFfcvxl0TSUhrZbwolkKdoPK00X+nwx1mBaz4Lu+Xf9Y6t6VvlLQb/XikzRv9wu+yopsczPLntYF/JMg2oXUo6xYL1ASyHIuc1CZnIYvCv44JWtK8xsWczrsujIHrfRH7Nkha0ncb61W61Bl1pKSb+xcHaONERbqW5dk3gkq6PKUhAIIaMayKbh8/r+dhHhwAr7mSI/+57hnHbil3t18eNdWOKtWmma23Z5lzbXeg5emO/7jKcftgTZH3yNnuPFCIGGBJdlbCvXuswqCjGuLlJwlZYctNxLVcwqNEjdtGOz4qVqyORcmceJOE+8/vorvv3F57g0k50l1cInn/2U5z/6gOocu/2R3veMpxNTLkyl8tHz5wRbOMyZw2nm5uaGq40nDAN9NdgpUqeZnBPOQdcZLrYX/M5v/pTh8oLT6wceDyPjPIvHrJdwMQTHi2etT3fk8nKD7zr2j3uC9QwBTE7kbNhebZli4XF/guOsrkQbLlygGkciE7NUMrFIj7wg5LpKJZggalOD4+bqgmEYZN7XeHKaiAX6YElViD3eWYIXbeZh0y/CN9ZbNhcDPnSk00wmUQ1stp2oYgUdSdSKPASxzstUri82wu5NmfF04nLj2WxkLMeURHAdXfBcX24E/SgF7IDxnvvHA/vdke12Qz90hFKxxjEWw7C5wCO9y+OUmbNWdiliSsU6Q6xRSFIFqjP4TU8/SLHS973M+pZCSSKDWLIkD7YPMkNqnQQNL89h1ufI+yDkKG/oBk/fOza9IwQZ4TrFCCVhTOHyaiDlWQKWUw/wWRSYXJX2TqWQy4wfOu73Oy42PZs+sDUbjjGS5qhKXllNbLJAtEZIorjKsA3EJJV1NRXfe0wS7oTzbplcqVqhNkhSyFNa7WrsqEZkPe+nE9/cPfL8+YZN8HK+9FmUiQxRM1NOryALEn2F2FsLOQHVYXv3/SvEE3bs++399n57v73f3m/vt/9Tth+YFnq/vd/eb++399v77f32f+T2PuC+395v77f32/vt/fafYHsfcN9v77f32/vt/fZ++0+wvQ+477f32/vt/fZ+e7/9J9jeB9z32/vt/fZ+e7+93/4TbP8rdD/inLXg5QcAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Input image size: (50, 50)\n", + "Output image size: torch.Size([1600, 1600])\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Input image size: (33, 33)\n", + "Output image size: torch.Size([1600, 1600])\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "image_path = parameters[task]['image_path'] # change image_path to your image\n", + "with torch.no_grad():\n", + " frame = cv2.imread(image_path)\n", + " frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", + " paras = get_video_crop_parameter(frame, landmarkpredictor)\n", + " \n", + " h,w,top,bottom,left,right,scale = paras\n", + " H, W = int(bottom-top), int(right-left)\n", + " frame = cv2.resize(frame, (w, h))[top:bottom, left:right]\n", + " #x2 = align_face(frame, landmarkpredictor)\n", + " for i in [4,8,12]:\n", + " x1 = PIL.Image.fromarray(np.uint8(frame))\n", + " x1 = augmentations.BilinearResize(factors=[i])(x1)\n", + " x1_up = x1.resize((W, H))\n", + " x2_up = align_face(np.array(x1_up), landmarkpredictor)\n", + "\n", + " x1_up = transforms.ToTensor()(x1_up).unsqueeze(dim=0).to(device) * 2 - 1\n", + " x2_up = transform(x2_up).unsqueeze(dim=0).to(device)\n", + " print('Input image size:', x1.size)\n", + "\n", + " y_hat = torch.clamp(pspex(x1=x1_up, x2=x2_up, use_skip=pspex.opts.use_skip, resize=False), -1, 1)\n", + " print('Output image size:', y_hat.shape[2:])\n", + " \n", + " visualize(torch.cat((F.interpolate(x1_up, scale_factor=4)[0].cpu(), y_hat[0].cpu()), dim=2), 60)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fD4icy1_1Bx-" + }, + "source": [ + "# Sketch2Face\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E-mF1aAU1Bx-" + }, + "source": [ + "We will download the pre-trained model to perform sketch-to-face translation,\n", + "and perform style mixing for multi-modal translation.\n", + "- Input: should be a one-channel sketch image\n", + "- Style mixing: applying random color and texture to the target image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TW4_h3nh1Bx_" + }, + "outputs": [], + "source": [ + "task = 'sketch2face'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sS5Fv-1T1Bx_", + "outputId": "3f4e67f1-328b-4a56-8f05-02240112f1c8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "c768bd63fbb2422b811d4e92d5efe697", + "f95685872e1d4840b7dfd506b55fe108", + "a6c483c0fb0d4da9b8909c49b4cf76e9", + "7560836663e74896946013098e235146", + "640aa16340ee48a6a37f127c5394ca97", + "98f2b70cd9f94eab99bf2766553bcdc2", + "9f7a7df113164b318888af0474c839ae", + "5b02b83f7520497faa62dbf4a9fecb6a", + "03ed63ac9dc34c7985381ac48ffd9124", + "754624c0d19b44389bb1d652eae9030f", + "88cbc268f2d94f2f8eb67d4546d361e9" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading (…)ganex_sketch2face.pt: 0%| | 0.00/1.23G [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "image_path = parameters[task]['image_path'] # change image_path to your image\n", + "torch.manual_seed(32)\n", + "with torch.no_grad():\n", + " x1 = transforms.ToTensor()(PIL.Image.open(image_path)).unsqueeze(0).to(device)\n", + " x1_viz = torch.clamp(x1.repeat(1,3,1,1), -1, 1) * 2 - 1\n", + " pspex.train()\n", + " y_hats = [F.interpolate(x1_viz, scale_factor=4)]\n", + " # randomly sample four style appearances \n", + " for j in range(4):\n", + " y_hat = pspex(x1=x1, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=pspex.opts.use_skip,\n", + " inject_latent= pspex.decoder.style(torch.randn(1, 512).to(device)).unsqueeze(1).repeat(1,18,1) * 0.6) \n", + " y_hat = torch.clamp(y_hat, -1, 1)\n", + " y_hats += [y_hat]\n", + " pspex.eval()\n", + "\n", + "viz = torchvision.utils.make_grid(torch.cat(y_hats, dim=0), 5, 2) \n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S_FVo8_Z1Bx_" + }, + "source": [ + "# Mask2Face\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RGihRIfT1Bx_" + }, + "source": [ + "We will download the pre-trained model to perform mask-to-face translation,\n", + "and perform style mixing for multi-modal translation.\n", + "- Input: should be a real face image. We will extract its segmentation mask as the input\n", + "- Style mixing: applying random color and texture to the target image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j32pNTc21Bx_" + }, + "outputs": [], + "source": [ + "task = 'mask2face'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "d1G4NXPu1Bx_", + "outputId": "8a58c631-e1ea-4284-d67c-239aca1fa8e0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "a5a1eace1ecb45958139415c560a64c9", + "0dd6319ca69d4e2fb41c1f03f028a683", + "16069c2c528448d48340bc826395c1c2", + "a8d8214f406646189636a12ba21089d1", + "69ce3759e522496d9483ab71075f730f", + "155de707983e400d9531c1baffe523a8", + "9e727bbe4fc040c3a1798c71c16c3619", + "9e42c996b0c343b892adebe19bfb0cd0", + "387982859ae249a3a42e6331026c92d5", + "d23e831f19d54f80ba47e98a6238875e", + "117c1974502841a9800510db5000ec92" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading (…)leganex_mask2face.pt: 0%| | 0.00/1.25G [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "image_path = parameters[task]['image_path'] # change image_path to your image\n", + "torch.manual_seed(2)\n", + "with torch.no_grad():\n", + " # convert face image to segmentation mask\n", + " x1 = to_tensor(PIL.Image.open(image_path)).unsqueeze(0).to(device)\n", + " # upsample image for precise segmentation\n", + " x1 = F.interpolate(x1, scale_factor=2, mode='bilinear') \n", + " x1 = maskpredictor(x1)[0]\n", + " x1 = F.interpolate(x1, scale_factor=0.5).argmax(dim=1)\n", + " x1 = F.one_hot(x1, num_classes=19).permute(0, 3, 1, 2).float().to(device)\n", + " x1_viz = transform(tensor2label(x1[0], 19)/192).unsqueeze(0).to(device) \n", + " \n", + " pspex.train()\n", + " y_hats = [F.interpolate(x1_viz, scale_factor=4)]\n", + " # randomly sample four style appearances \n", + " for j in range(4):\n", + " y_hat = pspex(x1=x1, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=pspex.opts.use_skip,\n", + " inject_latent= pspex.decoder.style(torch.randn(1, 512).to(device)).unsqueeze(1).repeat(1,18,1) * 0.75) \n", + " y_hat = torch.clamp(y_hat, -1, 1)\n", + " y_hats += [y_hat]\n", + " pspex.eval()\n", + "\n", + "viz = torchvision.utils.make_grid(torch.cat(y_hats, dim=0), 5, 2) \n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pv-q7_oU1ByA" + }, + "source": [ + "# Video Face Editing\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CzfLd99a1ByA" + }, + "source": [ + "We will download the pre-trained model to perform two video face editings.\n", + "- Hair editing: control color on a single frame, and adjust the hair color on multiple frames\n", + "- Age editing: control age on a single frame, and adjust the age on multiple frames" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XZd9K7121ByA" + }, + "source": [ + "Hair color editing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kWCWDvBG1ByA" + }, + "outputs": [], + "source": [ + "task = 'edit_hair'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xr_agH1U1ByA", + "outputId": "56603fae-64d4-469b-e84f-f8da51cfcb81", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "d7fa559b14c44b738764a8612e6bf030", + "75b5c242fb0b4873ba07f604d85f0c96", + "6fd56ab253524624aaafc3ca2fac91a1", + "ac099c0e1730418a92e493cc852c7c26", + "ce2b94754e474cbb8b082bdc004c793f", + "47cca4be42114361b78e30ec09c6700a", + "22cf5ae6d34846aa8b816dc4cdfdc092", + "148677cf313c41fda02efd92fd965439", + "d16104958ccc4379b2d0d18f52286e61", + "15c862a59bf04972b8ac873d350dc391", + "05317bd965674c1db5628a00a527357a" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading (…)leganex_edit_hair.pt: 0%| | 0.00/1.48G [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# color control on the fisrt frame\n", + "y_hats = []\n", + "with torch.no_grad():\n", + " for i in tqdm(range(7)):\n", + " y_hat = pspex(x1=x1, x2=x2, use_skip=pspex.opts.use_skip, zero_noise=True, \n", + " resize=False, editing_w=2*(-0.5 + i/6.0)*editing_w[0:1])\n", + " y_hat = torch.clamp(y_hat, -1, 1)\n", + " y_hats+=[y_hat.cpu()]\n", + " \n", + "viz = torchvision.utils.make_grid(torch.cat(y_hats, dim=0), 7, 2)\n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qHJhstvD1ByB", + "outputId": "d455a136-d913-4cf5-d285-37a436886f53", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 158 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 7/7 [00:03<00:00, 2.17it/s]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# a fixed hair color on multiple frames\n", + "\n", + "# uncomment the following lines to save the video\n", + "# fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n", + "# videoWriter = cv2.VideoWriter('./output/%s_edit_hair.mp4'%(os.path.basename(image_path).split('.')[0]), fourcc, video_cap.get(5), (4*W, 4*H))\n", + " \n", + "y_hats = []\n", + "with torch.no_grad():\n", + " for i in tqdm(range(7)):\n", + " success, frame = video_cap.read()\n", + " frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", + " frame = cv2.resize(frame, (w, h))[top:bottom, left:right]\n", + " x1 = transform(frame).unsqueeze(0).to(device)\n", + " y_hat = pspex(x1=x1, x2=x2, use_skip=pspex.opts.use_skip, zero_noise=True, \n", + " resize=False, editing_w=editing_w[0:1])\n", + " y_hat = torch.clamp(y_hat, -1, 1)\n", + " #videoWriter.write(tensor2cv2(y_hat[0].cpu()))\n", + " y_hats+=[y_hat.cpu()] \n", + "#videoWriter.release()\n", + "\n", + "viz = torchvision.utils.make_grid(torch.cat(y_hats, dim=0), 7, 2)\n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s7wLzn7W1ByB" + }, + "source": [ + "Age editing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TwRgS9Ac1ByB" + }, + "outputs": [], + "source": [ + "task = 'edit_age'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pWeevBe11ByB", + "outputId": "1026df62-8fe9-4f05-8968-712cb3884a09", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "b4e454cd6c2b4519baf8755936adc9a4", + "275a6c3f911741dca2114552b18b4edf", + "02d8ce08cdc1489c8f8ac84563c86024", + "a7b9ed9ad87549ea83ee900a318f4ff3", + "d0e7182a6f7340688bd901dbfcf161be", + "f8cf14490b5d4d3db503ef4ec3793860", + "829e85dfd2c34cdea61e70f4cbfcab5f", + "64a4656a80d648a78dbf2a1145c02dc1", + "8f9ec03c5793498d90a98ebffa36141b", + "5e43d8dc8653423887756490c14a981c", + "7a7a32301bc24f13b8f771d1eb3d13cc" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading styleganex_edit_age.pt: 0%| | 0.00/1.48G [00:00" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# age control on the fisrt frame\n", + "y_hats = []\n", + "with torch.no_grad():\n", + " for i in tqdm(range(7)):\n", + " y_hat = pspex(x1=x1, x2=x2, use_skip=pspex.opts.use_skip, zero_noise=True, \n", + " resize=False, editing_w=5*(-0.5 + i/6.0)*editing_w[0:1])\n", + " y_hat = torch.clamp(y_hat, -1, 1)\n", + " y_hats+=[y_hat.cpu()]\n", + " \n", + "viz = torchvision.utils.make_grid(torch.cat(y_hats, dim=0), 7, 2)\n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NEw8NEmk1ByB", + "outputId": "4609a7e5-b957-4280-d2cd-15d62cc32ed7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 158 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 7/7 [00:03<00:00, 2.15it/s]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "# a fixed age on multiple frames\n", + "\n", + "# uncomment the following lines to save the video\n", + "# fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n", + "# videoWriter = cv2.VideoWriter('./output/%s_edit_age.mp4'%(os.path.basename(image_path).split('.')[0]), fourcc, video_cap.get(5), (4*W, 4*H))\n", + " \n", + "y_hats = []\n", + "with torch.no_grad():\n", + " for i in tqdm(range(7)):\n", + " success, frame = video_cap.read()\n", + " frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n", + " frame = cv2.resize(frame, (w, h))[top:bottom, left:right]\n", + " x1 = transform(frame).unsqueeze(0).to(device)\n", + " y_hat = pspex(x1=x1, x2=x2, use_skip=pspex.opts.use_skip, zero_noise=True, \n", + " resize=False, editing_w=-2.5*editing_w[0:1])\n", + " y_hat = torch.clamp(y_hat, -1, 1)\n", + " #videoWriter.write(tensor2cv2(y_hat[0].cpu()))\n", + " y_hats+=[y_hat.cpu()] \n", + "#videoWriter.release()\n", + "\n", + "viz = torchvision.utils.make_grid(torch.cat(y_hats, dim=0), 7, 2)\n", + "visualize(viz, 120)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rncjOQsM1ByC" + }, + "source": [ + "# Video Face Toonification\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d4CEzOsS1ByC" + }, + "source": [ + "We will download the pre-trained model to perform vidoe face toonification. Three styles are supported: \n", + "- Pixar\n", + "- Cartoon\n", + "- Arcane" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ee4IXp_Q1ByC" + }, + "outputs": [], + "source": [ + "# choose different style type\n", + "\n", + "task = 'toonify_pixar'\n", + "#task = 'toonify_cartoon'\n", + "#task = 'toonify_arcane'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ogPLwQ6F1ByC", + "outputId": "98643527-b2de-40f6-cea3-3fcf32110195", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88, + "referenced_widgets": [ + "ea6f0f8c56034482858b85253f81ff56", + "bf478133152743d0abb0890972ede78d", + "b557b0e0059642508d4404063fa017d5", + "e2b6cfee1b0240dc8fac7234f7030a3a", + "0ffd331fa50d4ddea4a9f082d0c48878", + "fb2481f6df8841caa55b39fc95aa6006", + "a14e12e9abaf4c2fb112020ad9fe6316", + "7e074ff96e444288865fd682686cf1a0", + "50ff3708cba54b9e81b32b35d3ca915d", + "13375b96d06746ed953d063f37eb2a70", + "48ad1a70be124f6db971cb68c9d377ed" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading (…)nex_toonify_pixar.pt: 0%| | 0.00/1.44G [00:00" + ], + "image/png": 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120)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "psp_env", + "language": "python", + "name": "psp_env" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + }, + "colab": { + "provenance": [], + "toc_visible": true + }, + "accelerator": "GPU", + "gpuClass": "standard", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "9bc04897c9964fe095f516eedead27a4": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + 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torch.nn.functional as F +import torchvision +from torchvision import transforms, utils +from argparse import Namespace +from datasets import augmentations +from scripts.align_all_parallel import align_face +from latent_optimization import latent_optimization +from utils.inference_utils import save_image, load_image, visualize, get_video_crop_parameter, tensor2cv2, tensor2label, labelcolormap + +class TestOptions(): + def __init__(self): + + self.parser = argparse.ArgumentParser(description="StyleGANEX Inversion") + self.parser.add_argument("--data_path", type=str, default='./data/ILip77SbmOE.png', help="path of the target image") + self.parser.add_argument("--ckpt", type=str, default='pretrained_models/styleganex_inversion.pt', help="path of the saved model") + self.parser.add_argument("--output_path", type=str, default='./output/', help="path of the output images") + self.parser.add_argument("--cpu", action="store_true", help="if true, only use cpu") + + def parse(self): + self.opt = self.parser.parse_args() + args = vars(self.opt) + print('Load options') + for name, value in sorted(args.items()): + print('%s: %s' % (str(name), str(value))) + return self.opt + + +if __name__ == "__main__": + + parser = TestOptions() + args = parser.parse() + print('*'*98) + + device = "cpu" if args.cpu else "cuda" + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), + ]) + + ckpt = torch.load(args.ckpt, map_location='cpu') + opts = ckpt['opts'] + opts['checkpoint_path'] = args.ckpt + opts['device'] = device + opts = Namespace(**opts) + pspex = pSp(opts).to(device).eval() + pspex.latent_avg = pspex.latent_avg.to(device) + + modelname = 'pretrained_models/shape_predictor_68_face_landmarks.dat' + if not os.path.exists(modelname): + import wget, bz2 + wget.download('http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2', modelname+'.bz2') + zipfile = bz2.BZ2File(modelname+'.bz2') + data = zipfile.read() + open(modelname, 'wb').write(data) + landmarkpredictor = dlib.shape_predictor(modelname) + + print('Load models successfully!') + + image_path = args.data_path + with torch.no_grad(): + frame = cv2.imread(image_path) + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + paras = get_video_crop_parameter(frame, landmarkpredictor) + assert paras is not None, 'StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ + You can try several times or use other videos until a face is detected, \ + then switch back to the original video.' + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + + wplus_hat, f_hat, noises_hat, _, _ = latent_optimization(frame, pspex, landmarkpredictor, step=500, device=device) + + with torch.no_grad(): + y_hat, _ = pspex.decoder([wplus_hat], input_is_latent=True, randomize_noise=False, + first_layer_feature=f_hat, noise=noises_hat) + + y_hat = torch.clamp(y_hat, -1, 1) + + save_dict = { + 'wplus': wplus_hat.detach().cpu(), + 'f': [f.detach().cpu() for f in f_hat], + #'noise': [n.detach().cpu() for n in noises_hat], + } + torch.save(save_dict, '%s/%s_inversion.pt'%(args.output_path, os.path.basename(image_path).split('.')[0])) + save_image(y_hat[0].cpu(), '%s/%s_inversion.jpg'%(args.output_path, os.path.basename(image_path).split('.')[0])) + + # how to use the saved pt + ''' + latents = torch.load('./output/XXXXX_inversion.pt') + wplus_hat = latents['wplus'].to(device) + f_hat = [latents['f'][0].to(device)] + with torch.no_grad(): + y_hat, _ = pspex.decoder([wplus_hat], input_is_latent=True, randomize_noise=True, + first_layer_feature=f_hat, noise=None) + y_hat = torch.clamp(y_hat, -1, 1) + ''' + + print('Inversion successfully!') diff --git a/upsampler/latent_optimization.py b/upsampler/latent_optimization.py new file mode 100644 index 0000000000000000000000000000000000000000..a29a5cbd1e31ed14f95f37601a2b6956bb7de803 --- /dev/null +++ b/upsampler/latent_optimization.py @@ -0,0 +1,107 @@ +import models.stylegan2.lpips as lpips +from torch import autograd, optim +from torchvision import transforms, utils +from tqdm import tqdm +import torch +from scripts.align_all_parallel import align_face +from utils.inference_utils import noise_regularize, noise_normalize_, get_lr, latent_noise, visualize + +def latent_optimization(frame, pspex, landmarkpredictor, step=500, device='cuda'): + percept = lpips.PerceptualLoss( + model="net-lin", net="vgg", use_gpu=device.startswith("cuda") + ) + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), + ]) + + with torch.no_grad(): + + noise_sample = torch.randn(1000, 512, device=device) + latent_out = pspex.decoder.style(noise_sample) + latent_mean = latent_out.mean(0) + latent_std = ((latent_out - latent_mean).pow(2).sum() / 1000) ** 0.5 + + y = transform(frame).unsqueeze(dim=0).to(device) + I_ = align_face(frame, landmarkpredictor) + I_ = transform(I_).unsqueeze(dim=0).to(device) + wplus = pspex.encoder(I_) + pspex.latent_avg.unsqueeze(0) + _, f = pspex.encoder(y, return_feat=True) + latent_in = wplus.detach().clone() + feat = [f[0].detach().clone(), f[1].detach().clone()] + + + + # wplus and f to optimize + latent_in.requires_grad = True + feat[0].requires_grad = True + feat[1].requires_grad = True + + noises_single = pspex.decoder.make_noise() + basic_height, basic_width = int(y.shape[2]*32/256), int(y.shape[3]*32/256) + noises = [] + for noise in noises_single: + noises.append(noise.new_empty(y.shape[0], 1, max(basic_height, int(y.shape[2]*noise.shape[2]/256)), + max(basic_width, int(y.shape[3]*noise.shape[2]/256))).normal_()) + for noise in noises: + noise.requires_grad = True + + init_lr=0.05 + optimizer = optim.Adam(feat + noises, lr=init_lr) + optimizer2 = optim.Adam([latent_in], lr=init_lr) + noise_weight = 0.05 * 0.2 + + pbar = tqdm(range(step)) + latent_path = [] + + for i in pbar: + t = i / step + lr = get_lr(t, init_lr) + optimizer.param_groups[0]["lr"] = lr + optimizer2.param_groups[0]["lr"] = get_lr(t, init_lr) + + noise_strength = latent_std * noise_weight * max(0, 1 - t / 0.75) ** 2 + latent_n = latent_noise(latent_in, noise_strength.item()) + + y_hat, _ = pspex.decoder([latent_n], input_is_latent=True, randomize_noise=False, + first_layer_feature=feat, noise=noises) + + + batch, channel, height, width = y_hat.shape + + if height > y.shape[2]: + factor = height // y.shape[2] + + y_hat = y_hat.reshape( + batch, channel, height // factor, factor, width // factor, factor + ) + y_hat = y_hat.mean([3, 5]) + + p_loss = percept(y_hat, y).sum() + n_loss = noise_regularize(noises) * 1e3 + + loss = p_loss + n_loss + + optimizer.zero_grad() + optimizer2.zero_grad() + loss.backward() + optimizer.step() + optimizer2.step() + + noise_normalize_(noises) + + ''' for visualization + if (i + 1) % 100 == 0 or i == 0: + viz = torch.cat((y_hat,y,y_hat-y), dim=3) + visualize(torch.clamp(viz[0].cpu(),-1,1), 60) + ''' + + pbar.set_description( + ( + f"perceptual: {p_loss.item():.4f}; noise regularize: {n_loss.item():.4f};" + f" lr: {lr:.4f}" + ) + ) + + return latent_n, feat, noises, wplus, f \ No newline at end of file diff --git a/upsampler/models/__init__.py b/upsampler/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/models/bisenet/LICENSE b/upsampler/models/bisenet/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..bfae0b0c29f885a118e382b445b6eaeca0d3b3e6 --- /dev/null +++ b/upsampler/models/bisenet/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2019 zll + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/upsampler/models/bisenet/README.md b/upsampler/models/bisenet/README.md new file mode 100644 index 0000000000000000000000000000000000000000..849d55e2789c8852e01707d1ff755dc74e63a7f5 --- /dev/null +++ b/upsampler/models/bisenet/README.md @@ -0,0 +1,68 @@ +# face-parsing.PyTorch + +

+ + + +

+ +### Contents +- [Training](#training) +- [Demo](#Demo) +- [References](#references) + +## Training + +1. Prepare training data: + -- download [CelebAMask-HQ dataset](https://github.com/switchablenorms/CelebAMask-HQ) + + -- change file path in the `prepropess_data.py` and run +```Shell +python prepropess_data.py +``` + +2. Train the model using CelebAMask-HQ dataset: +Just run the train script: +``` + $ CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py +``` + +If you do not wish to train the model, you can download [our pre-trained model](https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812) and save it in `res/cp`. + + +## Demo +1. Evaluate the trained model using: +```Shell +# evaluate using GPU +python test.py +``` + +## Face makeup using parsing maps +[**face-makeup.PyTorch**](https://github.com/zllrunning/face-makeup.PyTorch) + + + + + + + + + + + + + + + + + + + + + + +
 HairLip
Original InputOriginal InputOriginal Input
ColorColorColor
+ + +## References +- [BiSeNet](https://github.com/CoinCheung/BiSeNet) \ No newline at end of file diff --git a/upsampler/models/bisenet/model.py b/upsampler/models/bisenet/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1d2a16ca7533c7b92c600c4dddb89f5f68191d4f --- /dev/null +++ b/upsampler/models/bisenet/model.py @@ -0,0 +1,283 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +from models.bisenet.resnet import Resnet18 +# from modules.bn import InPlaceABNSync as BatchNorm2d + + +class ConvBNReLU(nn.Module): + def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d(in_chan, + out_chan, + kernel_size = ks, + stride = stride, + padding = padding, + bias = False) + self.bn = nn.BatchNorm2d(out_chan) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = F.relu(self.bn(x)) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + +class BiSeNetOutput(nn.Module): + def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs): + super(BiSeNetOutput, self).__init__() + self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) + self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = self.conv_out(x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class AttentionRefinementModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(AttentionRefinementModule, self).__init__() + self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) + self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False) + self.bn_atten = nn.BatchNorm2d(out_chan) + self.sigmoid_atten = nn.Sigmoid() + self.init_weight() + + def forward(self, x): + feat = self.conv(x) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv_atten(atten) + atten = self.bn_atten(atten) + atten = self.sigmoid_atten(atten) + out = torch.mul(feat, atten) + return out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + +class ContextPath(nn.Module): + def __init__(self, *args, **kwargs): + super(ContextPath, self).__init__() + self.resnet = Resnet18() + self.arm16 = AttentionRefinementModule(256, 128) + self.arm32 = AttentionRefinementModule(512, 128) + self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) + + self.init_weight() + + def forward(self, x): + H0, W0 = x.size()[2:] + feat8, feat16, feat32 = self.resnet(x) + H8, W8 = feat8.size()[2:] + H16, W16 = feat16.size()[2:] + H32, W32 = feat32.size()[2:] + + avg = F.avg_pool2d(feat32, feat32.size()[2:]) + avg = self.conv_avg(avg) + avg_up = F.interpolate(avg, (H32, W32), mode='nearest') + + feat32_arm = self.arm32(feat32) + feat32_sum = feat32_arm + avg_up + feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest') + feat32_up = self.conv_head32(feat32_up) + + feat16_arm = self.arm16(feat16) + feat16_sum = feat16_arm + feat32_up + feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest') + feat16_up = self.conv_head16(feat16_up) + + return feat8, feat16_up, feat32_up # x8, x8, x16 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +### This is not used, since I replace this with the resnet feature with the same size +class SpatialPath(nn.Module): + def __init__(self, *args, **kwargs): + super(SpatialPath, self).__init__() + self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) + self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) + self.init_weight() + + def forward(self, x): + feat = self.conv1(x) + feat = self.conv2(feat) + feat = self.conv3(feat) + feat = self.conv_out(feat) + return feat + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class FeatureFusionModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(FeatureFusionModule, self).__init__() + self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) + self.conv1 = nn.Conv2d(out_chan, + out_chan//4, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.conv2 = nn.Conv2d(out_chan//4, + out_chan, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.relu = nn.ReLU(inplace=True) + self.sigmoid = nn.Sigmoid() + self.init_weight() + + def forward(self, fsp, fcp): + fcat = torch.cat([fsp, fcp], dim=1) + feat = self.convblk(fcat) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv1(atten) + atten = self.relu(atten) + atten = self.conv2(atten) + atten = self.sigmoid(atten) + feat_atten = torch.mul(feat, atten) + feat_out = feat_atten + feat + return feat_out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class BiSeNet(nn.Module): + def __init__(self, n_classes, *args, **kwargs): + super(BiSeNet, self).__init__() + self.cp = ContextPath() + ## here self.sp is deleted + self.ffm = FeatureFusionModule(256, 256) + self.conv_out = BiSeNetOutput(256, 256, n_classes) + self.conv_out16 = BiSeNetOutput(128, 64, n_classes) + self.conv_out32 = BiSeNetOutput(128, 64, n_classes) + self.init_weight() + + def forward(self, x): + H, W = x.size()[2:] + feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature + feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature + feat_fuse = self.ffm(feat_sp, feat_cp8) + + feat_out = self.conv_out(feat_fuse) + feat_out16 = self.conv_out16(feat_cp8) + feat_out32 = self.conv_out32(feat_cp16) + + feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True) + feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True) + feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True) + return feat_out, feat_out16, feat_out32 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] + for name, child in self.named_children(): + child_wd_params, child_nowd_params = child.get_params() + if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput): + lr_mul_wd_params += child_wd_params + lr_mul_nowd_params += child_nowd_params + else: + wd_params += child_wd_params + nowd_params += child_nowd_params + return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params + + +if __name__ == "__main__": + net = BiSeNet(19) + net.cuda() + net.eval() + in_ten = torch.randn(16, 3, 640, 480).cuda() + out, out16, out32 = net(in_ten) + print(out.shape) + + net.get_params() diff --git a/upsampler/models/bisenet/resnet.py b/upsampler/models/bisenet/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..aa2bf95130e9815ba378cb6f73207068b81a04b9 --- /dev/null +++ b/upsampler/models/bisenet/resnet.py @@ -0,0 +1,109 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + +# from modules.bn import InPlaceABNSync as BatchNorm2d + +resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = self.conv1(x) + residual = F.relu(self.bn1(residual)) + residual = self.conv2(residual) + residual = self.bn2(residual) + + shortcut = x + if self.downsample is not None: + shortcut = self.downsample(x) + + out = shortcut + residual + out = self.relu(out) + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class Resnet18(nn.Module): + def __init__(self): + super(Resnet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + self.init_weight() + + def forward(self, x): + x = self.conv1(x) + x = F.relu(self.bn1(x)) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self): + state_dict = modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if 'fc' in k: continue + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +if __name__ == "__main__": + net = Resnet18() + x = torch.randn(16, 3, 224, 224) + out = net(x) + print(out[0].size()) + print(out[1].size()) + print(out[2].size()) + net.get_params() diff --git a/upsampler/models/encoders/__init__.py b/upsampler/models/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/models/encoders/helpers.py b/upsampler/models/encoders/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..b51fdf97141407fcc1c9d249a086ddbfd042469f --- /dev/null +++ b/upsampler/models/encoders/helpers.py @@ -0,0 +1,119 @@ +from collections import namedtuple +import torch +from torch.nn import Conv2d, BatchNorm2d, PReLU, ReLU, Sigmoid, MaxPool2d, AdaptiveAvgPool2d, Sequential, Module + +""" +ArcFace implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Flatten(Module): + def forward(self, input): + return input.view(input.size(0), -1) + + +def l2_norm(input, axis=1): + norm = torch.norm(input, 2, axis, True) + output = torch.div(input, norm) + return output + + +class Bottleneck(namedtuple('Block', ['in_channel', 'depth', 'stride'])): + """ A named tuple describing a ResNet block. """ + + +def get_block(in_channel, depth, num_units, stride=2): + return [Bottleneck(in_channel, depth, stride)] + [Bottleneck(depth, depth, 1) for i in range(num_units - 1)] + + +def get_blocks(num_layers): + if num_layers == 50: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=4), + get_block(in_channel=128, depth=256, num_units=14), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 100: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=13), + get_block(in_channel=128, depth=256, num_units=30), + get_block(in_channel=256, depth=512, num_units=3) + ] + elif num_layers == 152: + blocks = [ + get_block(in_channel=64, depth=64, num_units=3), + get_block(in_channel=64, depth=128, num_units=8), + get_block(in_channel=128, depth=256, num_units=36), + get_block(in_channel=256, depth=512, num_units=3) + ] + else: + raise ValueError("Invalid number of layers: {}. Must be one of [50, 100, 152]".format(num_layers)) + return blocks + + +class SEModule(Module): + def __init__(self, channels, reduction): + super(SEModule, self).__init__() + self.avg_pool = AdaptiveAvgPool2d(1) + self.fc1 = Conv2d(channels, channels // reduction, kernel_size=1, padding=0, bias=False) + self.relu = ReLU(inplace=True) + self.fc2 = Conv2d(channels // reduction, channels, kernel_size=1, padding=0, bias=False) + self.sigmoid = Sigmoid() + + def forward(self, x): + module_input = x + x = self.avg_pool(x) + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + return module_input * x + + +class bottleneck_IR(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), BatchNorm2d(depth) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut + + +class bottleneck_IR_SE(Module): + def __init__(self, in_channel, depth, stride): + super(bottleneck_IR_SE, self).__init__() + if in_channel == depth: + self.shortcut_layer = MaxPool2d(1, stride) + else: + self.shortcut_layer = Sequential( + Conv2d(in_channel, depth, (1, 1), stride, bias=False), + BatchNorm2d(depth) + ) + self.res_layer = Sequential( + BatchNorm2d(in_channel), + Conv2d(in_channel, depth, (3, 3), (1, 1), 1, bias=False), + PReLU(depth), + Conv2d(depth, depth, (3, 3), stride, 1, bias=False), + BatchNorm2d(depth), + SEModule(depth, 16) + ) + + def forward(self, x): + shortcut = self.shortcut_layer(x) + res = self.res_layer(x) + return res + shortcut diff --git a/upsampler/models/encoders/model_irse.py b/upsampler/models/encoders/model_irse.py new file mode 100644 index 0000000000000000000000000000000000000000..bc41ace0ba04cf4285c283a28e6c36113a18e6d6 --- /dev/null +++ b/upsampler/models/encoders/model_irse.py @@ -0,0 +1,84 @@ +from torch.nn import Linear, Conv2d, BatchNorm1d, BatchNorm2d, PReLU, Dropout, Sequential, Module +from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE, l2_norm + +""" +Modified Backbone implementation from [TreB1eN](https://github.com/TreB1eN/InsightFace_Pytorch) +""" + + +class Backbone(Module): + def __init__(self, input_size, num_layers, mode='ir', drop_ratio=0.4, affine=True): + super(Backbone, self).__init__() + assert input_size in [112, 224], "input_size should be 112 or 224" + assert num_layers in [50, 100, 152], "num_layers should be 50, 100 or 152" + assert mode in ['ir', 'ir_se'], "mode should be ir or ir_se" + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + if input_size == 112: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 7 * 7, 512), + BatchNorm1d(512, affine=affine)) + else: + self.output_layer = Sequential(BatchNorm2d(512), + Dropout(drop_ratio), + Flatten(), + Linear(512 * 14 * 14, 512), + BatchNorm1d(512, affine=affine)) + + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + def forward(self, x): + x = self.input_layer(x) + x = self.body(x) + x = self.output_layer(x) + return l2_norm(x) + + +def IR_50(input_size): + """Constructs a ir-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_101(input_size): + """Constructs a ir-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_152(input_size): + """Constructs a ir-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_50(input_size): + """Constructs a ir_se-50 model.""" + model = Backbone(input_size, num_layers=50, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_101(input_size): + """Constructs a ir_se-101 model.""" + model = Backbone(input_size, num_layers=100, mode='ir_se', drop_ratio=0.4, affine=False) + return model + + +def IR_SE_152(input_size): + """Constructs a ir_se-152 model.""" + model = Backbone(input_size, num_layers=152, mode='ir_se', drop_ratio=0.4, affine=False) + return model diff --git a/upsampler/models/encoders/psp_encoders.py b/upsampler/models/encoders/psp_encoders.py new file mode 100644 index 0000000000000000000000000000000000000000..b8ed6a10130312fa44923db44f953be90936f26d --- /dev/null +++ b/upsampler/models/encoders/psp_encoders.py @@ -0,0 +1,357 @@ +import numpy as np +import torch +import torch.nn.functional as F +from torch import nn +from torch.nn import Linear, Conv2d, BatchNorm2d, PReLU, Sequential, Module + +from models.encoders.helpers import get_blocks, Flatten, bottleneck_IR, bottleneck_IR_SE +from models.stylegan2.model import EqualLinear + + +class GradualStyleBlock(Module): + def __init__(self, in_c, out_c, spatial, max_pooling=False): + super(GradualStyleBlock, self).__init__() + self.out_c = out_c + self.spatial = spatial + self.max_pooling = max_pooling + num_pools = int(np.log2(spatial)) + modules = [] + modules += [Conv2d(in_c, out_c, kernel_size=3, stride=2, padding=1), + nn.LeakyReLU()] + for i in range(num_pools - 1): + modules += [ + Conv2d(out_c, out_c, kernel_size=3, stride=2, padding=1), + nn.LeakyReLU() + ] + self.convs = nn.Sequential(*modules) + self.linear = EqualLinear(out_c, out_c, lr_mul=1) + + def forward(self, x): + x = self.convs(x) + # To make E accept more general H*W images, we add global average pooling to + # resize all features to 1*1*512 before mapping to latent codes + if self.max_pooling: + x = F.adaptive_max_pool2d(x, 1) ##### modified + else: + x = F.adaptive_avg_pool2d(x, 1) ##### modified + x = x.view(-1, self.out_c) + x = self.linear(x) + return x + +class AdaptiveInstanceNorm(nn.Module): + def __init__(self, fin, style_dim=512): + super().__init__() + + self.norm = nn.InstanceNorm2d(fin, affine=False) + self.style = nn.Linear(style_dim, fin * 2) + + self.style.bias.data[:fin] = 1 + self.style.bias.data[fin:] = 0 + + def forward(self, input, style): + style = self.style(style).unsqueeze(2).unsqueeze(3) + gamma, beta = style.chunk(2, 1) + out = self.norm(input) + out = gamma * out + beta + return out + + +class FusionLayer(Module): ##### modified + def __init__(self, inchannel, outchannel, use_skip_torgb=True, use_att=0): + super(FusionLayer, self).__init__() + + self.transform = nn.Sequential(nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=1, padding=1), + nn.LeakyReLU()) + self.fusion_out = nn.Conv2d(outchannel*2, outchannel, kernel_size=3, stride=1, padding=1) + self.fusion_out.weight.data *= 0.01 + self.fusion_out.weight[:,0:outchannel,1,1].data += torch.eye(outchannel) + + self.use_skip_torgb = use_skip_torgb + if use_skip_torgb: + self.fusion_skip = nn.Conv2d(3+outchannel, 3, kernel_size=3, stride=1, padding=1) + self.fusion_skip.weight.data *= 0.01 + self.fusion_skip.weight[:,0:3,1,1].data += torch.eye(3) + + self.use_att = use_att + if use_att: + modules = [] + modules.append(nn.Linear(512, outchannel)) + for _ in range(use_att): + modules.append(nn.LeakyReLU(negative_slope=0.2, inplace=True)) + modules.append(nn.Linear(outchannel, outchannel)) + modules.append(nn.LeakyReLU(negative_slope=0.2, inplace=True)) + self.linear = Sequential(*modules) + self.norm = AdaptiveInstanceNorm(outchannel*2, outchannel) + self.conv = nn.Conv2d(outchannel*2, 1, 3, 1, 1, bias=True) + + def forward(self, feat, out, skip, editing_w=None): + x = self.transform(feat) + # similar to VToonify, use editing vector as condition + # fuse encoder feature and decoder feature with a predicted attention mask m_E + # if self.use_att = False, just fuse them with a simple conv layer + if self.use_att and editing_w is not None: + label = self.linear(editing_w) + m_E = (F.relu(self.conv(self.norm(torch.cat([out, abs(out-x)], dim=1), label)))).tanh() + x = x * m_E + out = self.fusion_out(torch.cat((out, x), dim=1)) + if self.use_skip_torgb: + skip = self.fusion_skip(torch.cat((skip, x), dim=1)) + return out, skip + + +class ResnetBlock(nn.Module): + def __init__(self, dim): + super(ResnetBlock, self).__init__() + + self.conv_block = nn.Sequential(Conv2d(dim, dim, 3, 1, 1), + nn.LeakyReLU(), + Conv2d(dim, dim, 3, 1, 1)) + self.relu = nn.LeakyReLU() + + def forward(self, x): + out = x + self.conv_block(x) + return self.relu(out) + +# trainable light-weight translation network T +# for sketch/mask-to-face translation, +# we add a trainable T to map y to an intermediate domain where E can more easily extract features. +class ResnetGenerator(nn.Module): + def __init__(self, in_channel=19, res_num=2): + super(ResnetGenerator, self).__init__() + + modules = [] + modules.append(Conv2d(in_channel, 16, 3, 2, 1)) + modules.append(nn.LeakyReLU()) + modules.append(Conv2d(16, 16, 3, 2, 1)) + modules.append(nn.LeakyReLU()) + for _ in range(res_num): + modules.append(ResnetBlock(16)) + for _ in range(2): + modules.append(nn.ConvTranspose2d(16, 16, 3, 2, 1, output_padding=1)) + modules.append(nn.LeakyReLU()) + modules.append(Conv2d(16, 64, 3, 1, 1, bias=False)) + modules.append(BatchNorm2d(64)) + modules.append(PReLU(64)) + self.model = Sequential(*modules) + + def forward(self, input): + return self.model(input) + +class GradualStyleEncoder(Module): + def __init__(self, num_layers, mode='ir', opts=None): + super(GradualStyleEncoder, self).__init__() + assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' + assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + + # for sketch/mask-to-face translation, add a new network T + if opts.input_nc != 3: + self.input_label_layer = ResnetGenerator(opts.input_nc, opts.res_num) + + self.input_layer = Sequential(Conv2d(3, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + self.styles = nn.ModuleList() + self.style_count = opts.n_styles + self.coarse_ind = 3 + self.middle_ind = 7 + for i in range(self.style_count): + if i < self.coarse_ind: + style = GradualStyleBlock(512, 512, 16, 'max_pooling' in opts and opts.max_pooling) + elif i < self.middle_ind: + style = GradualStyleBlock(512, 512, 32, 'max_pooling' in opts and opts.max_pooling) + else: + style = GradualStyleBlock(512, 512, 64, 'max_pooling' in opts and opts.max_pooling) + self.styles.append(style) + self.latlayer1 = nn.Conv2d(256, 512, kernel_size=1, stride=1, padding=0) + self.latlayer2 = nn.Conv2d(128, 512, kernel_size=1, stride=1, padding=0) + + # we concatenate pSp features in the middle layers and + # add a convolution layer to map the concatenated features to the first-layer input feature f of G. + self.featlayer = nn.Conv2d(768, 512, kernel_size=1, stride=1, padding=0) ##### modified + self.skiplayer = nn.Conv2d(768, 3, kernel_size=1, stride=1, padding=0) ##### modified + + # skip connection + if 'use_skip' in opts and opts.use_skip: ##### modified + self.fusion = nn.ModuleList() + channels = [[256,512], [256,512], [256,512], [256,512], [128,512], [64,256], [64,128]] + # opts.skip_max_layer: how many layers are skipped to the decoder + for inc, outc in channels[:max(1, min(7, opts.skip_max_layer))]: # from 4 to 256 + self.fusion.append(FusionLayer(inc, outc, opts.use_skip_torgb, opts.use_att)) + + def _upsample_add(self, x, y): + '''Upsample and add two feature maps. + Args: + x: (Variable) top feature map to be upsampled. + y: (Variable) lateral feature map. + Returns: + (Variable) added feature map. + Note in PyTorch, when input size is odd, the upsampled feature map + with `F.upsample(..., scale_factor=2, mode='nearest')` + maybe not equal to the lateral feature map size. + e.g. + original input size: [N,_,15,15] -> + conv2d feature map size: [N,_,8,8] -> + upsampled feature map size: [N,_,16,16] + So we choose bilinear upsample which supports arbitrary output sizes. + ''' + _, _, H, W = y.size() + return F.interpolate(x, size=(H, W), mode='bilinear', align_corners=True) + y + + # return_feat: return f + # return_full: return f and the skipped encoder features + # return [out, feats] + # out is the style latent code w+ + # feats[0] is f for the 1st conv layer, feats[1] is f for the 1st torgb layer + # feats[2-8] is the skipped encoder features + def forward(self, x, return_feat=False, return_full=False): ##### modified + if x.shape[1] != 3: + x = self.input_label_layer(x) + else: + x = self.input_layer(x) + c256 = x ##### modified + + latents = [] + modulelist = list(self.body._modules.values()) + for i, l in enumerate(modulelist): + x = l(x) + if i == 2: ##### modified + c128 = x + elif i == 6: + c1 = x + elif i == 10: ##### modified + c21 = x ##### modified + elif i == 15: ##### modified + c22 = x ##### modified + elif i == 20: + c2 = x + elif i == 23: + c3 = x + + for j in range(self.coarse_ind): + latents.append(self.styles[j](c3)) + + p2 = self._upsample_add(c3, self.latlayer1(c2)) + for j in range(self.coarse_ind, self.middle_ind): + latents.append(self.styles[j](p2)) + + p1 = self._upsample_add(p2, self.latlayer2(c1)) + for j in range(self.middle_ind, self.style_count): + latents.append(self.styles[j](p1)) + + out = torch.stack(latents, dim=1) + + if not return_feat: + return out + + feats = [self.featlayer(torch.cat((c21, c22, c2), dim=1)), self.skiplayer(torch.cat((c21, c22, c2), dim=1))] + + if return_full: ##### modified + feats += [c2, c2, c22, c21, c1, c128, c256] + + return out, feats + + + # only compute the first-layer feature f + # E_F in the paper + def get_feat(self, x): ##### modified + # for sketch/mask-to-face translation + # use a trainable light-weight translation network T + if x.shape[1] != 3: + x = self.input_label_layer(x) + else: + x = self.input_layer(x) + + latents = [] + modulelist = list(self.body._modules.values()) + for i, l in enumerate(modulelist): + x = l(x) + if i == 10: ##### modified + c21 = x ##### modified + elif i == 15: ##### modified + c22 = x ##### modified + elif i == 20: + c2 = x + break + return self.featlayer(torch.cat((c21, c22, c2), dim=1)) + +class BackboneEncoderUsingLastLayerIntoW(Module): + def __init__(self, num_layers, mode='ir', opts=None): + super(BackboneEncoderUsingLastLayerIntoW, self).__init__() + print('Using BackboneEncoderUsingLastLayerIntoW') + assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' + assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + self.output_pool = torch.nn.AdaptiveAvgPool2d((1, 1)) + self.linear = EqualLinear(512, 512, lr_mul=1) + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + def forward(self, x): + x = self.input_layer(x) + x = self.body(x) + x = self.output_pool(x) + x = x.view(-1, 512) + x = self.linear(x) + return x + + +class BackboneEncoderUsingLastLayerIntoWPlus(Module): + def __init__(self, num_layers, mode='ir', opts=None): + super(BackboneEncoderUsingLastLayerIntoWPlus, self).__init__() + print('Using BackboneEncoderUsingLastLayerIntoWPlus') + assert num_layers in [50, 100, 152], 'num_layers should be 50,100, or 152' + assert mode in ['ir', 'ir_se'], 'mode should be ir or ir_se' + blocks = get_blocks(num_layers) + if mode == 'ir': + unit_module = bottleneck_IR + elif mode == 'ir_se': + unit_module = bottleneck_IR_SE + self.n_styles = opts.n_styles + self.input_layer = Sequential(Conv2d(opts.input_nc, 64, (3, 3), 1, 1, bias=False), + BatchNorm2d(64), + PReLU(64)) + self.output_layer_2 = Sequential(BatchNorm2d(512), + torch.nn.AdaptiveAvgPool2d((7, 7)), + Flatten(), + Linear(512 * 7 * 7, 512)) + self.linear = EqualLinear(512, 512 * self.n_styles, lr_mul=1) + modules = [] + for block in blocks: + for bottleneck in block: + modules.append(unit_module(bottleneck.in_channel, + bottleneck.depth, + bottleneck.stride)) + self.body = Sequential(*modules) + + def forward(self, x): + x = self.input_layer(x) + x = self.body(x) + x = self.output_layer_2(x) + x = self.linear(x) + x = x.view(-1, self.n_styles, 512) + return x diff --git a/upsampler/models/mtcnn/__init__.py b/upsampler/models/mtcnn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/models/mtcnn/mtcnn.py b/upsampler/models/mtcnn/mtcnn.py new file mode 100644 index 0000000000000000000000000000000000000000..4deacabaaf35e315c363c9eada9ff0c41f2561e5 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn.py @@ -0,0 +1,156 @@ +import numpy as np +import torch +from PIL import Image +from models.mtcnn.mtcnn_pytorch.src.get_nets import PNet, RNet, ONet +from models.mtcnn.mtcnn_pytorch.src.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square +from models.mtcnn.mtcnn_pytorch.src.first_stage import run_first_stage +from models.mtcnn.mtcnn_pytorch.src.align_trans import get_reference_facial_points, warp_and_crop_face + +device = 'cuda:0' + + +class MTCNN(): + def __init__(self): + print(device) + self.pnet = PNet().to(device) + self.rnet = RNet().to(device) + self.onet = ONet().to(device) + self.pnet.eval() + self.rnet.eval() + self.onet.eval() + self.refrence = get_reference_facial_points(default_square=True) + + def align(self, img): + _, landmarks = self.detect_faces(img) + if len(landmarks) == 0: + return None, None + facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)] + warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112)) + return Image.fromarray(warped_face), tfm + + def align_multi(self, img, limit=None, min_face_size=30.0): + boxes, landmarks = self.detect_faces(img, min_face_size) + if limit: + boxes = boxes[:limit] + landmarks = landmarks[:limit] + faces = [] + tfms = [] + for landmark in landmarks: + facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)] + warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112)) + faces.append(Image.fromarray(warped_face)) + tfms.append(tfm) + return boxes, faces, tfms + + def detect_faces(self, image, min_face_size=20.0, + thresholds=[0.15, 0.25, 0.35], + nms_thresholds=[0.7, 0.7, 0.7]): + """ + Arguments: + image: an instance of PIL.Image. + min_face_size: a float number. + thresholds: a list of length 3. + nms_thresholds: a list of length 3. + + Returns: + two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10], + bounding boxes and facial landmarks. + """ + + # BUILD AN IMAGE PYRAMID + width, height = image.size + min_length = min(height, width) + + min_detection_size = 12 + factor = 0.707 # sqrt(0.5) + + # scales for scaling the image + scales = [] + + # scales the image so that + # minimum size that we can detect equals to + # minimum face size that we want to detect + m = min_detection_size / min_face_size + min_length *= m + + factor_count = 0 + while min_length > min_detection_size: + scales.append(m * factor ** factor_count) + min_length *= factor + factor_count += 1 + + # STAGE 1 + + # it will be returned + bounding_boxes = [] + + with torch.no_grad(): + # run P-Net on different scales + for s in scales: + boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0]) + bounding_boxes.append(boxes) + + # collect boxes (and offsets, and scores) from different scales + bounding_boxes = [i for i in bounding_boxes if i is not None] + bounding_boxes = np.vstack(bounding_boxes) + + keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0]) + bounding_boxes = bounding_boxes[keep] + + # use offsets predicted by pnet to transform bounding boxes + bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:]) + # shape [n_boxes, 5] + + bounding_boxes = convert_to_square(bounding_boxes) + bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4]) + + # STAGE 2 + + img_boxes = get_image_boxes(bounding_boxes, image, size=24) + img_boxes = torch.FloatTensor(img_boxes).to(device) + + output = self.rnet(img_boxes) + offsets = output[0].cpu().data.numpy() # shape [n_boxes, 4] + probs = output[1].cpu().data.numpy() # shape [n_boxes, 2] + + keep = np.where(probs[:, 1] > thresholds[1])[0] + bounding_boxes = bounding_boxes[keep] + bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,)) + offsets = offsets[keep] + + keep = nms(bounding_boxes, nms_thresholds[1]) + bounding_boxes = bounding_boxes[keep] + bounding_boxes = calibrate_box(bounding_boxes, offsets[keep]) + bounding_boxes = convert_to_square(bounding_boxes) + bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4]) + + # STAGE 3 + + img_boxes = get_image_boxes(bounding_boxes, image, size=48) + if len(img_boxes) == 0: + return [], [] + img_boxes = torch.FloatTensor(img_boxes).to(device) + output = self.onet(img_boxes) + landmarks = output[0].cpu().data.numpy() # shape [n_boxes, 10] + offsets = output[1].cpu().data.numpy() # shape [n_boxes, 4] + probs = output[2].cpu().data.numpy() # shape [n_boxes, 2] + + keep = np.where(probs[:, 1] > thresholds[2])[0] + bounding_boxes = bounding_boxes[keep] + bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,)) + offsets = offsets[keep] + landmarks = landmarks[keep] + + # compute landmark points + width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0 + height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0 + xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1] + landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5] + landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10] + + bounding_boxes = calibrate_box(bounding_boxes, offsets) + keep = nms(bounding_boxes, nms_thresholds[2], mode='min') + bounding_boxes = bounding_boxes[keep] + landmarks = landmarks[keep] + + return bounding_boxes, landmarks diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/__init__.py b/upsampler/models/mtcnn/mtcnn_pytorch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/__init__.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..617ba38c34b1801b2db2e0209b4e886c9d24c490 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/__init__.py @@ -0,0 +1,2 @@ +from .visualization_utils import show_bboxes +from .detector import detect_faces diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/align_trans.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/align_trans.py new file mode 100644 index 0000000000000000000000000000000000000000..ab5f1df002bc19556ae8a75cabf56310084785a9 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/align_trans.py @@ -0,0 +1,304 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Apr 24 15:43:29 2017 +@author: zhaoy +""" +import numpy as np +import cv2 + +# from scipy.linalg import lstsq +# from scipy.ndimage import geometric_transform # , map_coordinates + +from models.mtcnn.mtcnn_pytorch.src.matlab_cp2tform import get_similarity_transform_for_cv2 + +# reference facial points, a list of coordinates (x,y) +REFERENCE_FACIAL_POINTS = [ + [30.29459953, 51.69630051], + [65.53179932, 51.50139999], + [48.02519989, 71.73660278], + [33.54930115, 92.3655014], + [62.72990036, 92.20410156] +] + +DEFAULT_CROP_SIZE = (96, 112) + + +class FaceWarpException(Exception): + def __str__(self): + return 'In File {}:{}'.format( + __file__, super.__str__(self)) + + +def get_reference_facial_points(output_size=None, + inner_padding_factor=0.0, + outer_padding=(0, 0), + default_square=False): + """ + Function: + ---------- + get reference 5 key points according to crop settings: + 0. Set default crop_size: + if default_square: + crop_size = (112, 112) + else: + crop_size = (96, 112) + 1. Pad the crop_size by inner_padding_factor in each side; + 2. Resize crop_size into (output_size - outer_padding*2), + pad into output_size with outer_padding; + 3. Output reference_5point; + Parameters: + ---------- + @output_size: (w, h) or None + size of aligned face image + @inner_padding_factor: (w_factor, h_factor) + padding factor for inner (w, h) + @outer_padding: (w_pad, h_pad) + each row is a pair of coordinates (x, y) + @default_square: True or False + if True: + default crop_size = (112, 112) + else: + default crop_size = (96, 112); + !!! make sure, if output_size is not None: + (output_size - outer_padding) + = some_scale * (default crop_size * (1.0 + inner_padding_factor)) + Returns: + ---------- + @reference_5point: 5x2 np.array + each row is a pair of transformed coordinates (x, y) + """ + # print('\n===> get_reference_facial_points():') + + # print('---> Params:') + # print(' output_size: ', output_size) + # print(' inner_padding_factor: ', inner_padding_factor) + # print(' outer_padding:', outer_padding) + # print(' default_square: ', default_square) + + tmp_5pts = np.array(REFERENCE_FACIAL_POINTS) + tmp_crop_size = np.array(DEFAULT_CROP_SIZE) + + # 0) make the inner region a square + if default_square: + size_diff = max(tmp_crop_size) - tmp_crop_size + tmp_5pts += size_diff / 2 + tmp_crop_size += size_diff + + # print('---> default:') + # print(' crop_size = ', tmp_crop_size) + # print(' reference_5pts = ', tmp_5pts) + + if (output_size and + output_size[0] == tmp_crop_size[0] and + output_size[1] == tmp_crop_size[1]): + # print('output_size == DEFAULT_CROP_SIZE {}: return default reference points'.format(tmp_crop_size)) + return tmp_5pts + + if (inner_padding_factor == 0 and + outer_padding == (0, 0)): + if output_size is None: + # print('No paddings to do: return default reference points') + return tmp_5pts + else: + raise FaceWarpException( + 'No paddings to do, output_size must be None or {}'.format(tmp_crop_size)) + + # check output size + if not (0 <= inner_padding_factor <= 1.0): + raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)') + + if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) + and output_size is None): + output_size = tmp_crop_size * \ + (1 + inner_padding_factor * 2).astype(np.int32) + output_size += np.array(outer_padding) + # print(' deduced from paddings, output_size = ', output_size) + + if not (outer_padding[0] < output_size[0] + and outer_padding[1] < output_size[1]): + raise FaceWarpException('Not (outer_padding[0] < output_size[0]' + 'and outer_padding[1] < output_size[1])') + + # 1) pad the inner region according inner_padding_factor + # print('---> STEP1: pad the inner region according inner_padding_factor') + if inner_padding_factor > 0: + size_diff = tmp_crop_size * inner_padding_factor * 2 + tmp_5pts += size_diff / 2 + tmp_crop_size += np.round(size_diff).astype(np.int32) + + # print(' crop_size = ', tmp_crop_size) + # print(' reference_5pts = ', tmp_5pts) + + # 2) resize the padded inner region + # print('---> STEP2: resize the padded inner region') + size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2 + # print(' crop_size = ', tmp_crop_size) + # print(' size_bf_outer_pad = ', size_bf_outer_pad) + + if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]: + raise FaceWarpException('Must have (output_size - outer_padding)' + '= some_scale * (crop_size * (1.0 + inner_padding_factor)') + + scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0] + # print(' resize scale_factor = ', scale_factor) + tmp_5pts = tmp_5pts * scale_factor + # size_diff = tmp_crop_size * (scale_factor - min(scale_factor)) + # tmp_5pts = tmp_5pts + size_diff / 2 + tmp_crop_size = size_bf_outer_pad + # print(' crop_size = ', tmp_crop_size) + # print(' reference_5pts = ', tmp_5pts) + + # 3) add outer_padding to make output_size + reference_5point = tmp_5pts + np.array(outer_padding) + tmp_crop_size = output_size + # print('---> STEP3: add outer_padding to make output_size') + # print(' crop_size = ', tmp_crop_size) + # print(' reference_5pts = ', tmp_5pts) + + # print('===> end get_reference_facial_points\n') + + return reference_5point + + +def get_affine_transform_matrix(src_pts, dst_pts): + """ + Function: + ---------- + get affine transform matrix 'tfm' from src_pts to dst_pts + Parameters: + ---------- + @src_pts: Kx2 np.array + source points matrix, each row is a pair of coordinates (x, y) + @dst_pts: Kx2 np.array + destination points matrix, each row is a pair of coordinates (x, y) + Returns: + ---------- + @tfm: 2x3 np.array + transform matrix from src_pts to dst_pts + """ + + tfm = np.float32([[1, 0, 0], [0, 1, 0]]) + n_pts = src_pts.shape[0] + ones = np.ones((n_pts, 1), src_pts.dtype) + src_pts_ = np.hstack([src_pts, ones]) + dst_pts_ = np.hstack([dst_pts, ones]) + + # #print(('src_pts_:\n' + str(src_pts_)) + # #print(('dst_pts_:\n' + str(dst_pts_)) + + A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_) + + # #print(('np.linalg.lstsq return A: \n' + str(A)) + # #print(('np.linalg.lstsq return res: \n' + str(res)) + # #print(('np.linalg.lstsq return rank: \n' + str(rank)) + # #print(('np.linalg.lstsq return s: \n' + str(s)) + + if rank == 3: + tfm = np.float32([ + [A[0, 0], A[1, 0], A[2, 0]], + [A[0, 1], A[1, 1], A[2, 1]] + ]) + elif rank == 2: + tfm = np.float32([ + [A[0, 0], A[1, 0], 0], + [A[0, 1], A[1, 1], 0] + ]) + + return tfm + + +def warp_and_crop_face(src_img, + facial_pts, + reference_pts=None, + crop_size=(96, 112), + align_type='smilarity'): + """ + Function: + ---------- + apply affine transform 'trans' to uv + Parameters: + ---------- + @src_img: 3x3 np.array + input image + @facial_pts: could be + 1)a list of K coordinates (x,y) + or + 2) Kx2 or 2xK np.array + each row or col is a pair of coordinates (x, y) + @reference_pts: could be + 1) a list of K coordinates (x,y) + or + 2) Kx2 or 2xK np.array + each row or col is a pair of coordinates (x, y) + or + 3) None + if None, use default reference facial points + @crop_size: (w, h) + output face image size + @align_type: transform type, could be one of + 1) 'similarity': use similarity transform + 2) 'cv2_affine': use the first 3 points to do affine transform, + by calling cv2.getAffineTransform() + 3) 'affine': use all points to do affine transform + Returns: + ---------- + @face_img: output face image with size (w, h) = @crop_size + """ + + if reference_pts is None: + if crop_size[0] == 96 and crop_size[1] == 112: + reference_pts = REFERENCE_FACIAL_POINTS + else: + default_square = False + inner_padding_factor = 0 + outer_padding = (0, 0) + output_size = crop_size + + reference_pts = get_reference_facial_points(output_size, + inner_padding_factor, + outer_padding, + default_square) + + ref_pts = np.float32(reference_pts) + ref_pts_shp = ref_pts.shape + if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2: + raise FaceWarpException( + 'reference_pts.shape must be (K,2) or (2,K) and K>2') + + if ref_pts_shp[0] == 2: + ref_pts = ref_pts.T + + src_pts = np.float32(facial_pts) + src_pts_shp = src_pts.shape + if max(src_pts_shp) < 3 or min(src_pts_shp) != 2: + raise FaceWarpException( + 'facial_pts.shape must be (K,2) or (2,K) and K>2') + + if src_pts_shp[0] == 2: + src_pts = src_pts.T + + # #print('--->src_pts:\n', src_pts + # #print('--->ref_pts\n', ref_pts + + if src_pts.shape != ref_pts.shape: + raise FaceWarpException( + 'facial_pts and reference_pts must have the same shape') + + if align_type is 'cv2_affine': + tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3]) + # #print(('cv2.getAffineTransform() returns tfm=\n' + str(tfm)) + elif align_type is 'affine': + tfm = get_affine_transform_matrix(src_pts, ref_pts) + # #print(('get_affine_transform_matrix() returns tfm=\n' + str(tfm)) + else: + tfm = get_similarity_transform_for_cv2(src_pts, ref_pts) + # #print(('get_similarity_transform_for_cv2() returns tfm=\n' + str(tfm)) + + # #print('--->Transform matrix: ' + # #print(('type(tfm):' + str(type(tfm))) + # #print(('tfm.dtype:' + str(tfm.dtype)) + # #print( tfm + + face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1])) + + return face_img, tfm diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/box_utils.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/box_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1e8081b73639a7d70e4391b3d45417569550ddc6 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/box_utils.py @@ -0,0 +1,238 @@ +import numpy as np +from PIL import Image + + +def nms(boxes, overlap_threshold=0.5, mode='union'): + """Non-maximum suppression. + + Arguments: + boxes: a float numpy array of shape [n, 5], + where each row is (xmin, ymin, xmax, ymax, score). + overlap_threshold: a float number. + mode: 'union' or 'min'. + + Returns: + list with indices of the selected boxes + """ + + # if there are no boxes, return the empty list + if len(boxes) == 0: + return [] + + # list of picked indices + pick = [] + + # grab the coordinates of the bounding boxes + x1, y1, x2, y2, score = [boxes[:, i] for i in range(5)] + + area = (x2 - x1 + 1.0) * (y2 - y1 + 1.0) + ids = np.argsort(score) # in increasing order + + while len(ids) > 0: + + # grab index of the largest value + last = len(ids) - 1 + i = ids[last] + pick.append(i) + + # compute intersections + # of the box with the largest score + # with the rest of boxes + + # left top corner of intersection boxes + ix1 = np.maximum(x1[i], x1[ids[:last]]) + iy1 = np.maximum(y1[i], y1[ids[:last]]) + + # right bottom corner of intersection boxes + ix2 = np.minimum(x2[i], x2[ids[:last]]) + iy2 = np.minimum(y2[i], y2[ids[:last]]) + + # width and height of intersection boxes + w = np.maximum(0.0, ix2 - ix1 + 1.0) + h = np.maximum(0.0, iy2 - iy1 + 1.0) + + # intersections' areas + inter = w * h + if mode == 'min': + overlap = inter / np.minimum(area[i], area[ids[:last]]) + elif mode == 'union': + # intersection over union (IoU) + overlap = inter / (area[i] + area[ids[:last]] - inter) + + # delete all boxes where overlap is too big + ids = np.delete( + ids, + np.concatenate([[last], np.where(overlap > overlap_threshold)[0]]) + ) + + return pick + + +def convert_to_square(bboxes): + """Convert bounding boxes to a square form. + + Arguments: + bboxes: a float numpy array of shape [n, 5]. + + Returns: + a float numpy array of shape [n, 5], + squared bounding boxes. + """ + + square_bboxes = np.zeros_like(bboxes) + x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)] + h = y2 - y1 + 1.0 + w = x2 - x1 + 1.0 + max_side = np.maximum(h, w) + square_bboxes[:, 0] = x1 + w * 0.5 - max_side * 0.5 + square_bboxes[:, 1] = y1 + h * 0.5 - max_side * 0.5 + square_bboxes[:, 2] = square_bboxes[:, 0] + max_side - 1.0 + square_bboxes[:, 3] = square_bboxes[:, 1] + max_side - 1.0 + return square_bboxes + + +def calibrate_box(bboxes, offsets): + """Transform bounding boxes to be more like true bounding boxes. + 'offsets' is one of the outputs of the nets. + + Arguments: + bboxes: a float numpy array of shape [n, 5]. + offsets: a float numpy array of shape [n, 4]. + + Returns: + a float numpy array of shape [n, 5]. + """ + x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)] + w = x2 - x1 + 1.0 + h = y2 - y1 + 1.0 + w = np.expand_dims(w, 1) + h = np.expand_dims(h, 1) + + # this is what happening here: + # tx1, ty1, tx2, ty2 = [offsets[:, i] for i in range(4)] + # x1_true = x1 + tx1*w + # y1_true = y1 + ty1*h + # x2_true = x2 + tx2*w + # y2_true = y2 + ty2*h + # below is just more compact form of this + + # are offsets always such that + # x1 < x2 and y1 < y2 ? + + translation = np.hstack([w, h, w, h]) * offsets + bboxes[:, 0:4] = bboxes[:, 0:4] + translation + return bboxes + + +def get_image_boxes(bounding_boxes, img, size=24): + """Cut out boxes from the image. + + Arguments: + bounding_boxes: a float numpy array of shape [n, 5]. + img: an instance of PIL.Image. + size: an integer, size of cutouts. + + Returns: + a float numpy array of shape [n, 3, size, size]. + """ + + num_boxes = len(bounding_boxes) + width, height = img.size + + [dy, edy, dx, edx, y, ey, x, ex, w, h] = correct_bboxes(bounding_boxes, width, height) + img_boxes = np.zeros((num_boxes, 3, size, size), 'float32') + + for i in range(num_boxes): + img_box = np.zeros((h[i], w[i], 3), 'uint8') + + img_array = np.asarray(img, 'uint8') + img_box[dy[i]:(edy[i] + 1), dx[i]:(edx[i] + 1), :] = \ + img_array[y[i]:(ey[i] + 1), x[i]:(ex[i] + 1), :] + + # resize + img_box = Image.fromarray(img_box) + img_box = img_box.resize((size, size), Image.BILINEAR) + img_box = np.asarray(img_box, 'float32') + + img_boxes[i, :, :, :] = _preprocess(img_box) + + return img_boxes + + +def correct_bboxes(bboxes, width, height): + """Crop boxes that are too big and get coordinates + with respect to cutouts. + + Arguments: + bboxes: a float numpy array of shape [n, 5], + where each row is (xmin, ymin, xmax, ymax, score). + width: a float number. + height: a float number. + + Returns: + dy, dx, edy, edx: a int numpy arrays of shape [n], + coordinates of the boxes with respect to the cutouts. + y, x, ey, ex: a int numpy arrays of shape [n], + corrected ymin, xmin, ymax, xmax. + h, w: a int numpy arrays of shape [n], + just heights and widths of boxes. + + in the following order: + [dy, edy, dx, edx, y, ey, x, ex, w, h]. + """ + + x1, y1, x2, y2 = [bboxes[:, i] for i in range(4)] + w, h = x2 - x1 + 1.0, y2 - y1 + 1.0 + num_boxes = bboxes.shape[0] + + # 'e' stands for end + # (x, y) -> (ex, ey) + x, y, ex, ey = x1, y1, x2, y2 + + # we need to cut out a box from the image. + # (x, y, ex, ey) are corrected coordinates of the box + # in the image. + # (dx, dy, edx, edy) are coordinates of the box in the cutout + # from the image. + dx, dy = np.zeros((num_boxes,)), np.zeros((num_boxes,)) + edx, edy = w.copy() - 1.0, h.copy() - 1.0 + + # if box's bottom right corner is too far right + ind = np.where(ex > width - 1.0)[0] + edx[ind] = w[ind] + width - 2.0 - ex[ind] + ex[ind] = width - 1.0 + + # if box's bottom right corner is too low + ind = np.where(ey > height - 1.0)[0] + edy[ind] = h[ind] + height - 2.0 - ey[ind] + ey[ind] = height - 1.0 + + # if box's top left corner is too far left + ind = np.where(x < 0.0)[0] + dx[ind] = 0.0 - x[ind] + x[ind] = 0.0 + + # if box's top left corner is too high + ind = np.where(y < 0.0)[0] + dy[ind] = 0.0 - y[ind] + y[ind] = 0.0 + + return_list = [dy, edy, dx, edx, y, ey, x, ex, w, h] + return_list = [i.astype('int32') for i in return_list] + + return return_list + + +def _preprocess(img): + """Preprocessing step before feeding the network. + + Arguments: + img: a float numpy array of shape [h, w, c]. + + Returns: + a float numpy array of shape [1, c, h, w]. + """ + img = img.transpose((2, 0, 1)) + img = np.expand_dims(img, 0) + img = (img - 127.5) * 0.0078125 + return img diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/detector.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/detector.py new file mode 100644 index 0000000000000000000000000000000000000000..b162cff3194cc0114abd1a840e5dc772a55edd25 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/detector.py @@ -0,0 +1,126 @@ +import numpy as np +import torch +from torch.autograd import Variable +from .get_nets import PNet, RNet, ONet +from .box_utils import nms, calibrate_box, get_image_boxes, convert_to_square +from .first_stage import run_first_stage + + +def detect_faces(image, min_face_size=20.0, + thresholds=[0.6, 0.7, 0.8], + nms_thresholds=[0.7, 0.7, 0.7]): + """ + Arguments: + image: an instance of PIL.Image. + min_face_size: a float number. + thresholds: a list of length 3. + nms_thresholds: a list of length 3. + + Returns: + two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10], + bounding boxes and facial landmarks. + """ + + # LOAD MODELS + pnet = PNet() + rnet = RNet() + onet = ONet() + onet.eval() + + # BUILD AN IMAGE PYRAMID + width, height = image.size + min_length = min(height, width) + + min_detection_size = 12 + factor = 0.707 # sqrt(0.5) + + # scales for scaling the image + scales = [] + + # scales the image so that + # minimum size that we can detect equals to + # minimum face size that we want to detect + m = min_detection_size / min_face_size + min_length *= m + + factor_count = 0 + while min_length > min_detection_size: + scales.append(m * factor ** factor_count) + min_length *= factor + factor_count += 1 + + # STAGE 1 + + # it will be returned + bounding_boxes = [] + + with torch.no_grad(): + # run P-Net on different scales + for s in scales: + boxes = run_first_stage(image, pnet, scale=s, threshold=thresholds[0]) + bounding_boxes.append(boxes) + + # collect boxes (and offsets, and scores) from different scales + bounding_boxes = [i for i in bounding_boxes if i is not None] + bounding_boxes = np.vstack(bounding_boxes) + + keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0]) + bounding_boxes = bounding_boxes[keep] + + # use offsets predicted by pnet to transform bounding boxes + bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:]) + # shape [n_boxes, 5] + + bounding_boxes = convert_to_square(bounding_boxes) + bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4]) + + # STAGE 2 + + img_boxes = get_image_boxes(bounding_boxes, image, size=24) + img_boxes = torch.FloatTensor(img_boxes) + + output = rnet(img_boxes) + offsets = output[0].data.numpy() # shape [n_boxes, 4] + probs = output[1].data.numpy() # shape [n_boxes, 2] + + keep = np.where(probs[:, 1] > thresholds[1])[0] + bounding_boxes = bounding_boxes[keep] + bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,)) + offsets = offsets[keep] + + keep = nms(bounding_boxes, nms_thresholds[1]) + bounding_boxes = bounding_boxes[keep] + bounding_boxes = calibrate_box(bounding_boxes, offsets[keep]) + bounding_boxes = convert_to_square(bounding_boxes) + bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4]) + + # STAGE 3 + + img_boxes = get_image_boxes(bounding_boxes, image, size=48) + if len(img_boxes) == 0: + return [], [] + img_boxes = torch.FloatTensor(img_boxes) + output = onet(img_boxes) + landmarks = output[0].data.numpy() # shape [n_boxes, 10] + offsets = output[1].data.numpy() # shape [n_boxes, 4] + probs = output[2].data.numpy() # shape [n_boxes, 2] + + keep = np.where(probs[:, 1] > thresholds[2])[0] + bounding_boxes = bounding_boxes[keep] + bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,)) + offsets = offsets[keep] + landmarks = landmarks[keep] + + # compute landmark points + width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0 + height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0 + xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1] + landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5] + landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10] + + bounding_boxes = calibrate_box(bounding_boxes, offsets) + keep = nms(bounding_boxes, nms_thresholds[2], mode='min') + bounding_boxes = bounding_boxes[keep] + landmarks = landmarks[keep] + + return bounding_boxes, landmarks diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/first_stage.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/first_stage.py new file mode 100644 index 0000000000000000000000000000000000000000..d646f91d5e0348e23bd426701f6afa6000a9b6d1 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/first_stage.py @@ -0,0 +1,101 @@ +import torch +from torch.autograd import Variable +import math +from PIL import Image +import numpy as np +from .box_utils import nms, _preprocess + +# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") +device = 'cuda:0' + + +def run_first_stage(image, net, scale, threshold): + """Run P-Net, generate bounding boxes, and do NMS. + + Arguments: + image: an instance of PIL.Image. + net: an instance of pytorch's nn.Module, P-Net. + scale: a float number, + scale width and height of the image by this number. + threshold: a float number, + threshold on the probability of a face when generating + bounding boxes from predictions of the net. + + Returns: + a float numpy array of shape [n_boxes, 9], + bounding boxes with scores and offsets (4 + 1 + 4). + """ + + # scale the image and convert it to a float array + width, height = image.size + sw, sh = math.ceil(width * scale), math.ceil(height * scale) + img = image.resize((sw, sh), Image.BILINEAR) + img = np.asarray(img, 'float32') + + img = torch.FloatTensor(_preprocess(img)).to(device) + with torch.no_grad(): + output = net(img) + probs = output[1].cpu().data.numpy()[0, 1, :, :] + offsets = output[0].cpu().data.numpy() + # probs: probability of a face at each sliding window + # offsets: transformations to true bounding boxes + + boxes = _generate_bboxes(probs, offsets, scale, threshold) + if len(boxes) == 0: + return None + + keep = nms(boxes[:, 0:5], overlap_threshold=0.5) + return boxes[keep] + + +def _generate_bboxes(probs, offsets, scale, threshold): + """Generate bounding boxes at places + where there is probably a face. + + Arguments: + probs: a float numpy array of shape [n, m]. + offsets: a float numpy array of shape [1, 4, n, m]. + scale: a float number, + width and height of the image were scaled by this number. + threshold: a float number. + + Returns: + a float numpy array of shape [n_boxes, 9] + """ + + # applying P-Net is equivalent, in some sense, to + # moving 12x12 window with stride 2 + stride = 2 + cell_size = 12 + + # indices of boxes where there is probably a face + inds = np.where(probs > threshold) + + if inds[0].size == 0: + return np.array([]) + + # transformations of bounding boxes + tx1, ty1, tx2, ty2 = [offsets[0, i, inds[0], inds[1]] for i in range(4)] + # they are defined as: + # w = x2 - x1 + 1 + # h = y2 - y1 + 1 + # x1_true = x1 + tx1*w + # x2_true = x2 + tx2*w + # y1_true = y1 + ty1*h + # y2_true = y2 + ty2*h + + offsets = np.array([tx1, ty1, tx2, ty2]) + score = probs[inds[0], inds[1]] + + # P-Net is applied to scaled images + # so we need to rescale bounding boxes back + bounding_boxes = np.vstack([ + np.round((stride * inds[1] + 1.0) / scale), + np.round((stride * inds[0] + 1.0) / scale), + np.round((stride * inds[1] + 1.0 + cell_size) / scale), + np.round((stride * inds[0] + 1.0 + cell_size) / scale), + score, offsets + ]) + # why one is added? + + return bounding_boxes.T diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/get_nets.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/get_nets.py new file mode 100644 index 0000000000000000000000000000000000000000..0b5d3cc64734f0d05b19969fda31dc2bff9b18c6 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/get_nets.py @@ -0,0 +1,171 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from collections import OrderedDict +import numpy as np + +from configs.paths_config import model_paths +PNET_PATH = model_paths["mtcnn_pnet"] +ONET_PATH = model_paths["mtcnn_onet"] +RNET_PATH = model_paths["mtcnn_rnet"] + + +class Flatten(nn.Module): + + def __init__(self): + super(Flatten, self).__init__() + + def forward(self, x): + """ + Arguments: + x: a float tensor with shape [batch_size, c, h, w]. + Returns: + a float tensor with shape [batch_size, c*h*w]. + """ + + # without this pretrained model isn't working + x = x.transpose(3, 2).contiguous() + + return x.view(x.size(0), -1) + + +class PNet(nn.Module): + + def __init__(self): + super().__init__() + + # suppose we have input with size HxW, then + # after first layer: H - 2, + # after pool: ceil((H - 2)/2), + # after second conv: ceil((H - 2)/2) - 2, + # after last conv: ceil((H - 2)/2) - 4, + # and the same for W + + self.features = nn.Sequential(OrderedDict([ + ('conv1', nn.Conv2d(3, 10, 3, 1)), + ('prelu1', nn.PReLU(10)), + ('pool1', nn.MaxPool2d(2, 2, ceil_mode=True)), + + ('conv2', nn.Conv2d(10, 16, 3, 1)), + ('prelu2', nn.PReLU(16)), + + ('conv3', nn.Conv2d(16, 32, 3, 1)), + ('prelu3', nn.PReLU(32)) + ])) + + self.conv4_1 = nn.Conv2d(32, 2, 1, 1) + self.conv4_2 = nn.Conv2d(32, 4, 1, 1) + + weights = np.load(PNET_PATH, allow_pickle=True)[()] + for n, p in self.named_parameters(): + p.data = torch.FloatTensor(weights[n]) + + def forward(self, x): + """ + Arguments: + x: a float tensor with shape [batch_size, 3, h, w]. + Returns: + b: a float tensor with shape [batch_size, 4, h', w']. + a: a float tensor with shape [batch_size, 2, h', w']. + """ + x = self.features(x) + a = self.conv4_1(x) + b = self.conv4_2(x) + a = F.softmax(a, dim=-1) + return b, a + + +class RNet(nn.Module): + + def __init__(self): + super().__init__() + + self.features = nn.Sequential(OrderedDict([ + ('conv1', nn.Conv2d(3, 28, 3, 1)), + ('prelu1', nn.PReLU(28)), + ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)), + + ('conv2', nn.Conv2d(28, 48, 3, 1)), + ('prelu2', nn.PReLU(48)), + ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)), + + ('conv3', nn.Conv2d(48, 64, 2, 1)), + ('prelu3', nn.PReLU(64)), + + ('flatten', Flatten()), + ('conv4', nn.Linear(576, 128)), + ('prelu4', nn.PReLU(128)) + ])) + + self.conv5_1 = nn.Linear(128, 2) + self.conv5_2 = nn.Linear(128, 4) + + weights = np.load(RNET_PATH, allow_pickle=True)[()] + for n, p in self.named_parameters(): + p.data = torch.FloatTensor(weights[n]) + + def forward(self, x): + """ + Arguments: + x: a float tensor with shape [batch_size, 3, h, w]. + Returns: + b: a float tensor with shape [batch_size, 4]. + a: a float tensor with shape [batch_size, 2]. + """ + x = self.features(x) + a = self.conv5_1(x) + b = self.conv5_2(x) + a = F.softmax(a, dim=-1) + return b, a + + +class ONet(nn.Module): + + def __init__(self): + super().__init__() + + self.features = nn.Sequential(OrderedDict([ + ('conv1', nn.Conv2d(3, 32, 3, 1)), + ('prelu1', nn.PReLU(32)), + ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)), + + ('conv2', nn.Conv2d(32, 64, 3, 1)), + ('prelu2', nn.PReLU(64)), + ('pool2', nn.MaxPool2d(3, 2, ceil_mode=True)), + + ('conv3', nn.Conv2d(64, 64, 3, 1)), + ('prelu3', nn.PReLU(64)), + ('pool3', nn.MaxPool2d(2, 2, ceil_mode=True)), + + ('conv4', nn.Conv2d(64, 128, 2, 1)), + ('prelu4', nn.PReLU(128)), + + ('flatten', Flatten()), + ('conv5', nn.Linear(1152, 256)), + ('drop5', nn.Dropout(0.25)), + ('prelu5', nn.PReLU(256)), + ])) + + self.conv6_1 = nn.Linear(256, 2) + self.conv6_2 = nn.Linear(256, 4) + self.conv6_3 = nn.Linear(256, 10) + + weights = np.load(ONET_PATH, allow_pickle=True)[()] + for n, p in self.named_parameters(): + p.data = torch.FloatTensor(weights[n]) + + def forward(self, x): + """ + Arguments: + x: a float tensor with shape [batch_size, 3, h, w]. + Returns: + c: a float tensor with shape [batch_size, 10]. + b: a float tensor with shape [batch_size, 4]. + a: a float tensor with shape [batch_size, 2]. + """ + x = self.features(x) + a = self.conv6_1(x) + b = self.conv6_2(x) + c = self.conv6_3(x) + a = F.softmax(a, dim=-1) + return c, b, a diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/matlab_cp2tform.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/matlab_cp2tform.py new file mode 100644 index 0000000000000000000000000000000000000000..025b18ec2e64472bd4c0c636f9ae061526bdc8cd --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/matlab_cp2tform.py @@ -0,0 +1,350 @@ +# -*- coding: utf-8 -*- +""" +Created on Tue Jul 11 06:54:28 2017 + +@author: zhaoyafei +""" + +import numpy as np +from numpy.linalg import inv, norm, lstsq +from numpy.linalg import matrix_rank as rank + + +class MatlabCp2tormException(Exception): + def __str__(self): + return 'In File {}:{}'.format( + __file__, super.__str__(self)) + + +def tformfwd(trans, uv): + """ + Function: + ---------- + apply affine transform 'trans' to uv + + Parameters: + ---------- + @trans: 3x3 np.array + transform matrix + @uv: Kx2 np.array + each row is a pair of coordinates (x, y) + + Returns: + ---------- + @xy: Kx2 np.array + each row is a pair of transformed coordinates (x, y) + """ + uv = np.hstack(( + uv, np.ones((uv.shape[0], 1)) + )) + xy = np.dot(uv, trans) + xy = xy[:, 0:-1] + return xy + + +def tforminv(trans, uv): + """ + Function: + ---------- + apply the inverse of affine transform 'trans' to uv + + Parameters: + ---------- + @trans: 3x3 np.array + transform matrix + @uv: Kx2 np.array + each row is a pair of coordinates (x, y) + + Returns: + ---------- + @xy: Kx2 np.array + each row is a pair of inverse-transformed coordinates (x, y) + """ + Tinv = inv(trans) + xy = tformfwd(Tinv, uv) + return xy + + +def findNonreflectiveSimilarity(uv, xy, options=None): + options = {'K': 2} + + K = options['K'] + M = xy.shape[0] + x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector + y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector + # print('--->x, y:\n', x, y + + tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1)))) + tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1)))) + X = np.vstack((tmp1, tmp2)) + # print('--->X.shape: ', X.shape + # print('X:\n', X + + u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector + v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector + U = np.vstack((u, v)) + # print('--->U.shape: ', U.shape + # print('U:\n', U + + # We know that X * r = U + if rank(X) >= 2 * K: + r, _, _, _ = lstsq(X, U, rcond=None) # Make sure this is what I want + r = np.squeeze(r) + else: + raise Exception('cp2tform:twoUniquePointsReq') + + # print('--->r:\n', r + + sc = r[0] + ss = r[1] + tx = r[2] + ty = r[3] + + Tinv = np.array([ + [sc, -ss, 0], + [ss, sc, 0], + [tx, ty, 1] + ]) + + # print('--->Tinv:\n', Tinv + + T = inv(Tinv) + # print('--->T:\n', T + + T[:, 2] = np.array([0, 0, 1]) + + return T, Tinv + + +def findSimilarity(uv, xy, options=None): + options = {'K': 2} + + # uv = np.array(uv) + # xy = np.array(xy) + + # Solve for trans1 + trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options) + + # Solve for trans2 + + # manually reflect the xy data across the Y-axis + xyR = xy + xyR[:, 0] = -1 * xyR[:, 0] + + trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options) + + # manually reflect the tform to undo the reflection done on xyR + TreflectY = np.array([ + [-1, 0, 0], + [0, 1, 0], + [0, 0, 1] + ]) + + trans2 = np.dot(trans2r, TreflectY) + + # Figure out if trans1 or trans2 is better + xy1 = tformfwd(trans1, uv) + norm1 = norm(xy1 - xy) + + xy2 = tformfwd(trans2, uv) + norm2 = norm(xy2 - xy) + + if norm1 <= norm2: + return trans1, trans1_inv + else: + trans2_inv = inv(trans2) + return trans2, trans2_inv + + +def get_similarity_transform(src_pts, dst_pts, reflective=True): + """ + Function: + ---------- + Find Similarity Transform Matrix 'trans': + u = src_pts[:, 0] + v = src_pts[:, 1] + x = dst_pts[:, 0] + y = dst_pts[:, 1] + [x, y, 1] = [u, v, 1] * trans + + Parameters: + ---------- + @src_pts: Kx2 np.array + source points, each row is a pair of coordinates (x, y) + @dst_pts: Kx2 np.array + destination points, each row is a pair of transformed + coordinates (x, y) + @reflective: True or False + if True: + use reflective similarity transform + else: + use non-reflective similarity transform + + Returns: + ---------- + @trans: 3x3 np.array + transform matrix from uv to xy + trans_inv: 3x3 np.array + inverse of trans, transform matrix from xy to uv + """ + + if reflective: + trans, trans_inv = findSimilarity(src_pts, dst_pts) + else: + trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts) + + return trans, trans_inv + + +def cvt_tform_mat_for_cv2(trans): + """ + Function: + ---------- + Convert Transform Matrix 'trans' into 'cv2_trans' which could be + directly used by cv2.warpAffine(): + u = src_pts[:, 0] + v = src_pts[:, 1] + x = dst_pts[:, 0] + y = dst_pts[:, 1] + [x, y].T = cv_trans * [u, v, 1].T + + Parameters: + ---------- + @trans: 3x3 np.array + transform matrix from uv to xy + + Returns: + ---------- + @cv2_trans: 2x3 np.array + transform matrix from src_pts to dst_pts, could be directly used + for cv2.warpAffine() + """ + cv2_trans = trans[:, 0:2].T + + return cv2_trans + + +def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True): + """ + Function: + ---------- + Find Similarity Transform Matrix 'cv2_trans' which could be + directly used by cv2.warpAffine(): + u = src_pts[:, 0] + v = src_pts[:, 1] + x = dst_pts[:, 0] + y = dst_pts[:, 1] + [x, y].T = cv_trans * [u, v, 1].T + + Parameters: + ---------- + @src_pts: Kx2 np.array + source points, each row is a pair of coordinates (x, y) + @dst_pts: Kx2 np.array + destination points, each row is a pair of transformed + coordinates (x, y) + reflective: True or False + if True: + use reflective similarity transform + else: + use non-reflective similarity transform + + Returns: + ---------- + @cv2_trans: 2x3 np.array + transform matrix from src_pts to dst_pts, could be directly used + for cv2.warpAffine() + """ + trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective) + cv2_trans = cvt_tform_mat_for_cv2(trans) + + return cv2_trans + + +if __name__ == '__main__': + """ + u = [0, 6, -2] + v = [0, 3, 5] + x = [-1, 0, 4] + y = [-1, -10, 4] + + # In Matlab, run: + # + # uv = [u'; v']; + # xy = [x'; y']; + # tform_sim=cp2tform(uv,xy,'similarity'); + # + # trans = tform_sim.tdata.T + # ans = + # -0.0764 -1.6190 0 + # 1.6190 -0.0764 0 + # -3.2156 0.0290 1.0000 + # trans_inv = tform_sim.tdata.Tinv + # ans = + # + # -0.0291 0.6163 0 + # -0.6163 -0.0291 0 + # -0.0756 1.9826 1.0000 + # xy_m=tformfwd(tform_sim, u,v) + # + # xy_m = + # + # -3.2156 0.0290 + # 1.1833 -9.9143 + # 5.0323 2.8853 + # uv_m=tforminv(tform_sim, x,y) + # + # uv_m = + # + # 0.5698 1.3953 + # 6.0872 2.2733 + # -2.6570 4.3314 + """ + u = [0, 6, -2] + v = [0, 3, 5] + x = [-1, 0, 4] + y = [-1, -10, 4] + + uv = np.array((u, v)).T + xy = np.array((x, y)).T + + print('\n--->uv:') + print(uv) + print('\n--->xy:') + print(xy) + + trans, trans_inv = get_similarity_transform(uv, xy) + + print('\n--->trans matrix:') + print(trans) + + print('\n--->trans_inv matrix:') + print(trans_inv) + + print('\n---> apply transform to uv') + print('\nxy_m = uv_augmented * trans') + uv_aug = np.hstack(( + uv, np.ones((uv.shape[0], 1)) + )) + xy_m = np.dot(uv_aug, trans) + print(xy_m) + + print('\nxy_m = tformfwd(trans, uv)') + xy_m = tformfwd(trans, uv) + print(xy_m) + + print('\n---> apply inverse transform to xy') + print('\nuv_m = xy_augmented * trans_inv') + xy_aug = np.hstack(( + xy, np.ones((xy.shape[0], 1)) + )) + uv_m = np.dot(xy_aug, trans_inv) + print(uv_m) + + print('\nuv_m = tformfwd(trans_inv, xy)') + uv_m = tformfwd(trans_inv, xy) + print(uv_m) + + uv_m = tforminv(trans, xy) + print('\nuv_m = tforminv(trans, xy)') + print(uv_m) diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/visualization_utils.py b/upsampler/models/mtcnn/mtcnn_pytorch/src/visualization_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bab02be31a6ca44486f98d57de4ab4bfa89394b7 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/visualization_utils.py @@ -0,0 +1,31 @@ +from PIL import ImageDraw + + +def show_bboxes(img, bounding_boxes, facial_landmarks=[]): + """Draw bounding boxes and facial landmarks. + + Arguments: + img: an instance of PIL.Image. + bounding_boxes: a float numpy array of shape [n, 5]. + facial_landmarks: a float numpy array of shape [n, 10]. + + Returns: + an instance of PIL.Image. + """ + + img_copy = img.copy() + draw = ImageDraw.Draw(img_copy) + + for b in bounding_boxes: + draw.rectangle([ + (b[0], b[1]), (b[2], b[3]) + ], outline='white') + + for p in facial_landmarks: + for i in range(5): + draw.ellipse([ + (p[i] - 1.0, p[i + 5] - 1.0), + (p[i] + 1.0, p[i + 5] + 1.0) + ], outline='blue') + + return img_copy diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/onet.npy b/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/onet.npy new file mode 100644 index 0000000000000000000000000000000000000000..cdca73b8bbd154e574b4be82945e3e10982acd56 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/onet.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:313141c3646bebb73cb8350a2d5fee4c7f044fb96304b46ccc21aeea8b818f83 +size 2345483 diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/pnet.npy b/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/pnet.npy new file mode 100644 index 0000000000000000000000000000000000000000..344e6beba228f3ce0d191d45125ac2c6954c3fca --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/pnet.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:03e19e5c473932ab38f5a6308fe6210624006994a687e858d1dcda53c66f18cb +size 41271 diff --git a/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/rnet.npy b/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/rnet.npy new file mode 100644 index 0000000000000000000000000000000000000000..08699a2123aa6742b146e8c0a5dada489359a1b8 --- /dev/null +++ b/upsampler/models/mtcnn/mtcnn_pytorch/src/weights/rnet.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:5660aad67688edc9e8a3dd4e47ed120932835e06a8a711a423252a6f2c747083 +size 604651 diff --git a/upsampler/models/psp.py b/upsampler/models/psp.py new file mode 100644 index 0000000000000000000000000000000000000000..607a05aa8aa0d29ca58a4959e78c9b2065953a9e --- /dev/null +++ b/upsampler/models/psp.py @@ -0,0 +1,148 @@ +""" +This file defines the core research contribution +""" +import matplotlib +matplotlib.use('Agg') +import math + +import torch +from torch import nn +from models.encoders import psp_encoders +from models.stylegan2.model import Generator +from configs.paths_config import model_paths +import torch.nn.functional as F + +def get_keys(d, name): + if 'state_dict' in d: + d = d['state_dict'] + d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name} + return d_filt + + +class pSp(nn.Module): + + def __init__(self, opts, ckpt=None): + super(pSp, self).__init__() + self.set_opts(opts) + # compute number of style inputs based on the output resolution + self.opts.n_styles = int(math.log(self.opts.output_size, 2)) * 2 - 2 + # Define architecture + self.encoder = self.set_encoder() + self.decoder = Generator(self.opts.output_size, 512, 8) + self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256)) + # Load weights if needed + self.load_weights(ckpt) + + def set_encoder(self): + if self.opts.encoder_type == 'GradualStyleEncoder': + encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts) + elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoW': + encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoW(50, 'ir_se', self.opts) + elif self.opts.encoder_type == 'BackboneEncoderUsingLastLayerIntoWPlus': + encoder = psp_encoders.BackboneEncoderUsingLastLayerIntoWPlus(50, 'ir_se', self.opts) + else: + raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type)) + return encoder + + def load_weights(self, ckpt=None): + if self.opts.checkpoint_path is not None: + print('Loading pSp from checkpoint: {}'.format(self.opts.checkpoint_path)) + if ckpt is None: + ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') + self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=False) + self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=False) + self.__load_latent_avg(ckpt) + else: + print('Loading encoders weights from irse50!') + encoder_ckpt = torch.load(model_paths['ir_se50']) + # if input to encoder is not an RGB image, do not load the input layer weights + if self.opts.label_nc != 0: + encoder_ckpt = {k: v for k, v in encoder_ckpt.items() if "input_layer" not in k} + self.encoder.load_state_dict(encoder_ckpt, strict=False) + print('Loading decoder weights from pretrained!') + ckpt = torch.load(self.opts.stylegan_weights) + self.decoder.load_state_dict(ckpt['g_ema'], strict=False) + if self.opts.learn_in_w: + self.__load_latent_avg(ckpt, repeat=1) + else: + self.__load_latent_avg(ckpt, repeat=self.opts.n_styles) + # for video toonification, we load G0' model + if self.opts.toonify_weights is not None: ##### modified + ckpt = torch.load(self.opts.toonify_weights) + self.decoder.load_state_dict(ckpt['g_ema'], strict=False) + self.opts.toonify_weights = None + + # x1: image for first-layer feature f. + # x2: image for style latent code w+. If not specified, x2=x1. + # inject_latent: for sketch/mask-to-face translation, another latent code to fuse with w+ + # latent_mask: fuse w+ and inject_latent with the mask (1~7 use w+ and 8~18 use inject_latent) + # use_feature: use f. Otherwise, use the orginal StyleGAN first-layer constant 4*4 feature + # first_layer_feature_ind: always=0, means the 1st layer of G accept f + # use_skip: use skip connection. + # zero_noise: use zero noises. + # editing_w: the editing vector v for video face editing + def forward(self, x1, x2=None, resize=True, latent_mask=None, randomize_noise=True, + inject_latent=None, return_latents=False, alpha=None, use_feature=True, + first_layer_feature_ind=0, use_skip=False, zero_noise=False, editing_w=None): ##### modified + + feats = None # f and the skipped encoder features + codes, feats = self.encoder(x1, return_feat=True, return_full=use_skip) ##### modified + if x2 is not None: ##### modified + codes = self.encoder(x2) ##### modified + # normalize with respect to the center of an average face + if self.opts.start_from_latent_avg: + if self.opts.learn_in_w: + codes = codes + self.latent_avg.repeat(codes.shape[0], 1) + else: + codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) + + # E_W^{1:7}(T(x1)) concatenate E_W^{8:18}(w~) + if latent_mask is not None: + for i in latent_mask: + if inject_latent is not None: + if alpha is not None: + codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i] + else: + codes[:, i] = inject_latent[:, i] + else: + codes[:, i] = 0 + + first_layer_feats, skip_layer_feats, fusion = None, None, None ##### modified + if use_feature: ##### modified + first_layer_feats = feats[0:2] # use f + if use_skip: ##### modified + skip_layer_feats = feats[2:] # use skipped encoder feature + fusion = self.encoder.fusion # use fusion layer to fuse encoder feature and decoder feature. + + images, result_latent = self.decoder([codes], + input_is_latent=True, + randomize_noise=randomize_noise, + return_latents=return_latents, + first_layer_feature=first_layer_feats, + first_layer_feature_ind=first_layer_feature_ind, + skip_layer_feature=skip_layer_feats, + fusion_block=fusion, + zero_noise=zero_noise, + editing_w=editing_w) ##### modified + + if resize: + if self.opts.output_size == 1024: ##### modified + images = F.adaptive_avg_pool2d(images, (images.shape[2]//4, images.shape[3]//4)) ##### modified + else: + images = self.face_pool(images) + + if return_latents: + return images, result_latent + else: + return images + + def set_opts(self, opts): + self.opts = opts + + def __load_latent_avg(self, ckpt, repeat=None): + if 'latent_avg' in ckpt: + self.latent_avg = ckpt['latent_avg'].to(self.opts.device) + if repeat is not None: + self.latent_avg = self.latent_avg.repeat(repeat, 1) + else: + self.latent_avg = None diff --git a/upsampler/models/stylegan2/__init__.py b/upsampler/models/stylegan2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/models/stylegan2/lpips/__init__.py b/upsampler/models/stylegan2/lpips/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..22252ce8594c5bd2c9dc17e75f977ed21c94447f --- /dev/null +++ b/upsampler/models/stylegan2/lpips/__init__.py @@ -0,0 +1,161 @@ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +#from skimage.measure import compare_ssim +from skimage.metrics import structural_similarity as compare_ssim +import torch +from torch.autograd import Variable + +from models.stylegan2.lpips import dist_model + +class PerceptualLoss(torch.nn.Module): + def __init__(self, model='net-lin', net='alex', colorspace='rgb', spatial=False, use_gpu=True, gpu_ids=[0]): # VGG using our perceptually-learned weights (LPIPS metric) + # def __init__(self, model='net', net='vgg', use_gpu=True): # "default" way of using VGG as a perceptual loss + super(PerceptualLoss, self).__init__() + print('Setting up Perceptual loss...') + self.use_gpu = use_gpu + self.spatial = spatial + self.gpu_ids = gpu_ids + self.model = dist_model.DistModel() + self.model.initialize(model=model, net=net, use_gpu=use_gpu, colorspace=colorspace, spatial=self.spatial, gpu_ids=gpu_ids) + print('...[%s] initialized'%self.model.name()) + print('...Done') + + def forward(self, pred, target, normalize=False): + """ + Pred and target are Variables. + If normalize is True, assumes the images are between [0,1] and then scales them between [-1,+1] + If normalize is False, assumes the images are already between [-1,+1] + + Inputs pred and target are Nx3xHxW + Output pytorch Variable N long + """ + + if normalize: + target = 2 * target - 1 + pred = 2 * pred - 1 + + return self.model.forward(target, pred) + +def normalize_tensor(in_feat,eps=1e-10): + norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True)) + return in_feat/(norm_factor+eps) + +def l2(p0, p1, range=255.): + return .5*np.mean((p0 / range - p1 / range)**2) + +def psnr(p0, p1, peak=255.): + return 10*np.log10(peak**2/np.mean((1.*p0-1.*p1)**2)) + +def dssim(p0, p1, range=255.): + return (1 - compare_ssim(p0, p1, data_range=range, multichannel=True)) / 2. + +def rgb2lab(in_img,mean_cent=False): + from skimage import color + img_lab = color.rgb2lab(in_img) + if(mean_cent): + img_lab[:,:,0] = img_lab[:,:,0]-50 + return img_lab + +def tensor2np(tensor_obj): + # change dimension of a tensor object into a numpy array + return tensor_obj[0].cpu().float().numpy().transpose((1,2,0)) + +def np2tensor(np_obj): + # change dimenion of np array into tensor array + return torch.Tensor(np_obj[:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + +def tensor2tensorlab(image_tensor,to_norm=True,mc_only=False): + # image tensor to lab tensor + from skimage import color + + img = tensor2im(image_tensor) + img_lab = color.rgb2lab(img) + if(mc_only): + img_lab[:,:,0] = img_lab[:,:,0]-50 + if(to_norm and not mc_only): + img_lab[:,:,0] = img_lab[:,:,0]-50 + img_lab = img_lab/100. + + return np2tensor(img_lab) + +def tensorlab2tensor(lab_tensor,return_inbnd=False): + from skimage import color + import warnings + warnings.filterwarnings("ignore") + + lab = tensor2np(lab_tensor)*100. + lab[:,:,0] = lab[:,:,0]+50 + + rgb_back = 255.*np.clip(color.lab2rgb(lab.astype('float')),0,1) + if(return_inbnd): + # convert back to lab, see if we match + lab_back = color.rgb2lab(rgb_back.astype('uint8')) + mask = 1.*np.isclose(lab_back,lab,atol=2.) + mask = np2tensor(np.prod(mask,axis=2)[:,:,np.newaxis]) + return (im2tensor(rgb_back),mask) + else: + return im2tensor(rgb_back) + +def rgb2lab(input): + from skimage import color + return color.rgb2lab(input / 255.) + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) + +def tensor2vec(vector_tensor): + return vector_tensor.data.cpu().numpy()[:, :, 0, 0] + +def voc_ap(rec, prec, use_07_metric=False): + """ ap = voc_ap(rec, prec, [use_07_metric]) + Compute VOC AP given precision and recall. + If use_07_metric is true, uses the + VOC 07 11 point method (default:False). + """ + if use_07_metric: + # 11 point metric + ap = 0. + for t in np.arange(0., 1.1, 0.1): + if np.sum(rec >= t) == 0: + p = 0 + else: + p = np.max(prec[rec >= t]) + ap = ap + p / 11. + else: + # correct AP calculation + # first append sentinel values at the end + mrec = np.concatenate(([0.], rec, [1.])) + mpre = np.concatenate(([0.], prec, [0.])) + + # compute the precision envelope + for i in range(mpre.size - 1, 0, -1): + mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) + + # to calculate area under PR curve, look for points + # where X axis (recall) changes value + i = np.where(mrec[1:] != mrec[:-1])[0] + + # and sum (\Delta recall) * prec + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) + return ap + +def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=255./2.): +# def tensor2im(image_tensor, imtype=np.uint8, cent=1., factor=1.): + image_numpy = image_tensor[0].cpu().float().numpy() + image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + cent) * factor + return image_numpy.astype(imtype) + +def im2tensor(image, imtype=np.uint8, cent=1., factor=255./2.): +# def im2tensor(image, imtype=np.uint8, cent=1., factor=1.): + return torch.Tensor((image / factor - cent) + [:, :, :, np.newaxis].transpose((3, 2, 0, 1))) diff --git a/upsampler/models/stylegan2/lpips/base_model.py b/upsampler/models/stylegan2/lpips/base_model.py new file mode 100644 index 0000000000000000000000000000000000000000..8de1d16f0c7fa52d8067139abc6e769e96d0a6a1 --- /dev/null +++ b/upsampler/models/stylegan2/lpips/base_model.py @@ -0,0 +1,58 @@ +import os +import numpy as np +import torch +from torch.autograd import Variable +from pdb import set_trace as st +from IPython import embed + +class BaseModel(): + def __init__(self): + pass; + + def name(self): + return 'BaseModel' + + def initialize(self, use_gpu=True, gpu_ids=[0]): + self.use_gpu = use_gpu + self.gpu_ids = gpu_ids + + def forward(self): + pass + + def get_image_paths(self): + pass + + def optimize_parameters(self): + pass + + def get_current_visuals(self): + return self.input + + def get_current_errors(self): + return {} + + def save(self, label): + pass + + # helper saving function that can be used by subclasses + def save_network(self, network, path, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(path, save_filename) + torch.save(network.state_dict(), save_path) + + # helper loading function that can be used by subclasses + def load_network(self, network, network_label, epoch_label): + save_filename = '%s_net_%s.pth' % (epoch_label, network_label) + save_path = os.path.join(self.save_dir, save_filename) + print('Loading network from %s'%save_path) + network.load_state_dict(torch.load(save_path)) + + def update_learning_rate(): + pass + + def get_image_paths(self): + return self.image_paths + + def save_done(self, flag=False): + np.save(os.path.join(self.save_dir, 'done_flag'),flag) + np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') diff --git a/upsampler/models/stylegan2/lpips/dist_model.py b/upsampler/models/stylegan2/lpips/dist_model.py new file mode 100644 index 0000000000000000000000000000000000000000..117fd18899608ce9c7398bafa62d75c8b6efc603 --- /dev/null +++ b/upsampler/models/stylegan2/lpips/dist_model.py @@ -0,0 +1,284 @@ + +from __future__ import absolute_import + +import sys +import numpy as np +import torch +from torch import nn +import os +from collections import OrderedDict +from torch.autograd import Variable +import itertools +from models.stylegan2.lpips.base_model import BaseModel +from scipy.ndimage import zoom +import fractions +import functools +import skimage.transform +from tqdm import tqdm + +from IPython import embed + +from models.stylegan2.lpips import networks_basic as networks +import models.stylegan2.lpips as util + +class DistModel(BaseModel): + def name(self): + return self.model_name + + def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, model_path=None, + use_gpu=True, printNet=False, spatial=False, + is_train=False, lr=.0001, beta1=0.5, version='0.1', gpu_ids=[0]): + ''' + INPUTS + model - ['net-lin'] for linearly calibrated network + ['net'] for off-the-shelf network + ['L2'] for L2 distance in Lab colorspace + ['SSIM'] for ssim in RGB colorspace + net - ['squeeze','alex','vgg'] + model_path - if None, will look in weights/[NET_NAME].pth + colorspace - ['Lab','RGB'] colorspace to use for L2 and SSIM + use_gpu - bool - whether or not to use a GPU + printNet - bool - whether or not to print network architecture out + spatial - bool - whether to output an array containing varying distances across spatial dimensions + spatial_shape - if given, output spatial shape. if None then spatial shape is determined automatically via spatial_factor (see below). + spatial_factor - if given, specifies upsampling factor relative to the largest spatial extent of a convolutional layer. if None then resized to size of input images. + spatial_order - spline order of filter for upsampling in spatial mode, by default 1 (bilinear). + is_train - bool - [True] for training mode + lr - float - initial learning rate + beta1 - float - initial momentum term for adam + version - 0.1 for latest, 0.0 was original (with a bug) + gpu_ids - int array - [0] by default, gpus to use + ''' + BaseModel.initialize(self, use_gpu=use_gpu, gpu_ids=gpu_ids) + + self.model = model + self.net = net + self.is_train = is_train + self.spatial = spatial + self.gpu_ids = gpu_ids + self.model_name = '%s [%s]'%(model,net) + + if(self.model == 'net-lin'): # pretrained net + linear layer + self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_tune=pnet_tune, pnet_type=net, + use_dropout=True, spatial=spatial, version=version, lpips=True) + kw = {} + if not use_gpu: + kw['map_location'] = 'cpu' + if(model_path is None): + import inspect + model_path = os.path.abspath(os.path.join(inspect.getfile(self.initialize), '..', 'weights/v%s/%s.pth'%(version,net))) + + if(not is_train): + print('Loading model from: %s'%model_path) + self.net.load_state_dict(torch.load(model_path, **kw), strict=False) + + elif(self.model=='net'): # pretrained network + self.net = networks.PNetLin(pnet_rand=pnet_rand, pnet_type=net, lpips=False) + elif(self.model in ['L2','l2']): + self.net = networks.L2(use_gpu=use_gpu,colorspace=colorspace) # not really a network, only for testing + self.model_name = 'L2' + elif(self.model in ['DSSIM','dssim','SSIM','ssim']): + self.net = networks.DSSIM(use_gpu=use_gpu,colorspace=colorspace) + self.model_name = 'SSIM' + else: + raise ValueError("Model [%s] not recognized." % self.model) + + self.parameters = list(self.net.parameters()) + + if self.is_train: # training mode + # extra network on top to go from distances (d0,d1) => predicted human judgment (h*) + self.rankLoss = networks.BCERankingLoss() + self.parameters += list(self.rankLoss.net.parameters()) + self.lr = lr + self.old_lr = lr + self.optimizer_net = torch.optim.Adam(self.parameters, lr=lr, betas=(beta1, 0.999)) + else: # test mode + self.net.eval() + + if(use_gpu): + self.net.to(gpu_ids[0]) + self.net = torch.nn.DataParallel(self.net, device_ids=gpu_ids) + if(self.is_train): + self.rankLoss = self.rankLoss.to(device=gpu_ids[0]) # just put this on GPU0 + + if(printNet): + print('---------- Networks initialized -------------') + networks.print_network(self.net) + print('-----------------------------------------------') + + def forward(self, in0, in1, retPerLayer=False): + ''' Function computes the distance between image patches in0 and in1 + INPUTS + in0, in1 - torch.Tensor object of shape Nx3xXxY - image patch scaled to [-1,1] + OUTPUT + computed distances between in0 and in1 + ''' + + return self.net.forward(in0, in1, retPerLayer=retPerLayer) + + # ***** TRAINING FUNCTIONS ***** + def optimize_parameters(self): + self.forward_train() + self.optimizer_net.zero_grad() + self.backward_train() + self.optimizer_net.step() + self.clamp_weights() + + def clamp_weights(self): + for module in self.net.modules(): + if(hasattr(module, 'weight') and module.kernel_size==(1,1)): + module.weight.data = torch.clamp(module.weight.data,min=0) + + def set_input(self, data): + self.input_ref = data['ref'] + self.input_p0 = data['p0'] + self.input_p1 = data['p1'] + self.input_judge = data['judge'] + + if(self.use_gpu): + self.input_ref = self.input_ref.to(device=self.gpu_ids[0]) + self.input_p0 = self.input_p0.to(device=self.gpu_ids[0]) + self.input_p1 = self.input_p1.to(device=self.gpu_ids[0]) + self.input_judge = self.input_judge.to(device=self.gpu_ids[0]) + + self.var_ref = Variable(self.input_ref,requires_grad=True) + self.var_p0 = Variable(self.input_p0,requires_grad=True) + self.var_p1 = Variable(self.input_p1,requires_grad=True) + + def forward_train(self): # run forward pass + # print(self.net.module.scaling_layer.shift) + # print(torch.norm(self.net.module.net.slice1[0].weight).item(), torch.norm(self.net.module.lin0.model[1].weight).item()) + + self.d0 = self.forward(self.var_ref, self.var_p0) + self.d1 = self.forward(self.var_ref, self.var_p1) + self.acc_r = self.compute_accuracy(self.d0,self.d1,self.input_judge) + + self.var_judge = Variable(1.*self.input_judge).view(self.d0.size()) + + self.loss_total = self.rankLoss.forward(self.d0, self.d1, self.var_judge*2.-1.) + + return self.loss_total + + def backward_train(self): + torch.mean(self.loss_total).backward() + + def compute_accuracy(self,d0,d1,judge): + ''' d0, d1 are Variables, judge is a Tensor ''' + d1_lt_d0 = (d1 %f' % (type,self.old_lr, lr)) + self.old_lr = lr + +def score_2afc_dataset(data_loader, func, name=''): + ''' Function computes Two Alternative Forced Choice (2AFC) score using + distance function 'func' in dataset 'data_loader' + INPUTS + data_loader - CustomDatasetDataLoader object - contains a TwoAFCDataset inside + func - callable distance function - calling d=func(in0,in1) should take 2 + pytorch tensors with shape Nx3xXxY, and return numpy array of length N + OUTPUTS + [0] - 2AFC score in [0,1], fraction of time func agrees with human evaluators + [1] - dictionary with following elements + d0s,d1s - N arrays containing distances between reference patch to perturbed patches + gts - N array in [0,1], preferred patch selected by human evaluators + (closer to "0" for left patch p0, "1" for right patch p1, + "0.6" means 60pct people preferred right patch, 40pct preferred left) + scores - N array in [0,1], corresponding to what percentage function agreed with humans + CONSTS + N - number of test triplets in data_loader + ''' + + d0s = [] + d1s = [] + gts = [] + + for data in tqdm(data_loader.load_data(), desc=name): + d0s+=func(data['ref'],data['p0']).data.cpu().numpy().flatten().tolist() + d1s+=func(data['ref'],data['p1']).data.cpu().numpy().flatten().tolist() + gts+=data['judge'].cpu().numpy().flatten().tolist() + + d0s = np.array(d0s) + d1s = np.array(d1s) + gts = np.array(gts) + scores = (d0s 1: + kernel = kernel * (upsample_factor ** 2) + + self.register_buffer('kernel', kernel) + + self.pad = pad + + def forward(self, input): + out = upfirdn2d(input, self.kernel, pad=self.pad) + + return out + + +class EqualConv2d(nn.Module): + def __init__( + self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dilation=1 ## modified + ): + super().__init__() + + self.weight = nn.Parameter( + torch.randn(out_channel, in_channel, kernel_size, kernel_size) + ) + self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2) + + self.stride = stride + self.padding = padding + self.dilation = dilation ## modified + + if bias: + self.bias = nn.Parameter(torch.zeros(out_channel)) + + else: + self.bias = None + + def forward(self, input): + out = F.conv2d( + input, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + dilation=self.dilation, ## modified + ) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]}," + f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding}, dilation={self.dilation})" ## modified + ) + + +class EqualLinear(nn.Module): + def __init__( + self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None + ): + super().__init__() + + self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul)) + + if bias: + self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init)) + + else: + self.bias = None + + self.activation = activation + + self.scale = (1 / math.sqrt(in_dim)) * lr_mul + self.lr_mul = lr_mul + + def forward(self, input): + if self.activation: + out = F.linear(input, self.weight * self.scale) + out = fused_leaky_relu(out, self.bias * self.lr_mul) + + else: + out = F.linear( + input, self.weight * self.scale, bias=self.bias * self.lr_mul + ) + + return out + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})' + ) + + +class ScaledLeakyReLU(nn.Module): + def __init__(self, negative_slope=0.2): + super().__init__() + + self.negative_slope = negative_slope + + def forward(self, input): + out = F.leaky_relu(input, negative_slope=self.negative_slope) + + return out * math.sqrt(2) + + +class ModulatedConv2d(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + demodulate=True, + upsample=False, + downsample=False, + blur_kernel=[1, 3, 3, 1], + dilation=1, ##### modified + ): + super().__init__() + + self.eps = 1e-8 + self.kernel_size = kernel_size + self.in_channel = in_channel + self.out_channel = out_channel + self.upsample = upsample + self.downsample = downsample + self.dilation = dilation ##### modified + + if upsample: + factor = 2 + p = (len(blur_kernel) - factor) - (kernel_size - 1) + pad0 = (p + 1) // 2 + factor - 1 + pad1 = p // 2 + 1 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor) + + # to simulate transconv + blur + # we use dilated transposed conv with blur kernel as weight + dilated transconv + if dilation > 1: ##### modified + blur_weight = torch.randn(1, 1, 3, 3) * 0 + 1 + blur_weight[:,:,0,1] = 2 + blur_weight[:,:,1,0] = 2 + blur_weight[:,:,1,2] = 2 + blur_weight[:,:,2,1] = 2 + blur_weight[:,:,1,1] = 4 + blur_weight = blur_weight / 16.0 + self.register_buffer("blur_weight", blur_weight) + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + self.blur = Blur(blur_kernel, pad=(pad0, pad1)) + + fan_in = in_channel * kernel_size ** 2 + self.scale = 1 / math.sqrt(fan_in) + self.padding = (kernel_size - 1) // 2 * dilation ##### modified + + self.weight = nn.Parameter( + torch.randn(1, out_channel, in_channel, kernel_size, kernel_size) + ) + + self.modulation = EqualLinear(style_dim, in_channel, bias_init=1) + + self.demodulate = demodulate + + def __repr__(self): + return ( + f'{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, ' + f'upsample={self.upsample}, downsample={self.downsample})' + ) + + def forward(self, input, style): + batch, in_channel, height, width = input.shape + + style = self.modulation(style).view(batch, 1, in_channel, 1, 1) + weight = self.scale * self.weight * style + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8) + weight = weight * demod.view(batch, self.out_channel, 1, 1, 1) + + weight = weight.view( + batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + + if self.upsample: + input = input.view(1, batch * in_channel, height, width) + weight = weight.view( + batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size + ) + weight = weight.transpose(1, 2).reshape( + batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size + ) + + if self.dilation > 1: ##### modified + # to simulate out = self.blur(out) + out = F.conv_transpose2d( + input, self.blur_weight.repeat(batch*in_channel,1,1,1), padding=0, groups=batch*in_channel, dilation=self.dilation//2) + # to simulate the next line + out = F.conv_transpose2d( + out, weight, padding=self.dilation, groups=batch, dilation=self.dilation//2) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + return out + + out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + out = self.blur(out) + + elif self.downsample: + input = self.blur(input) + _, _, height, width = input.shape + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=0, stride=2, groups=batch) + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + else: + input = input.view(1, batch * in_channel, height, width) + out = F.conv2d(input, weight, padding=self.padding, groups=batch, dilation=self.dilation) ##### modified + _, _, height, width = out.shape + out = out.view(batch, self.out_channel, height, width) + + return out + + +class NoiseInjection(nn.Module): + def __init__(self): + super().__init__() + + self.weight = nn.Parameter(torch.zeros(1)) + + def forward(self, image, noise=None): + if noise is None: + batch, _, height, width = image.shape + noise = image.new_empty(batch, 1, height, width).normal_() + else: ##### modified, to make the resolution matches + batch, _, height, width = image.shape + _, _, height1, width1 = noise.shape + if height != height1 or width != width1: + noise = F.adaptive_avg_pool2d(noise, (height, width)) + + return image + self.weight * noise + + +class ConstantInput(nn.Module): + def __init__(self, channel, size=4): + super().__init__() + + self.input = nn.Parameter(torch.randn(1, channel, size, size)) + + def forward(self, input): + batch = input.shape[0] + out = self.input.repeat(batch, 1, 1, 1) + + return out + + +class StyledConv(nn.Module): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=False, + blur_kernel=[1, 3, 3, 1], + demodulate=True, + dilation=1, ##### modified + ): + super().__init__() + + self.conv = ModulatedConv2d( + in_channel, + out_channel, + kernel_size, + style_dim, + upsample=upsample, + blur_kernel=blur_kernel, + demodulate=demodulate, + dilation=dilation, ##### modified + ) + + self.noise = NoiseInjection() + self.activate = FusedLeakyReLU(out_channel) + + def forward(self, input, style, noise=None): + out = self.conv(input, style) + out = self.noise(out, noise=noise) + out = self.activate(out) + + return out + + +class ToRGB(nn.Module): + def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1], dilation=1): ##### modified + super().__init__() + + if upsample: + self.upsample = Upsample(blur_kernel) + + self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + self.dilation = dilation ##### modified + if dilation > 1: ##### modified + blur_weight = torch.randn(1, 1, 3, 3) * 0 + 1 + blur_weight[:,:,0,1] = 2 + blur_weight[:,:,1,0] = 2 + blur_weight[:,:,1,2] = 2 + blur_weight[:,:,2,1] = 2 + blur_weight[:,:,1,1] = 4 + blur_weight = blur_weight / 16.0 + self.register_buffer("blur_weight", blur_weight) + + def forward(self, input, style, skip=None): + out = self.conv(input, style) + out = out + self.bias + + if skip is not None: + if self.dilation == 1: + skip = self.upsample(skip) + else: ##### modified, to simulate skip = self.upsample(skip) + batch, in_channel, _, _ = skip.shape + skip = F.conv2d(skip, self.blur_weight.repeat(in_channel,1,1,1), + padding=self.dilation//2, groups=in_channel, dilation=self.dilation//2) + + out = out + skip + + return out + + +class Generator(nn.Module): + def __init__( + self, + size, + style_dim, + n_mlp, + channel_multiplier=2, + blur_kernel=[1, 3, 3, 1], + lr_mlp=0.01, + ): + super().__init__() + + self.size = size + + self.style_dim = style_dim + + layers = [PixelNorm()] + + for i in range(n_mlp): + layers.append( + EqualLinear( + style_dim, style_dim, lr_mul=lr_mlp, activation='fused_lrelu' + ) + ) + + self.style = nn.Sequential(*layers) + + self.channels = { + 4: 512, + 8: 512, + 16: 512, + 32: 512, + 64: 256 * channel_multiplier, + 128: 128 * channel_multiplier, + 256: 64 * channel_multiplier, + 512: 32 * channel_multiplier, + 1024: 16 * channel_multiplier, + } + + self.input = ConstantInput(self.channels[4]) + self.conv1 = StyledConv( + self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel, dilation=8 ##### modified + ) + self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False) + + self.log_size = int(math.log(size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + + self.convs = nn.ModuleList() + self.upsamples = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channel = self.channels[4] + + for layer_idx in range(self.num_layers): + res = (layer_idx + 5) // 2 + shape = [1, 1, 2 ** res, 2 ** res] + self.noises.register_buffer(f'noise_{layer_idx}', torch.randn(*shape)) + + for i in range(3, self.log_size + 1): + out_channel = self.channels[2 ** i] + + self.convs.append( + StyledConv( + in_channel, + out_channel, + 3, + style_dim, + upsample=True, + blur_kernel=blur_kernel, + dilation=max(1, 32 // (2**(i-1))) ##### modified + ) + ) + + self.convs.append( + StyledConv( + out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel, dilation=max(1, 32 // (2**i)) ##### modified + ) + ) + + self.to_rgbs.append(ToRGB(out_channel, style_dim, dilation=max(1, 32 // (2**(i-1))))) ##### modified + + in_channel = out_channel + + self.n_latent = self.log_size * 2 - 2 + + def make_noise(self): + device = self.input.input.device + + noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device)) + + return noises + + def mean_latent(self, n_latent): + latent_in = torch.randn( + n_latent, self.style_dim, device=self.input.input.device + ) + latent = self.style(latent_in).mean(0, keepdim=True) + + return latent + + def get_latent(self, input): + return self.style(input) + + # styles is the latent code w+ + # first_layer_feature is the first-layer input feature f + # first_layer_feature_ind indicate which layer of G accepts f (should always=0, the first layer) + # skip_layer_feature is the encoder features sent by skip connection + # fusion_block is the network to fuse the encoder feature and decoder feature + # zero_noise is to force the noise to be zero (to avoid flickers for videos) + # editing_w is the editing vector v used in video face editing + def forward( + self, + styles, + return_latents=False, + return_features=False, + inject_index=None, + truncation=1, + truncation_latent=None, + input_is_latent=False, + noise=None, + randomize_noise=True, + first_layer_feature = None, ##### modified + first_layer_feature_ind = 0, ##### modified + skip_layer_feature = None, ##### modified + fusion_block = None, ##### modified + zero_noise = False, ##### modified + editing_w = None, ##### modified + ): + if not input_is_latent: + styles = [self.style(s) for s in styles] + + if zero_noise: + noise = [ + getattr(self.noises, f'noise_{i}') * 0.0 for i in range(self.num_layers) + ] + elif noise is None: + if randomize_noise: + noise = [None] * self.num_layers + else: + noise = [ + getattr(self.noises, f'noise_{i}') for i in range(self.num_layers) + ] + + if truncation < 1: + style_t = [] + + for style in styles: + style_t.append( + truncation_latent + truncation * (style - truncation_latent) + ) + + styles = style_t + + if len(styles) < 2: + inject_index = self.n_latent + + if styles[0].ndim < 3: + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: + latent = styles[0] + + else: + if inject_index is None: + inject_index = random.randint(1, self.n_latent - 1) + + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1) + + latent = torch.cat([latent, latent2], 1) + + # w+ + v for video face editing + if editing_w is not None: ##### modified + latent = latent + editing_w + + # the original StyleGAN + if first_layer_feature is None: ##### modified + out = self.input(latent) + out = F.adaptive_avg_pool2d(out, 32) ##### modified + out = self.conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + # the default StyleGANEX, replacing the first layer of G + elif first_layer_feature_ind == 0: ##### modified + out = first_layer_feature[0] ##### modified + out = self.conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + # maybe we can also use the second layer of G to accept f? + else: ##### modified + out = first_layer_feature[0] ##### modified + skip = first_layer_feature[1] ##### modified + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs + ): + # these layers accepts skipped encoder layer, use fusion block to fuse the encoder feature and decoder feature + if skip_layer_feature and fusion_block and i//2 < len(skip_layer_feature) and i//2 < len(fusion_block): + if editing_w is None: + out, skip = fusion_block[i//2](skip_layer_feature[i//2], out, skip) + else: + out, skip = fusion_block[i//2](skip_layer_feature[i//2], out, skip, editing_w[:,i]) + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + + i += 2 + + image = skip + + if return_latents: + return image, latent + elif return_features: + return image, out + else: + return image, None + + +class ConvLayer(nn.Sequential): + def __init__( + self, + in_channel, + out_channel, + kernel_size, + downsample=False, + blur_kernel=[1, 3, 3, 1], + bias=True, + activate=True, + dilation=1, ## modified + ): + layers = [] + + if downsample: + factor = 2 + p = (len(blur_kernel) - factor) + (kernel_size - 1) + pad0 = (p + 1) // 2 + pad1 = p // 2 + + layers.append(Blur(blur_kernel, pad=(pad0, pad1))) + + stride = 2 + self.padding = 0 + + else: + stride = 1 + self.padding = (kernel_size - 1) // 2 * dilation ## modified + + layers.append( + EqualConv2d( + in_channel, + out_channel, + kernel_size, + padding=self.padding, + stride=stride, + bias=bias and not activate, + dilation=dilation, ## modified + ) + ) + + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channel)) + + else: + layers.append(ScaledLeakyReLU(0.2)) + + super().__init__(*layers) + + +class ResBlock(nn.Module): + def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]): + super().__init__() + + self.conv1 = ConvLayer(in_channel, in_channel, 3) + self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True) + + self.skip = ConvLayer( + in_channel, out_channel, 1, downsample=True, activate=False, bias=False + ) + + def forward(self, input): + out = self.conv1(input) + out = self.conv2(out) + + skip = self.skip(input) + out = (out + skip) / math.sqrt(2) + + return out + + +class Discriminator(nn.Module): + def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1], img_channel=3): + super().__init__() + + channels = { + 4: 512, + 8: 512, + 16: 512, + 32: 512, + 64: 256 * channel_multiplier, + 128: 128 * channel_multiplier, + 256: 64 * channel_multiplier, + 512: 32 * channel_multiplier, + 1024: 16 * channel_multiplier, + } + + convs = [ConvLayer(img_channel, channels[size], 1)] + + log_size = int(math.log(size, 2)) + + in_channel = channels[size] + + for i in range(log_size, 2, -1): + out_channel = channels[2 ** (i - 1)] + + convs.append(ResBlock(in_channel, out_channel, blur_kernel)) + + in_channel = out_channel + + self.convs = nn.Sequential(*convs) + + self.stddev_group = 4 + self.stddev_feat = 1 + + self.final_conv = ConvLayer(in_channel + 1, channels[4], 3) + self.final_linear = nn.Sequential( + EqualLinear(channels[4] * 4 * 4, channels[4], activation='fused_lrelu'), + EqualLinear(channels[4], 1), + ) + + self.size = size ##### modified + + def forward(self, input): + # for input that not satisfies the target size, we crop it to extract a small image of the target size. + _, _, h, w = input.shape ##### modified + i, j = torch.randint(0, h+1-self.size, size=(1,)).item(), torch.randint(0, w+1-self.size, size=(1,)).item() ##### modified + out = self.convs(input[:,:,i:i+self.size,j:j+self.size]) ##### modified + + batch, channel, height, width = out.shape + group = min(batch, self.stddev_group) + stddev = out.view( + group, -1, self.stddev_feat, channel // self.stddev_feat, height, width + ) + stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8) + stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2) + stddev = stddev.repeat(group, 1, height, width) + out = torch.cat([out, stddev], 1) + + out = self.final_conv(out) + + out = out.view(batch, -1) + out = self.final_linear(out) + + return out diff --git a/upsampler/models/stylegan2/op/__init__.py b/upsampler/models/stylegan2/op/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d0918d92285955855be89f00096b888ee5597ce3 --- /dev/null +++ b/upsampler/models/stylegan2/op/__init__.py @@ -0,0 +1,2 @@ +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d diff --git a/upsampler/models/stylegan2/op/conv2d_gradfix.py b/upsampler/models/stylegan2/op/conv2d_gradfix.py new file mode 100644 index 0000000000000000000000000000000000000000..c4485b11991c5426939e87e6c363307eb9017438 --- /dev/null +++ b/upsampler/models/stylegan2/op/conv2d_gradfix.py @@ -0,0 +1,227 @@ +import contextlib +import warnings + +import torch +from torch import autograd +from torch.nn import functional as F + +enabled = True +weight_gradients_disabled = False + + +@contextlib.contextmanager +def no_weight_gradients(): + global weight_gradients_disabled + + old = weight_gradients_disabled + weight_gradients_disabled = True + yield + weight_gradients_disabled = old + + +def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): + if could_use_op(input): + return conv2d_gradfix( + transpose=False, + weight_shape=weight.shape, + stride=stride, + padding=padding, + output_padding=0, + dilation=dilation, + groups=groups, + ).apply(input, weight, bias) + + return F.conv2d( + input=input, + weight=weight, + bias=bias, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + ) + + +def conv_transpose2d( + input, + weight, + bias=None, + stride=1, + padding=0, + output_padding=0, + groups=1, + dilation=1, +): + if could_use_op(input): + return conv2d_gradfix( + transpose=True, + weight_shape=weight.shape, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + dilation=dilation, + ).apply(input, weight, bias) + + return F.conv_transpose2d( + input=input, + weight=weight, + bias=bias, + stride=stride, + padding=padding, + output_padding=output_padding, + dilation=dilation, + groups=groups, + ) + + +def could_use_op(input): + if (not enabled) or (not torch.backends.cudnn.enabled): + return False + + if input.device.type != "cuda": + return False + + if any(torch.__version__.startswith(x) for x in ["1.7.", "1.8."]): + return True + + warnings.warn( + f"conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d()." + ) + + return False + + +def ensure_tuple(xs, ndim): + xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim + + return xs + + +conv2d_gradfix_cache = dict() + + +def conv2d_gradfix( + transpose, weight_shape, stride, padding, output_padding, dilation, groups +): + ndim = 2 + weight_shape = tuple(weight_shape) + stride = ensure_tuple(stride, ndim) + padding = ensure_tuple(padding, ndim) + output_padding = ensure_tuple(output_padding, ndim) + dilation = ensure_tuple(dilation, ndim) + + key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) + if key in conv2d_gradfix_cache: + return conv2d_gradfix_cache[key] + + common_kwargs = dict( + stride=stride, padding=padding, dilation=dilation, groups=groups + ) + + def calc_output_padding(input_shape, output_shape): + if transpose: + return [0, 0] + + return [ + input_shape[i + 2] + - (output_shape[i + 2] - 1) * stride[i] + - (1 - 2 * padding[i]) + - dilation[i] * (weight_shape[i + 2] - 1) + for i in range(ndim) + ] + + class Conv2d(autograd.Function): + @staticmethod + def forward(ctx, input, weight, bias): + if not transpose: + out = F.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) + + else: + out = F.conv_transpose2d( + input=input, + weight=weight, + bias=bias, + output_padding=output_padding, + **common_kwargs, + ) + + ctx.save_for_backward(input, weight) + + return out + + @staticmethod + def backward(ctx, grad_output): + input, weight = ctx.saved_tensors + grad_input, grad_weight, grad_bias = None, None, None + + if ctx.needs_input_grad[0]: + p = calc_output_padding( + input_shape=input.shape, output_shape=grad_output.shape + ) + grad_input = conv2d_gradfix( + transpose=(not transpose), + weight_shape=weight_shape, + output_padding=p, + **common_kwargs, + ).apply(grad_output, weight, None) + + if ctx.needs_input_grad[1] and not weight_gradients_disabled: + grad_weight = Conv2dGradWeight.apply(grad_output, input) + + if ctx.needs_input_grad[2]: + grad_bias = grad_output.sum((0, 2, 3)) + + return grad_input, grad_weight, grad_bias + + class Conv2dGradWeight(autograd.Function): + @staticmethod + def forward(ctx, grad_output, input): + op = torch._C._jit_get_operation( + "aten::cudnn_convolution_backward_weight" + if not transpose + else "aten::cudnn_convolution_transpose_backward_weight" + ) + flags = [ + torch.backends.cudnn.benchmark, + torch.backends.cudnn.deterministic, + torch.backends.cudnn.allow_tf32, + ] + grad_weight = op( + weight_shape, + grad_output, + input, + padding, + stride, + dilation, + groups, + *flags, + ) + ctx.save_for_backward(grad_output, input) + + return grad_weight + + @staticmethod + def backward(ctx, grad_grad_weight): + grad_output, input = ctx.saved_tensors + grad_grad_output, grad_grad_input = None, None + + if ctx.needs_input_grad[0]: + grad_grad_output = Conv2d.apply(input, grad_grad_weight, None) + + if ctx.needs_input_grad[1]: + p = calc_output_padding( + input_shape=input.shape, output_shape=grad_output.shape + ) + grad_grad_input = conv2d_gradfix( + transpose=(not transpose), + weight_shape=weight_shape, + output_padding=p, + **common_kwargs, + ).apply(grad_output, grad_grad_weight, None) + + return grad_grad_output, grad_grad_input + + conv2d_gradfix_cache[key] = Conv2d + + return Conv2d diff --git a/upsampler/models/stylegan2/op/fused_act.py b/upsampler/models/stylegan2/op/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..74815adafbf7a37d5d4def41ac60dbdeefdbff30 --- /dev/null +++ b/upsampler/models/stylegan2/op/fused_act.py @@ -0,0 +1,34 @@ +import torch +from torch import nn +from torch.nn import functional as F + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5): + super().__init__() + + if bias: + self.bias = nn.Parameter(torch.zeros(channel)) + + else: + self.bias = None + + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, inputs): + return fused_leaky_relu(inputs, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(inputs, bias=None, negative_slope=0.2, scale=2 ** 0.5): + if bias is not None: + rest_dim = [1] * (inputs.ndim - bias.ndim - 1) + return ( + F.leaky_relu( + inputs + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope + ) + * scale + ) + + else: + return F.leaky_relu(inputs, negative_slope=negative_slope) * scale \ No newline at end of file diff --git a/upsampler/models/stylegan2/op/readme.md b/upsampler/models/stylegan2/op/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..f886dbf33c303cf146224f0c3a59d8592077c228 --- /dev/null +++ b/upsampler/models/stylegan2/op/readme.md @@ -0,0 +1,9 @@ +Code from [rosinality-stylegan2-pytorch-cp](https://github.com/senior-sigan/rosinality-stylegan2-pytorch-cpu) + +Scripts to convert rosinality/stylegan2-pytorch to the CPU compatible format + +If you would like to use CPU for testing or have a problem regarding the cpp extention (fused and upfirdn2d), please make the following changes: + +Change `models.stylegan2.op_old` to `models.stylegan2.op` + +https://github.com/williamyang1991/StyleGANEX/blob/73b580cc7eb757e36701c094456e9ee02078d03e/models/stylegan2/model.py#L8 diff --git a/upsampler/models/stylegan2/op/upfirdn2d.py b/upsampler/models/stylegan2/op/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..d509eb5e11e8cd01468dded5e5b53f5326057706 --- /dev/null +++ b/upsampler/models/stylegan2/op/upfirdn2d.py @@ -0,0 +1,61 @@ +from collections import abc + +import torch +from torch.nn import functional as F + + +def upfirdn2d(inputs, kernel, up=1, down=1, pad=(0, 0)): + if not isinstance(up, abc.Iterable): + up = (up, up) + + if not isinstance(down, abc.Iterable): + down = (down, down) + + if len(pad) == 2: + pad = (pad[0], pad[1], pad[0], pad[1]) + + return upfirdn2d_native(inputs, kernel, *up, *down, *pad) + + +def upfirdn2d_native( + inputs, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = inputs.shape + inputs = inputs.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = inputs.shape + kernel_h, kernel_w = kernel.shape + + out = inputs.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x + + return out.view(-1, channel, out_h, out_w) \ No newline at end of file diff --git a/upsampler/models/stylegan2/op2/__init__.py b/upsampler/models/stylegan2/op2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4d63b8dad1793115ca3ea6c372209d07118dbbf2 --- /dev/null +++ b/upsampler/models/stylegan2/op2/__init__.py @@ -0,0 +1 @@ +from .upfirdn2d import upfirdn2d diff --git a/upsampler/models/stylegan2/op2/upfirdn2d.cpp b/upsampler/models/stylegan2/op2/upfirdn2d.cpp new file mode 100644 index 0000000000000000000000000000000000000000..73928ece8150f847d98af65a95685a29fcceecde --- /dev/null +++ b/upsampler/models/stylegan2/op2/upfirdn2d.cpp @@ -0,0 +1,31 @@ +#include +#include + +torch::Tensor upfirdn2d_op(const torch::Tensor &input, + const torch::Tensor &kernel, int up_x, int up_y, + int down_x, int down_y, int pad_x0, int pad_x1, + int pad_y0, int pad_y1); + +#define CHECK_CUDA(x) \ + TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) \ + TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) \ + CHECK_CUDA(x); \ + CHECK_CONTIGUOUS(x) + +torch::Tensor upfirdn2d(const torch::Tensor &input, const torch::Tensor &kernel, + int up_x, int up_y, int down_x, int down_y, int pad_x0, + int pad_x1, int pad_y0, int pad_y1) { + CHECK_INPUT(input); + CHECK_INPUT(kernel); + + at::DeviceGuard guard(input.device()); + + return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, + pad_y0, pad_y1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); +} \ No newline at end of file diff --git a/upsampler/models/stylegan2/op2/upfirdn2d.py b/upsampler/models/stylegan2/op2/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..b133575dd953ced72cc521f0b2f4875e55a91461 --- /dev/null +++ b/upsampler/models/stylegan2/op2/upfirdn2d.py @@ -0,0 +1,209 @@ +from collections import abc +import os + +import torch +from torch.nn import functional as F +from torch.autograd import Function +from torch.utils.cpp_extension import load + + +module_path = os.path.dirname(__file__) +upfirdn2d_op = load( + "upfirdn2d", + sources=[ + os.path.join(module_path, "upfirdn2d.cpp"), + os.path.join(module_path, "upfirdn2d_kernel.cu"), + ], +) + + +class UpFirDn2dBackward(Function): + @staticmethod + def forward( + ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size + ): + + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1).contiguous() + + grad_input = upfirdn2d_op.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + kernel, = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_op.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view( + ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] + ) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + batch, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_op.upfirdn2d( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 + ) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = None + + if ctx.needs_input_grad[0]: + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + if not isinstance(up, abc.Iterable): + up = (up, up) + + if not isinstance(down, abc.Iterable): + down = (down, down) + + if len(pad) == 2: + pad = (pad[0], pad[1], pad[0], pad[1]) + + if input.device.type == "cpu": + out = upfirdn2d_native(input, kernel, *up, *down, *pad) + + else: + out = UpFirDn2d.apply(input, kernel, up, down, pad) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x + + return out.view(-1, channel, out_h, out_w) diff --git a/upsampler/models/stylegan2/op2/upfirdn2d_kernel.cu b/upsampler/models/stylegan2/op2/upfirdn2d_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..190514c2d703634092836332211fb4fefa805cfa --- /dev/null +++ b/upsampler/models/stylegan2/op2/upfirdn2d_kernel.cu @@ -0,0 +1,369 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + +static __host__ __device__ __forceinline__ int floor_div(int a, int b) { + int c = a / b; + + if (c * b > a) { + c--; + } + + return c; +} + +struct UpFirDn2DKernelParams { + int up_x; + int up_y; + int down_x; + int down_y; + int pad_x0; + int pad_x1; + int pad_y0; + int pad_y1; + + int major_dim; + int in_h; + int in_w; + int minor_dim; + int kernel_h; + int kernel_w; + int out_h; + int out_w; + int loop_major; + int loop_x; +}; + +template +__global__ void upfirdn2d_kernel_large(scalar_t *out, const scalar_t *input, + const scalar_t *kernel, + const UpFirDn2DKernelParams p) { + int minor_idx = blockIdx.x * blockDim.x + threadIdx.x; + int out_y = minor_idx / p.minor_dim; + minor_idx -= out_y * p.minor_dim; + int out_x_base = blockIdx.y * p.loop_x * blockDim.y + threadIdx.y; + int major_idx_base = blockIdx.z * p.loop_major; + + if (out_x_base >= p.out_w || out_y >= p.out_h || + major_idx_base >= p.major_dim) { + return; + } + + int mid_y = out_y * p.down_y + p.up_y - 1 - p.pad_y0; + int in_y = min(max(floor_div(mid_y, p.up_y), 0), p.in_h); + int h = min(max(floor_div(mid_y + p.kernel_h, p.up_y), 0), p.in_h) - in_y; + int kernel_y = mid_y + p.kernel_h - (in_y + 1) * p.up_y; + + for (int loop_major = 0, major_idx = major_idx_base; + loop_major < p.loop_major && major_idx < p.major_dim; + loop_major++, major_idx++) { + for (int loop_x = 0, out_x = out_x_base; + loop_x < p.loop_x && out_x < p.out_w; loop_x++, out_x += blockDim.y) { + int mid_x = out_x * p.down_x + p.up_x - 1 - p.pad_x0; + int in_x = min(max(floor_div(mid_x, p.up_x), 0), p.in_w); + int w = min(max(floor_div(mid_x + p.kernel_w, p.up_x), 0), p.in_w) - in_x; + int kernel_x = mid_x + p.kernel_w - (in_x + 1) * p.up_x; + + const scalar_t *x_p = + &input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + + minor_idx]; + const scalar_t *k_p = &kernel[kernel_y * p.kernel_w + kernel_x]; + int x_px = p.minor_dim; + int k_px = -p.up_x; + int x_py = p.in_w * p.minor_dim; + int k_py = -p.up_y * p.kernel_w; + + scalar_t v = 0.0f; + + for (int y = 0; y < h; y++) { + for (int x = 0; x < w; x++) { + v += static_cast(*x_p) * static_cast(*k_p); + x_p += x_px; + k_p += k_px; + } + + x_p += x_py - w * x_px; + k_p += k_py - w * k_px; + } + + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + + minor_idx] = v; + } + } +} + +template +__global__ void upfirdn2d_kernel(scalar_t *out, const scalar_t *input, + const scalar_t *kernel, + const UpFirDn2DKernelParams p) { + const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; + const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; + + __shared__ volatile float sk[kernel_h][kernel_w]; + __shared__ volatile float sx[tile_in_h][tile_in_w]; + + int minor_idx = blockIdx.x; + int tile_out_y = minor_idx / p.minor_dim; + minor_idx -= tile_out_y * p.minor_dim; + tile_out_y *= tile_out_h; + int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; + int major_idx_base = blockIdx.z * p.loop_major; + + if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | + major_idx_base >= p.major_dim) { + return; + } + + for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; + tap_idx += blockDim.x) { + int ky = tap_idx / kernel_w; + int kx = tap_idx - ky * kernel_w; + scalar_t v = 0.0; + + if (kx < p.kernel_w & ky < p.kernel_h) { + v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; + } + + sk[ky][kx] = v; + } + + for (int loop_major = 0, major_idx = major_idx_base; + loop_major < p.loop_major & major_idx < p.major_dim; + loop_major++, major_idx++) { + for (int loop_x = 0, tile_out_x = tile_out_x_base; + loop_x < p.loop_x & tile_out_x < p.out_w; + loop_x++, tile_out_x += tile_out_w) { + int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; + int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; + int tile_in_x = floor_div(tile_mid_x, up_x); + int tile_in_y = floor_div(tile_mid_y, up_y); + + __syncthreads(); + + for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; + in_idx += blockDim.x) { + int rel_in_y = in_idx / tile_in_w; + int rel_in_x = in_idx - rel_in_y * tile_in_w; + int in_x = rel_in_x + tile_in_x; + int in_y = rel_in_y + tile_in_y; + + scalar_t v = 0.0; + + if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { + v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * + p.minor_dim + + minor_idx]; + } + + sx[rel_in_y][rel_in_x] = v; + } + + __syncthreads(); + for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; + out_idx += blockDim.x) { + int rel_out_y = out_idx / tile_out_w; + int rel_out_x = out_idx - rel_out_y * tile_out_w; + int out_x = rel_out_x + tile_out_x; + int out_y = rel_out_y + tile_out_y; + + int mid_x = tile_mid_x + rel_out_x * down_x; + int mid_y = tile_mid_y + rel_out_y * down_y; + int in_x = floor_div(mid_x, up_x); + int in_y = floor_div(mid_y, up_y); + int rel_in_x = in_x - tile_in_x; + int rel_in_y = in_y - tile_in_y; + int kernel_x = (in_x + 1) * up_x - mid_x - 1; + int kernel_y = (in_y + 1) * up_y - mid_y - 1; + + scalar_t v = 0.0; + +#pragma unroll + for (int y = 0; y < kernel_h / up_y; y++) +#pragma unroll + for (int x = 0; x < kernel_w / up_x; x++) + v += sx[rel_in_y + y][rel_in_x + x] * + sk[kernel_y + y * up_y][kernel_x + x * up_x]; + + if (out_x < p.out_w & out_y < p.out_h) { + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + + minor_idx] = v; + } + } + } + } +} + +torch::Tensor upfirdn2d_op(const torch::Tensor &input, + const torch::Tensor &kernel, int up_x, int up_y, + int down_x, int down_y, int pad_x0, int pad_x1, + int pad_y0, int pad_y1) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(); + + UpFirDn2DKernelParams p; + + auto x = input.contiguous(); + auto k = kernel.contiguous(); + + p.major_dim = x.size(0); + p.in_h = x.size(1); + p.in_w = x.size(2); + p.minor_dim = x.size(3); + p.kernel_h = k.size(0); + p.kernel_w = k.size(1); + p.up_x = up_x; + p.up_y = up_y; + p.down_x = down_x; + p.down_y = down_y; + p.pad_x0 = pad_x0; + p.pad_x1 = pad_x1; + p.pad_y0 = pad_y0; + p.pad_y1 = pad_y1; + + p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / + p.down_y; + p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / + p.down_x; + + auto out = + at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); + + int mode = -1; + + int tile_out_h = -1; + int tile_out_w = -1; + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 1; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 3 && p.kernel_w <= 3) { + mode = 2; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 3; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && + p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 4; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && + p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 5; + tile_out_h = 8; + tile_out_w = 32; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && + p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 6; + tile_out_h = 8; + tile_out_w = 32; + } + + dim3 block_size; + dim3 grid_size; + + if (tile_out_h > 0 && tile_out_w > 0) { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 1; + block_size = dim3(32 * 8, 1, 1); + grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, + (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } else { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 4; + block_size = dim3(4, 32, 1); + grid_size = dim3((p.out_h * p.minor_dim - 1) / block_size.x + 1, + (p.out_w - 1) / (p.loop_x * block_size.y) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { + switch (mode) { + case 1: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 2: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 3: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 4: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 5: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + case 6: + upfirdn2d_kernel + <<>>(out.data_ptr(), + x.data_ptr(), + k.data_ptr(), p); + + break; + + default: + upfirdn2d_kernel_large<<>>( + out.data_ptr(), x.data_ptr(), + k.data_ptr(), p); + } + }); + + return out; +} \ No newline at end of file diff --git a/upsampler/models/stylegan2/op_old/__init__.py b/upsampler/models/stylegan2/op_old/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d0918d92285955855be89f00096b888ee5597ce3 --- /dev/null +++ b/upsampler/models/stylegan2/op_old/__init__.py @@ -0,0 +1,2 @@ +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d diff --git a/upsampler/models/stylegan2/op_old/fused_act.py b/upsampler/models/stylegan2/op_old/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..973a84fffde53668d31397da5fb993bbc95f7be0 --- /dev/null +++ b/upsampler/models/stylegan2/op_old/fused_act.py @@ -0,0 +1,85 @@ +import os + +import torch +from torch import nn +from torch.autograd import Function +from torch.utils.cpp_extension import load + +module_path = os.path.dirname(__file__) +fused = load( + 'fused', + sources=[ + os.path.join(module_path, 'fused_bias_act.cpp'), + os.path.join(module_path, 'fused_bias_act_kernel.cu'), + ], +) + + +class FusedLeakyReLUFunctionBackward(Function): + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = fused.fused_bias_act( + grad_output, empty, out, 3, 1, negative_slope, scale + ) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + out, = ctx.saved_tensors + gradgrad_out = fused.fused_bias_act( + gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale + ) + + return gradgrad_out, None, None, None + + +class FusedLeakyReLUFunction(Function): + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + out = fused.fused_bias_act(input, bias, empty, 3, 0, negative_slope, scale) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + out, = ctx.saved_tensors + + grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( + grad_output, out, ctx.negative_slope, ctx.scale + ) + + return grad_input, grad_bias, None, None + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5): + return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/upsampler/models/stylegan2/op_old/fused_bias_act.cpp b/upsampler/models/stylegan2/op_old/fused_bias_act.cpp new file mode 100644 index 0000000000000000000000000000000000000000..02be898f970bcc8ea297867fcaa4e71b24b3d949 --- /dev/null +++ b/upsampler/models/stylegan2/op_old/fused_bias_act.cpp @@ -0,0 +1,21 @@ +#include + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor fused_bias_act(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + CHECK_CUDA(input); + CHECK_CUDA(bias); + + return fused_bias_act_op(input, bias, refer, act, grad, alpha, scale); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("fused_bias_act", &fused_bias_act, "fused bias act (CUDA)"); +} \ No newline at end of file diff --git a/upsampler/models/stylegan2/op_old/fused_bias_act_kernel.cu b/upsampler/models/stylegan2/op_old/fused_bias_act_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..c9fa56fea7ede7072dc8925cfb0148f136eb85b8 --- /dev/null +++ b/upsampler/models/stylegan2/op_old/fused_bias_act_kernel.cu @@ -0,0 +1,99 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +template +static __global__ void fused_bias_act_kernel(scalar_t* out, const scalar_t* p_x, const scalar_t* p_b, const scalar_t* p_ref, + int act, int grad, scalar_t alpha, scalar_t scale, int loop_x, int size_x, int step_b, int size_b, int use_bias, int use_ref) { + int xi = blockIdx.x * loop_x * blockDim.x + threadIdx.x; + + scalar_t zero = 0.0; + + for (int loop_idx = 0; loop_idx < loop_x && xi < size_x; loop_idx++, xi += blockDim.x) { + scalar_t x = p_x[xi]; + + if (use_bias) { + x += p_b[(xi / step_b) % size_b]; + } + + scalar_t ref = use_ref ? p_ref[xi] : zero; + + scalar_t y; + + switch (act * 10 + grad) { + default: + case 10: y = x; break; + case 11: y = x; break; + case 12: y = 0.0; break; + + case 30: y = (x > 0.0) ? x : x * alpha; break; + case 31: y = (ref > 0.0) ? x : x * alpha; break; + case 32: y = 0.0; break; + } + + out[xi] = y * scale; + } +} + + +torch::Tensor fused_bias_act_op(const torch::Tensor& input, const torch::Tensor& bias, const torch::Tensor& refer, + int act, int grad, float alpha, float scale) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + auto x = input.contiguous(); + auto b = bias.contiguous(); + auto ref = refer.contiguous(); + + int use_bias = b.numel() ? 1 : 0; + int use_ref = ref.numel() ? 1 : 0; + + int size_x = x.numel(); + int size_b = b.numel(); + int step_b = 1; + + for (int i = 1 + 1; i < x.dim(); i++) { + step_b *= x.size(i); + } + + int loop_x = 4; + int block_size = 4 * 32; + int grid_size = (size_x - 1) / (loop_x * block_size) + 1; + + auto y = torch::empty_like(x); + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "fused_bias_act_kernel", [&] { + fused_bias_act_kernel<<>>( + y.data_ptr(), + x.data_ptr(), + b.data_ptr(), + ref.data_ptr(), + act, + grad, + alpha, + scale, + loop_x, + size_x, + step_b, + size_b, + use_bias, + use_ref + ); + }); + + return y; +} \ No newline at end of file diff --git a/upsampler/models/stylegan2/op_old/upfirdn2d.cpp b/upsampler/models/stylegan2/op_old/upfirdn2d.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d2e633dc896433c205e18bc3e455539192ff968e --- /dev/null +++ b/upsampler/models/stylegan2/op_old/upfirdn2d.cpp @@ -0,0 +1,23 @@ +#include + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1); + +#define CHECK_CUDA(x) TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor") +#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") +#define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x) + +torch::Tensor upfirdn2d(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + CHECK_CUDA(input); + CHECK_CUDA(kernel); + + return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1); +} + +PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { + m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)"); +} \ No newline at end of file diff --git a/upsampler/models/stylegan2/op_old/upfirdn2d.py b/upsampler/models/stylegan2/op_old/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..e9cb52219689592e2745600abb19fad02740a139 --- /dev/null +++ b/upsampler/models/stylegan2/op_old/upfirdn2d.py @@ -0,0 +1,184 @@ +import os + +import torch +from torch.autograd import Function +from torch.utils.cpp_extension import load + +module_path = os.path.dirname(__file__) +upfirdn2d_op = load( + 'upfirdn2d', + sources=[ + os.path.join(module_path, 'upfirdn2d.cpp'), + os.path.join(module_path, 'upfirdn2d_kernel.cu'), + ], +) + + +class UpFirDn2dBackward(Function): + @staticmethod + def forward( + ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size + ): + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_op.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + kernel, = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_op.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view( + ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] + ) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + batch, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_op.upfirdn2d( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 + ) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + out = UpFirDn2d.apply( + input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) + ) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0): out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0): out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + + return out[:, ::down_y, ::down_x, :] \ No newline at end of file diff --git a/upsampler/models/stylegan2/op_old/upfirdn2d_kernel.cu b/upsampler/models/stylegan2/op_old/upfirdn2d_kernel.cu new file mode 100644 index 0000000000000000000000000000000000000000..2a710aa6adc3d43ac93136a1814e3c39970e1c7e --- /dev/null +++ b/upsampler/models/stylegan2/op_old/upfirdn2d_kernel.cu @@ -0,0 +1,272 @@ +// Copyright (c) 2019, NVIDIA Corporation. All rights reserved. +// +// This work is made available under the Nvidia Source Code License-NC. +// To view a copy of this license, visit +// https://nvlabs.github.io/stylegan2/license.html + +#include + +#include +#include +#include +#include + +#include +#include + + +static __host__ __device__ __forceinline__ int floor_div(int a, int b) { + int c = a / b; + + if (c * b > a) { + c--; + } + + return c; +} + + +struct UpFirDn2DKernelParams { + int up_x; + int up_y; + int down_x; + int down_y; + int pad_x0; + int pad_x1; + int pad_y0; + int pad_y1; + + int major_dim; + int in_h; + int in_w; + int minor_dim; + int kernel_h; + int kernel_w; + int out_h; + int out_w; + int loop_major; + int loop_x; +}; + + +template +__global__ void upfirdn2d_kernel(scalar_t* out, const scalar_t* input, const scalar_t* kernel, const UpFirDn2DKernelParams p) { + const int tile_in_h = ((tile_out_h - 1) * down_y + kernel_h - 1) / up_y + 1; + const int tile_in_w = ((tile_out_w - 1) * down_x + kernel_w - 1) / up_x + 1; + + __shared__ volatile float sk[kernel_h][kernel_w]; + __shared__ volatile float sx[tile_in_h][tile_in_w]; + + int minor_idx = blockIdx.x; + int tile_out_y = minor_idx / p.minor_dim; + minor_idx -= tile_out_y * p.minor_dim; + tile_out_y *= tile_out_h; + int tile_out_x_base = blockIdx.y * p.loop_x * tile_out_w; + int major_idx_base = blockIdx.z * p.loop_major; + + if (tile_out_x_base >= p.out_w | tile_out_y >= p.out_h | major_idx_base >= p.major_dim) { + return; + } + + for (int tap_idx = threadIdx.x; tap_idx < kernel_h * kernel_w; tap_idx += blockDim.x) { + int ky = tap_idx / kernel_w; + int kx = tap_idx - ky * kernel_w; + scalar_t v = 0.0; + + if (kx < p.kernel_w & ky < p.kernel_h) { + v = kernel[(p.kernel_h - 1 - ky) * p.kernel_w + (p.kernel_w - 1 - kx)]; + } + + sk[ky][kx] = v; + } + + for (int loop_major = 0, major_idx = major_idx_base; loop_major < p.loop_major & major_idx < p.major_dim; loop_major++, major_idx++) { + for (int loop_x = 0, tile_out_x = tile_out_x_base; loop_x < p.loop_x & tile_out_x < p.out_w; loop_x++, tile_out_x += tile_out_w) { + int tile_mid_x = tile_out_x * down_x + up_x - 1 - p.pad_x0; + int tile_mid_y = tile_out_y * down_y + up_y - 1 - p.pad_y0; + int tile_in_x = floor_div(tile_mid_x, up_x); + int tile_in_y = floor_div(tile_mid_y, up_y); + + __syncthreads(); + + for (int in_idx = threadIdx.x; in_idx < tile_in_h * tile_in_w; in_idx += blockDim.x) { + int rel_in_y = in_idx / tile_in_w; + int rel_in_x = in_idx - rel_in_y * tile_in_w; + int in_x = rel_in_x + tile_in_x; + int in_y = rel_in_y + tile_in_y; + + scalar_t v = 0.0; + + if (in_x >= 0 & in_y >= 0 & in_x < p.in_w & in_y < p.in_h) { + v = input[((major_idx * p.in_h + in_y) * p.in_w + in_x) * p.minor_dim + minor_idx]; + } + + sx[rel_in_y][rel_in_x] = v; + } + + __syncthreads(); + for (int out_idx = threadIdx.x; out_idx < tile_out_h * tile_out_w; out_idx += blockDim.x) { + int rel_out_y = out_idx / tile_out_w; + int rel_out_x = out_idx - rel_out_y * tile_out_w; + int out_x = rel_out_x + tile_out_x; + int out_y = rel_out_y + tile_out_y; + + int mid_x = tile_mid_x + rel_out_x * down_x; + int mid_y = tile_mid_y + rel_out_y * down_y; + int in_x = floor_div(mid_x, up_x); + int in_y = floor_div(mid_y, up_y); + int rel_in_x = in_x - tile_in_x; + int rel_in_y = in_y - tile_in_y; + int kernel_x = (in_x + 1) * up_x - mid_x - 1; + int kernel_y = (in_y + 1) * up_y - mid_y - 1; + + scalar_t v = 0.0; + + #pragma unroll + for (int y = 0; y < kernel_h / up_y; y++) + #pragma unroll + for (int x = 0; x < kernel_w / up_x; x++) + v += sx[rel_in_y + y][rel_in_x + x] * sk[kernel_y + y * up_y][kernel_x + x * up_x]; + + if (out_x < p.out_w & out_y < p.out_h) { + out[((major_idx * p.out_h + out_y) * p.out_w + out_x) * p.minor_dim + minor_idx] = v; + } + } + } + } +} + + +torch::Tensor upfirdn2d_op(const torch::Tensor& input, const torch::Tensor& kernel, + int up_x, int up_y, int down_x, int down_y, + int pad_x0, int pad_x1, int pad_y0, int pad_y1) { + int curDevice = -1; + cudaGetDevice(&curDevice); + cudaStream_t stream = at::cuda::getCurrentCUDAStream(curDevice); + + UpFirDn2DKernelParams p; + + auto x = input.contiguous(); + auto k = kernel.contiguous(); + + p.major_dim = x.size(0); + p.in_h = x.size(1); + p.in_w = x.size(2); + p.minor_dim = x.size(3); + p.kernel_h = k.size(0); + p.kernel_w = k.size(1); + p.up_x = up_x; + p.up_y = up_y; + p.down_x = down_x; + p.down_y = down_y; + p.pad_x0 = pad_x0; + p.pad_x1 = pad_x1; + p.pad_y0 = pad_y0; + p.pad_y1 = pad_y1; + + p.out_h = (p.in_h * p.up_y + p.pad_y0 + p.pad_y1 - p.kernel_h + p.down_y) / p.down_y; + p.out_w = (p.in_w * p.up_x + p.pad_x0 + p.pad_x1 - p.kernel_w + p.down_x) / p.down_x; + + auto out = at::empty({p.major_dim, p.out_h, p.out_w, p.minor_dim}, x.options()); + + int mode = -1; + + int tile_out_h; + int tile_out_w; + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 1; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 3 && p.kernel_w <= 3) { + mode = 2; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 3; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 2 && p.up_y == 2 && p.down_x == 1 && p.down_y == 1 && p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 4; + tile_out_h = 16; + tile_out_w = 64; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 4 && p.kernel_w <= 4) { + mode = 5; + tile_out_h = 8; + tile_out_w = 32; + } + + if (p.up_x == 1 && p.up_y == 1 && p.down_x == 2 && p.down_y == 2 && p.kernel_h <= 2 && p.kernel_w <= 2) { + mode = 6; + tile_out_h = 8; + tile_out_w = 32; + } + + dim3 block_size; + dim3 grid_size; + + if (tile_out_h > 0 && tile_out_w) { + p.loop_major = (p.major_dim - 1) / 16384 + 1; + p.loop_x = 1; + block_size = dim3(32 * 8, 1, 1); + grid_size = dim3(((p.out_h - 1) / tile_out_h + 1) * p.minor_dim, + (p.out_w - 1) / (p.loop_x * tile_out_w) + 1, + (p.major_dim - 1) / p.loop_major + 1); + } + + AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] { + switch (mode) { + case 1: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 2: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 3: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 4: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 5: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + + case 6: + upfirdn2d_kernel<<>>( + out.data_ptr(), x.data_ptr(), k.data_ptr(), p + ); + + break; + } + }); + + return out; +} \ No newline at end of file diff --git a/upsampler/models/stylegan2/simple_augment.py b/upsampler/models/stylegan2/simple_augment.py new file mode 100644 index 0000000000000000000000000000000000000000..77776cd134046dc012e021d0ab80c1e0b90d2275 --- /dev/null +++ b/upsampler/models/stylegan2/simple_augment.py @@ -0,0 +1,478 @@ +import math + +import torch +from torch import autograd +from torch.nn import functional as F +import numpy as np + +from torch import distributed as dist +#from distributed import reduce_sum +from models.stylegan2.op2 import upfirdn2d + +def reduce_sum(tensor): + if not dist.is_available(): + return tensor + + if not dist.is_initialized(): + return tensor + + tensor = tensor.clone() + dist.all_reduce(tensor, op=dist.ReduceOp.SUM) + + return tensor + + +class AdaptiveAugment: + def __init__(self, ada_aug_target, ada_aug_len, update_every, device): + self.ada_aug_target = ada_aug_target + self.ada_aug_len = ada_aug_len + self.update_every = update_every + + self.ada_update = 0 + self.ada_aug_buf = torch.tensor([0.0, 0.0], device=device) + self.r_t_stat = 0 + self.ada_aug_p = 0 + + @torch.no_grad() + def tune(self, real_pred): + self.ada_aug_buf += torch.tensor( + (torch.sign(real_pred).sum().item(), real_pred.shape[0]), + device=real_pred.device, + ) + self.ada_update += 1 + + if self.ada_update % self.update_every == 0: + self.ada_aug_buf = reduce_sum(self.ada_aug_buf) + pred_signs, n_pred = self.ada_aug_buf.tolist() + + self.r_t_stat = pred_signs / n_pred + + if self.r_t_stat > self.ada_aug_target: + sign = 1 + + else: + sign = -1 + + self.ada_aug_p += sign * n_pred / self.ada_aug_len + self.ada_aug_p = min(1, max(0, self.ada_aug_p)) + self.ada_aug_buf.mul_(0) + self.ada_update = 0 + + return self.ada_aug_p + + +SYM6 = ( + 0.015404109327027373, + 0.0034907120842174702, + -0.11799011114819057, + -0.048311742585633, + 0.4910559419267466, + 0.787641141030194, + 0.3379294217276218, + -0.07263752278646252, + -0.021060292512300564, + 0.04472490177066578, + 0.0017677118642428036, + -0.007800708325034148, +) + + +def translate_mat(t_x, t_y, device="cpu"): + batch = t_x.shape[0] + + mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1) + translate = torch.stack((t_x, t_y), 1) + mat[:, :2, 2] = translate + + return mat + + +def rotate_mat(theta, device="cpu"): + batch = theta.shape[0] + + mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1) + sin_t = torch.sin(theta) + cos_t = torch.cos(theta) + rot = torch.stack((cos_t, -sin_t, sin_t, cos_t), 1).view(batch, 2, 2) + mat[:, :2, :2] = rot + + return mat + + +def scale_mat(s_x, s_y, device="cpu"): + batch = s_x.shape[0] + + mat = torch.eye(3, device=device).unsqueeze(0).repeat(batch, 1, 1) + mat[:, 0, 0] = s_x + mat[:, 1, 1] = s_y + + return mat + + +def translate3d_mat(t_x, t_y, t_z): + batch = t_x.shape[0] + + mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) + translate = torch.stack((t_x, t_y, t_z), 1) + mat[:, :3, 3] = translate + + return mat + + +def rotate3d_mat(axis, theta): + batch = theta.shape[0] + + u_x, u_y, u_z = axis + + eye = torch.eye(3).unsqueeze(0) + cross = torch.tensor([(0, -u_z, u_y), (u_z, 0, -u_x), (-u_y, u_x, 0)]).unsqueeze(0) + outer = torch.tensor(axis) + outer = (outer.unsqueeze(1) * outer).unsqueeze(0) + + sin_t = torch.sin(theta).view(-1, 1, 1) + cos_t = torch.cos(theta).view(-1, 1, 1) + + rot = cos_t * eye + sin_t * cross + (1 - cos_t) * outer + + eye_4 = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) + eye_4[:, :3, :3] = rot + + return eye_4 + + +def scale3d_mat(s_x, s_y, s_z): + batch = s_x.shape[0] + + mat = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) + mat[:, 0, 0] = s_x + mat[:, 1, 1] = s_y + mat[:, 2, 2] = s_z + + return mat + + +def luma_flip_mat(axis, i): + batch = i.shape[0] + + eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) + axis = torch.tensor(axis + (0,)) + flip = 2 * torch.ger(axis, axis) * i.view(-1, 1, 1) + + return eye - flip + + +def saturation_mat(axis, i): + batch = i.shape[0] + + eye = torch.eye(4).unsqueeze(0).repeat(batch, 1, 1) + axis = torch.tensor(axis + (0,)) + axis = torch.ger(axis, axis) + saturate = axis + (eye - axis) * i.view(-1, 1, 1) + + return saturate + + +def lognormal_sample(size, mean=0, std=1, device="cpu"): + return torch.empty(size, device=device).log_normal_(mean=mean, std=std) + + +def category_sample(size, categories, device="cpu"): + category = torch.tensor(categories, device=device) + sample = torch.randint(high=len(categories), size=(size,), device=device) + + return category[sample] + + +def uniform_sample(size, low, high, device="cpu"): + return torch.empty(size, device=device).uniform_(low, high) + + +def normal_sample(size, mean=0, std=1, device="cpu"): + return torch.empty(size, device=device).normal_(mean, std) + + +def bernoulli_sample(size, p, device="cpu"): + return torch.empty(size, device=device).bernoulli_(p) + + +def random_mat_apply(p, transform, prev, eye, device="cpu"): + size = transform.shape[0] + select = bernoulli_sample(size, p, device=device).view(size, 1, 1) + select_transform = select * transform + (1 - select) * eye + + return select_transform @ prev + + +def sample_affine(p, size, height, width, device="cpu"): + G = torch.eye(3, device=device).unsqueeze(0).repeat(size, 1, 1) + eye = G + + # flip + #param = category_sample(size, (0, 1)) + #Gc = scale_mat(1 - 2.0 * param, torch.ones(size), device=device) + #G = random_mat_apply(p, Gc, G, eye, device=device) + # print('flip', G, scale_mat(1 - 2.0 * param, torch.ones(size)), sep='\n') + + # 90 rotate + #param = category_sample(size, (0, 3)) + #Gc = rotate_mat(-math.pi / 2 * param, device=device) + #G = random_mat_apply(p, Gc, G, eye, device=device) + # print('90 rotate', G, rotate_mat(-math.pi / 2 * param), sep='\n') + + # integer translate + param = uniform_sample(size, -0.125, 0.125) + param_height = torch.round(param * height) / height + param_width = torch.round(param * width) / width + Gc = translate_mat(param_width, param_height, device=device) + G = random_mat_apply(p, Gc, G, eye, device=device) + # print('integer translate', G, translate_mat(param_width, param_height), sep='\n') + + # isotropic scale + param = lognormal_sample(size, std=0.1 * math.log(2)) + Gc = scale_mat(param, param, device=device) + G = random_mat_apply(p, Gc, G, eye, device=device) + # print('isotropic scale', G, scale_mat(param, param), sep='\n') + + p_rot = 1 - math.sqrt(1 - p) + + # pre-rotate + param = uniform_sample(size, -math.pi * 0.25, math.pi * 0.25) + Gc = rotate_mat(-param, device=device) + G = random_mat_apply(p_rot, Gc, G, eye, device=device) + # print('pre-rotate', G, rotate_mat(-param), sep='\n') + + # anisotropic scale + param = lognormal_sample(size, std=0.1 * math.log(2)) + Gc = scale_mat(param, 1 / param, device=device) + G = random_mat_apply(p, Gc, G, eye, device=device) + # print('anisotropic scale', G, scale_mat(param, 1 / param), sep='\n') + + # post-rotate + param = uniform_sample(size, -math.pi * 0.25, math.pi * 0.25) + Gc = rotate_mat(-param, device=device) + G = random_mat_apply(p_rot, Gc, G, eye, device=device) + # print('post-rotate', G, rotate_mat(-param), sep='\n') + + # fractional translate + param = normal_sample(size, std=0.125) + Gc = translate_mat(param, param, device=device) + G = random_mat_apply(p, Gc, G, eye, device=device) + # print('fractional translate', G, translate_mat(param, param), sep='\n') + + return G + + +def sample_color(p, size): + C = torch.eye(4).unsqueeze(0).repeat(size, 1, 1) + eye = C + axis_val = 1 / math.sqrt(3) + axis = (axis_val, axis_val, axis_val) + + # brightness + param = normal_sample(size, std=0.2) + Cc = translate3d_mat(param, param, param) + C = random_mat_apply(p, Cc, C, eye) + + # contrast + param = lognormal_sample(size, std=0.5 * math.log(2)) + Cc = scale3d_mat(param, param, param) + C = random_mat_apply(p, Cc, C, eye) + + # luma flip + param = category_sample(size, (0, 1)) + Cc = luma_flip_mat(axis, param) + C = random_mat_apply(p, Cc, C, eye) + + # hue rotation + param = uniform_sample(size, -math.pi, math.pi) + Cc = rotate3d_mat(axis, param) + C = random_mat_apply(p, Cc, C, eye) + + # saturation + param = lognormal_sample(size, std=1 * math.log(2)) + Cc = saturation_mat(axis, param) + C = random_mat_apply(p, Cc, C, eye) + + return C + + +def make_grid(shape, x0, x1, y0, y1, device): + n, c, h, w = shape + grid = torch.empty(n, h, w, 3, device=device) + grid[:, :, :, 0] = torch.linspace(x0, x1, w, device=device) + grid[:, :, :, 1] = torch.linspace(y0, y1, h, device=device).unsqueeze(-1) + grid[:, :, :, 2] = 1 + + return grid + + +def affine_grid(grid, mat): + n, h, w, _ = grid.shape + return (grid.view(n, h * w, 3) @ mat.transpose(1, 2)).view(n, h, w, 2) + + +def get_padding(G, height, width, kernel_size): + device = G.device + + cx = (width - 1) / 2 + cy = (height - 1) / 2 + cp = torch.tensor( + [(-cx, -cy, 1), (cx, -cy, 1), (cx, cy, 1), (-cx, cy, 1)], device=device + ) + cp = G @ cp.T + + pad_k = kernel_size // 4 + + pad = cp[:, :2, :].permute(1, 0, 2).flatten(1) + pad = torch.cat((-pad, pad)).max(1).values + pad = pad + torch.tensor([pad_k * 2 - cx, pad_k * 2 - cy] * 2, device=device) + pad = pad.max(torch.tensor([0, 0] * 2, device=device)) + pad = pad.min(torch.tensor([width - 1, height - 1] * 2, device=device)) + + pad_x1, pad_y1, pad_x2, pad_y2 = pad.ceil().to(torch.int32) + + return pad_x1, pad_x2, pad_y1, pad_y2 + + +def try_sample_affine_and_pad(img, p, kernel_size, G=None): + batch, _, height, width = img.shape + + G_try = G + + if G is None: + G_try = torch.inverse(sample_affine(p, batch, height, width)) + + pad_x1, pad_x2, pad_y1, pad_y2 = get_padding(G_try, height, width, kernel_size) + + img_pad = F.pad(img, (pad_x1, pad_x2, pad_y1, pad_y2), mode="reflect") + + return img_pad, G_try, (pad_x1, pad_x2, pad_y1, pad_y2) + + +class GridSampleForward(autograd.Function): + @staticmethod + def forward(ctx, input, grid): + out = F.grid_sample( + input, grid, mode="bilinear", padding_mode="zeros", align_corners=False + ) + ctx.save_for_backward(input, grid) + + return out + + @staticmethod + def backward(ctx, grad_output): + input, grid = ctx.saved_tensors + grad_input, grad_grid = GridSampleBackward.apply(grad_output, input, grid) + + return grad_input, grad_grid + + +class GridSampleBackward(autograd.Function): + @staticmethod + def forward(ctx, grad_output, input, grid): + op = torch._C._jit_get_operation("aten::grid_sampler_2d_backward") + grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) + ctx.save_for_backward(grid) + + return grad_input, grad_grid + + @staticmethod + def backward(ctx, grad_grad_input, grad_grad_grid): + grid, = ctx.saved_tensors + grad_grad_output = None + + if ctx.needs_input_grad[0]: + grad_grad_output = GridSampleForward.apply(grad_grad_input, grid) + + return grad_grad_output, None, None + + +grid_sample = GridSampleForward.apply + + +def scale_mat_single(s_x, s_y): + return torch.tensor(((s_x, 0, 0), (0, s_y, 0), (0, 0, 1)), dtype=torch.float32) + + +def translate_mat_single(t_x, t_y): + return torch.tensor(((1, 0, t_x), (0, 1, t_y), (0, 0, 1)), dtype=torch.float32) + + +def random_apply_affine(img, p, G=None, antialiasing_kernel=SYM6): + kernel = antialiasing_kernel + len_k = len(kernel) + + kernel = torch.as_tensor(kernel).to(img) + # kernel = torch.ger(kernel, kernel).to(img) + kernel_flip = torch.flip(kernel, (0,)) + + img_pad, G, (pad_x1, pad_x2, pad_y1, pad_y2) = try_sample_affine_and_pad( + img, p, len_k, G + ) + + G_inv = ( + translate_mat_single((pad_x1 - pad_x2).item() / 2, (pad_y1 - pad_y2).item() / 2) + @ G + ) + up_pad = ( + (len_k + 2 - 1) // 2, + (len_k - 2) // 2, + (len_k + 2 - 1) // 2, + (len_k - 2) // 2, + ) + img_2x = upfirdn2d(img_pad, kernel.unsqueeze(0), up=(2, 1), pad=(*up_pad[:2], 0, 0)) + img_2x = upfirdn2d(img_2x, kernel.unsqueeze(1), up=(1, 2), pad=(0, 0, *up_pad[2:])) + G_inv = scale_mat_single(2, 2) @ G_inv @ scale_mat_single(1 / 2, 1 / 2) + G_inv = translate_mat_single(-0.5, -0.5) @ G_inv @ translate_mat_single(0.5, 0.5) + batch_size, channel, height, width = img.shape + pad_k = len_k // 4 + shape = (batch_size, channel, (height + pad_k * 2) * 2, (width + pad_k * 2) * 2) + G_inv = ( + scale_mat_single(2 / img_2x.shape[3], 2 / img_2x.shape[2]) + @ G_inv + @ scale_mat_single(1 / (2 / shape[3]), 1 / (2 / shape[2])) + ) + grid = F.affine_grid(G_inv[:, :2, :].to(img_2x), shape, align_corners=False) + img_affine = grid_sample(img_2x, grid) + d_p = -pad_k * 2 + down_pad = ( + d_p + (len_k - 2 + 1) // 2, + d_p + (len_k - 2) // 2, + d_p + (len_k - 2 + 1) // 2, + d_p + (len_k - 2) // 2, + ) + img_down = upfirdn2d( + img_affine, kernel_flip.unsqueeze(0), down=(2, 1), pad=(*down_pad[:2], 0, 0) + ) + img_down = upfirdn2d( + img_down, kernel_flip.unsqueeze(1), down=(1, 2), pad=(0, 0, *down_pad[2:]) + ) + + return img_down, G + + +def apply_color(img, mat): + batch = img.shape[0] + img = img.permute(0, 2, 3, 1) + mat_mul = mat[:, :3, :3].transpose(1, 2).view(batch, 1, 3, 3) + mat_add = mat[:, :3, 3].view(batch, 1, 1, 3) + img = img @ mat_mul + mat_add + img = img.permute(0, 3, 1, 2) + + return img + + +def random_apply_color(img, p, C=None): + if C is None: + C = sample_color(p, img.shape[0]) + + img = apply_color(img, C.to(img)) + + return img, C + + +def augment(img, p, transform_matrix=(None, None)): + img, G = random_apply_affine(img, p, transform_matrix[0]) + img, C = random_apply_color(img, p, transform_matrix[1]) + + return img, (G, C) diff --git a/upsampler/options/__init__.py b/upsampler/options/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/options/test_options.py b/upsampler/options/test_options.py new file mode 100644 index 0000000000000000000000000000000000000000..8bcffaa065bc81b131844531090609a7e282e3b4 --- /dev/null +++ b/upsampler/options/test_options.py @@ -0,0 +1,33 @@ +from argparse import ArgumentParser + + +class TestOptions: + + def __init__(self): + self.parser = ArgumentParser() + self.initialize() + + def initialize(self): + # arguments for inference script + self.parser.add_argument('--exp_dir', type=str, help='Path to experiment output directory') + self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to pSp model checkpoint') + self.parser.add_argument('--data_path', type=str, default='gt_images', help='Path to directory of images to evaluate') + self.parser.add_argument('--couple_outputs', action='store_true', help='Whether to also save inputs + outputs side-by-side') + self.parser.add_argument('--resize_outputs', action='store_true', help='Whether to resize outputs to 256x256 or keep at 1024x1024') + + self.parser.add_argument('--test_batch_size', default=2, type=int, help='Batch size for testing and inference') + self.parser.add_argument('--test_workers', default=2, type=int, help='Number of test/inference dataloader workers') + + # arguments for style-mixing script + self.parser.add_argument('--n_images', type=int, default=None, help='Number of images to output. If None, run on all data') + self.parser.add_argument('--n_outputs_to_generate', type=int, default=5, help='Number of outputs to generate per input image.') + self.parser.add_argument('--mix_alpha', type=float, default=None, help='Alpha value for style-mixing') + self.parser.add_argument('--latent_mask', type=str, default=None, help='Comma-separated list of latents to perform style-mixing with') + + # arguments for super-resolution + self.parser.add_argument('--resize_factors', type=str, default=None, + help='Downsampling factor for super-res (should be a single value for inference).') + + def parse(self): + opts = self.parser.parse_args() + return opts \ No newline at end of file diff --git a/upsampler/options/train_options.py b/upsampler/options/train_options.py new file mode 100644 index 0000000000000000000000000000000000000000..5656a6b2e00059de72bc790e87daa3b15f5f6703 --- /dev/null +++ b/upsampler/options/train_options.py @@ -0,0 +1,81 @@ +from argparse import ArgumentParser +from configs.paths_config import model_paths + + +class TrainOptions: + + def __init__(self): + self.parser = ArgumentParser() + self.initialize() + + def initialize(self): + self.parser.add_argument('--exp_dir', type=str, help='Path to experiment output directory') + self.parser.add_argument('--dataset_type', default='ffhq_encode', type=str, help='Type of dataset/experiment to run') + self.parser.add_argument('--encoder_type', default='GradualStyleEncoder', type=str, help='Which encoder to use') + self.parser.add_argument('--input_nc', default=3, type=int, help='Number of input image channels to the psp encoder') + self.parser.add_argument('--label_nc', default=0, type=int, help='Number of input label channels to the psp encoder') + self.parser.add_argument('--output_size', default=1024, type=int, help='Output size of generator') + + # new options for StyleGANEX + self.parser.add_argument('--feat_ind', default=0, type=int, help='Layer index of G to accept the first-layer feature') + self.parser.add_argument('--max_pooling', action="store_true", help='Apply max pooling or average pooling') + self.parser.add_argument('--use_skip', action="store_true", help='Using skip connection from the encoder to the styleconv layers of G') + self.parser.add_argument('--use_skip_torgb', action="store_true", help='Using skip connection from the encoder to the toRGB layers of G.') + self.parser.add_argument('--skip_max_layer', default=7, type=int, help='Layer used for skip connection. 1,2,3,4,5,6,7 correspond to 4,8,16,32,64,128,256') + self.parser.add_argument('--crop_face', action="store_true", help='Use aligned cropped face to predict style latent code w+') + self.parser.add_argument('--affine_augment', action="store_true", help='Apply random affine transformation during training') + self.parser.add_argument('--random_crop', action="store_true", help='Apply random crop during training') + # for SR + self.parser.add_argument('--resize_factors', type=str, default=None, help='For super-res, comma-separated resize factors to use for inference.') + self.parser.add_argument('--blind_sr', action="store_true", help='Whether training blind SR (will use ./datasetsffhq_degradation_dataset.py)') + # for sketch/mask to face translation + self.parser.add_argument('--use_latent_mask', action="store_true", help='For segmentation/sketch to face translation, fuse w+ from two sources') + self.parser.add_argument('--latent_mask', type=str, default='8,9,10,11,12,13,14,15,16,17', help='Comma-separated list of latents to perform style-mixing with') + self.parser.add_argument('--res_num', default=2, type=int, help='Layer number of the resblocks of the translation network T') + # for video face toonify + self.parser.add_argument('--toonify_weights', default=None, type=str, help='Path to Toonify StyleGAN model weights') + # for video face editing + self.parser.add_argument('--generate_training_data', action="store_true", help='Whether generating training data (for video editing) or load real data') + self.parser.add_argument('--use_att', default=0, type=int, help='Layer of MLP used for attention, 0 not use attention') + self.parser.add_argument('--editing_w_path', type=str, default=None, help='Path to the editing vector v') + self.parser.add_argument('--zero_noise', action="store_true", help='Whether using zero noises') + self.parser.add_argument('--direction_path', type=str, default=None, help='Path to the direction vector to augment generated data') + + self.parser.add_argument('--batch_size', default=4, type=int, help='Batch size for training') + self.parser.add_argument('--test_batch_size', default=8, type=int, help='Batch size for testing and inference') + self.parser.add_argument('--workers', default=4, type=int, help='Number of train dataloader workers') + self.parser.add_argument('--test_workers', default=8, type=int, help='Number of test/inference dataloader workers') + + self.parser.add_argument('--learning_rate', default=0.0001, type=float, help='Optimizer learning rate') + self.parser.add_argument('--optim_name', default='ranger', type=str, help='Which optimizer to use') + self.parser.add_argument('--train_decoder', default=False, type=bool, help='Whether to train the decoder model') + self.parser.add_argument('--start_from_latent_avg', action='store_true', help='Whether to add average latent vector to generate codes from encoder.') + self.parser.add_argument('--learn_in_w', action='store_true', help='Whether to learn in w space instead of w+') + + self.parser.add_argument('--lpips_lambda', default=0.8, type=float, help='LPIPS loss multiplier factor') + self.parser.add_argument('--id_lambda', default=0, type=float, help='ID loss multiplier factor') + self.parser.add_argument('--l2_lambda', default=1.0, type=float, help='L2 loss multiplier factor') + self.parser.add_argument('--w_norm_lambda', default=0, type=float, help='W-norm loss multiplier factor') + self.parser.add_argument('--lpips_lambda_crop', default=0, type=float, help='LPIPS loss multiplier factor for inner image region') + self.parser.add_argument('--l2_lambda_crop', default=0, type=float, help='L2 loss multiplier factor for inner image region') + self.parser.add_argument('--moco_lambda', default=0, type=float, help='Moco-based feature similarity loss multiplier factor') + self.parser.add_argument('--adv_lambda', default=0, type=float, help='Adversarial loss multiplier factor') + self.parser.add_argument('--d_reg_every', default=16, type=int, help='Interval of the applying r1 regularization') + self.parser.add_argument('--r1', default=1, type=float, help="weight of the r1 regularization") + self.parser.add_argument('--tmp_lambda', default=0, type=float, help='Temporal loss multiplier factor') + + self.parser.add_argument('--stylegan_weights', default=model_paths['stylegan_ffhq'], type=str, help='Path to StyleGAN model weights') + self.parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to pSp model checkpoint') + + self.parser.add_argument('--max_steps', default=500000, type=int, help='Maximum number of training steps') + self.parser.add_argument('--image_interval', default=100, type=int, help='Interval for logging train images during training') + self.parser.add_argument('--board_interval', default=50, type=int, help='Interval for logging metrics to tensorboard') + self.parser.add_argument('--val_interval', default=1000, type=int, help='Validation interval') + self.parser.add_argument('--save_interval', default=None, type=int, help='Model checkpoint interval') + + # arguments for weights & biases support + self.parser.add_argument('--use_wandb', action="store_true", help='Whether to use Weights & Biases to track experiment.') + + def parse(self): + opts = self.parser.parse_args() + return opts diff --git a/upsampler/output/ILip77SbmOE_inversion.pt b/upsampler/output/ILip77SbmOE_inversion.pt new file mode 100644 index 0000000000000000000000000000000000000000..a221f7d77d0215d7b5ab17d07372733fff998ece --- /dev/null +++ b/upsampler/output/ILip77SbmOE_inversion.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:218288bc1f0e07ccf842fbbf8f0709ecfaea6c53b4cf9736328a474c8f2b75b0 +size 4209674 diff --git a/upsampler/pretrained_models/readme.md b/upsampler/pretrained_models/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..287c322f305668ef3a9b94461807e311e0851bae --- /dev/null +++ b/upsampler/pretrained_models/readme.md @@ -0,0 +1,36 @@ +## pretained model for testing + +styleganex_toonify_arcane.pt + +styleganex_toonify_cartoon.pt + +styleganex_toonify_pixar.pt + +styleganex_mask2face.pt + +styleganex_sketch2face.pt + +styleganex_sr32.pt + +styleganex_sr.pt + +styleganex_inversion.pt + + +## supporting model for training + +faceparsing.pth + +model_ir_se50.pth + +shape_predictor_68_face_landmarks.dat + +styleganex_pretrain.pt + +stylegan2-ffhq-config-f.pt + +psp_ffhq_encode.pt + +editing_w_age.pt/editing_w_hair.pt + +augment_w.pt diff --git a/upsampler/scripts/align_all_parallel.py b/upsampler/scripts/align_all_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..85d23ca8142b29e97421d92b8e9ddadec04d15de --- /dev/null +++ b/upsampler/scripts/align_all_parallel.py @@ -0,0 +1,215 @@ +""" +brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset) +author: lzhbrian (https://lzhbrian.me) +date: 2020.1.5 +note: code is heavily borrowed from + https://github.com/NVlabs/ffhq-dataset + http://dlib.net/face_landmark_detection.py.html + +requirements: + apt install cmake + conda install Pillow numpy scipy + pip install dlib + # download face landmark model from: + # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 +""" +from argparse import ArgumentParser +import time +import numpy as np +import PIL +import PIL.Image +import os +import scipy +import scipy.ndimage +import dlib +import multiprocessing as mp +import math + +from configs.paths_config import model_paths +SHAPE_PREDICTOR_PATH = model_paths["shape_predictor"] + + +def get_landmark(filepath, predictor): + """get landmark with dlib + :return: np.array shape=(68, 2) + """ + detector = dlib.get_frontal_face_detector() + if type(filepath) == str: + img = dlib.load_rgb_image(filepath) + else: + img = filepath + dets = detector(img, 1) + + if len(dets) == 0: + print('Error: no face detected! If you are sure there are faces in your input, you may rerun the code or change the image several times until the face is detected. Sometimes the detector is unstable.') + return None + + shape = None + for k, d in enumerate(dets): + shape = predictor(img, d) + + t = list(shape.parts()) + a = [] + for tt in t: + a.append([tt.x, tt.y]) + lm = np.array(a) + return lm + + +def align_face(filepath, predictor): + """ + :param filepath: str + :return: PIL Image + """ + + lm = get_landmark(filepath, predictor) + if lm is None: + return None + + lm_chin = lm[0: 17] # left-right + lm_eyebrow_left = lm[17: 22] # left-right + lm_eyebrow_right = lm[22: 27] # left-right + lm_nose = lm[27: 31] # top-down + lm_nostrils = lm[31: 36] # top-down + lm_eye_left = lm[36: 42] # left-clockwise + lm_eye_right = lm[42: 48] # left-clockwise + lm_mouth_outer = lm[48: 60] # left-clockwise + lm_mouth_inner = lm[60: 68] # left-clockwise + + # Calculate auxiliary vectors. + eye_left = np.mean(lm_eye_left, axis=0) + eye_right = np.mean(lm_eye_right, axis=0) + eye_avg = (eye_left + eye_right) * 0.5 + eye_to_eye = eye_right - eye_left + mouth_left = lm_mouth_outer[0] + mouth_right = lm_mouth_outer[6] + mouth_avg = (mouth_left + mouth_right) * 0.5 + eye_to_mouth = mouth_avg - eye_avg + + # Choose oriented crop rectangle. + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + x /= np.hypot(*x) + x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) + y = np.flipud(x) * [-1, 1] + c = eye_avg + eye_to_mouth * 0.1 + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + qsize = np.hypot(*x) * 2 + + # read image + if type(filepath) == str: + img = PIL.Image.open(filepath) + else: + img = PIL.Image.fromarray(filepath) + + output_size = 256 + transform_size = 256 + enable_padding = True + + # Shrink. + shrink = int(np.floor(qsize / output_size * 0.5)) + if shrink > 1: + rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) + img = img.resize(rsize, PIL.Image.ANTIALIAS) + quad /= shrink + qsize /= shrink + + # Crop. + border = max(int(np.rint(qsize * 0.1)), 3) + crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), + min(crop[3] + border, img.size[1])) + if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: + img = img.crop(crop) + quad -= crop[0:2] + + # Pad. + pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), + max(pad[3] - img.size[1] + border, 0)) + if enable_padding and max(pad) > border - 4: + pad = np.maximum(pad, int(np.rint(qsize * 0.3))) + img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + h, w, _ = img.shape + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]), + 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3])) + blur = qsize * 0.02 + img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) + img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') + quad += pad[:2] + + # Transform. + img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) + if output_size < transform_size: + img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) + + # Save aligned image. + return img + + +def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + for i in range(0, len(lst), n): + yield lst[i:i + n] + + +def extract_on_paths(file_paths): + predictor = dlib.shape_predictor(SHAPE_PREDICTOR_PATH) + pid = mp.current_process().name + print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths))) + tot_count = len(file_paths) + count = 0 + for file_path, res_path in file_paths: + count += 1 + if count % 100 == 0: + print('{} done with {}/{}'.format(pid, count, tot_count)) + try: + res = align_face(file_path, predictor) + res = res.convert('RGB') + os.makedirs(os.path.dirname(res_path), exist_ok=True) + res.save(res_path) + except Exception: + continue + print('\tDone!') + + +def parse_args(): + parser = ArgumentParser(add_help=False) + parser.add_argument('--num_threads', type=int, default=1) + parser.add_argument('--root_path', type=str, default='') + args = parser.parse_args() + return args + + +def run(args): + root_path = args.root_path + out_crops_path = root_path + '_crops' + if not os.path.exists(out_crops_path): + os.makedirs(out_crops_path, exist_ok=True) + + file_paths = [] + for root, dirs, files in os.walk(root_path): + for file in files: + file_path = os.path.join(root, file) + fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path)) + res_path = '{}.jpg'.format(os.path.splitext(fname)[0]) + if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path): + continue + file_paths.append((file_path, res_path)) + + file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads)))) + print(len(file_chunks)) + pool = mp.Pool(args.num_threads) + print('Running on {} paths\nHere we goooo'.format(len(file_paths))) + tic = time.time() + pool.map(extract_on_paths, file_chunks) + toc = time.time() + print('Mischief managed in {}s'.format(toc - tic)) + + +if __name__ == '__main__': + args = parse_args() + run(args) diff --git a/upsampler/scripts/calc_id_loss_parallel.py b/upsampler/scripts/calc_id_loss_parallel.py new file mode 100644 index 0000000000000000000000000000000000000000..efc82bf851b252e92c45be3c87be877616f44ead --- /dev/null +++ b/upsampler/scripts/calc_id_loss_parallel.py @@ -0,0 +1,119 @@ +from argparse import ArgumentParser +import time +import numpy as np +import os +import json +import sys +from PIL import Image +import multiprocessing as mp +import math +import torch +import torchvision.transforms as trans + +sys.path.append(".") +sys.path.append("..") + +from models.mtcnn.mtcnn import MTCNN +from models.encoders.model_irse import IR_101 +from configs.paths_config import model_paths +CIRCULAR_FACE_PATH = model_paths['circular_face'] + + +def chunks(lst, n): + """Yield successive n-sized chunks from lst.""" + for i in range(0, len(lst), n): + yield lst[i:i + n] + + +def extract_on_paths(file_paths): + facenet = IR_101(input_size=112) + facenet.load_state_dict(torch.load(CIRCULAR_FACE_PATH)) + facenet.cuda() + facenet.eval() + mtcnn = MTCNN() + id_transform = trans.Compose([ + trans.ToTensor(), + trans.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]) + ]) + + pid = mp.current_process().name + print('\t{} is starting to extract on {} images'.format(pid, len(file_paths))) + tot_count = len(file_paths) + count = 0 + + scores_dict = {} + for res_path, gt_path in file_paths: + count += 1 + if count % 100 == 0: + print('{} done with {}/{}'.format(pid, count, tot_count)) + if True: + input_im = Image.open(res_path) + input_im, _ = mtcnn.align(input_im) + if input_im is None: + print('{} skipping {}'.format(pid, res_path)) + continue + + input_id = facenet(id_transform(input_im).unsqueeze(0).cuda())[0] + + result_im = Image.open(gt_path) + result_im, _ = mtcnn.align(result_im) + if result_im is None: + print('{} skipping {}'.format(pid, gt_path)) + continue + + result_id = facenet(id_transform(result_im).unsqueeze(0).cuda())[0] + score = float(input_id.dot(result_id)) + scores_dict[os.path.basename(gt_path)] = score + + return scores_dict + + +def parse_args(): + parser = ArgumentParser(add_help=False) + parser.add_argument('--num_threads', type=int, default=4) + parser.add_argument('--data_path', type=str, default='results') + parser.add_argument('--gt_path', type=str, default='gt_images') + args = parser.parse_args() + return args + + +def run(args): + file_paths = [] + for f in os.listdir(args.data_path): + image_path = os.path.join(args.data_path, f) + gt_path = os.path.join(args.gt_path, f) + if f.endswith(".jpg") or f.endswith('.png'): + file_paths.append([image_path, gt_path.replace('.png','.jpg')]) + + file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads)))) + pool = mp.Pool(args.num_threads) + print('Running on {} paths\nHere we goooo'.format(len(file_paths))) + + tic = time.time() + results = pool.map(extract_on_paths, file_chunks) + scores_dict = {} + for d in results: + scores_dict.update(d) + + all_scores = list(scores_dict.values()) + mean = np.mean(all_scores) + std = np.std(all_scores) + result_str = 'New Average score is {:.2f}+-{:.2f}'.format(mean, std) + print(result_str) + + out_path = os.path.join(os.path.dirname(args.data_path), 'inference_metrics') + if not os.path.exists(out_path): + os.makedirs(out_path) + + with open(os.path.join(out_path, 'stat_id.txt'), 'w') as f: + f.write(result_str) + with open(os.path.join(out_path, 'scores_id.json'), 'w') as f: + json.dump(scores_dict, f) + + toc = time.time() + print('Mischief managed in {}s'.format(toc - tic)) + + +if __name__ == '__main__': + args = parse_args() + run(args) diff --git a/upsampler/scripts/calc_losses_on_images.py b/upsampler/scripts/calc_losses_on_images.py new file mode 100644 index 0000000000000000000000000000000000000000..436348db28a625d94f63bbb86ff779b92d28b419 --- /dev/null +++ b/upsampler/scripts/calc_losses_on_images.py @@ -0,0 +1,84 @@ +from argparse import ArgumentParser +import os +import json +import sys +from tqdm import tqdm +import numpy as np +import torch +from torch.utils.data import DataLoader +import torchvision.transforms as transforms + +sys.path.append(".") +sys.path.append("..") + +from criteria.lpips.lpips import LPIPS +from datasets.gt_res_dataset import GTResDataset + + +def parse_args(): + parser = ArgumentParser(add_help=False) + parser.add_argument('--mode', type=str, default='lpips', choices=['lpips', 'l2']) + parser.add_argument('--data_path', type=str, default='results') + parser.add_argument('--gt_path', type=str, default='gt_images') + parser.add_argument('--workers', type=int, default=4) + parser.add_argument('--batch_size', type=int, default=4) + args = parser.parse_args() + return args + + +def run(args): + + transform = transforms.Compose([transforms.Resize((256, 256)), + transforms.ToTensor(), + transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])]) + + print('Loading dataset') + dataset = GTResDataset(root_path=args.data_path, + gt_dir=args.gt_path, + transform=transform) + + dataloader = DataLoader(dataset, + batch_size=args.batch_size, + shuffle=False, + num_workers=int(args.workers), + drop_last=True) + + if args.mode == 'lpips': + loss_func = LPIPS(net_type='alex') + elif args.mode == 'l2': + loss_func = torch.nn.MSELoss() + else: + raise Exception('Not a valid mode!') + loss_func.cuda() + + global_i = 0 + scores_dict = {} + all_scores = [] + for result_batch, gt_batch in tqdm(dataloader): + for i in range(args.batch_size): + loss = float(loss_func(result_batch[i:i+1].cuda(), gt_batch[i:i+1].cuda())) + all_scores.append(loss) + im_path = dataset.pairs[global_i][0] + scores_dict[os.path.basename(im_path)] = loss + global_i += 1 + + all_scores = list(scores_dict.values()) + mean = np.mean(all_scores) + std = np.std(all_scores) + result_str = 'Average loss is {:.2f}+-{:.2f}'.format(mean, std) + print('Finished with ', args.data_path) + print(result_str) + + out_path = os.path.join(os.path.dirname(args.data_path), 'inference_metrics') + if not os.path.exists(out_path): + os.makedirs(out_path) + + with open(os.path.join(out_path, 'stat_{}.txt'.format(args.mode)), 'w') as f: + f.write(result_str) + with open(os.path.join(out_path, 'scores_{}.json'.format(args.mode)), 'w') as f: + json.dump(scores_dict, f) + + +if __name__ == '__main__': + args = parse_args() + run(args) diff --git a/upsampler/scripts/download_ffhq1280.py b/upsampler/scripts/download_ffhq1280.py new file mode 100644 index 0000000000000000000000000000000000000000..7418ef20b56e971e3c71540610a65bbc246a210d --- /dev/null +++ b/upsampler/scripts/download_ffhq1280.py @@ -0,0 +1,468 @@ +# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. +# +# This work is licensed under the Creative Commons +# Attribution-NonCommercial-ShareAlike 4.0 International License. +# To view a copy of this license, visit +# http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to +# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. + +"""Download Flickr-Faces-HQ (FFHQ) dataset to current working directory.""" + +import os +import sys +import requests +import html +import hashlib +import PIL.Image +import PIL.ImageFile +import numpy as np +import scipy.ndimage +import threading +import queue +import time +import json +import uuid +import glob +import argparse +import itertools +import shutil +from collections import OrderedDict, defaultdict + +PIL.ImageFile.LOAD_TRUNCATED_IMAGES = True # avoid "Decompressed Data Too Large" error + +#---------------------------------------------------------------------------- + +json_spec = dict(file_url='https://drive.google.com/uc?id=16N0RV4fHI6joBuKbQAoG34V_cQk7vxSA', file_path='ffhq-dataset-v2.json', file_size=267793842, file_md5='425ae20f06a4da1d4dc0f46d40ba5fd6') + +tfrecords_specs = [ + dict(file_url='https://drive.google.com/uc?id=1LnhoytWihRRJ7CfhLQ76F8YxwxRDlZN3', file_path='tfrecords/ffhq/ffhq-r02.tfrecords', file_size=6860000, file_md5='63e062160f1ef9079d4f51206a95ba39'), + dict(file_url='https://drive.google.com/uc?id=1LWeKZGZ_x2rNlTenqsaTk8s7Cpadzjbh', file_path='tfrecords/ffhq/ffhq-r03.tfrecords', file_size=17290000, file_md5='54fb32a11ebaf1b86807cc0446dd4ec5'), + dict(file_url='https://drive.google.com/uc?id=1Lr7Tiufr1Za85HQ18yg3XnJXstiI2BAC', file_path='tfrecords/ffhq/ffhq-r04.tfrecords', file_size=57610000, file_md5='7164cc5531f6828bf9c578bdc3320e49'), + dict(file_url='https://drive.google.com/uc?id=1LnyiayZ-XJFtatxGFgYePcs9bdxuIJO_', file_path='tfrecords/ffhq/ffhq-r05.tfrecords', file_size=218890000, file_md5='050cc7e5fd07a1508eaa2558dafbd9ed'), + dict(file_url='https://drive.google.com/uc?id=1Lt6UP201zHnpH8zLNcKyCIkbC-aMb5V_', file_path='tfrecords/ffhq/ffhq-r06.tfrecords', file_size=864010000, file_md5='90bedc9cc07007cd66615b2b1255aab8'), + dict(file_url='https://drive.google.com/uc?id=1LwOP25fJ4xN56YpNCKJZM-3mSMauTxeb', file_path='tfrecords/ffhq/ffhq-r07.tfrecords', file_size=3444980000, file_md5='bff839e0dda771732495541b1aff7047'), + dict(file_url='https://drive.google.com/uc?id=1LxxgVBHWgyN8jzf8bQssgVOrTLE8Gv2v', file_path='tfrecords/ffhq/ffhq-r08.tfrecords', file_size=13766900000, file_md5='74de4f07dc7bfb07c0ad4471fdac5e67'), + dict(file_url='https://drive.google.com/uc?id=1M-ulhD5h-J7sqSy5Y1njUY_80LPcrv3V', file_path='tfrecords/ffhq/ffhq-r09.tfrecords', file_size=55054580000, file_md5='05355aa457a4bd72709f74a81841b46d'), + dict(file_url='https://drive.google.com/uc?id=1M11BIdIpFCiapUqV658biPlaXsTRvYfM', file_path='tfrecords/ffhq/ffhq-r10.tfrecords', file_size=220205650000, file_md5='bf43cab9609ab2a27892fb6c2415c11b'), +] + +license_specs = { + 'json': dict(file_url='https://drive.google.com/uc?id=1SHafCugkpMZzYhbgOz0zCuYiy-hb9lYX', file_path='LICENSE.txt', file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'), + 'images': dict(file_url='https://drive.google.com/uc?id=1sP2qz8TzLkzG2gjwAa4chtdB31THska4', file_path='images1024x1024/LICENSE.txt', file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'), + 'thumbs': dict(file_url='https://drive.google.com/uc?id=1iaL1S381LS10VVtqu-b2WfF9TiY75Kmj', file_path='thumbnails128x128/LICENSE.txt', file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'), + 'wilds': dict(file_url='https://drive.google.com/uc?id=1rsfFOEQvkd6_Z547qhpq5LhDl2McJEzw', file_path='in-the-wild-images/LICENSE.txt', file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'), + 'tfrecords': dict(file_url='https://drive.google.com/uc?id=1SYUmqKdLoTYq-kqsnPsniLScMhspvl5v', file_path='tfrecords/ffhq/LICENSE.txt', file_size=1610, file_md5='724f3831aaecd61a84fe98500079abc2'), +} + +#---------------------------------------------------------------------------- + +def download_file(session, file_spec, stats, chunk_size=128, num_attempts=10, **kwargs): + file_path = file_spec['file_path'] + file_url = file_spec['file_url'] + file_dir = os.path.dirname(file_path) + tmp_path = file_path + '.tmp.' + uuid.uuid4().hex + if file_dir: + os.makedirs(file_dir, exist_ok=True) + + for attempts_left in reversed(range(num_attempts)): + data_size = 0 + try: + # Download. + data_md5 = hashlib.md5() + with session.get(file_url, stream=True) as res: + res.raise_for_status() + with open(tmp_path, 'wb') as f: + for chunk in res.iter_content(chunk_size=chunk_size<<10): + f.write(chunk) + data_size += len(chunk) + data_md5.update(chunk) + with stats['lock']: + stats['bytes_done'] += len(chunk) + + # Validate. + if 'file_size' in file_spec and data_size != file_spec['file_size']: + raise IOError('Incorrect file size', file_path) + if 'file_md5' in file_spec and data_md5.hexdigest() != file_spec['file_md5']: + raise IOError('Incorrect file MD5', file_path) + if 'pixel_size' in file_spec or 'pixel_md5' in file_spec: + with PIL.Image.open(tmp_path) as image: + if 'pixel_size' in file_spec and list(image.size) != file_spec['pixel_size']: + raise IOError('Incorrect pixel size', file_path) + if 'pixel_md5' in file_spec and hashlib.md5(np.array(image)).hexdigest() != file_spec['pixel_md5']: + raise IOError('Incorrect pixel MD5', file_path) + break + + except: + with stats['lock']: + stats['bytes_done'] -= data_size + + # Handle known failure cases. + if data_size > 0 and data_size < 8192: + with open(tmp_path, 'rb') as f: + data = f.read() + data_str = data.decode('utf-8') + + # Google Drive virus checker nag. + links = [html.unescape(link) for link in data_str.split('"') if 'export=download' in link] + if len(links) == 1: + if attempts_left: + file_url = requests.compat.urljoin(file_url, links[0]) + continue + + # Google Drive quota exceeded. + if 'Google Drive - Quota exceeded' in data_str: + if not attempts_left: + raise IOError("Google Drive download quota exceeded -- please try again later") + + # Last attempt => raise error. + if not attempts_left: + raise + + # Rename temp file to the correct name. + os.replace(tmp_path, file_path) # atomic + with stats['lock']: + stats['files_done'] += 1 + + # Attempt to clean up any leftover temps. + for filename in glob.glob(file_path + '.tmp.*'): + try: + os.remove(filename) + except: + pass + +#---------------------------------------------------------------------------- + +def choose_bytes_unit(num_bytes): + b = int(np.rint(num_bytes)) + if b < (100 << 0): return 'B', (1 << 0) + if b < (100 << 10): return 'kB', (1 << 10) + if b < (100 << 20): return 'MB', (1 << 20) + if b < (100 << 30): return 'GB', (1 << 30) + return 'TB', (1 << 40) + +#---------------------------------------------------------------------------- + +def format_time(seconds): + s = int(np.rint(seconds)) + if s < 60: return '%ds' % s + if s < 60 * 60: return '%dm %02ds' % (s // 60, s % 60) + if s < 24 * 60 * 60: return '%dh %02dm' % (s // (60 * 60), (s // 60) % 60) + if s < 100 * 24 * 60 * 60: return '%dd %02dh' % (s // (24 * 60 * 60), (s // (60 * 60)) % 24) + return '>100d' + +#---------------------------------------------------------------------------- + +def download_files(file_specs, num_threads=32, status_delay=0.2, timing_window=50, **download_kwargs): + + # Determine which files to download. + done_specs = {spec['file_path']: spec for spec in file_specs if os.path.isfile(spec['file_path'])} + missing_specs = [spec for spec in file_specs if spec['file_path'] not in done_specs] + files_total = len(file_specs) + bytes_total = sum(spec['file_size'] for spec in file_specs) + stats = dict(files_done=len(done_specs), bytes_done=sum(spec['file_size'] for spec in done_specs.values()), lock=threading.Lock()) + if len(done_specs) == files_total: + print('All files already downloaded -- skipping.') + return + + # Launch worker threads. + spec_queue = queue.Queue() + exception_queue = queue.Queue() + for spec in missing_specs: + spec_queue.put(spec) + thread_kwargs = dict(spec_queue=spec_queue, exception_queue=exception_queue, stats=stats, download_kwargs=download_kwargs) + for _thread_idx in range(min(num_threads, len(missing_specs))): + threading.Thread(target=_download_thread, kwargs=thread_kwargs, daemon=True).start() + + # Monitor status until done. + bytes_unit, bytes_div = choose_bytes_unit(bytes_total) + spinner = '/-\\|' + timing = [] + while True: + with stats['lock']: + files_done = stats['files_done'] + bytes_done = stats['bytes_done'] + spinner = spinner[1:] + spinner[:1] + timing = timing[max(len(timing) - timing_window + 1, 0):] + [(time.time(), bytes_done)] + bandwidth = max((timing[-1][1] - timing[0][1]) / max(timing[-1][0] - timing[0][0], 1e-8), 0) + bandwidth_unit, bandwidth_div = choose_bytes_unit(bandwidth) + eta = format_time((bytes_total - bytes_done) / max(bandwidth, 1)) + + print('\r%s %6.2f%% done %d/%d files %-13s %-10s ETA: %-7s ' % ( + spinner[0], + bytes_done / bytes_total * 100, + files_done, files_total, + '%.2f/%.2f %s' % (bytes_done / bytes_div, bytes_total / bytes_div, bytes_unit), + '%.2f %s/s' % (bandwidth / bandwidth_div, bandwidth_unit), + 'done' if bytes_total == bytes_done else '...' if len(timing) < timing_window or bandwidth == 0 else eta, + ), end='', flush=True) + + if files_done == files_total: + print() + break + + try: + exc_info = exception_queue.get(timeout=status_delay) + raise exc_info[1].with_traceback(exc_info[2]) + except queue.Empty: + pass + +def _download_thread(spec_queue, exception_queue, stats, download_kwargs): + with requests.Session() as session: + while not spec_queue.empty(): + spec = spec_queue.get() + try: + download_file(session, spec, stats, **download_kwargs) + except: + exception_queue.put(sys.exc_info()) + +#---------------------------------------------------------------------------- + +def print_statistics(json_data): + categories = defaultdict(int) + licenses = defaultdict(int) + countries = defaultdict(int) + for item in json_data.values(): + categories[item['category']] += 1 + licenses[item['metadata']['license']] += 1 + country = item['metadata']['country'] + countries[country if country else ''] += 1 + + for name in [name for name, num in countries.items() if num / len(json_data) < 1e-3]: + countries[''] += countries.pop(name) + + rows = [[]] * 2 + rows += [['Category', 'Images', '% of all']] + rows += [['---'] * 3] + for name, num in sorted(categories.items(), key=lambda x: -x[1]): + rows += [[name, '%d' % num, '%.2f' % (100.0 * num / len(json_data))]] + + rows += [[]] * 2 + rows += [['License', 'Images', '% of all']] + rows += [['---'] * 3] + for name, num in sorted(licenses.items(), key=lambda x: -x[1]): + rows += [[name, '%d' % num, '%.2f' % (100.0 * num / len(json_data))]] + + rows += [[]] * 2 + rows += [['Country', 'Images', '% of all', '% of known']] + rows += [['---'] * 4] + for name, num in sorted(countries.items(), key=lambda x: -x[1] if x[0] != '' else 0): + rows += [[name, '%d' % num, '%.2f' % (100.0 * num / len(json_data)), + '%.2f' % (0 if name == '' else 100.0 * num / (len(json_data) - countries['']))]] + + rows += [[]] * 2 + widths = [max(len(cell) for cell in column if cell is not None) for column in itertools.zip_longest(*rows)] + for row in rows: + print(" ".join(cell + " " * (width - len(cell)) for cell, width in zip(row, widths))) + +#---------------------------------------------------------------------------- + +def find_coeffs(pa, pb): + matrix = [] + for p1, p2 in zip(pa, pb): + matrix.append([p1[0], p1[1], 1, 0, 0, 0, -p2[0]*p1[0], -p2[0]*p1[1]]) + matrix.append([0, 0, 0, p1[0], p1[1], 1, -p2[1]*p1[0], -p2[1]*p1[1]]) + + A = np.matrix(matrix, dtype=np.float64) + B = np.array(pb).reshape(8) + + res = np.dot(np.linalg.inv(A.T * A) * A.T, B) + return np.array(res).reshape(8) + + +def recreate_aligned_images(json_data, source_dir, dst_dir='realign1280x1280', output_size=1280, transform_size=5120, enable_padding=True, rotate_level=True, random_shift=0.0, retry_crops=False): + print('Recreating aligned images...') + + # Fix random seed for reproducibility + np.random.seed(12345) + # The following random numbers are unused in present implementation, but we consume them for reproducibility + _ = np.random.normal(0, 1, (len(json_data.values()), 2)) + + if dst_dir: + os.makedirs(dst_dir, exist_ok=True) + shutil.copyfile('LICENSE.txt', os.path.join(dst_dir, 'LICENSE.txt')) + + for item_idx, item in enumerate(json_data.values()): + print('\r%d / %d ... ' % (item_idx, len(json_data)), end='', flush=True) + + # Parse landmarks. + # pylint: disable=unused-variable + lm = np.array(item['in_the_wild']['face_landmarks']) + lm_chin = lm[0 : 17] # left-right + lm_eyebrow_left = lm[17 : 22] # left-right + lm_eyebrow_right = lm[22 : 27] # left-right + lm_nose = lm[27 : 31] # top-down + lm_nostrils = lm[31 : 36] # top-down + lm_eye_left = lm[36 : 42] # left-clockwise + lm_eye_right = lm[42 : 48] # left-clockwise + lm_mouth_outer = lm[48 : 60] # left-clockwise + lm_mouth_inner = lm[60 : 68] # left-clockwise + + # Calculate auxiliary vectors. + eye_left = np.mean(lm_eye_left, axis=0) + eye_right = np.mean(lm_eye_right, axis=0) + eye_avg = (eye_left + eye_right) * 0.5 + eye_to_eye = eye_right - eye_left + mouth_left = lm_mouth_outer[0] + mouth_right = lm_mouth_outer[6] + mouth_avg = (mouth_left + mouth_right) * 0.5 + eye_to_mouth = mouth_avg - eye_avg + + # Choose oriented crop rectangle. + if rotate_level: + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + x /= np.hypot(*x) + x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) + y = np.flipud(x) * [-1, 1] + c0 = eye_avg + eye_to_mouth * 0.1 + else: + x = np.array([1, 0], dtype=np.float64) + x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) + y = np.flipud(x) * [-1, 1] + c0 = eye_avg + eye_to_mouth * 0.1 + + # Load in-the-wild image. + src_file = os.path.join(source_dir, item['in_the_wild']['file_path']) + if not os.path.isfile(src_file): + print('\nCannot find source image. Please run "--wilds" before "--align".') + return + img = PIL.Image.open(src_file) + + quad = np.stack([c0 - x - y, c0 - x + y, c0 + x + y, c0 + x - y]) + qsize = np.hypot(*x) * 2 + + original = np.array([[ 0, 0], [ 0, 1024], [1024, 1024], [1024, 0]]) + tmp = find_coeffs(original, quad) + new = np.array([[ -128, -128], [ -128, 1152], [1152, 1152], [1152, -128]]) + quad = np.concatenate(((tmp[0] * new[:,0:1] + tmp[1] * new[:,1:2] + tmp[2])/(tmp[6] * new[:,0:1] + tmp[7] * new[:,1:2] + 1), + (tmp[3] * new[:,0:1] + tmp[4] * new[:,1:2] + tmp[5])/(tmp[6] * new[:,0:1] + tmp[7] * new[:,1:2] + 1)), axis=1) + qsize = qsize * 1.25 + + # Keep drawing new random crop offsets until we find one that is contained in the image + # and does not require padding + if random_shift != 0: + for _ in range(1000): + # Offset the crop rectange center by a random shift proportional to image dimension + # and the requested standard deviation + c = (c0 + np.hypot(*x)*2 * random_shift * np.random.normal(0, 1, c0.shape)) + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1])))) + if not retry_crops or not (crop[0] < 0 or crop[1] < 0 or crop[2] >= img.width or crop[3] >= img.height): + # We're happy with this crop (either it fits within the image, or retries are disabled) + break + else: + # rejected N times, give up and move to next image + # (does not happen in practice with the FFHQ data) + print('rejected image') + return + + # Shrink. + shrink = int(np.floor(qsize / output_size * 0.5)) + if shrink > 1: + rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink))) + img = img.resize(rsize, PIL.Image.ANTIALIAS) + quad /= shrink + qsize /= shrink + + # Crop. + border = max(int(np.rint(qsize * 0.1)), 3) + crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1])))) + crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1])) + if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: + img = img.crop(crop) + quad -= crop[0:2] + + # Pad. + pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1])))) + pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0)) + if enable_padding and max(pad) > border - 4: + pad = np.maximum(pad, int(np.rint(qsize * 0.3))) + img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + h, w, _ = img.shape + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3])) + blur = qsize * 0.02 + img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0) + img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') + quad += pad[:2] + + # Transform. + img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR) + if output_size < transform_size: + img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) + + # Save aligned image. + #dst_subdir = os.path.join(dst_dir, '%05d' % (item_idx - item_idx % 1000)) + dst_subdir = dst_dir + #os.makedirs(dst_subdir, exist_ok=True) + img.save(os.path.join(dst_subdir, '%05d.jpg' % item_idx)) + + # All done. + print('\r%d / %d ... done' % (len(json_data), len(json_data))) + +#---------------------------------------------------------------------------- + +def run(tasks, **download_kwargs): + if not os.path.isfile(json_spec['file_path']) or not os.path.isfile('LICENSE.txt'): + print('Downloading JSON metadata...') + download_files([json_spec, license_specs['json']], **download_kwargs) + + print('Parsing JSON metadata...') + with open(json_spec['file_path'], 'rb') as f: + json_data = json.load(f, object_pairs_hook=OrderedDict) + + if 'stats' in tasks: + print_statistics(json_data) + + specs = [] + if 'images' in tasks: + specs += [item['image'] for item in json_data.values()] + [license_specs['images']] + if 'thumbs' in tasks: + specs += [item['thumbnail'] for item in json_data.values()] + [license_specs['thumbs']] + if 'wilds' in tasks: + specs += [item['in_the_wild'] for item in json_data.values()] + [license_specs['wilds']] + if 'tfrecords' in tasks: + specs += tfrecords_specs + [license_specs['tfrecords']] + + if len(specs): + print('Downloading %d files...' % len(specs)) + np.random.shuffle(specs) # to make the workload more homogeneous + download_files(specs, **download_kwargs) + + if 'align' in tasks: + recreate_aligned_images(json_data, source_dir=download_kwargs['source_dir'], rotate_level=not download_kwargs['no_rotation'], random_shift=download_kwargs['random_shift'], enable_padding=not download_kwargs['no_padding'], retry_crops=download_kwargs['retry_crops']) + +#---------------------------------------------------------------------------- + +def run_cmdline(argv): + parser = argparse.ArgumentParser(prog=argv[0], description='Download Flickr-Face-HQ (FFHQ) dataset to current working directory.') + parser.add_argument('-j', '--json', help='download metadata as JSON (254 MB)', dest='tasks', action='append_const', const='json') + parser.add_argument('-s', '--stats', help='print statistics about the dataset', dest='tasks', action='append_const', const='stats') + parser.add_argument('-i', '--images', help='download 1024x1024 images as PNG (89.1 GB)', dest='tasks', action='append_const', const='images') + parser.add_argument('-t', '--thumbs', help='download 128x128 thumbnails as PNG (1.95 GB)', dest='tasks', action='append_const', const='thumbs') + parser.add_argument('-w', '--wilds', help='download in-the-wild images as PNG (955 GB)', dest='tasks', action='append_const', const='wilds') + parser.add_argument('-r', '--tfrecords', help='download multi-resolution TFRecords (273 GB)', dest='tasks', action='append_const', const='tfrecords') + parser.add_argument('-a', '--align', help='recreate 1024x1024 images from in-the-wild images', dest='tasks', action='append_const', const='align') + parser.add_argument('--num_threads', help='number of concurrent download threads (default: 32)', type=int, default=32, metavar='NUM') + parser.add_argument('--status_delay', help='time between download status prints (default: 0.2)', type=float, default=0.2, metavar='SEC') + parser.add_argument('--timing_window', help='samples for estimating download eta (default: 50)', type=int, default=50, metavar='LEN') + parser.add_argument('--chunk_size', help='chunk size for each download thread (default: 128)', type=int, default=128, metavar='KB') + parser.add_argument('--num_attempts', help='number of download attempts per file (default: 10)', type=int, default=10, metavar='NUM') + parser.add_argument('--random-shift', help='standard deviation of random crop rectangle jitter', type=float, default=0.0, metavar='SHIFT') + parser.add_argument('--retry-crops', help='retry random shift if crop rectangle falls outside image (up to 1000 times)', dest='retry_crops', default=False, action='store_true') + parser.add_argument('--no-rotation', help='keep the original orientation of images', dest='no_rotation', default=False, action='store_true') + parser.add_argument('--no-padding', help='do not apply blur-padding outside and near the image borders', dest='no_padding', default=False, action='store_true') + parser.add_argument('--source-dir', help='where to find already downloaded FFHQ source data', default='', metavar='DIR') + + args = parser.parse_args() + if not args.tasks: + print('No tasks specified. Please see "-h" for help.') + exit(1) + run(**vars(args)) + +#---------------------------------------------------------------------------- + +if __name__ == "__main__": + run_cmdline(sys.argv) + +#---------------------------------------------------------------------------- diff --git a/upsampler/scripts/generate_sketch_data.py b/upsampler/scripts/generate_sketch_data.py new file mode 100644 index 0000000000000000000000000000000000000000..a13acf949bf2efb3449f13922b7489e5c06880a3 --- /dev/null +++ b/upsampler/scripts/generate_sketch_data.py @@ -0,0 +1,62 @@ +from torchvision import transforms +from torchvision.utils import save_image +from torch.utils.serialization import load_lua +import os +import cv2 +import numpy as np + +""" +NOTE!: Must have torch==0.4.1 and torchvision==0.2.1 +The sketch simplification model (sketch_gan.t7) from Simo Serra et al. can be downloaded from their official implementation: + https://github.com/bobbens/sketch_simplification +""" + + +def sobel(img): + opImgx = cv2.Sobel(img, cv2.CV_8U, 0, 1, ksize=3) + opImgy = cv2.Sobel(img, cv2.CV_8U, 1, 0, ksize=3) + return cv2.bitwise_or(opImgx, opImgy) + + +def sketch(frame): + frame = cv2.GaussianBlur(frame, (3, 3), 0) + invImg = 255 - frame + edgImg0 = sobel(frame) + edgImg1 = sobel(invImg) + edgImg = cv2.addWeighted(edgImg0, 0.75, edgImg1, 0.75, 0) + opImg = 255 - edgImg + return opImg + + +def get_sketch_image(image_path): + original = cv2.imread(image_path) + original = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY) + sketch_image = sketch(original) + return sketch_image[:, :, np.newaxis] + + +use_cuda = True + +cache = load_lua("/path/to/sketch_gan.t7") +model = cache.model +immean = cache.mean +imstd = cache.std +model.evaluate() + +data_path = "/path/to/data/imgs" +images = [os.path.join(data_path, f) for f in os.listdir(data_path)] + +output_dir = "/path/to/data/edges" +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +for idx, image_path in enumerate(images): + if idx % 50 == 0: + print("{} out of {}".format(idx, len(images))) + data = get_sketch_image(image_path) + data = ((transforms.ToTensor()(data) - immean) / imstd).unsqueeze(0) + if use_cuda: + pred = model.cuda().forward(data.cuda()).float() + else: + pred = model.forward(data) + save_image(pred[0], os.path.join(output_dir, "{}_edges.jpg".format(image_path.split("/")[-1].split('.')[0]))) diff --git a/upsampler/scripts/inference.py b/upsampler/scripts/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..9250d4b5b05d8a31527603d42823fd8b10234ce9 --- /dev/null +++ b/upsampler/scripts/inference.py @@ -0,0 +1,136 @@ +import os +from argparse import Namespace + +from tqdm import tqdm +import time +import numpy as np +import torch +from PIL import Image +from torch.utils.data import DataLoader +import sys + +sys.path.append(".") +sys.path.append("..") + +from configs import data_configs +from datasets.inference_dataset import InferenceDataset +from utils.common import tensor2im, log_input_image +from options.test_options import TestOptions +from models.psp import pSp + + +def run(): + test_opts = TestOptions().parse() + + if test_opts.resize_factors is not None: + assert len( + test_opts.resize_factors.split(',')) == 1, "When running inference, provide a single downsampling factor!" + out_path_results = os.path.join(test_opts.exp_dir, 'inference_results', + 'downsampling_{}'.format(test_opts.resize_factors)) + out_path_coupled = os.path.join(test_opts.exp_dir, 'inference_coupled', + 'downsampling_{}'.format(test_opts.resize_factors)) + else: + out_path_results = os.path.join(test_opts.exp_dir, 'inference_results') + out_path_coupled = os.path.join(test_opts.exp_dir, 'inference_coupled') + + os.makedirs(out_path_results, exist_ok=True) + os.makedirs(out_path_coupled, exist_ok=True) + + # update test options with options used during training + ckpt = torch.load(test_opts.checkpoint_path, map_location='cpu') + opts = ckpt['opts'] + opts.update(vars(test_opts)) + if 'learn_in_w' not in opts: + opts['learn_in_w'] = False + if 'output_size' not in opts: + opts['output_size'] = 1024 + opts = Namespace(**opts) + + net = pSp(opts) + net.eval() + net.cuda() + + print('Loading dataset for {}'.format(opts.dataset_type)) + dataset_args = data_configs.DATASETS[opts.dataset_type] + transforms_dict = dataset_args['transforms'](opts).get_transforms() + dataset = InferenceDataset(root=opts.data_path, + transform=transforms_dict['transform_inference'], + opts=opts) + dataloader = DataLoader(dataset, + batch_size=opts.test_batch_size, + shuffle=False, + num_workers=int(opts.test_workers), + drop_last=True) + + if opts.n_images is None: + opts.n_images = len(dataset) + + global_i = 0 + global_time = [] + for input_batch in tqdm(dataloader): + if global_i >= opts.n_images: + break + with torch.no_grad(): + input_cuda = input_batch.cuda().float() + tic = time.time() + result_batch = run_on_batch(input_cuda, net, opts) + toc = time.time() + global_time.append(toc - tic) + + for i in range(opts.test_batch_size): + result = tensor2im(result_batch[i]) + im_path = dataset.paths[global_i] + + if opts.couple_outputs or global_i % 100 == 0: + input_im = log_input_image(input_batch[i], opts) + resize_amount = (256, 256) if opts.resize_outputs else (opts.output_size, opts.output_size) + if opts.resize_factors is not None: + # for super resolution, save the original, down-sampled, and output + source = Image.open(im_path) + res = np.concatenate([np.array(source.resize(resize_amount)), + np.array(input_im.resize(resize_amount, resample=Image.NEAREST)), + np.array(result.resize(resize_amount))], axis=1) + else: + # otherwise, save the original and output + res = np.concatenate([np.array(input_im.resize(resize_amount)), + np.array(result.resize(resize_amount))], axis=1) + Image.fromarray(res).save(os.path.join(out_path_coupled, os.path.basename(im_path))) + + im_save_path = os.path.join(out_path_results, os.path.basename(im_path)) + Image.fromarray(np.array(result)).save(im_save_path) + + global_i += 1 + + stats_path = os.path.join(opts.exp_dir, 'stats.txt') + result_str = 'Runtime {:.4f}+-{:.4f}'.format(np.mean(global_time), np.std(global_time)) + print(result_str) + + with open(stats_path, 'w') as f: + f.write(result_str) + + +def run_on_batch(inputs, net, opts): + if opts.latent_mask is None: + result_batch = net(inputs, randomize_noise=False, resize=opts.resize_outputs) + else: + latent_mask = [int(l) for l in opts.latent_mask.split(",")] + result_batch = [] + for image_idx, input_image in enumerate(inputs): + # get latent vector to inject into our input image + vec_to_inject = np.random.randn(1, 512).astype('float32') + _, latent_to_inject = net(torch.from_numpy(vec_to_inject).to("cuda"), + input_code=True, + return_latents=True) + # get output image with injected style vector + res = net(input_image.unsqueeze(0).to("cuda").float(), + latent_mask=latent_mask, + inject_latent=latent_to_inject, + alpha=opts.mix_alpha, + resize=opts.resize_outputs) + result_batch.append(res) + result_batch = torch.cat(result_batch, dim=0) + return result_batch + + +if __name__ == '__main__': + run() diff --git a/upsampler/scripts/pretrain.py b/upsampler/scripts/pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..043ac981f72e2cf36fb15efc5e816d9e171b721a --- /dev/null +++ b/upsampler/scripts/pretrain.py @@ -0,0 +1,140 @@ +import os +import sys +import torch +import dlib +import cv2 +import PIL +import argparse +from tqdm import tqdm +import numpy as np +import torch.nn.functional as F +import torchvision +from torchvision import transforms, utils +from argparse import Namespace +from torch import autograd, optim +from utils.inference_utils import save_image +from models.psp import pSp +from models.stylegan2.model import Downsample +import models.stylegan2.lpips as lpips + +def requires_grad(model, flag=True): + for p in model.parameters(): + p.requires_grad = flag + + +class TrainOptions(): + def __init__(self): + + self.parser = argparse.ArgumentParser(description="StyleGANEX Pretraining") + self.parser.add_argument('--exp_dir', type=str, default='./logs/styleganex_pretrain/', help='Path to experiment output directory') + self.parser.add_argument("--ckpt", type=str, default='./pretrained_models/psp_ffhq_encode.pt', help="path of the original psp model") + self.parser.add_argument('--batch_size', default=8, type=int, help='Batch size for training') + self.parser.add_argument('--learning_rate', default=0.0001, type=float, help='Optimizer learning rate') + self.parser.add_argument('--max_steps', default=5000, type=int, help='Maximum number of training steps') + self.parser.add_argument('--image_interval', default=100, type=int, help='Interval for logging train images during training') + + def parse(self): + self.opt = self.parser.parse_args() + args = vars(self.opt) + print('Load options') + for name, value in sorted(args.items()): + print('%s: %s' % (str(name), str(value))) + return self.opt + +if __name__ == "__main__": + parser = TrainOptions() + args = parser.parse() + print('*'*98) + + device = "cuda" + + os.makedirs(args.exp_dir, exist_ok=True) + checkpoint_path = os.path.join(args.exp_dir, 'checkpoints') + log_path = os.path.join(args.exp_dir, 'logs') + os.makedirs(checkpoint_path, exist_ok=True) + os.makedirs(log_path, exist_ok=True) + + ckpt = torch.load(args.ckpt, map_location='cpu') + opts = ckpt['opts'] + opts['checkpoint_path'] = args.ckpt + if 'output_size' not in opts: + opts['output_size'] = 1024 + if 'toonify_weights' not in opts: + opts['toonify_weights'] = None + opts = Namespace(**opts) + pspex = pSp(opts).to(device).eval() + pspex.latent_avg = pspex.latent_avg.to(device) + requires_grad(pspex, False) + requires_grad(pspex.encoder.featlayer, True) + requires_grad(pspex.encoder.skiplayer, True) + + down = Downsample([1, 3, 3, 1], 2).to(device) + requires_grad(down, False) + + percept = lpips.PerceptualLoss(model="net-lin", net="vgg", use_gpu=device.startswith("cuda")) + requires_grad(percept.model.net, False) + + e_optim = optim.Adam( + list(pspex.encoder.featlayer.parameters()) + list(pspex.encoder.skiplayer.parameters()), + lr=args.learning_rate, + betas=(0.9, 0.99), + ) + + pbar = tqdm(range(args.max_steps), initial=0, dynamic_ncols=True, smoothing=0.01) + recon_loss = torch.tensor(0.0, device=device) + loss_dict = {} + + accum = 0.5 ** (32 / (10 * 1000)) + + for idx in pbar: + with torch.no_grad(): + noise_sample = torch.randn(args.batch_size, 512).cuda() + img_gen, _ = pspex.decoder([noise_sample], input_is_latent=False, truncation=0.7, truncation_latent=0, randomize_noise=False) + img_gen = torch.clamp(img_gen, -1, 1).detach() + img_real = img_gen.clone() + real_input = down(down(img_gen)).detach() + style = pspex.encoder(real_input) + pspex.latent_avg.unsqueeze(0) + if idx == 0: + samplein = real_input.clone().detach() + samplestyle = style.clone().detach() + + _, feat = pspex.encoder(real_input, return_feat=True) + fake_img, _ = pspex.decoder([style], input_is_latent=True, randomize_noise=False, first_layer_feature=feat) + + recon_loss = F.mse_loss(fake_img, img_real) * 10 + perct_loss = percept(down(fake_img), down(img_real).detach()).sum() + e_loss = recon_loss + perct_loss + + loss_dict["er"] = recon_loss + loss_dict["ef"] = perct_loss + + pspex.zero_grad() + e_loss.backward() + e_optim.step() + + er_loss_val = loss_dict["er"].mean().item() + ef_loss_val = loss_dict["ef"].mean().item() + + pbar.set_description( + ( + f"iter: {idx:d}; er: {er_loss_val:.3f}; ef: {ef_loss_val:.3f}" + ) + ) + + if idx % args.image_interval == 0 or (idx+1) == args.max_steps: + with torch.no_grad(): + _, sample_feat = pspex.encoder(samplein, return_feat=True) + sample, _ = pspex.decoder([samplestyle], input_is_latent=True, randomize_noise=False, first_layer_feature=sample_feat) + sample = torch.cat((samplein, down(down(sample))), dim=0) + save_image(torchvision.utils.make_grid(sample, args.batch_size, 1), f"%s/%05d.jpg"%(log_path, idx+1)) + + save_dict = { + 'state_dict': pspex.state_dict(), + 'opts': vars(pspex.opts) + } + if pspex.opts.start_from_latent_avg: + save_dict['latent_avg'] = pspex.latent_avg + torch.save( + save_dict, + f"%s/%05d.pt"%(checkpoint_path, idx+1), + ) \ No newline at end of file diff --git a/upsampler/scripts/style_mixing.py b/upsampler/scripts/style_mixing.py new file mode 100644 index 0000000000000000000000000000000000000000..e252b418adb26ac5dc9e30998d44279c2ff60cb7 --- /dev/null +++ b/upsampler/scripts/style_mixing.py @@ -0,0 +1,101 @@ +import os +from argparse import Namespace + +from tqdm import tqdm +import numpy as np +from PIL import Image +import torch +from torch.utils.data import DataLoader +import sys + +sys.path.append(".") +sys.path.append("..") + +from configs import data_configs +from datasets.inference_dataset import InferenceDataset +from utils.common import tensor2im, log_input_image +from options.test_options import TestOptions +from models.psp import pSp + + +def run(): + test_opts = TestOptions().parse() + + if test_opts.resize_factors is not None: + factors = test_opts.resize_factors.split(',') + assert len(factors) == 1, "When running inference, please provide a single downsampling factor!" + mixed_path_results = os.path.join(test_opts.exp_dir, 'style_mixing', + 'downsampling_{}'.format(test_opts.resize_factors)) + else: + mixed_path_results = os.path.join(test_opts.exp_dir, 'style_mixing') + os.makedirs(mixed_path_results, exist_ok=True) + + # update test options with options used during training + ckpt = torch.load(test_opts.checkpoint_path, map_location='cpu') + opts = ckpt['opts'] + opts.update(vars(test_opts)) + if 'learn_in_w' not in opts: + opts['learn_in_w'] = False + if 'output_size' not in opts: + opts['output_size'] = 1024 + opts = Namespace(**opts) + + net = pSp(opts) + net.eval() + net.cuda() + + print('Loading dataset for {}'.format(opts.dataset_type)) + dataset_args = data_configs.DATASETS[opts.dataset_type] + transforms_dict = dataset_args['transforms'](opts).get_transforms() + dataset = InferenceDataset(root=opts.data_path, + transform=transforms_dict['transform_inference'], + opts=opts) + dataloader = DataLoader(dataset, + batch_size=opts.test_batch_size, + shuffle=False, + num_workers=int(opts.test_workers), + drop_last=True) + + latent_mask = [int(l) for l in opts.latent_mask.split(",")] + if opts.n_images is None: + opts.n_images = len(dataset) + + global_i = 0 + for input_batch in tqdm(dataloader): + if global_i >= opts.n_images: + break + with torch.no_grad(): + input_batch = input_batch.cuda() + for image_idx, input_image in enumerate(input_batch): + # generate random vectors to inject into input image + vecs_to_inject = np.random.randn(opts.n_outputs_to_generate, 512).astype('float32') + multi_modal_outputs = [] + for vec_to_inject in vecs_to_inject: + cur_vec = torch.from_numpy(vec_to_inject).unsqueeze(0).to("cuda") + # get latent vector to inject into our input image + _, latent_to_inject = net(cur_vec, + input_code=True, + return_latents=True) + # get output image with injected style vector + res = net(input_image.unsqueeze(0).to("cuda").float(), + latent_mask=latent_mask, + inject_latent=latent_to_inject, + alpha=opts.mix_alpha, + resize=opts.resize_outputs) + multi_modal_outputs.append(res[0]) + + # visualize multi modal outputs + input_im_path = dataset.paths[global_i] + image = input_batch[image_idx] + input_image = log_input_image(image, opts) + resize_amount = (256, 256) if opts.resize_outputs else (opts.output_size, opts.output_size) + res = np.array(input_image.resize(resize_amount)) + for output in multi_modal_outputs: + output = tensor2im(output) + res = np.concatenate([res, np.array(output.resize(resize_amount))], axis=1) + Image.fromarray(res).save(os.path.join(mixed_path_results, os.path.basename(input_im_path))) + global_i += 1 + + +if __name__ == '__main__': + run() diff --git a/upsampler/scripts/train.py b/upsampler/scripts/train.py new file mode 100644 index 0000000000000000000000000000000000000000..21026ebf1619cf19dda8fb5a05909b22f0f0fcbc --- /dev/null +++ b/upsampler/scripts/train.py @@ -0,0 +1,32 @@ +""" +This file runs the main training/val loop +""" +import os +import json +import sys +import pprint + +sys.path.append(".") +sys.path.append("..") + +from options.train_options import TrainOptions +from training.coach import Coach + + +def main(): + opts = TrainOptions().parse() + if os.path.exists(opts.exp_dir): + raise Exception('Oops... {} already exists'.format(opts.exp_dir)) + os.makedirs(opts.exp_dir) + + opts_dict = vars(opts) + pprint.pprint(opts_dict) + with open(os.path.join(opts.exp_dir, 'opt.json'), 'w') as f: + json.dump(opts_dict, f, indent=4, sort_keys=True) + + coach = Coach(opts) + coach.train() + + +if __name__ == '__main__': + main() diff --git a/upsampler/training/__init__.py b/upsampler/training/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/training/coach.py b/upsampler/training/coach.py new file mode 100644 index 0000000000000000000000000000000000000000..58ec5da8f6c3c8259de34f0e17424be03aef49a4 --- /dev/null +++ b/upsampler/training/coach.py @@ -0,0 +1,598 @@ +import os +import matplotlib +import matplotlib.pyplot as plt + +matplotlib.use('Agg') + +import torch +from torch import nn, autograd ##### modified +from torch.utils.data import DataLoader +from torch.utils.tensorboard import SummaryWriter +import torch.nn.functional as F +import numpy as np + +from utils import common, train_utils +from criteria import id_loss, w_norm, moco_loss +from configs import data_configs +from datasets.images_dataset import ImagesDataset +from criteria.lpips.lpips import LPIPS +from models.psp import pSp +from models.stylegan2.model import Discriminator ##### modified +from training.ranger import Ranger +from models.stylegan2.simple_augment import random_apply_affine +from datasets.ffhq_degradation_dataset import FFHQDegradationDataset ##### modified, for blind SR + +class Coach: + def __init__(self, opts): + self.opts = opts + + self.global_step = 0 + + self.device = 'cuda:0' # TODO: Allow multiple GPU? currently using CUDA_VISIBLE_DEVICES + self.opts.device = self.device + + if self.opts.use_wandb: + from utils.wandb_utils import WBLogger + self.wb_logger = WBLogger(self.opts) + + # Initialize network + self.net = pSp(self.opts).to(self.device) + if self.opts.adv_lambda > 0: ##### modified, add discriminator + self.discriminator = Discriminator(1024, channel_multiplier=2, img_channel=3) + if self.opts.checkpoint_path is not None: + ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') + if 'discriminator' in ckpt.keys(): + print('Loading discriminator from checkpoint: {}'.format(self.opts.checkpoint_path)) + self.discriminator.load_state_dict(ckpt['discriminator'], strict=False) + self.discriminator = self.discriminator.to(self.device) + self.discriminator_optimizer = torch.optim.Adam(list(self.discriminator.parameters()), + lr=self.opts.learning_rate) + + # Estimate latent_avg via dense sampling if latent_avg is not available + if self.net.latent_avg is None: + self.net.latent_avg = self.net.decoder.mean_latent(int(1e5))[0].detach() + + # Initialize loss + if self.opts.id_lambda > 0 and self.opts.moco_lambda > 0: + raise ValueError('Both ID and MoCo loss have lambdas > 0! Please select only one to have non-zero lambda!') + + self.mse_loss = nn.MSELoss().to(self.device).eval() + if self.opts.lpips_lambda > 0: + self.lpips_loss = LPIPS(net_type='alex').to(self.device).eval() + if self.opts.id_lambda > 0: + self.id_loss = id_loss.IDLoss().to(self.device).eval() + if self.opts.w_norm_lambda > 0: + self.w_norm_loss = w_norm.WNormLoss(start_from_latent_avg=self.opts.start_from_latent_avg) + if self.opts.moco_lambda > 0: + self.moco_loss = moco_loss.MocoLoss().to(self.device).eval() + + # Initialize optimizer + self.optimizer = self.configure_optimizers() + + # Initialize dataset + self.train_dataset, self.test_dataset = self.configure_datasets() + self.train_dataloader = DataLoader(self.train_dataset, + batch_size=self.opts.batch_size, + shuffle=True, + num_workers=int(self.opts.workers), + drop_last=True) + self.test_dataloader = DataLoader(self.test_dataset, + batch_size=self.opts.test_batch_size, + shuffle=False, + num_workers=int(self.opts.test_workers), + drop_last=True) + + # Initialize logger + log_dir = os.path.join(opts.exp_dir, 'logs') + os.makedirs(log_dir, exist_ok=True) + self.logger = SummaryWriter(log_dir=log_dir) + + # Initialize checkpoint dir + self.checkpoint_dir = os.path.join(opts.exp_dir, 'checkpoints') + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.best_val_loss = None + if self.opts.save_interval is None: + self.opts.save_interval = self.opts.max_steps + + # for sketch/mask-to-face translation, indicate which layers from x, which layers from y + if self.opts.use_latent_mask: ##### modified + self.latent_mask = [int(l) for l in self.opts.latent_mask.split(",")] + + # for video face editing, the editing vector v + self.editing_w = None + if self.opts.editing_w_path is not None: + self.editing_w = torch.load(self.opts.editing_w_path).to(self.device) + + # for video face editing, to augment face attribute when generating training data + self.directions = None + if self.opts.direction_path is not None: + self.directions = torch.load(self.opts.direction_path).to(self.device) + + def train(self): + self.net.train() + while self.global_step < self.opts.max_steps: + for batch_idx, batch in enumerate(self.train_dataloader): + self.optimizer.zero_grad() + + #************************ Data Preparation ************************** + + # x is the input, y is the ground truth + # the faces in x and y are aligned, we will apply geometric transformation to make them unaligned. + x, y = batch + + # for video face editing, generating paired data (x,y) + editing_w = None + if self.opts.generate_training_data and self.editing_w is not None: + with torch.no_grad(): + noise_sample = torch.randn(x.shape[0], 512).to(self.device) + ws = self.net.decoder.style(noise_sample).unsqueeze(1).repeat(1,18,1) + ws = ws + self.directions[torch.randint(0, self.directions.shape[0], (x.shape[0],))] + x, _ = self.net.decoder([ws], input_is_latent=True, truncation=0.75, truncation_latent=0, randomize_noise=False) + x = F.adaptive_avg_pool2d(x, (x.shape[2]//4, x.shape[3]//4)).detach() + scale_factor = np.random.choice([0.0,0.25,0.5,0.75,1.0,1.25], 1)[0] + editing_w = self.editing_w[torch.randint(0, self.editing_w.shape[0], (1,))] * scale_factor + y, _ = self.net.decoder([ws], input_is_latent=True, truncation=0.75, truncation_latent=0, + randomize_noise=False, editing_w=editing_w) + y = y.detach() + + x, y = x.to(self.device).float(), y.to(self.device).float() + + # the shape of y should be H*W or H/8*W/8, the shape of x should always be H/8*W/8 + scale = int(y.shape[2] // x.shape[2]) + assert(int(y.shape[3] // x.shape[3]) == scale) + + # prepare aligned images for w+ extraction + x_tilde = None + y_tilde = y.clone() if scale ==1 else F.interpolate(y, (x.shape[2], x.shape[3]), mode='bilinear') + # crop the centered 256*256 region from a H/8*W/8 image + if self.opts.crop_face: + crop_size = int((x.shape[2] - 256) // 2) + x_tilde = x.clone() + if crop_size > 0: + x_tilde = x_tilde[:,:,crop_size:-crop_size,crop_size:-crop_size] + if self.opts.use_latent_mask: + y_tilde = y_tilde[:,:,crop_size:-crop_size,crop_size:-crop_size] + + # for flicker suppression loss in video-related tasks + y0_hat = None + if self.opts.tmp_lambda > 0 and self.global_step * 2 >= self.opts.max_steps: + if self.opts.use_latent_mask: # for sketch/mask-to-face translation. not used in the paper + y0_hat = self.net.forward(x1=x, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + latent_mask=self.latent_mask, inject_latent=self.net.encoder(y_tilde), + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w) + else: + y0_hat = self.net.forward(x1=x, x2=x_tilde, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w) + + # making the faces unaligned for the following training + if self.opts.affine_augment: + x, affine_T = random_apply_affine(x, 0.2, None) + y, _ = random_apply_affine(y, 1.0, affine_T) + x = x.detach() + y = y.detach() + if y0_hat is not None and self.opts.tmp_lambda > 0: + y0_hat, _ = random_apply_affine(y0_hat, 1.0, affine_T) + + # making the resolution of the image variable + if self.opts.random_crop: + _, _, h, w = x.shape + th, tw = torch.randint(32, 41, size=(1,)).item() * 8, torch.randint(32, 41, size=(1,)).item() * 8 + i, j = torch.randint(0, h - th + 1, size=(1,)).item(), torch.randint(0, w - tw + 1, size=(1,)).item() + x = x[:,:,i:i+th,j:j+tw].detach() + y = y[:,:,i*scale:(i+th)*scale,j*scale:(j+tw)*scale].detach() + if y0_hat is not None and self.opts.tmp_lambda > 0: + y0_hat = y0_hat[:,:,i*scale:(i+th)*scale,j*scale:(j+tw)*scale] + + # if opts.crop_face=False, using unaligned faces to extract w+ + if self.opts.use_latent_mask and (not self.opts.crop_face): + y_tilde = y.clone() if scale == 1 else F.interpolate(y, (x.shape[2], x.shape[3]), mode='bilinear') + + # Now we have prepare the input data (x,x_tilde,y_tilde) and ground truth data (y, y0_hat) + # x is the input image, y is the ground truth output + # (if opts.crop_face=True) x_tilde is the cropped aligned version of x, y_tilde is the cropped aligned version of y + # y0_hat is the geometrically transformed output, which is used to suppress flickers + + #************************ generate y_hat given the data ************************** + + # y_hat is the output image, latent is the extracted w+ + if self.opts.use_latent_mask: # for sketch/mask-to-face translation + y_hat, latent = self.net.forward(x1=x, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + latent_mask=self.latent_mask, inject_latent=self.net.encoder(y_tilde), + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w, return_latents=True) + else: # for other tasks + y_hat, latent = self.net.forward(x1=x, x2=x_tilde, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w, return_latents=True) + # adversarial loss + if self.opts.adv_lambda > 0: + d_loss_dict = self.train_discriminator(y, y_hat) + + # calculate losses + loss, loss_dict, id_logs = self.calc_loss(x, y, y_hat, latent, y0_hat) + + if self.opts.adv_lambda > 0: + loss_dict = {**d_loss_dict, **loss_dict} + + loss.backward() + self.optimizer.step() + + #************************ logging and saving model************************** + + # Logging related + with torch.no_grad(): ##### modified for SR task, since x, y and y_hat may have different resolution + y = F.adaptive_avg_pool2d(y, (x.shape[2], x.shape[3])) + y_hat = F.adaptive_avg_pool2d(y_hat, (x.shape[2], x.shape[3])) + x = torch.clamp(x, -1, 1) + + if self.global_step % self.opts.image_interval == 0 or (self.global_step < 1000 and self.global_step % 25 == 0): + self.parse_and_log_images(id_logs, x, y, y_hat, title='images/train/faces') + if self.global_step % self.opts.board_interval == 0: + self.print_metrics(loss_dict, prefix='train') + self.log_metrics(loss_dict, prefix='train') + + # Log images of first batch to wandb + if self.opts.use_wandb and batch_idx == 0: + self.wb_logger.log_images_to_wandb(x, y, y_hat, id_logs, prefix="train", step=self.global_step, opts=self.opts) + + # Validation related + val_loss_dict = None + if self.global_step % self.opts.val_interval == 0 or self.global_step == self.opts.max_steps: + val_loss_dict = self.validate() + if val_loss_dict and (self.best_val_loss is None or val_loss_dict['loss'] < self.best_val_loss): + self.best_val_loss = val_loss_dict['loss'] + self.checkpoint_me(val_loss_dict, is_best=True) + + if self.global_step % self.opts.save_interval == 0 or self.global_step == self.opts.max_steps: + if val_loss_dict is not None: + self.checkpoint_me(val_loss_dict, is_best=False) + else: + self.checkpoint_me(loss_dict, is_best=False) + + if self.global_step == self.opts.max_steps: + print('OMG, finished training!') + break + + self.global_step += 1 + + def validate(self): + self.net.eval() + agg_loss_dict = [] + for batch_idx, batch in enumerate(self.test_dataloader): + x, y = batch + + editing_w = None + if self.editing_w is not None: + editing_w = self.editing_w[torch.randint(0, self.editing_w.shape[0], (1,))] + + with torch.no_grad(): + x, y = x.to(self.device).float(), y.to(self.device).float() + scale = int(y.shape[2] // x.shape[2]) + assert(int(y.shape[3] // x.shape[3]) == scale) + + # prepare aligned images for w+ extraction + x_tilde = None + y_tilde = y.clone() if scale ==1 else F.interpolate(y, (x.shape[2], x.shape[3]), mode='bilinear') + # crop the centered 256*256 region from a H/8*W/8 image + if self.opts.crop_face: + crop_size = int((x.shape[2] - 256) // 2) + x_tilde = x.clone() + if crop_size > 0: + x_tilde = x_tilde[:,:,crop_size:-crop_size,crop_size:-crop_size] + if self.opts.use_latent_mask: + y_tilde = y_tilde[:,:,crop_size:-crop_size,crop_size:-crop_size] + + # for flicker suppression loss in video-related tasks + y0_hat = None + if self.opts.tmp_lambda > 0 and self.global_step * 2 >= self.opts.max_steps: + if self.opts.use_latent_mask: # for sketch/mask-to-face translation. not used in the paper + y0_hat = self.net.forward(x1=x, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + latent_mask=self.latent_mask, inject_latent=self.net.encoder(y_tilde), + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w) + else: + y0_hat = self.net.forward(x1=x, x2=x_tilde, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w) + y0_hat = y0_hat.detach() + + # making the faces unaligned for the following training + if self.opts.affine_augment: + x, affine_T = random_apply_affine(x, 0.2, None) + y, _ = random_apply_affine(y, 1.0, affine_T) + x = x.detach() + y = y.detach() + if y0_hat is not None and self.opts.tmp_lambda > 0: + y0_hat, _ = random_apply_affine(y0_hat, 1.0, affine_T) + y0_hat = y0_hat.detach() + + # making the resolution of the image variable + if self.opts.random_crop: + _, _, h, w = x.shape + th, tw = torch.randint(32, 41, size=(1,)).item() * 8, torch.randint(32, 41, size=(1,)).item() * 8 + i, j = torch.randint(0, h - th + 1, size=(1,)).item(), torch.randint(0, w - tw + 1, size=(1,)).item() + x = x[:,:,i:i+th,j:j+tw].detach() + y = y[:,:,i*scale:(i+th)*scale,j*scale:(j+tw)*scale].detach() + if y0_hat is not None and self.opts.tmp_lambda > 0: + y0_hat = y0_hat[:,:,i*scale:(i+th)*scale,j*scale:(j+tw)*scale].detach() + + + # if opts.crop_face=False, using unaligned faces to extract w+ + if self.opts.use_latent_mask and (not self.opts.crop_face): + y_tilde = y.clone() if scale == 1 else F.interpolate(y, (x.shape[2], x.shape[3]), mode='bilinear') + + # y_hat is the output image, latent is the extracted w+ + if self.opts.use_latent_mask: # for sketch/mask-to-face translation + y_hat, latent = self.net.forward(x1=x, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + latent_mask=self.latent_mask, inject_latent=self.net.encoder(y_tilde), + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w, return_latents=True) + else: # for other tasks + y_hat, latent = self.net.forward(x1=x, x2=x_tilde, resize=(x.shape[2:]==y.shape[2:]), zero_noise=self.opts.zero_noise, + first_layer_feature_ind=self.opts.feat_ind, use_skip=self.opts.use_skip, + editing_w=editing_w, return_latents=True) + + # adversarial loss + if self.opts.adv_lambda > 0: + cur_d_loss_dict = self.validate_discriminator(y, y_hat) + + loss, cur_loss_dict, id_logs = self.calc_loss(x, y, y_hat, latent, y0_hat) + + if self.opts.adv_lambda > 0: + cur_loss_dict = {**cur_d_loss_dict, **cur_loss_dict} + + agg_loss_dict.append(cur_loss_dict) + + # Logging related + with torch.no_grad(): ##### modified for SR task + y = F.adaptive_avg_pool2d(y, (x.shape[2], x.shape[3])) + y_hat = F.adaptive_avg_pool2d(y_hat, (x.shape[2], x.shape[3])) + x = torch.clamp(x, -1, 1) ##### modified + + self.parse_and_log_images(id_logs, x, y, y_hat, + title='images/test/faces', + subscript='{:04d}'.format(batch_idx)) + + # Log images of first batch to wandb + if self.opts.use_wandb and batch_idx == 0: + self.wb_logger.log_images_to_wandb(x, y, y_hat, id_logs, prefix="test", step=self.global_step, opts=self.opts) + + # For first step just do sanity test on small amount of data + if self.global_step == 0 and batch_idx >= 4: + self.net.train() + return None # Do not log, inaccurate in first batch + + loss_dict = train_utils.aggregate_loss_dict(agg_loss_dict) + self.log_metrics(loss_dict, prefix='test') + self.print_metrics(loss_dict, prefix='test') + + self.net.train() + return loss_dict + + def checkpoint_me(self, loss_dict, is_best): + save_name = 'best_model.pt' if is_best else f'iteration_{self.global_step}.pt' + save_dict = self.__get_save_dict() + checkpoint_path = os.path.join(self.checkpoint_dir, save_name) + torch.save(save_dict, checkpoint_path) + with open(os.path.join(self.checkpoint_dir, 'timestamp.txt'), 'a') as f: + if is_best: + f.write(f'**Best**: Step - {self.global_step}, Loss - {self.best_val_loss} \n{loss_dict}\n') + if self.opts.use_wandb: + self.wb_logger.log_best_model() + else: + f.write(f'Step - {self.global_step}, \n{loss_dict}\n') + + def configure_optimizers(self): + if hasattr(self.opts, 'pretrain_model') and self.opts.pretrain_model == 'input_label_layer': ##### modified + params = list(self.net.encoder.input_label_layer.parameters()) + else: + params = list(self.net.encoder.parameters()) + if self.opts.train_decoder: + params += list(self.net.decoder.parameters()) + if self.opts.optim_name == 'adam': + optimizer = torch.optim.Adam(params, lr=self.opts.learning_rate) + else: + optimizer = Ranger(params, lr=self.opts.learning_rate) + return optimizer + + def configure_datasets(self): + if self.opts.dataset_type not in data_configs.DATASETS.keys(): + Exception(f'{self.opts.dataset_type} is not a valid dataset_type') + print(f'Loading dataset for {self.opts.dataset_type}') + dataset_args = data_configs.DATASETS[self.opts.dataset_type] + transforms_dict = dataset_args['transforms'](self.opts).get_transforms() + if self.opts.blind_sr: + import yaml + with open("./configs/dataset_config.yml", 'r') as stream: + parsed_yaml=yaml.safe_load(stream) + parsed_yaml['datasets']['train']['dataroot_gt'] = dataset_args['train_target_root'] + factors = [int(f) for f in self.opts.resize_factors.split(",")] + if '320' in self.opts.dataset_type: + parsed_yaml['datasets']['train']['scale'] = 1 + rescale = parsed_yaml['datasets']['train']['scale'] + parsed_yaml['datasets']['train']['downsample_range'] = [min(factors) * 0.75 * rescale, max(factors)* 1.5 * rescale] + train_dataset = FFHQDegradationDataset(parsed_yaml['datasets']['train']) + else: + train_dataset = ImagesDataset(source_root=dataset_args['train_source_root'], + target_root=dataset_args['train_target_root'], + source_transform=transforms_dict['transform_source'], + target_transform=transforms_dict['transform_gt_train'], + opts=self.opts) + test_dataset = ImagesDataset(source_root=dataset_args['test_source_root'], + target_root=dataset_args['test_target_root'], + source_transform=transforms_dict['transform_source'], + target_transform=transforms_dict['transform_test'], + opts=self.opts) + if self.opts.use_wandb: + self.wb_logger.log_dataset_wandb(train_dataset, dataset_name="Train") + self.wb_logger.log_dataset_wandb(test_dataset, dataset_name="Test") + print(f"Number of training samples: {len(train_dataset)}") + print(f"Number of test samples: {len(test_dataset)}") + return train_dataset, test_dataset + + def calc_loss(self, x, y, y_hat, latent, y0_hat=None): + loss_dict = {} + loss = 0.0 + id_logs = None + if self.opts.id_lambda > 0: + loss_id, sim_improvement, id_logs = self.id_loss(y_hat, y, x) + loss_dict['loss_id'] = float(loss_id) + loss_dict['id_improve'] = float(sim_improvement) + loss = loss_id * self.opts.id_lambda + if self.opts.l2_lambda > 0: + loss_l2 = F.mse_loss(y_hat, y) + loss_dict['loss_l2'] = float(loss_l2) + loss += loss_l2 * self.opts.l2_lambda + if self.opts.lpips_lambda > 0: + loss_lpips = self.lpips_loss(y_hat, y) + loss_dict['loss_lpips'] = float(loss_lpips) + loss += loss_lpips * self.opts.lpips_lambda + if self.opts.lpips_lambda_crop > 0: + loss_lpips_crop = self.lpips_loss(y_hat[:, :, 35:223, 32:220], y[:, :, 35:223, 32:220]) + loss_dict['loss_lpips_crop'] = float(loss_lpips_crop) + loss += loss_lpips_crop * self.opts.lpips_lambda_crop + if self.opts.l2_lambda_crop > 0: + loss_l2_crop = F.mse_loss(y_hat[:, :, 35:223, 32:220], y[:, :, 35:223, 32:220]) + loss_dict['loss_l2_crop'] = float(loss_l2_crop) + loss += loss_l2_crop * self.opts.l2_lambda_crop + if self.opts.w_norm_lambda > 0: + loss_w_norm = self.w_norm_loss(latent, self.net.latent_avg) + loss_dict['loss_w_norm'] = float(loss_w_norm) + loss += loss_w_norm * self.opts.w_norm_lambda + if self.opts.moco_lambda > 0: + loss_moco, sim_improvement, id_logs = self.moco_loss(y_hat, y, x) + loss_dict['loss_moco'] = float(loss_moco) + loss_dict['id_improve'] = float(sim_improvement) + loss += loss_moco * self.opts.moco_lambda + if self.opts.adv_lambda > 0: ##### modified + loss_g = F.softplus(-self.discriminator(y_hat)).mean() + loss_dict['loss_g'] = float(loss_g) + loss += loss_g * self.opts.adv_lambda + if self.opts.tmp_lambda > 0 and y0_hat is not None: ##### modified + loss_tmp = ((y_hat-y0_hat)**2).mean() + loss_dict['loss_tmp'] = float(loss_tmp) + loss += loss_tmp * self.opts.tmp_lambda * min(1, 4.0*(self.global_step/self.opts.max_steps-0.5)) + loss_dict['loss'] = float(loss) + return loss, loss_dict, id_logs + + def log_metrics(self, metrics_dict, prefix): + for key, value in metrics_dict.items(): + self.logger.add_scalar(f'{prefix}/{key}', value, self.global_step) + if self.opts.use_wandb: + self.wb_logger.log(prefix, metrics_dict, self.global_step) + + def print_metrics(self, metrics_dict, prefix): + print(f'Metrics for {prefix}, step {self.global_step}') + for key, value in metrics_dict.items(): + print(f'\t{key} = ', value) + + def parse_and_log_images(self, id_logs, x, y, y_hat, title, subscript=None, display_count=2): + im_data = [] + for i in range(display_count): + cur_im_data = { + 'input_face': common.log_input_image(x[i], self.opts), + 'target_face': common.tensor2im(y[i]), + 'output_face': common.tensor2im(y_hat[i]), + } + if id_logs is not None: + for key in id_logs[i]: + cur_im_data[key] = id_logs[i][key] + im_data.append(cur_im_data) + self.log_images(title, im_data=im_data, subscript=subscript) + + def log_images(self, name, im_data, subscript=None, log_latest=False): + fig = common.vis_faces(im_data) + step = self.global_step + if log_latest: + step = 0 + if subscript: + path = os.path.join(self.logger.log_dir, name, f'{subscript}_{step:04d}.jpg') + else: + path = os.path.join(self.logger.log_dir, name, f'{step:04d}.jpg') + os.makedirs(os.path.dirname(path), exist_ok=True) + fig.savefig(path) + plt.close(fig) + + def __get_save_dict(self): + save_dict = { + 'state_dict': self.net.state_dict(), + 'opts': vars(self.opts) + } + if self.opts.adv_lambda > 0: ##### modified + save_dict['discriminator'] = self.discriminator.state_dict() + if self.opts.editing_w_path is not None: + save_dict['editing_w'] = self.editing_w.cpu() + # save the latent avg in state_dict for inference if truncation of w was used during training + if self.opts.start_from_latent_avg: + save_dict['latent_avg'] = self.net.latent_avg + return save_dict + + ##### modified + @staticmethod + def discriminator_loss(real_pred, fake_pred, loss_dict): + real_loss = F.softplus(-real_pred).mean() + fake_loss = F.softplus(fake_pred).mean() + + loss_dict['loss_d_real'] = float(real_loss) + loss_dict['loss_d_fake'] = float(fake_loss) + + return real_loss + fake_loss + + @staticmethod + def discriminator_r1_loss(real_pred, real_w): + grad_real, = autograd.grad( + outputs=real_pred.sum(), inputs=real_w, create_graph=True + ) + grad_penalty = grad_real.pow(2).reshape(grad_real.shape[0], -1).sum(1).mean() + + return grad_penalty + + @staticmethod + def requires_grad(model, flag=True): + for p in model.parameters(): + p.requires_grad = flag + + def train_discriminator(self, real_img, fake_img): + loss_dict = {} + self.requires_grad(self.discriminator, True) + + real_pred = self.discriminator(real_img) + fake_pred = self.discriminator(fake_img.detach()) + loss = self.discriminator_loss(real_pred, fake_pred, loss_dict) + loss_dict['loss_d'] = float(loss) + loss = loss * self.opts.adv_lambda + + self.discriminator_optimizer.zero_grad() + loss.backward() + self.discriminator_optimizer.step() + + # r1 regularization + d_regularize = self.global_step % self.opts.d_reg_every == 0 + if d_regularize: + real_img = real_img.detach() + real_img.requires_grad = True + real_pred = self.discriminator(real_img) + r1_loss = self.discriminator_r1_loss(real_pred, real_img) + + self.discriminator.zero_grad() + r1_final_loss = self.opts.r1 / 2 * r1_loss * self.opts.d_reg_every + 0 * real_pred[0] + r1_final_loss.backward() + self.discriminator_optimizer.step() + loss_dict['loss_r1'] = float(r1_final_loss) + + # Reset to previous state + self.requires_grad(self.discriminator, False) + + return loss_dict + + def validate_discriminator(self, real_img, fake_img): + with torch.no_grad(): + loss_dict = {} + real_pred = self.discriminator(real_img) + fake_pred = self.discriminator(fake_img.detach()) + loss = self.discriminator_loss(real_pred, fake_pred, loss_dict) + loss_dict['loss_d'] = float(loss) + loss = loss * self.opts.adv_lambda + return loss_dict \ No newline at end of file diff --git a/upsampler/training/ranger.py b/upsampler/training/ranger.py new file mode 100644 index 0000000000000000000000000000000000000000..3d63264dda6df0ee40cac143440f0b5f8977a9ad --- /dev/null +++ b/upsampler/training/ranger.py @@ -0,0 +1,164 @@ +# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer. + +# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer +# and/or +# https://github.com/lessw2020/Best-Deep-Learning-Optimizers + +# Ranger has now been used to capture 12 records on the FastAI leaderboard. + +# This version = 20.4.11 + +# Credits: +# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github: https://github.com/Yonghongwei/Gradient-Centralization +# RAdam --> https://github.com/LiyuanLucasLiu/RAdam +# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code. +# Lookahead paper --> MZhang,G Hinton https://arxiv.org/abs/1907.08610 + +# summary of changes: +# 4/11/20 - add gradient centralization option. Set new testing benchmark for accuracy with it, toggle with use_gc flag at init. +# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights), +# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues. +# changes 8/31/19 - fix references to *self*.N_sma_threshold; +# changed eps to 1e-5 as better default than 1e-8. + +import math +import torch +from torch.optim.optimizer import Optimizer + + +class Ranger(Optimizer): + + def __init__(self, params, lr=1e-3, # lr + alpha=0.5, k=6, N_sma_threshhold=5, # Ranger options + betas=(.95, 0.999), eps=1e-5, weight_decay=0, # Adam options + use_gc=True, gc_conv_only=False + # Gradient centralization on or off, applied to conv layers only or conv + fc layers + ): + + # parameter checks + if not 0.0 <= alpha <= 1.0: + raise ValueError(f'Invalid slow update rate: {alpha}') + if not 1 <= k: + raise ValueError(f'Invalid lookahead steps: {k}') + if not lr > 0: + raise ValueError(f'Invalid Learning Rate: {lr}') + if not eps > 0: + raise ValueError(f'Invalid eps: {eps}') + + # parameter comments: + # beta1 (momentum) of .95 seems to work better than .90... + # N_sma_threshold of 5 seems better in testing than 4. + # In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you. + + # prep defaults and init torch.optim base + defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, + eps=eps, weight_decay=weight_decay) + super().__init__(params, defaults) + + # adjustable threshold + self.N_sma_threshhold = N_sma_threshhold + + # look ahead params + + self.alpha = alpha + self.k = k + + # radam buffer for state + self.radam_buffer = [[None, None, None] for ind in range(10)] + + # gc on or off + self.use_gc = use_gc + + # level of gradient centralization + self.gc_gradient_threshold = 3 if gc_conv_only else 1 + + def __setstate__(self, state): + super(Ranger, self).__setstate__(state) + + def step(self, closure=None): + loss = None + + # Evaluate averages and grad, update param tensors + for group in self.param_groups: + + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data.float() + + if grad.is_sparse: + raise RuntimeError('Ranger optimizer does not support sparse gradients') + + p_data_fp32 = p.data.float() + + state = self.state[p] # get state dict for this param + + if len(state) == 0: # if first time to run...init dictionary with our desired entries + # if self.first_run_check==0: + # self.first_run_check=1 + # print("Initializing slow buffer...should not see this at load from saved model!") + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p_data_fp32) + state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + + # look ahead weight storage now in state dict + state['slow_buffer'] = torch.empty_like(p.data) + state['slow_buffer'].copy_(p.data) + + else: + state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) + state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) + + # begin computations + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + beta1, beta2 = group['betas'] + + # GC operation for Conv layers and FC layers + if grad.dim() > self.gc_gradient_threshold: + grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True)) + + state['step'] += 1 + + # compute variance mov avg + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + # compute mean moving avg + exp_avg.mul_(beta1).add_(1 - beta1, grad) + + buffered = self.radam_buffer[int(state['step'] % 10)] + + if state['step'] == buffered[0]: + N_sma, step_size = buffered[1], buffered[2] + else: + buffered[0] = state['step'] + beta2_t = beta2 ** state['step'] + N_sma_max = 2 / (1 - beta2) - 1 + N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) + buffered[1] = N_sma + if N_sma > self.N_sma_threshhold: + step_size = math.sqrt( + (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / ( + N_sma_max - 2)) / (1 - beta1 ** state['step']) + else: + step_size = 1.0 / (1 - beta1 ** state['step']) + buffered[2] = step_size + + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + + # apply lr + if N_sma > self.N_sma_threshhold: + denom = exp_avg_sq.sqrt().add_(group['eps']) + p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom) + else: + p_data_fp32.add_(-step_size * group['lr'], exp_avg) + + p.data.copy_(p_data_fp32) + + # integrated look ahead... + # we do it at the param level instead of group level + if state['step'] % group['k'] == 0: + slow_p = state['slow_buffer'] # get access to slow param tensor + slow_p.add_(self.alpha, p.data - slow_p) # (fast weights - slow weights) * alpha + p.data.copy_(slow_p) # copy interpolated weights to RAdam param tensor + + return loss \ No newline at end of file diff --git a/upsampler/utils/__init__.py b/upsampler/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/upsampler/utils/common.py b/upsampler/utils/common.py new file mode 100644 index 0000000000000000000000000000000000000000..4813fe311ee40720697e4862c5fbfad811d39237 --- /dev/null +++ b/upsampler/utils/common.py @@ -0,0 +1,87 @@ +import cv2 +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt + + +# Log images +def log_input_image(x, opts): + if opts.label_nc == 0: + return tensor2im(x) + elif opts.label_nc == 1: + return tensor2sketch(x) + else: + return tensor2map(x) + + +def tensor2im(var): + var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy() + var = ((var + 1) / 2) + var[var < 0] = 0 + var[var > 1] = 1 + var = var * 255 + return Image.fromarray(var.astype('uint8')) + + +def tensor2map(var): + mask = np.argmax(var.data.cpu().numpy(), axis=0) + colors = get_colors() + mask_image = np.ones(shape=(mask.shape[0], mask.shape[1], 3)) + for class_idx in np.unique(mask): + mask_image[mask == class_idx] = colors[class_idx] + mask_image = mask_image.astype('uint8') + return Image.fromarray(mask_image) + + +def tensor2sketch(var): + im = var[0].cpu().detach().numpy() + im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) + im = (im * 255).astype(np.uint8) + return Image.fromarray(im) + + +# Visualization utils +def get_colors(): + # currently support up to 19 classes (for the celebs-hq-mask dataset) + colors = [[0, 0, 0], [204, 0, 0], [76, 153, 0], [204, 204, 0], [51, 51, 255], [204, 0, 204], [0, 255, 255], + [255, 204, 204], [102, 51, 0], [255, 0, 0], [102, 204, 0], [255, 255, 0], [0, 0, 153], [0, 0, 204], + [255, 51, 153], [0, 204, 204], [0, 51, 0], [255, 153, 51], [0, 204, 0]] + return colors + + +def vis_faces(log_hooks): + display_count = len(log_hooks) + fig = plt.figure(figsize=(8, 4 * display_count)) + gs = fig.add_gridspec(display_count, 3) + for i in range(display_count): + hooks_dict = log_hooks[i] + fig.add_subplot(gs[i, 0]) + if 'diff_input' in hooks_dict: + vis_faces_with_id(hooks_dict, fig, gs, i) + else: + vis_faces_no_id(hooks_dict, fig, gs, i) + plt.tight_layout() + return fig + + +def vis_faces_with_id(hooks_dict, fig, gs, i): + plt.imshow(hooks_dict['input_face']) + plt.title('Input\nOut Sim={:.2f}'.format(float(hooks_dict['diff_input']))) + fig.add_subplot(gs[i, 1]) + plt.imshow(hooks_dict['target_face']) + plt.title('Target\nIn={:.2f}, Out={:.2f}'.format(float(hooks_dict['diff_views']), + float(hooks_dict['diff_target']))) + fig.add_subplot(gs[i, 2]) + plt.imshow(hooks_dict['output_face']) + plt.title('Output\n Target Sim={:.2f}'.format(float(hooks_dict['diff_target']))) + + +def vis_faces_no_id(hooks_dict, fig, gs, i): + plt.imshow(hooks_dict['input_face'], cmap="gray") + plt.title('Input') + fig.add_subplot(gs[i, 1]) + plt.imshow(hooks_dict['target_face']) + plt.title('Target') + fig.add_subplot(gs[i, 2]) + plt.imshow(hooks_dict['output_face']) + plt.title('Output') diff --git a/upsampler/utils/data_utils.py b/upsampler/utils/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f1ba79f4a2d5cc2b97dce76d87bf6e7cdebbc257 --- /dev/null +++ b/upsampler/utils/data_utils.py @@ -0,0 +1,25 @@ +""" +Code adopted from pix2pixHD: +https://github.com/NVIDIA/pix2pixHD/blob/master/data/image_folder.py +""" +import os + +IMG_EXTENSIONS = [ + '.jpg', '.JPG', '.jpeg', '.JPEG', + '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tiff' +] + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def make_dataset(dir): + images = [] + assert os.path.isdir(dir), '%s is not a valid directory' % dir + for root, _, fnames in sorted(os.walk(dir)): + for fname in fnames: + if is_image_file(fname): + path = os.path.join(root, fname) + images.append(path) + return images diff --git a/upsampler/utils/inference_utils.py b/upsampler/utils/inference_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..4e993cac404d3e0d6749cad54005179a7b375a10 --- /dev/null +++ b/upsampler/utils/inference_utils.py @@ -0,0 +1,182 @@ +import numpy as np +import matplotlib.pyplot as plt +from PIL import Image +import cv2 +import random +import math +import argparse +import torch +from torch.utils import data +from torch.nn import functional as F +from torch import autograd +from torch.nn import init +import torchvision.transforms as transforms +from scripts.align_all_parallel import get_landmark + +def visualize(img_arr, dpi): + plt.figure(figsize=(10,10),dpi=dpi) + plt.imshow(((img_arr.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8)) + plt.axis('off') + plt.show() + +def save_image(img, filename): + tmp = ((img.detach().cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8) + cv2.imwrite(filename, cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR)) + +def load_image(filename): + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), + ]) + + img = Image.open(filename) + img = transform(img) + return img.unsqueeze(dim=0) + +def get_video_crop_parameter(filepath, predictor, padding=[256,256,256,256]): + if type(filepath) == str: + img = dlib.load_rgb_image(filepath) + else: + img = filepath + lm = get_landmark(img, predictor) + if lm is None: + return None + lm_chin = lm[0 : 17] # left-right + lm_eyebrow_left = lm[17 : 22] # left-right + lm_eyebrow_right = lm[22 : 27] # left-right + lm_nose = lm[27 : 31] # top-down + lm_nostrils = lm[31 : 36] # top-down + lm_eye_left = lm[36 : 42] # left-clockwise + lm_eye_right = lm[42 : 48] # left-clockwise + lm_mouth_outer = lm[48 : 60] # left-clockwise + lm_mouth_inner = lm[60 : 68] # left-clockwise + + scale = 64. / (np.mean(lm_eye_right[:,0])-np.mean(lm_eye_left[:,0])) + center = ((np.mean(lm_eye_right, axis=0)+np.mean(lm_eye_left, axis=0)) / 2) * scale + h, w = round(img.shape[0] * scale), round(img.shape[1] * scale) + left = max(round(center[0] - padding[0]), 0) // 8 * 8 + right = min(round(center[0] + padding[1]), w) // 8 * 8 + top = max(round(center[1] - padding[2]), 0) // 8 * 8 + bottom = min(round(center[1] + padding[3]), h) // 8 * 8 + return h,w,top,bottom,left,right,scale + +def tensor2cv2(img): + tmp = ((img.cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8) + return cv2.cvtColor(tmp, cv2.COLOR_RGB2BGR) + +def noise_regularize(noises): + loss = 0 + + for noise in noises: + size = noise.shape[2] + + while True: + loss = ( + loss + + (noise * torch.roll(noise, shifts=1, dims=3)).mean().pow(2) + + (noise * torch.roll(noise, shifts=1, dims=2)).mean().pow(2) + ) + + if size <= 8: + break + + #noise = noise.reshape([-1, 1, size // 2, 2, size // 2, 2]) + #noise = noise.mean([3, 5]) + noise = F.interpolate(noise, scale_factor=0.5, mode='bilinear') + size //= 2 + + return loss + + +def noise_normalize_(noises): + for noise in noises: + mean = noise.mean() + std = noise.std() + + noise.data.add_(-mean).div_(std) + + +def get_lr(t, initial_lr, rampdown=0.25, rampup=0.05): + lr_ramp = min(1, (1 - t) / rampdown) + lr_ramp = 0.5 - 0.5 * math.cos(lr_ramp * math.pi) + lr_ramp = lr_ramp * min(1, t / rampup) + + return initial_lr * lr_ramp + + +def latent_noise(latent, strength): + noise = torch.randn_like(latent) * strength + + return latent + noise + + +def make_image(tensor): + return ( + tensor.detach() + .clamp_(min=-1, max=1) + .add(1) + .div_(2) + .mul(255) + .type(torch.uint8) + .permute(0, 2, 3, 1) + .to("cpu") + .numpy() + ) + + +# from pix2pixeHD +# Converts a one-hot tensor into a colorful label map +def tensor2label(label_tensor, n_label, imtype=np.uint8): + if n_label == 0: + return tensor2im(label_tensor, imtype) + label_tensor = label_tensor.cpu().float() + if label_tensor.size()[0] > 1: + label_tensor = label_tensor.max(0, keepdim=True)[1] + label_tensor = Colorize(n_label)(label_tensor) + label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0)) + return label_numpy.astype(imtype) + +def uint82bin(n, count=8): + """returns the binary of integer n, count refers to amount of bits""" + return ''.join([str((n >> y) & 1) for y in range(count-1, -1, -1)]) + +def labelcolormap(N): + if N == 35: # cityscape + cmap = np.array([( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), ( 0, 0, 0), (111, 74, 0), ( 81, 0, 81), + (128, 64,128), (244, 35,232), (250,170,160), (230,150,140), ( 70, 70, 70), (102,102,156), (190,153,153), + (180,165,180), (150,100,100), (150,120, 90), (153,153,153), (153,153,153), (250,170, 30), (220,220, 0), + (107,142, 35), (152,251,152), ( 70,130,180), (220, 20, 60), (255, 0, 0), ( 0, 0,142), ( 0, 0, 70), + ( 0, 60,100), ( 0, 0, 90), ( 0, 0,110), ( 0, 80,100), ( 0, 0,230), (119, 11, 32), ( 0, 0,142)], + dtype=np.uint8) + else: + cmap = np.zeros((N, 3), dtype=np.uint8) + for i in range(N): + r, g, b = 0, 0, 0 + id = i + for j in range(7): + str_id = uint82bin(id) + r = r ^ (np.uint8(str_id[-1]) << (7-j)) + g = g ^ (np.uint8(str_id[-2]) << (7-j)) + b = b ^ (np.uint8(str_id[-3]) << (7-j)) + id = id >> 3 + cmap[i, 0] = r + cmap[i, 1] = g + cmap[i, 2] = b + return cmap + +class Colorize(object): + def __init__(self, n=35): + self.cmap = labelcolormap(n) + self.cmap = torch.from_numpy(self.cmap[:n]) + + def __call__(self, gray_image): + size = gray_image.size() + color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0) + + for label in range(0, len(self.cmap)): + mask = (label == gray_image[0]).cpu() + color_image[0][mask] = self.cmap[label][0] + color_image[1][mask] = self.cmap[label][1] + color_image[2][mask] = self.cmap[label][2] + + return color_image \ No newline at end of file diff --git a/upsampler/utils/train_utils.py b/upsampler/utils/train_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0c55177f7442010bc1fcc64de3d142585c22adc0 --- /dev/null +++ b/upsampler/utils/train_utils.py @@ -0,0 +1,13 @@ + +def aggregate_loss_dict(agg_loss_dict): + mean_vals = {} + for output in agg_loss_dict: + for key in output: + mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]] + for key in mean_vals: + if len(mean_vals[key]) > 0: + mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key]) + else: + print('{} has no value'.format(key)) + mean_vals[key] = 0 + return mean_vals diff --git a/upsampler/utils/wandb_utils.py b/upsampler/utils/wandb_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0061eb569dee40bbe68f244b286976412fe6dece --- /dev/null +++ b/upsampler/utils/wandb_utils.py @@ -0,0 +1,47 @@ +import datetime +import os +import numpy as np +import wandb + +from utils import common + + +class WBLogger: + + def __init__(self, opts): + wandb_run_name = os.path.basename(opts.exp_dir) + wandb.init(project="pixel2style2pixel", config=vars(opts), name=wandb_run_name) + + @staticmethod + def log_best_model(): + wandb.run.summary["best-model-save-time"] = datetime.datetime.now() + + @staticmethod + def log(prefix, metrics_dict, global_step): + log_dict = {f'{prefix}_{key}': value for key, value in metrics_dict.items()} + log_dict["global_step"] = global_step + wandb.log(log_dict) + + @staticmethod + def log_dataset_wandb(dataset, dataset_name, n_images=16): + idxs = np.random.choice(a=range(len(dataset)), size=n_images, replace=False) + data = [wandb.Image(dataset.source_paths[idx]) for idx in idxs] + wandb.log({f"{dataset_name} Data Samples": data}) + + @staticmethod + def log_images_to_wandb(x, y, y_hat, id_logs, prefix, step, opts): + im_data = [] + column_names = ["Source", "Target", "Output"] + if id_logs is not None: + column_names.append("ID Diff Output to Target") + for i in range(len(x)): + cur_im_data = [ + wandb.Image(common.log_input_image(x[i], opts)), + wandb.Image(common.tensor2im(y[i])), + wandb.Image(common.tensor2im(y_hat[i])), + ] + if id_logs is not None: + cur_im_data.append(id_logs[i]["diff_target"]) + im_data.append(cur_im_data) + outputs_table = wandb.Table(data=im_data, columns=column_names) + wandb.log({f"{prefix.title()} Step {step} Output Samples": outputs_table}) diff --git a/upsampler/video_editing.py b/upsampler/video_editing.py new file mode 100644 index 0000000000000000000000000000000000000000..7a2fb79d255d55694255d6e148707c01677ddabd --- /dev/null +++ b/upsampler/video_editing.py @@ -0,0 +1,124 @@ +import os +#os.environ['CUDA_VISIBLE_DEVICES'] = "0" + +from models.psp import pSp +import torch +import dlib +import cv2 +import PIL +import argparse +from tqdm import tqdm +import numpy as np +import torch.nn.functional as F +import torchvision +from torchvision import transforms, utils +from argparse import Namespace +from datasets import augmentations +from scripts.align_all_parallel import align_face +from latent_optimization import latent_optimization +from utils.inference_utils import save_image, load_image, visualize, get_video_crop_parameter, tensor2cv2, tensor2label, labelcolormap + +class TestOptions(): + def __init__(self): + + self.parser = argparse.ArgumentParser(description="StyleGANEX Video Editing") + self.parser.add_argument("--data_path", type=str, default='./data/390.mp4', help="path of the target image/video") + self.parser.add_argument("--ckpt", type=str, default='pretrained_models/styleganex_toonify_cartoon.pt', help="path of the saved model") + self.parser.add_argument("--output_path", type=str, default='./output/', help="path of the output results") + self.parser.add_argument("--scale_factor", type=float, default=1.0, help="scale of the editing degree") + self.parser.add_argument("--cpu", action="store_true", help="if true, only use cpu") + + def parse(self): + self.opt = self.parser.parse_args() + args = vars(self.opt) + print('Load options') + for name, value in sorted(args.items()): + print('%s: %s' % (str(name), str(value))) + return self.opt + + +if __name__ == "__main__": + parser = TestOptions() + args = parser.parse() + print('*'*98) + + device = "cpu" if args.cpu else "cuda" + + transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), + ]) + + ckpt = torch.load(args.ckpt, map_location='cpu') + opts = ckpt['opts'] + opts['checkpoint_path'] = args.ckpt + opts['device'] = device + opts = Namespace(**opts) + pspex = pSp(opts).to(device).eval() + pspex.latent_avg = pspex.latent_avg.to(device) + editing_w = None + if 'editing_w' in ckpt.keys(): + editing_w = ckpt['editing_w'].clone().to(device)[0:1] * args.scale_factor + + modelname = 'pretrained_models/shape_predictor_68_face_landmarks.dat' + if not os.path.exists(modelname): + import wget, bz2 + wget.download('http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2', modelname+'.bz2') + zipfile = bz2.BZ2File(modelname+'.bz2') + data = zipfile.read() + open(modelname, 'wb').write(data) + landmarkpredictor = dlib.shape_predictor(modelname) + + print('Load models successfully!') + + video_path = args.data_path + video_cap = cv2.VideoCapture(video_path) + success, frame = video_cap.read() + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + paras = get_video_crop_parameter(frame, landmarkpredictor) + assert paras is not None, 'StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ + You can try several times or use other videos until a face is detected, \ + then switch back to the original video.' + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + + x1 = transform(frame).unsqueeze(0).to(device) + with torch.no_grad(): + x2 = align_face(frame, landmarkpredictor) + x2 = transform(x2).unsqueeze(dim=0).to(device) + + save_name = '%s/%s_%s'%(args.output_path, os.path.basename(video_path).split('.')[0], os.path.basename(args.ckpt).split('.')[0]) + + num = int(video_cap.get(7)) + + if num == 1: # input is image + save_name = save_name + '.jpg' + else: # input is video + save_name = save_name + '.mp4' + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + videoWriter = cv2.VideoWriter(save_name, fourcc, video_cap.get(5), (4*W, 4*H)) + + with torch.no_grad(): + for i in tqdm(range(num)): + if i > 0: + success, frame = video_cap.read() + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + + x1 = transform(frame).unsqueeze(0).to(device) + y_hat = pspex(x1=x1, x2=x2, use_skip=pspex.opts.use_skip, zero_noise=True, + resize=False, editing_w=editing_w) + y_hat = torch.clamp(y_hat, -1, 1) + + if num > 1: + videoWriter.write(tensor2cv2(y_hat[0].cpu())) + + if num == 1: + save_image(y_hat[0].cpu(), save_name) + print('Image editing successfully!') + else: + videoWriter.release() + print('Video editing successfully!') + diff --git a/upsampler/webUI/app_task.py b/upsampler/webUI/app_task.py new file mode 100644 index 0000000000000000000000000000000000000000..d25e078746d93474c73c755300ee5617801dcba4 --- /dev/null +++ b/upsampler/webUI/app_task.py @@ -0,0 +1,314 @@ +from __future__ import annotations +from huggingface_hub import hf_hub_download +import numpy as np +import gradio as gr + + +def create_demo_sr(process): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## Face Super Resolution') + with gr.Row(): + with gr.Column(): + input_image = gr.Image(source='upload', type='filepath') + model_type = gr.Radio(label='Model Type', choices=['SR for 32x','SR for 4x-48x'], value='SR for 32x') + resize_scale = gr.Slider(label='Resize Scale', + minimum=4, + maximum=48, + value=32, + step=4) + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['pexels-daniel-xavier-1239291.jpg', 'SR for 32x', 32], + ['ILip77SbmOE.png', 'SR for 32x', 32], + ['ILip77SbmOE.png', 'SR for 4x-48x', 48], + ], + inputs = [input_image, model_type, resize_scale], + ) + with gr.Column(): + #lrinput = gr.Image(label='Low-resolution input',type='numpy', interactive=False) + #result = gr.Image(label='Output',type='numpy', interactive=False) + result = gr.Gallery(label='LR input and Output', + elem_id='gallery').style(grid=2, + height='auto') + + inputs = [ + input_image, + resize_scale, + model_type, + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[result], + api_name='sr') + return demo + +def create_demo_s2f(process): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## Sketch-to-Face Translation') + with gr.Row(): + with gr.Column(): + input_image = gr.Image(source='upload', type='filepath') + gr.Markdown("""Note: Input will be cropped if larger than 512x512.""") + seed = gr.Slider(label='Seed for appearance', + minimum=0, + maximum=2147483647, + step=1, + randomize=True) + #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['234_sketch.jpg', 1024]], + inputs = [input_image, seed], + ) + with gr.Column(): + result = gr.Image(label='Output',type='numpy', interactive=False) + + inputs = [ + input_image, seed + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[result], + api_name='s2f') + return demo + + +def create_demo_m2f(process): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## Mask-to-Face Translation') + with gr.Row(): + with gr.Column(): + input_image = gr.Image(source='upload', type='filepath') + input_type = gr.Radio(label='Input Type', choices=['color image','parsing mask'], value='color image') + seed = gr.Slider(label='Seed for appearance', + minimum=0, + maximum=2147483647, + step=1, + randomize=True) + #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['ILip77SbmOE.png', 'color image', 4], ['ILip77SbmOE_mask.png', 'parsing mask', 4]], + inputs = [input_image, input_type, seed], + ) + with gr.Column(): + #vizmask = gr.Image(label='Visualized mask',type='numpy', interactive=False) + #result = gr.Image(label='Output',type='numpy', interactive=False) + result = gr.Gallery(label='Visualized mask and Output', + elem_id='gallery').style(grid=2, + height='auto') + + inputs = [ + input_image, input_type, seed + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[result], + api_name='m2f') + return demo + +def create_demo_editing(process): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## Video Face Editing (for image input)') + with gr.Row(): + with gr.Column(): + input_image = gr.Image(source='upload', type='filepath') + model_type = gr.Radio(label='Editing Type', choices=['reduce age','light hair color'], value='color image') + scale_factor = gr.Slider(label='editing degree (-2~2)', + minimum=-2, + maximum=2, + value=1, + step=0.1) + #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['ILip77SbmOE.png', 'reduce age', -2], + ['ILip77SbmOE.png', 'light hair color', 1]], + inputs = [input_image, model_type, scale_factor], + ) + with gr.Column(): + result = gr.Image(label='Output',type='numpy', interactive=False) + + inputs = [ + input_image, scale_factor, model_type + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[result], + api_name='editing') + return demo + +def create_demo_toonify(process): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## Video Face Toonification (for image input)') + with gr.Row(): + with gr.Column(): + input_image = gr.Image(source='upload', type='filepath') + style_type = gr.Radio(label='Style Type', choices=['Pixar','Cartoon','Arcane'], value='Pixar') + #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['ILip77SbmOE.png', 'Pixar'], ['ILip77SbmOE.png', 'Cartoon'], ['ILip77SbmOE.png', 'Arcane']], + inputs = [input_image, style_type], + ) + with gr.Column(): + result = gr.Image(label='Output',type='numpy', interactive=False) + + inputs = [ + input_image, style_type + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[result], + api_name='toonify') + return demo + + +def create_demo_vediting(process, max_frame_num = 4): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## Video Face Editing (for video input)') + with gr.Row(): + with gr.Column(): + input_video = gr.Video(source='upload', mirror_webcam=False, type='filepath') + model_type = gr.Radio(label='Editing Type', choices=['reduce age','light hair color'], value='color image') + scale_factor = gr.Slider(label='editing degree (-2~2)', + minimum=-2, + maximum=2, + value=1, + step=0.1) + info = '' + if max_frame_num < 100: + info = '(full video editing is not allowed so as not to slow down the demo, \ + but you can duplicate the Space to modify the number limit to a large value)' + frame_num = gr.Slider(label='Number of frames to edit' + info, + minimum=1, + maximum=max_frame_num, + value=4, + step=1) + #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['684.mp4', 'reduce age', 1.5, 2], + ['684.mp4', 'light hair color', 0.7, 2]], + inputs = [input_video, model_type, scale_factor], + ) + with gr.Column(): + viz_result = gr.Gallery(label='Several edited frames', elem_id='gallery').style(grid=2, height='auto') + result = gr.Video(label='Output', type='mp4', interactive=False) + + inputs = [ + input_video, scale_factor, model_type, frame_num + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[viz_result, result], + api_name='vediting') + return demo + +def create_demo_vtoonify(process, max_frame_num = 4): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## Video Face Toonification (for video input)') + with gr.Row(): + with gr.Column(): + input_video = gr.Video(source='upload', mirror_webcam=False, type='filepath') + style_type = gr.Radio(label='Style Type', choices=['Pixar','Cartoon','Arcane'], value='Pixar') + info = '' + if max_frame_num < 100: + info = '(full video toonify is not allowed so as not to slow down the demo, \ + but you can duplicate the Space to modify the number limit from 4 to a large value)' + frame_num = gr.Slider(label='Number of frames to toonify' + info, + minimum=1, + maximum=max_frame_num, + value=4, + step=1) + #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['529_2.mp4', 'Arcane'], + ['pexels-anthony-shkraba-production-8136210.mp4', 'Pixar'], + ['684.mp4', 'Cartoon']], + inputs = [input_video, style_type], + ) + with gr.Column(): + viz_result = gr.Gallery(label='Several toonified frames', elem_id='gallery').style(grid=2, height='auto') + result = gr.Video(label='Output', type='mp4', interactive=False) + + inputs = [ + input_video, style_type, frame_num + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[viz_result, result], + api_name='vtoonify') + return demo + +def create_demo_inversion(process, allow_optimization=False): + with gr.Blocks() as demo: + with gr.Row(): + gr.Markdown('## StyleGANEX Inversion for Editing') + with gr.Row(): + with gr.Column(): + input_image = gr.Image(source='upload', type='filepath') + info = '' + if allow_optimization == False: + info = ' (latent optimization is not allowed so as not to slow down the demo, \ + but you can duplicate the Space to modify the option or directly upload an optimized latent file. \ + The file can be computed by inversion.py from the github page or colab)' + optimize = gr.Radio(label='Whether optimize latent' + info, choices=['No optimization','Latent optimization'], + value='No optimization', interactive=allow_optimization) + input_latent = gr.File(label='Optimized latent code (optional)', file_types=[".pt"]) + editing_options = gr.Dropdown(['None', 'Style Mixing', + 'Attribute Editing: smile', + 'Attribute Editing: open_eye', + 'Attribute Editing: open_mouth', + 'Attribute Editing: pose', + 'Attribute Editing: reduce_age', + 'Attribute Editing: glasses', + 'Attribute Editing: light_hair_color', + 'Attribute Editing: slender', + 'Domain Transfer: disney_princess', + 'Domain Transfer: vintage_comics', + 'Domain Transfer: pixar', + 'Domain Transfer: edvard_munch', + 'Domain Transfer: modigliani', + ], + label="editing options (based on StyleGAN-NADA, InterFaceGAN, LowRankGAN)", + value='None') + scale_factor = gr.Slider(label='editing degree (-2~2) for Attribute Editing', + minimum=-2, + maximum=2, + value=2, + step=0.1) + seed = gr.Slider(label='Appearance Seed for Style Mixing', + minimum=0, + maximum=2147483647, + step=1, + randomize=True) + #input_info = gr.Textbox(label='Process Information', interactive=False, value='n.a.') + run_button = gr.Button(label='Run') + gr.Examples( + examples =[['ILip77SbmOE.png', 'ILip77SbmOE_inversion.pt', 'Domain Transfer: vintage_comics'], + ['ILip77SbmOE.png', 'ILip77SbmOE_inversion.pt', 'Attribute Editing: smile'], + ['ILip77SbmOE.png', 'ILip77SbmOE_inversion.pt', 'Style Mixing'], + ], + inputs = [input_image, input_latent, editing_options], + ) + with gr.Column(): + result = gr.Image(label='Inversion output',type='numpy', interactive=False) + editing_result = gr.Image(label='Editing output',type='numpy', interactive=False) + + inputs = [ + input_image, optimize, input_latent, editing_options, scale_factor, seed + ] + run_button.click(fn=process, + inputs=inputs, + outputs=[result, editing_result], + api_name='inversion') + return demo diff --git a/upsampler/webUI/styleganex_model.py b/upsampler/webUI/styleganex_model.py new file mode 100644 index 0000000000000000000000000000000000000000..bc33ed9b25f27ca641e9a1c4af9f9bcd4d324e7b --- /dev/null +++ b/upsampler/webUI/styleganex_model.py @@ -0,0 +1,492 @@ +from __future__ import annotations +import numpy as np +import gradio as gr + +import os +import pathlib +import gc +import torch +import dlib +import cv2 +import PIL +from tqdm import tqdm +import numpy as np +import torch.nn.functional as F +import torchvision +from torchvision import transforms, utils +from argparse import Namespace +from datasets import augmentations +from huggingface_hub import hf_hub_download +from scripts.align_all_parallel import align_face +from latent_optimization import latent_optimization +from utils.inference_utils import save_image, load_image, visualize, get_video_crop_parameter, tensor2cv2, tensor2label, labelcolormap +from models.psp import pSp +from models.bisenet.model import BiSeNet +from models.stylegan2.model import Generator + +class Model(): + def __init__(self, device): + super().__init__() + + self.device = device + self.task_name = None + self.editing_w = None + self.pspex = None + self.landmarkpredictor = dlib.shape_predictor(hf_hub_download('PKUWilliamYang/VToonify', 'models/shape_predictor_68_face_landmarks.dat')) + self.transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5,0.5,0.5]), + ]) + self.to_tensor = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + self.maskpredictor = BiSeNet(n_classes=19) + self.maskpredictor.load_state_dict(torch.load(hf_hub_download('PKUWilliamYang/VToonify', 'models/faceparsing.pth'), map_location='cpu')) + self.maskpredictor.to(self.device).eval() + self.parameters = {} + self.parameters['inversion'] = {'path':'pretrained_models/styleganex_inversion.pt', 'image_path':'./data/ILip77SbmOE.png'} + self.parameters['sr-32'] = {'path':'pretrained_models/styleganex_sr32.pt', 'image_path':'./data/pexels-daniel-xavier-1239291.jpg'} + self.parameters['sr'] = {'path':'pretrained_models/styleganex_sr.pt', 'image_path':'./data/pexels-daniel-xavier-1239291.jpg'} + self.parameters['sketch2face'] = {'path':'pretrained_models/styleganex_sketch2face.pt', 'image_path':'./data/234_sketch.jpg'} + self.parameters['mask2face'] = {'path':'pretrained_models/styleganex_mask2face.pt', 'image_path':'./data/540.jpg'} + self.parameters['edit_age'] = {'path':'pretrained_models/styleganex_edit_age.pt', 'image_path':'./data/390.mp4'} + self.parameters['edit_hair'] = {'path':'pretrained_models/styleganex_edit_hair.pt', 'image_path':'./data/390.mp4'} + self.parameters['toonify_pixar'] = {'path':'pretrained_models/styleganex_toonify_pixar.pt', 'image_path':'./data/pexels-anthony-shkraba-production-8136210.mp4'} + self.parameters['toonify_cartoon'] = {'path':'pretrained_models/styleganex_toonify_cartoon.pt', 'image_path':'./data/pexels-anthony-shkraba-production-8136210.mp4'} + self.parameters['toonify_arcane'] = {'path':'pretrained_models/styleganex_toonify_arcane.pt', 'image_path':'./data/pexels-anthony-shkraba-production-8136210.mp4'} + self.print_log = True + self.editing_dicts = torch.load(hf_hub_download('PKUWilliamYang/StyleGANEX', 'direction_dics.pt')) + self.generator = Generator(1024, 512, 8) + self.model_type = None + self.error_info = 'Error: no face detected! \ + StyleGANEX uses dlib.get_frontal_face_detector but sometimes it fails to detect a face. \ + You can try several times or use other images until a face is detected, \ + then switch back to the original image.' + + def load_model(self, task_name: str) -> None: + if task_name == self.task_name: + return + if self.pspex is not None: + del self.pspex + torch.cuda.empty_cache() + gc.collect() + path = self.parameters[task_name]['path'] + local_path = hf_hub_download('PKUWilliamYang/StyleGANEX', path) + ckpt = torch.load(local_path, map_location='cpu') + opts = ckpt['opts'] + opts['checkpoint_path'] = local_path + opts['device'] = self.device + opts = Namespace(**opts) + self.pspex = pSp(opts, ckpt).to(self.device).eval() + self.pspex.latent_avg = self.pspex.latent_avg.to(self.device) + if 'editing_w' in ckpt.keys(): + self.editing_w = ckpt['editing_w'].clone().to(self.device) + self.task_name = task_name + torch.cuda.empty_cache() + gc.collect() + + def load_G_model(self, model_type: str) -> None: + if model_type == self.model_type: + return + torch.cuda.empty_cache() + gc.collect() + local_path = hf_hub_download('rinong/stylegan-nada-models', model_type+'.pt') + self.generator.load_state_dict(torch.load(local_path, map_location='cpu')['g_ema'], strict=False) + self.generator.to(self.device).eval() + self.model_type = model_type + torch.cuda.empty_cache() + gc.collect() + + def tensor2np(self, img): + tmp = ((img.cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8) + return tmp + + def process_sr(self, input_image: str, resize_scale: int, model: str) -> list[np.ndarray]: + #false_image = np.zeros((256,256,3), np.uint8) + #info = 'Error: no face detected! Please retry or change the photo.' + + if input_image is None: + #return [false_image, false_image], 'Error: fail to load empty file.' + raise gr.Error("Error: fail to load empty file.") + frame = cv2.imread(input_image) + if frame is None: + #return [false_image, false_image], 'Error: fail to load the image.' + raise gr.Error("Error: fail to load the image.") + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + if model is None or model == 'SR for 32x': + task_name = 'sr-32' + resize_scale = 32 + else: + task_name = 'sr' + + with torch.no_grad(): + paras = get_video_crop_parameter(frame, self.landmarkpredictor) + if paras is None: + #return [false_image, false_image], info + raise gr.Error(self.error_info) + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = PIL.Image.fromarray(np.uint8(frame)) + x1 = augmentations.BilinearResize(factors=[resize_scale//4])(x1) + x1_up = x1.resize((W, H)) + x2_up = align_face(np.array(x1_up), self.landmarkpredictor) + if x2_up is None: + #return [false_image, false_image], 'Error: no face detected! Please retry or change the photo.' + raise gr.Error(self.error_info) + x1_up = transforms.ToTensor()(x1_up).unsqueeze(dim=0).to(self.device) * 2 - 1 + x2_up = self.transform(x2_up).unsqueeze(dim=0).to(self.device) + if self.print_log: print('image loaded') + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + y_hat = torch.clamp(self.pspex(x1=x1_up, x2=x2_up, use_skip=self.pspex.opts.use_skip, resize=False), -1, 1) + + return [self.tensor2np(x1_up[0]), self.tensor2np(y_hat[0])] + + + def process_s2f(self, input_image: str, seed: int) -> np.ndarray: + task_name = 'sketch2face' + with torch.no_grad(): + x1 = transforms.ToTensor()(PIL.Image.open(input_image)).unsqueeze(0).to(self.device) + if x1.shape[2] > 513: + x1 = x1[:,:,(x1.shape[2]//2-256)//8*8:(x1.shape[2]//2+256)//8*8] + if x1.shape[3] > 513: + x1 = x1[:,:,:,(x1.shape[3]//2-256)//8*8:(x1.shape[3]//2+256)//8*8] + x1 = x1[:,0:1] # uploaded files will be transformed to 3-channel RGB image! + if self.print_log: print('image loaded') + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + self.pspex.train() + torch.manual_seed(seed) + y_hat = self.pspex(x1=x1, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=self.pspex.opts.use_skip, + inject_latent= self.pspex.decoder.style(torch.randn(1, 512).to(self.device)).unsqueeze(1).repeat(1,18,1) * 0.7) + y_hat = torch.clamp(y_hat, -1, 1) + self.pspex.eval() + return self.tensor2np(y_hat[0]) + + def process_m2f(self, input_image: str, input_type: str, seed: int) -> list[np.ndarray]: + #false_image = np.zeros((256,256,3), np.uint8) + if input_image is None: + raise gr.Error('Error: fail to load empty file.' ) + #return [false_image, false_image], 'Error: fail to load empty file.' + task_name = 'mask2face' + with torch.no_grad(): + if input_type == 'parsing mask': + x1 = PIL.Image.open(input_image).getchannel(0) # uploaded files will be transformed to 3-channel RGB image! + x1 = augmentations.ToOneHot(19)(x1) + x1 = transforms.ToTensor()(x1).unsqueeze(dim=0).float().to(self.device) + #print(x1.shape) + else: + frame = cv2.imread(input_image) + if frame is None: + #return [false_image, false_image], 'Error: fail to load the image.' + raise gr.Error('Error: fail to load the image.' ) + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + paras = get_video_crop_parameter(frame, self.landmarkpredictor) + if paras is None: + #return [false_image, false_image], 'Error: no face detected! Please retry or change the photo.' + raise gr.Error(self.error_info) + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + # convert face image to segmentation mask + x1 = self.to_tensor(frame).unsqueeze(0).to(self.device) + # upsample image for precise segmentation + x1 = F.interpolate(x1, scale_factor=2, mode='bilinear') + x1 = self.maskpredictor(x1)[0] + x1 = F.interpolate(x1, scale_factor=0.5).argmax(dim=1) + x1 = F.one_hot(x1, num_classes=19).permute(0, 3, 1, 2).float().to(self.device) + + if x1.shape[2] > 513: + x1 = x1[:,:,(x1.shape[2]//2-256)//8*8:(x1.shape[2]//2+256)//8*8] + if x1.shape[3] > 513: + x1 = x1[:,:,:,(x1.shape[3]//2-256)//8*8:(x1.shape[3]//2+256)//8*8] + + x1_viz = (tensor2label(x1[0], 19) / 192 * 256).astype(np.uint8) + + if self.print_log: print('image loaded') + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + self.pspex.train() + torch.manual_seed(seed) + y_hat = self.pspex(x1=x1, resize=False, latent_mask=[8,9,10,11,12,13,14,15,16,17], use_skip=self.pspex.opts.use_skip, + inject_latent= self.pspex.decoder.style(torch.randn(1, 512).to(self.device)).unsqueeze(1).repeat(1,18,1) * 0.7) + y_hat = torch.clamp(y_hat, -1, 1) + self.pspex.eval() + return [x1_viz, self.tensor2np(y_hat[0])] + + + def process_editing(self, input_image: str, scale_factor: float, model_type: str) -> np.ndarray: + #false_image = np.zeros((256,256,3), np.uint8) + #info = 'Error: no face detected! Please retry or change the photo.' + + if input_image is None: + #return false_image, false_image, 'Error: fail to load empty file.' + raise gr.Error('Error: fail to load empty file.') + frame = cv2.imread(input_image) + if frame is None: + #return false_image, false_image, 'Error: fail to load the image.' + raise gr.Error('Error: fail to load the image.') + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + if model_type is None or model_type == 'reduce age': + task_name = 'edit_age' + else: + task_name = 'edit_hair' + + with torch.no_grad(): + paras = get_video_crop_parameter(frame, self.landmarkpredictor) + if paras is None: + #return false_image, false_image, info + raise gr.Error(self.error_info) + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = self.transform(frame).unsqueeze(0).to(self.device) + x2 = align_face(frame, self.landmarkpredictor) + if x2 is None: + #return false_image, 'Error: no face detected! Please retry or change the photo.' + raise gr.Error(self.error_info) + x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) + if self.print_log: print('image loaded') + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, + resize=False, editing_w= - scale_factor* self.editing_w[0:1]) + y_hat = torch.clamp(y_hat, -1, 1) + + return self.tensor2np(y_hat[0]) + + def process_vediting(self, input_video: str, scale_factor: float, model_type: str, frame_num: int) -> tuple[list[np.ndarray], str]: + #false_image = np.zeros((256,256,3), np.uint8) + #info = 'Error: no face detected! Please retry or change the video.' + + if input_video is None: + #return [false_image], 'default.mp4', 'Error: fail to load empty file.' + raise gr.Error('Error: fail to load empty file.') + video_cap = cv2.VideoCapture(input_video) + success, frame = video_cap.read() + if success is False: + #return [false_image], 'default.mp4', 'Error: fail to load the video.' + raise gr.Error('Error: fail to load the video.') + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + if model_type is None or model_type == 'reduce age': + task_name = 'edit_age' + else: + task_name = 'edit_hair' + + with torch.no_grad(): + paras = get_video_crop_parameter(frame, self.landmarkpredictor) + if paras is None: + #return [false_image], 'default.mp4', info + raise gr.Error(self.error_info) + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = self.transform(frame).unsqueeze(0).to(self.device) + x2 = align_face(frame, self.landmarkpredictor) + if x2 is None: + #return [false_image], 'default.mp4', info + raise gr.Error(self.error_info) + x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) + if self.print_log: print('first frame loaded') + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + videoWriter = cv2.VideoWriter('output.mp4', fourcc, video_cap.get(5), (4*W, 4*H)) + + viz_frames = [] + for i in range(frame_num): + if i > 0: + success, frame = video_cap.read() + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = self.transform(frame).unsqueeze(0).to(self.device) + y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, + resize=False, editing_w= - scale_factor * self.editing_w[0:1]) + y_hat = torch.clamp(y_hat, -1, 1) + videoWriter.write(tensor2cv2(y_hat[0].cpu())) + if i < min(frame_num, 4): + viz_frames += [self.tensor2np(y_hat[0])] + + videoWriter.release() + + return viz_frames, 'output.mp4' + + + def process_toonify(self, input_image: str, style_type: str) -> np.ndarray: + #false_image = np.zeros((256,256,3), np.uint8) + #info = 'Error: no face detected! Please retry or change the photo.' + + if input_image is None: + raise gr.Error('Error: fail to load empty file.') + #return false_image, false_image, 'Error: fail to load empty file.' + frame = cv2.imread(input_image) + if frame is None: + raise gr.Error('Error: fail to load the image.') + #return false_image, false_image, 'Error: fail to load the image.' + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + if style_type is None or style_type == 'Pixar': + task_name = 'toonify_pixar' + elif style_type == 'Cartoon': + task_name = 'toonify_cartoon' + else: + task_name = 'toonify_arcane' + + with torch.no_grad(): + paras = get_video_crop_parameter(frame, self.landmarkpredictor) + if paras is None: + raise gr.Error(self.error_info) + #return false_image, false_image, info + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = self.transform(frame).unsqueeze(0).to(self.device) + x2 = align_face(frame, self.landmarkpredictor) + if x2 is None: + raise gr.Error(self.error_info) + #return false_image, 'Error: no face detected! Please retry or change the photo.' + x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) + if self.print_log: print('image loaded') + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, resize=False) + y_hat = torch.clamp(y_hat, -1, 1) + + return self.tensor2np(y_hat[0]) + + + def process_vtoonify(self, input_video: str, style_type: str, frame_num: int) -> tuple[list[np.ndarray], str]: + #false_image = np.zeros((256,256,3), np.uint8) + #info = 'Error: no face detected! Please retry or change the video.' + + if input_video is None: + raise gr.Error('Error: fail to load empty file.') + #return [false_image], 'default.mp4', 'Error: fail to load empty file.' + video_cap = cv2.VideoCapture(input_video) + success, frame = video_cap.read() + if success is False: + raise gr.Error('Error: fail to load the video.') + #return [false_image], 'default.mp4', 'Error: fail to load the video.' + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + if style_type is None or style_type == 'Pixar': + task_name = 'toonify_pixar' + elif style_type == 'Cartoon': + task_name = 'toonify_cartoon' + else: + task_name = 'toonify_arcane' + + with torch.no_grad(): + paras = get_video_crop_parameter(frame, self.landmarkpredictor) + if paras is None: + raise gr.Error(self.error_info) + #return [false_image], 'default.mp4', info + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = self.transform(frame).unsqueeze(0).to(self.device) + x2 = align_face(frame, self.landmarkpredictor) + if x2 is None: + raise gr.Error(self.error_info) + #return [false_image], 'default.mp4', info + x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) + if self.print_log: print('first frame loaded') + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + videoWriter = cv2.VideoWriter('output.mp4', fourcc, video_cap.get(5), (4*W, 4*H)) + + viz_frames = [] + for i in range(frame_num): + if i > 0: + success, frame = video_cap.read() + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = self.transform(frame).unsqueeze(0).to(self.device) + y_hat = self.pspex(x1=x1, x2=x2, use_skip=self.pspex.opts.use_skip, zero_noise=True, resize=False) + y_hat = torch.clamp(y_hat, -1, 1) + videoWriter.write(tensor2cv2(y_hat[0].cpu())) + if i < min(frame_num, 4): + viz_frames += [self.tensor2np(y_hat[0])] + + videoWriter.release() + + return viz_frames, 'output.mp4' + + + def process_inversion(self, input_image: str, optimize: str, input_latent: file-object, editing_options: str, + scale_factor: float, seed: int) -> tuple[np.ndarray, np.ndarray]: + #false_image = np.zeros((256,256,3), np.uint8) + #info = 'Error: no face detected! Please retry or change the photo.' + + if input_image is None: + raise gr.Error('Error: fail to load empty file.') + #return false_image, false_image, 'Error: fail to load empty file.' + frame = cv2.imread(input_image) + if frame is None: + raise gr.Error('Error: fail to load the image.') + #return false_image, false_image, 'Error: fail to load the image.' + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + task_name = 'inversion' + self.load_model(task_name) + if self.print_log: print('model %s loaded'%(task_name)) + if input_latent is not None: + if '.pt' not in input_latent.name: + raise gr.Error('Error: the latent format is wrong') + #return false_image, false_image, 'Error: the latent format is wrong' + latents = torch.load(input_latent.name) + if 'wplus' not in latents.keys() or 'f' not in latents.keys(): + raise gr.Error('Error: the latent format is wrong') + #return false_image, false_image, 'Error: the latent format is wrong' + wplus = latents['wplus'].to(self.device) # w+ + f = [latents['f'][0].to(self.device)] # f + elif optimize == 'Latent optimization': + wplus, f, _, _, _ = latent_optimization(frame, self.pspex, self.landmarkpredictor, + step=500, device=self.device) + else: + with torch.no_grad(): + paras = get_video_crop_parameter(frame, self.landmarkpredictor) + if paras is None: + raise gr.Error(self.error_info) + #return false_image, false_image, info + h,w,top,bottom,left,right,scale = paras + H, W = int(bottom-top), int(right-left) + frame = cv2.resize(frame, (w, h))[top:bottom, left:right] + x1 = self.transform(frame).unsqueeze(0).to(self.device) + x2 = align_face(frame, self.landmarkpredictor) + if x2 is None: + raise gr.Error(self.error_info) + #return false_image, false_image, 'Error: no face detected! Please retry or change the photo.' + x2 = self.transform(x2).unsqueeze(dim=0).to(self.device) + if self.print_log: print('image loaded') + wplus = self.pspex.encoder(x2) + self.pspex.latent_avg.unsqueeze(0) + _, f = self.pspex.encoder(x1, return_feat=True) + + with torch.no_grad(): + y_hat, _ = self.pspex.decoder([wplus], input_is_latent=True, first_layer_feature=f) + y_hat = torch.clamp(y_hat, -1, 1) + + if 'Style Mixing' in editing_options: + torch.manual_seed(seed) + wplus[:, 8:] = self.pspex.decoder.style(torch.randn(1, 512).to(self.device)).unsqueeze(1).repeat(1,10,1) * 0.7 + y_hat_edit, _ = self.pspex.decoder([wplus], input_is_latent=True, first_layer_feature=f) + elif 'Attribute Editing' in editing_options: + editing_w = self.editing_dicts[editing_options[19:]].to(self.device) + y_hat_edit, _ = self.pspex.decoder([wplus+scale_factor*editing_w], input_is_latent=True, first_layer_feature=f) + elif 'Domain Transfer' in editing_options: + self.load_G_model(editing_options[17:]) + if self.print_log: print('model %s loaded'%(editing_options[17:])) + y_hat_edit, _ = self.generator([wplus], input_is_latent=True, first_layer_feature=f) + else: + y_hat_edit = y_hat + y_hat_edit = torch.clamp(y_hat_edit, -1, 1) + + return self.tensor2np(y_hat[0]), self.tensor2np(y_hat_edit[0])