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Zero
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# Copyright (c) OpenMMLab. All rights reserved.
import os.path
import cv2
import mmengine
from mmengine.runner import ValLoop as MMENGINE_ValLoop
from mmengine.dist import broadcast_object_list, is_main_process, get_world_size, get_rank, barrier, collect_results
import math
import torch
from mmengine.model import is_model_wrapper
from types import MethodType
from xtuner.utils import PROMPT_TEMPLATE
from xtuner.tools.utils import get_stop_criteria
from transformers import GenerationConfig
from pycocotools import mask as _mask
from mmengine.visualization.visualizer import Visualizer
from vlm.utils import VideoReader
TORCH_DTYPE_MAP = dict(fp16=torch.float16, bf16=torch.bfloat16, fp32=torch.float32, auto='auto')
VID_INTERVAL = 4
def visualize(data_batch, prediction, visualize_path='work_dirs/visualize'):
if 'video_path' in data_batch:
vid_frames = VideoReader(data_batch['video_path'])[::VID_INTERVAL]
vid_id = os.path.basename(data_batch['video_path']).split('.')[0]
text_prompts = data_batch['text_prompts']
mmengine.mkdir_or_exist(os.path.join(visualize_path, vid_id))
visualizer = Visualizer()
mmengine.mkdir_or_exist(os.path.join(visualize_path, vid_id, "vid"))
for id_frame, img in enumerate(vid_frames):
out_path = os.path.join(visualize_path, vid_id, "vid", "{:06d}.jpg".format(id_frame))
cv2.imwrite(out_path, img)
for id_text, text in enumerate(text_prompts):
mmengine.mkdir_or_exist(os.path.join(visualize_path, vid_id, "sample_{:06d}".format(id_text)))
mmengine.put_text(text, os.path.join(visualize_path, vid_id, "sample_{:06d}".format(id_text), 'text.txt'))
for id_frame, img in enumerate(vid_frames):
visualizer.set_image(img)
mask = prediction['prediction_masks'][id_text][id_frame]
mask = _mask.decode(mask).astype(bool)
visualizer.draw_binary_masks(mask, colors='g')
visual_result = visualizer.get_image()
out_path = os.path.join(visualize_path, vid_id, "sample_{:06d}".format(id_text),
"{:06d}.jpg".format(id_frame))
cv2.imwrite(out_path, visual_result)
else:
images_files = data_batch['images']
vid_id = data_batch['video_id']
text_prompts = data_batch['text_prompts']
image_folder = data_batch['image_folder']
mmengine.mkdir_or_exist(os.path.join(visualize_path, "{:06d}".format(vid_id)))
visualizer = Visualizer()
mmengine.mkdir_or_exist(os.path.join(visualize_path, "{:06d}".format(vid_id), "vid"))
for id_frame, img_file in enumerate(images_files):
img = cv2.imread(os.path.join(image_folder, img_file))
out_path = os.path.join(visualize_path, "{:06d}".format(vid_id), "vid", os.path.basename(img_file))
cv2.imwrite(out_path, img)
for id_text, text in enumerate(text_prompts):
mmengine.mkdir_or_exist(os.path.join(visualize_path, "{:06d}".format(vid_id), "sample_{:06d}".format(id_text)))
mmengine.put_text(text, os.path.join(visualize_path, "{:06d}".format(vid_id), "sample_{:06d}".format(id_text),
'text.txt'))
for id_frame, img_file in enumerate(images_files):
img = cv2.imread(os.path.join(image_folder, img_file))
visualizer.set_image(img)
mask = prediction['prediction_masks'][id_text][id_frame]
mask = _mask.decode(mask).astype(bool)
visualizer.draw_binary_masks(mask, colors='g')
visual_result = visualizer.get_image()
out_path = os.path.join(visualize_path, "{:06d}".format(vid_id), "sample_{:06d}".format(id_text),
os.path.basename(img_file))
cv2.imwrite(out_path, visual_result)
class VideoTestLoop(MMENGINE_ValLoop):
def __init__(self, runner, dataloader, torch_dtype='fp16', select_metric='first', visualize=None, evaluator=None) -> None:
# must be concatset
super(MMENGINE_ValLoop, self).__init__(runner, dataloader)
self._runner = runner
self.torch_dtype = torch_dtype
if torch_dtype is not None:
self.torch_dtype = TORCH_DTYPE_MAP[torch_dtype]
self.select_metric = select_metric
self.visualize = visualize
self.evaluator = evaluator
def run(self) -> dict:
"""Launch Test."""
self.runner.logger.info('==================== Start test loop ===================')
self.runner.call_hook('before_test')
self.runner.call_hook('before_test_epoch')
if is_model_wrapper(self.runner.model):
model = self.runner.model.module
else:
model = self.runner.model
model.gradient_checkpointing_disable()
model.eval()
model.cuda()
rank = get_rank()
metrics = []
# Ensure that eta and log are displayed correctly.
current_run_total_ids = 0
for _, dataset in enumerate(self.dataloader.dataset.datasets):
if not hasattr(model, 'preparing_for_generation'):
model.preparing_for_generation = MethodType(default_preparing_for_generation, model)
print("Warning, the model do not have the preparing_for_generation() function, using the default!!!")
model.preparing_for_generation(dataset.metainfo)
# split per rank
results = []
n_samples = len(dataset)
per_rank_samples = math.ceil(n_samples / get_world_size())
running_tot = per_rank_samples * get_world_size()
assert running_tot >= n_samples
per_rank_ids = range(per_rank_samples * rank, per_rank_samples * (rank + 1))
for idx in per_rank_ids:
if n_samples <= idx:
data_batch = dataset[n_samples - 1]
else:
data_batch = dataset[idx]
self.run_iter(current_run_total_ids, data_batch, results, model)
current_run_total_ids += 1
barrier()
self.runner.logger.info('==================== Start collect results ===================')
results = collect_results(results, n_samples)
self.runner.logger.info('========= Starting the evaluation of a data ===========')
if is_main_process():
metric = dataset.evaluate(results, self.runner.work_dir)
objects = [metric]
else:
objects = [None]
broadcast_object_list(objects)
metric = objects[0]
metrics.append(metric)
# select metrics
if self.select_metric == 'first':
metrics = metrics[0]
else:
raise NotImplementedError
self.runner.logger.info('================ Ending test loop ================')
self.runner.call_hook('after_test_epoch', metrics=metrics)
self.runner.call_hook('after_test')
return metrics
@torch.no_grad()
def run_iter(self, idx, data_batch, results, model):
prediction = {'video_id': data_batch['video_id']}
self.runner.call_hook(
'before_test_iter', batch_idx=idx, data_batch=data_batch)
outputs = model.predict_forward(**data_batch)
prediction.update(outputs)
results.append(prediction)
if self.visualize:
# if not prediction['is_exists'][0].all():
# print(prediction['is_exists'])
visualize(data_batch=data_batch, prediction=prediction, visualize_path=self.visualize)
self.runner.call_hook(
'after_test_iter',
batch_idx=idx,
data_batch=data_batch,
outputs=outputs)
def default_preparing_for_generation(self, metainfo):
# set stop criteria and generation configs for model
assert hasattr(self, 'tokenizer'), "The Model does not have the tokenizer!!!"
self.bot_name = 'BOT'
template = PROMPT_TEMPLATE['internlm2_chat']
self.template = template
stop_words = []
stop_words += template.get('STOP_WORDS', [])
stop_criteria = get_stop_criteria(
tokenizer=self.tokenizer, stop_words=stop_words)
self.stop_criteria = stop_criteria
default_generation_kwargs = dict(
max_new_tokens=2048,
do_sample=False,
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=(
self.tokenizer.pad_token_id
if self.tokenizer.pad_token_id is not None
else self.tokenizer.eos_token_id
),
)
default_generation_kwargs.update(metainfo.get('generation_kwargs', {}))
self.gen_config = GenerationConfig(**default_generation_kwargs)
return
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