File size: 7,441 Bytes
cb2f529 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
import torch
from collections import Counter
from os import path as osp
from torch import distributed as dist
from tqdm import tqdm
from r_basicsr.metrics import calculate_metric
from r_basicsr.utils import get_root_logger, imwrite, tensor2img
from r_basicsr.utils.dist_util import get_dist_info
from r_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)
|