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video_base_model.py
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video_base_model.py
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import torch
from collections import Counter
from os import path as osp
from torch import distributed as dist
from tqdm import tqdm
from basicsr.metrics import calculate_metric
from basicsr.utils import get_root_logger, imwrite, tensor2img
from basicsr.utils.dist_util import get_dist_info
from basicsr.utils.registry import MODEL_REGISTRY
from .sr_model import SRModel
@MODEL_REGISTRY.register()
class VideoBaseModel(SRModel):
"""Base video SR model."""
def dist_validation(self, dataloader, current_iter, tb_logger, save_img):
dataset = dataloader.dataset
dataset_name = dataset.opt['name']
with_metrics = self.opt['val']['metrics'] is not None
# initialize self.metric_results
# It is a dict: {
# 'folder1': tensor (num_frame x len(metrics)),
# 'folder2': tensor (num_frame x len(metrics))
# }
if with_metrics:
if not hasattr(self, 'metric_results'): # only execute in the first run
self.metric_results = {}
num_frame_each_folder = Counter(dataset.data_info['folder'])
for folder, num_frame in num_frame_each_folder.items():
self.metric_results[folder] = torch.zeros(
num_frame, len(self.opt['val']['metrics']), dtype=torch.float32, device='cuda')
# initialize the best metric results
self._initialize_best_metric_results(dataset_name)
# zero self.metric_results
rank, world_size = get_dist_info()
if with_metrics:
for _, tensor in self.metric_results.items():
tensor.zero_()
metric_data = dict()
# record all frames (border and center frames)
if rank == 0:
pbar = tqdm(total=len(dataset), unit='frame')
for idx in range(rank, len(dataset), world_size):
val_data = dataset[idx]
val_data['lq'].unsqueeze_(0)
val_data['gt'].unsqueeze_(0)
folder = val_data['folder']
frame_idx, max_idx = val_data['idx'].split('/')
lq_path = val_data['lq_path']
self.feed_data(val_data)
self.test()
visuals = self.get_current_visuals()
result_img = tensor2img([visuals['result']])
metric_data['img'] = result_img
if 'gt' in visuals:
gt_img = tensor2img([visuals['gt']])
metric_data['img2'] = gt_img
del self.gt
# tentative for out of GPU memory
del self.lq
del self.output
torch.cuda.empty_cache()
if save_img:
if self.opt['is_train']:
raise NotImplementedError('saving image is not supported during training.')
else:
if 'vimeo' in dataset_name.lower(): # vimeo90k dataset
split_result = lq_path.split('/')
img_name = f'{split_result[-3]}_{split_result[-2]}_{split_result[-1].split(".")[0]}'
else: # other datasets, e.g., REDS, Vid4
img_name = osp.splitext(osp.basename(lq_path))[0]
if self.opt['val']['suffix']:
save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder,
f'{img_name}_{self.opt["val"]["suffix"]}.png')
else:
save_img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder,
f'{img_name}_{self.opt["name"]}.png')
imwrite(result_img, save_img_path)
if with_metrics:
# calculate metrics
for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()):
result = calculate_metric(metric_data, opt_)
self.metric_results[folder][int(frame_idx), metric_idx] += result
# progress bar
if rank == 0:
for _ in range(world_size):
pbar.update(1)
pbar.set_description(f'Test {folder}: {int(frame_idx) + world_size}/{max_idx}')
if rank == 0:
pbar.close()
if with_metrics:
if self.opt['dist']:
# collect data among GPUs
for _, tensor in self.metric_results.items():
dist.reduce(tensor, 0)
dist.barrier()
else:
pass # assume use one gpu in non-dist testing
if rank == 0:
self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
def nondist_validation(self, dataloader, current_iter, tb_logger, save_img):
logger = get_root_logger()
logger.warning('nondist_validation is not implemented. Run dist_validation.')
self.dist_validation(dataloader, current_iter, tb_logger, save_img)
def _log_validation_metric_values(self, current_iter, dataset_name, tb_logger):
# ----------------- calculate the average values for each folder, and for each metric ----------------- #
# average all frames for each sub-folder
# metric_results_avg is a dict:{
# 'folder1': tensor (len(metrics)),
# 'folder2': tensor (len(metrics))
# }
metric_results_avg = {
folder: torch.mean(tensor, dim=0).cpu()
for (folder, tensor) in self.metric_results.items()
}
# total_avg_results is a dict: {
# 'metric1': float,
# 'metric2': float
# }
total_avg_results = {metric: 0 for metric in self.opt['val']['metrics'].keys()}
for folder, tensor in metric_results_avg.items():
for idx, metric in enumerate(total_avg_results.keys()):
total_avg_results[metric] += metric_results_avg[folder][idx].item()
# average among folders
for metric in total_avg_results.keys():
total_avg_results[metric] /= len(metric_results_avg)
# update the best metric result
self._update_best_metric_result(dataset_name, metric, total_avg_results[metric], current_iter)
# ------------------------------------------ log the metric ------------------------------------------ #
log_str = f'Validation {dataset_name}\n'
for metric_idx, (metric, value) in enumerate(total_avg_results.items()):
log_str += f'\t # {metric}: {value:.4f}'
for folder, tensor in metric_results_avg.items():
log_str += f'\t # {folder}: {tensor[metric_idx].item():.4f}'
if hasattr(self, 'best_metric_results'):
log_str += (f'\n\t Best: {self.best_metric_results[dataset_name][metric]["val"]:.4f} @ '
f'{self.best_metric_results[dataset_name][metric]["iter"]} iter')
log_str += '\n'
logger = get_root_logger()
logger.info(log_str)
if tb_logger:
for metric_idx, (metric, value) in enumerate(total_avg_results.items()):
tb_logger.add_scalar(f'metrics/{metric}', value, current_iter)
for folder, tensor in metric_results_avg.items():
tb_logger.add_scalar(f'metrics/{metric}/{folder}', tensor[metric_idx].item(), current_iter)