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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 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 .video_base_model import VideoBaseModel
@MODEL_REGISTRY.register()
class VideoRecurrentModel(VideoBaseModel):
def __init__(self, opt):
super(VideoRecurrentModel, self).__init__(opt)
if self.is_train:
self.fix_flow_iter = opt['train'].get('fix_flow')
def setup_optimizers(self):
train_opt = self.opt['train']
flow_lr_mul = train_opt.get('flow_lr_mul', 1)
logger = get_root_logger()
logger.info(f'Multiple the learning rate for flow network with {flow_lr_mul}.')
if flow_lr_mul == 1:
optim_params = self.net_g.parameters()
else: # separate flow params and normal params for different lr
normal_params = []
flow_params = []
for name, param in self.net_g.named_parameters():
if 'spynet' in name:
flow_params.append(param)
else:
normal_params.append(param)
optim_params = [
{ # add normal params first
'params': normal_params,
'lr': train_opt['optim_g']['lr']
},
{
'params': flow_params,
'lr': train_opt['optim_g']['lr'] * flow_lr_mul
},
]
optim_type = train_opt['optim_g'].pop('type')
self.optimizer_g = self.get_optimizer(optim_type, optim_params, **train_opt['optim_g'])
self.optimizers.append(self.optimizer_g)
def optimize_parameters(self, current_iter):
if self.fix_flow_iter:
logger = get_root_logger()
if current_iter == 1:
logger.info(f'Fix flow network and feature extractor for {self.fix_flow_iter} iters.')
for name, param in self.net_g.named_parameters():
if 'spynet' in name or 'edvr' in name:
param.requires_grad_(False)
elif current_iter == self.fix_flow_iter:
logger.warning('Train all the parameters.')
self.net_g.requires_grad_(True)
super(VideoRecurrentModel, self).optimize_parameters(current_iter)
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()
num_folders = len(dataset)
num_pad = (world_size - (num_folders % world_size)) % world_size
if rank == 0:
pbar = tqdm(total=len(dataset), unit='folder')
# Will evaluate (num_folders + num_pad) times, but only the first num_folders results will be recorded.
# (To avoid wait-dead)
for i in range(rank, num_folders + num_pad, world_size):
idx = min(i, num_folders - 1)
val_data = dataset[idx]
folder = val_data['folder']
# compute outputs
val_data['lq'].unsqueeze_(0)
val_data['gt'].unsqueeze_(0)
self.feed_data(val_data)
val_data['lq'].squeeze_(0)
val_data['gt'].squeeze_(0)
self.test()
visuals = self.get_current_visuals()
# tentative for out of GPU memory
del self.lq
del self.output
if 'gt' in visuals:
del self.gt
torch.cuda.empty_cache()
if self.center_frame_only:
visuals['result'] = visuals['result'].unsqueeze(1)
if 'gt' in visuals:
visuals['gt'] = visuals['gt'].unsqueeze(1)
# evaluate
if i < num_folders:
for idx in range(visuals['result'].size(1)):
result = visuals['result'][0, idx, :, :, :]
result_img = tensor2img([result]) # uint8, bgr
metric_data['img'] = result_img
if 'gt' in visuals:
gt = visuals['gt'][0, idx, :, :, :]
gt_img = tensor2img([gt]) # uint8, bgr
metric_data['img2'] = gt_img
if save_img:
if self.opt['is_train']:
raise NotImplementedError('saving image is not supported during training.')
else:
if self.center_frame_only: # vimeo-90k
clip_ = val_data['lq_path'].split('/')[-3]
seq_ = val_data['lq_path'].split('/')[-2]
name_ = f'{clip_}_{seq_}'
img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder,
f"{name_}_{self.opt['name']}.png")
else: # others
img_path = osp.join(self.opt['path']['visualization'], dataset_name, folder,
f"{idx:08d}_{self.opt['name']}.png")
# image name only for REDS dataset
imwrite(result_img, img_path)
# calculate metrics
if with_metrics:
for metric_idx, opt_ in enumerate(self.opt['val']['metrics'].values()):
result = calculate_metric(metric_data, opt_)
self.metric_results[folder][idx, metric_idx] += result
# progress bar
if rank == 0:
for _ in range(world_size):
pbar.update(1)
pbar.set_description(f'Folder: {folder}')
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()
if rank == 0:
self._log_validation_metric_values(current_iter, dataset_name, tb_logger)
def test(self):
n = self.lq.size(1)
self.net_g.eval()
flip_seq = self.opt['val'].get('flip_seq', False)
self.center_frame_only = self.opt['val'].get('center_frame_only', False)
if flip_seq:
self.lq = torch.cat([self.lq, self.lq.flip(1)], dim=1)
with torch.no_grad():
self.output = self.net_g(self.lq)
if flip_seq:
output_1 = self.output[:, :n, :, :, :]
output_2 = self.output[:, n:, :, :, :].flip(1)
self.output = 0.5 * (output_1 + output_2)
if self.center_frame_only:
self.output = self.output[:, n // 2, :, :, :]
self.net_g.train()
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