44b89330c2
This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways:
1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped.
2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but
the model appears unable to fight the discriminator, so the G-loss steadily increases.
Going forwards, I want to try some bigger models. In particular, I want to change the generator
to increase complexity and capacity. I also want to add skip connections between the
disc and generator.
54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
"""create dataset and dataloader"""
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import logging
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import torch
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import torch.utils.data
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def create_dataloader(dataset, dataset_opt, opt=None, sampler=None):
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phase = dataset_opt['phase']
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if phase == 'train':
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if opt['dist']:
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world_size = torch.distributed.get_world_size()
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num_workers = dataset_opt['n_workers']
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assert dataset_opt['batch_size'] % world_size == 0
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batch_size = dataset_opt['batch_size'] // world_size
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shuffle = False
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else:
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num_workers = dataset_opt['n_workers'] * len(opt['gpu_ids'])
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batch_size = dataset_opt['batch_size']
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shuffle = True
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return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle,
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num_workers=num_workers, sampler=sampler, drop_last=True,
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pin_memory=False)
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else:
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batch_size = dataset_opt['batch_size'] or 1
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return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False, num_workers=0,
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pin_memory=False)
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def create_dataset(dataset_opt):
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mode = dataset_opt['mode']
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# datasets for image restoration
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if mode == 'LQ':
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from data.LQ_dataset import LQDataset as D
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elif mode == 'LQGT':
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from data.LQGT_dataset import LQGTDataset as D
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# datasets for image corruption
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elif mode == 'downsample':
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from data.Downsample_dataset import DownsampleDataset as D
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# datasets for video restoration
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elif mode == 'REDS':
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from data.REDS_dataset import REDSDataset as D
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elif mode == 'Vimeo90K':
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from data.Vimeo90K_dataset import Vimeo90KDataset as D
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elif mode == 'video_test':
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from data.video_test_dataset import VideoTestDataset as D
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else:
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raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
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dataset = D(dataset_opt)
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logger = logging.getLogger('base')
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logger.info('Dataset [{:s} - {:s}] is created.'.format(dataset.__class__.__name__,
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dataset_opt['name']))
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return dataset
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