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- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import _init_paths
-
- import os
- os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
- import torch
- import torch.utils.data
- from fhd_opts import opts
- # from opts import opts
- from models.model import create_model, load_model, save_model
- from models.data_parallel import DataParallel
- from logger import Logger
- from datasets.dataset_factory import get_dataset
- from trains.train_factory import train_factory
-
- from torch.utils.tensorboard import SummaryWriter
-
-
- def main(opt):
- torch.manual_seed(opt.seed)
- torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
- Dataset = get_dataset(opt.dataset, opt.task)
- opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
- print(opt)
-
- logger = Logger(opt)
-
- os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
- opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
-
- print('Creating model...')
- model = create_model(opt.arch, opt.heads, opt.head_conv)
- optimizer = torch.optim.Adam(model.parameters(), opt.lr)
- start_epoch = 0
- if opt.load_model != '':
- model, optimizer, start_epoch = load_model(
- model, opt.load_model, optimizer, opt.resume, opt.lr, opt.lr_step)
-
- Trainer = train_factory[opt.task]
- trainer = Trainer(opt, model, optimizer)
- trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
-
- print('Setting up data...')
- val_loader = torch.utils.data.DataLoader(
- Dataset(opt, 'val'),
- batch_size=1,
- shuffle=False,
- num_workers=1,
- pin_memory=True
- )
-
- if opt.test:
- _, preds = trainer.val(0, val_loader)
- val_loader.dataset.run_eval(preds, opt.save_dir)
- return
-
- train_loader = torch.utils.data.DataLoader(
- Dataset(opt, 'train'),
- batch_size=opt.batch_size,
- shuffle=True,
- num_workers=opt.num_workers,
- pin_memory=True,
- drop_last=True
- )
-
- print('Starting training...')
- best = 1e10
- for epoch in range(start_epoch + 1, opt.num_epochs + 1):
- mark = epoch if opt.save_all else 'last'
- log_dict_train, _ = trainer.train(epoch, train_loader)
- logger.write('epoch: {} |'.format(epoch))
- for k, v in log_dict_train.items():
- logger.scalar_summary('train_{}'.format(k), v, epoch)
- logger.write('{} {:8f} | '.format(k, v))
- if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
- save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),
- epoch, model, optimizer)
- with torch.no_grad():
- log_dict_val, preds = trainer.val(epoch, val_loader)
- for k, v in log_dict_val.items():
- logger.scalar_summary('val_{}'.format(k), v, epoch)
- logger.write('{} {:8f} | '.format(k, v))
- if log_dict_val[opt.metric] < best:
- best = log_dict_val[opt.metric]
- save_model(os.path.join(opt.save_dir, 'model_best.pth'),
- epoch, model)
- else:
- save_model(os.path.join(opt.save_dir, 'model_last.pth'),
- epoch, model, optimizer)
- logger.write('\n')
- if epoch in opt.lr_step:
- save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
- epoch, model, optimizer)
- lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1))
- print('Drop LR to', lr)
- for param_group in optimizer.param_groups:
- param_group['lr'] = lr
- logger.close()
-
- if __name__ == '__main__':
- opt = opts().parse()
- main(opt)
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