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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
# Please use this implementation in your products
# This implementation may produce slightly different results from Saining Xie's official implementations,
# but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations.
# Different from official models and other implementations, this is an RGB-input model (rather than BGR)
# and in this way it works better for gradio's RGB protocol
from abc import ABCMeta
import cv2
import numpy as np
import torch
from einops import rearrange
from scepter.modules.annotator.base_annotator import BaseAnnotator
from scepter.modules.annotator.registry import ANNOTATORS
from scepter.modules.utils.config import dict_to_yaml
from scepter.modules.utils.distribute import we
from scepter.modules.utils.file_system import FS
def nms(x, t, s):
x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
y = np.zeros_like(x)
for f in [f1, f2, f3, f4]:
np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
z = np.zeros_like(y, dtype=np.uint8)
z[y > t] = 255
return z
class DoubleConvBlock(torch.nn.Module):
def __init__(self, input_channel, output_channel, layer_number):
super().__init__()
self.convs = torch.nn.Sequential()
self.convs.append(
torch.nn.Conv2d(in_channels=input_channel,
out_channels=output_channel,
kernel_size=(3, 3),
stride=(1, 1),
padding=1))
for i in range(1, layer_number):
self.convs.append(
torch.nn.Conv2d(in_channels=output_channel,
out_channels=output_channel,
kernel_size=(3, 3),
stride=(1, 1),
padding=1))
self.projection = torch.nn.Conv2d(in_channels=output_channel,
out_channels=1,
kernel_size=(1, 1),
stride=(1, 1),
padding=0)
def __call__(self, x, down_sampling=False):
h = x
if down_sampling:
h = torch.nn.functional.max_pool2d(h,
kernel_size=(2, 2),
stride=(2, 2))
for conv in self.convs:
h = conv(h)
h = torch.nn.functional.relu(h)
return h, self.projection(h)
class ControlNetHED_Apache2(torch.nn.Module):
def __init__(self):
super().__init__()
self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1)))
self.block1 = DoubleConvBlock(input_channel=3,
output_channel=64,
layer_number=2)
self.block2 = DoubleConvBlock(input_channel=64,
output_channel=128,
layer_number=2)
self.block3 = DoubleConvBlock(input_channel=128,
output_channel=256,
layer_number=3)
self.block4 = DoubleConvBlock(input_channel=256,
output_channel=512,
layer_number=3)
self.block5 = DoubleConvBlock(input_channel=512,
output_channel=512,
layer_number=3)
def __call__(self, x):
h = x - self.norm
h, projection1 = self.block1(h)
h, projection2 = self.block2(h, down_sampling=True)
h, projection3 = self.block3(h, down_sampling=True)
h, projection4 = self.block4(h, down_sampling=True)
h, projection5 = self.block5(h, down_sampling=True)
return projection1, projection2, projection3, projection4, projection5
@ANNOTATORS.register_class()
class HedAnnotator(BaseAnnotator, metaclass=ABCMeta):
para_dict = {}
def __init__(self, cfg, logger=None):
super().__init__(cfg, logger=logger)
self.netNetwork = ControlNetHED_Apache2().float().eval()
pretrained_model = cfg.get('PRETRAINED_MODEL', None)
if pretrained_model:
with FS.get_from(pretrained_model, wait_finish=True) as local_path:
self.netNetwork.load_state_dict(torch.load(local_path))
@torch.no_grad()
@torch.inference_mode()
@torch.autocast('cuda', enabled=False)
def forward(self, image):
if isinstance(image, torch.Tensor):
if len(image.shape) == 3:
image = rearrange(image, 'h w c -> 1 c h w')
B, C, H, W = image.shape
else:
raise "Unsurpport input image's shape"
elif isinstance(image, np.ndarray):
image = torch.from_numpy(image.copy()).float()
if len(image.shape) == 3:
image = rearrange(image, 'h w c -> 1 c h w')
B, C, H, W = image.shape
else:
raise "Unsurpport input image's shape"
else:
raise "Unsurpport input image's type"
edges = self.netNetwork(image.to(we.device_id))
edges = [
e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges
]
edges = [
cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR)
for e in edges
]
edges = np.stack(edges, axis=2)
edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
edge = 255 - (edge * 255.0).clip(0, 255).astype(np.uint8)
return edge[..., None].repeat(3, 2)
@staticmethod
def get_config_template():
return dict_to_yaml('ANNOTATORS',
__class__.__name__,
HedAnnotator.para_dict,
set_name=True)
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