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Config files of RDN #260
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Config files of RDN #260
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,124 @@ | ||
| exp_name = 'rdn_x2c64b16_g1_1000k_div2k' | ||
|
|
||
| scale = 2 | ||
| # model settings | ||
| model = dict( | ||
| type='BasicRestorer', | ||
| generator=dict( | ||
| type='RDN', | ||
| in_channels=3, | ||
| out_channels=3, | ||
| mid_channels=64, | ||
| num_blocks=16, | ||
| upscale_factor=scale), | ||
| pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean')) | ||
| # model training and testing settings | ||
| train_cfg = None | ||
| test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale) | ||
|
|
||
| # dataset settings | ||
| train_dataset_type = 'SRAnnotationDataset' | ||
| val_dataset_type = 'SRFolderDataset' | ||
| train_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='lq', | ||
| flag='unchanged'), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='gt', | ||
| flag='unchanged'), | ||
| dict(type='RescaleToZeroOne', keys=['lq', 'gt']), | ||
| dict( | ||
| type='Normalize', | ||
| keys=['lq', 'gt'], | ||
| mean=[0, 0, 0], | ||
| std=[1, 1, 1], | ||
| to_rgb=True), | ||
| dict(type='PairedRandomCrop', gt_patch_size=64), | ||
| dict( | ||
| type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, | ||
| direction='horizontal'), | ||
| dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'), | ||
| dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5), | ||
| dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']), | ||
| dict(type='ImageToTensor', keys=['lq', 'gt']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='lq', | ||
| flag='unchanged'), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='gt', | ||
| flag='unchanged'), | ||
| dict(type='RescaleToZeroOne', keys=['lq', 'gt']), | ||
| dict( | ||
| type='Normalize', | ||
| keys=['lq', 'gt'], | ||
| mean=[0, 0, 0], | ||
| std=[1, 1, 1], | ||
| to_rgb=True), | ||
| dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']), | ||
| dict(type='ImageToTensor', keys=['lq', 'gt']) | ||
| ] | ||
|
|
||
| data = dict( | ||
| workers_per_gpu=1, | ||
| train_dataloader=dict(samples_per_gpu=16, drop_last=True), | ||
| val_dataloader=dict(samples_per_gpu=1), | ||
| test_dataloader=dict(samples_per_gpu=1), | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=1000, | ||
| dataset=dict( | ||
| type=train_dataset_type, | ||
| lq_folder='data/DIV2K/DIV2K_train_LR_bicubic/X2_sub', | ||
| gt_folder='data/DIV2K/DIV2K_train_HR_sub', | ||
| ann_file='data/DIV2K/meta_info_DIV2K800sub_GT.txt', | ||
| pipeline=train_pipeline, | ||
| scale=scale)), | ||
| val=dict( | ||
| type=val_dataset_type, | ||
| lq_folder='data/val_set5/Set5_bicLRx2', | ||
| gt_folder='data/val_set5/Set5_mod12', | ||
| pipeline=test_pipeline, | ||
| scale=scale, | ||
| filename_tmpl='{}'), | ||
| test=dict( | ||
| type=val_dataset_type, | ||
| lq_folder='data/val_set5/Set5_bicLRx2', | ||
| gt_folder='data/val_set5/Set5_mod12', | ||
| pipeline=test_pipeline, | ||
| scale=scale, | ||
| filename_tmpl='{}')) | ||
|
|
||
| # optimizer | ||
| optimizers = dict(generator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))) | ||
|
|
||
| # learning policy | ||
| total_iters = 1000000 | ||
| lr_config = dict( | ||
| policy='Step', | ||
| by_epoch=False, | ||
| step=[200000, 400000, 600000, 800000], | ||
| gamma=0.5) | ||
|
|
||
| checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False) | ||
| evaluation = dict(interval=5000, save_image=True, gpu_collect=True) | ||
| log_config = dict( | ||
| interval=100, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) | ||
| visual_config = None | ||
|
|
||
| # runtime settings | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = f'./work_dirs/{exp_name}' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,124 @@ | ||
| exp_name = 'rdn_x3c64b16_g1_1000k_div2k' | ||
|
|
||
| scale = 3 | ||
| # model settings | ||
| model = dict( | ||
| type='BasicRestorer', | ||
| generator=dict( | ||
| type='RDN', | ||
| in_channels=3, | ||
| out_channels=3, | ||
| mid_channels=64, | ||
| num_blocks=16, | ||
| upscale_factor=scale), | ||
| pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean')) | ||
| # model training and testing settings | ||
| train_cfg = None | ||
| test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale) | ||
|
|
||
| # dataset settings | ||
| train_dataset_type = 'SRAnnotationDataset' | ||
| val_dataset_type = 'SRFolderDataset' | ||
| train_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='lq', | ||
| flag='unchanged'), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='gt', | ||
| flag='unchanged'), | ||
| dict(type='RescaleToZeroOne', keys=['lq', 'gt']), | ||
| dict( | ||
| type='Normalize', | ||
| keys=['lq', 'gt'], | ||
| mean=[0, 0, 0], | ||
| std=[1, 1, 1], | ||
| to_rgb=True), | ||
| dict(type='PairedRandomCrop', gt_patch_size=96), | ||
| dict( | ||
| type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, | ||
| direction='horizontal'), | ||
| dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'), | ||
| dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5), | ||
| dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']), | ||
| dict(type='ImageToTensor', keys=['lq', 'gt']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='lq', | ||
| flag='unchanged'), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='gt', | ||
| flag='unchanged'), | ||
| dict(type='RescaleToZeroOne', keys=['lq', 'gt']), | ||
| dict( | ||
| type='Normalize', | ||
| keys=['lq', 'gt'], | ||
| mean=[0, 0, 0], | ||
| std=[1, 1, 1], | ||
| to_rgb=True), | ||
| dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']), | ||
| dict(type='ImageToTensor', keys=['lq', 'gt']) | ||
| ] | ||
|
|
||
| data = dict( | ||
| workers_per_gpu=1, | ||
| train_dataloader=dict(samples_per_gpu=16, drop_last=True), | ||
| val_dataloader=dict(samples_per_gpu=1), | ||
| test_dataloader=dict(samples_per_gpu=1), | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=1000, | ||
| dataset=dict( | ||
| type=train_dataset_type, | ||
| lq_folder='data/DIV2K/DIV2K_train_LR_bicubic/X3_sub', | ||
| gt_folder='data/DIV2K/DIV2K_train_HR_sub', | ||
| ann_file='data/DIV2K/meta_info_DIV2K800sub_GT.txt', | ||
| pipeline=train_pipeline, | ||
| scale=scale)), | ||
| val=dict( | ||
| type=val_dataset_type, | ||
| lq_folder='data/val_set5/Set5_bicLRx3', | ||
| gt_folder='data/val_set5/Set5_mod12', | ||
| pipeline=test_pipeline, | ||
| scale=scale, | ||
| filename_tmpl='{}'), | ||
| test=dict( | ||
| type=val_dataset_type, | ||
| lq_folder='data/val_set5/Set5_bicLRx3', | ||
| gt_folder='data/val_set5/Set5_mod12', | ||
| pipeline=test_pipeline, | ||
| scale=scale, | ||
| filename_tmpl='{}')) | ||
|
|
||
| # optimizer | ||
| optimizers = dict(generator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))) | ||
|
|
||
| # learning policy | ||
| total_iters = 1000000 | ||
| lr_config = dict( | ||
| policy='Step', | ||
| by_epoch=False, | ||
| step=[200000, 400000, 600000, 800000], | ||
| gamma=0.5) | ||
|
|
||
| checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False) | ||
| evaluation = dict(interval=5000, save_image=True, gpu_collect=True) | ||
| log_config = dict( | ||
| interval=100, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) | ||
| visual_config = None | ||
|
|
||
| # runtime settings | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = f'./work_dirs/{exp_name}' | ||
| load_from = None | ||
| resume_from = 'work_dirs/rdn_x3c64b16_g1_1000k_div2k/iter_315000.pth' | ||
| workflow = [('train', 1)] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,124 @@ | ||
| exp_name = 'rdn_x4c64b16_g1_100k_div2k' | ||
|
|
||
| scale = 4 | ||
| # model settings | ||
| model = dict( | ||
| type='BasicRestorer', | ||
| generator=dict( | ||
| type='RDN', | ||
| in_channels=3, | ||
| out_channels=3, | ||
| mid_channels=64, | ||
| num_blocks=16, | ||
| upscale_factor=scale), | ||
| pixel_loss=dict(type='L1Loss', loss_weight=1.0, reduction='mean')) | ||
| # model training and testing settings | ||
| train_cfg = None | ||
| test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=scale) | ||
|
|
||
| # dataset settings | ||
| train_dataset_type = 'SRAnnotationDataset' | ||
| val_dataset_type = 'SRFolderDataset' | ||
| train_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='lq', | ||
| flag='unchanged'), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='gt', | ||
| flag='unchanged'), | ||
| dict(type='RescaleToZeroOne', keys=['lq', 'gt']), | ||
| dict( | ||
| type='Normalize', | ||
| keys=['lq', 'gt'], | ||
| mean=[0, 0, 0], | ||
| std=[1, 1, 1], | ||
| to_rgb=True), | ||
| dict(type='PairedRandomCrop', gt_patch_size=128), | ||
| dict( | ||
| type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, | ||
| direction='horizontal'), | ||
| dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'), | ||
| dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5), | ||
| dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']), | ||
| dict(type='ImageToTensor', keys=['lq', 'gt']) | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='lq', | ||
| flag='unchanged'), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| io_backend='disk', | ||
| key='gt', | ||
| flag='unchanged'), | ||
| dict(type='RescaleToZeroOne', keys=['lq', 'gt']), | ||
| dict( | ||
| type='Normalize', | ||
| keys=['lq', 'gt'], | ||
| mean=[0, 0, 0], | ||
| std=[1, 1, 1], | ||
| to_rgb=True), | ||
| dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path']), | ||
| dict(type='ImageToTensor', keys=['lq', 'gt']) | ||
| ] | ||
|
|
||
| data = dict( | ||
| workers_per_gpu=1, | ||
| train_dataloader=dict(samples_per_gpu=16, drop_last=True), | ||
| val_dataloader=dict(samples_per_gpu=1), | ||
| test_dataloader=dict(samples_per_gpu=1), | ||
| train=dict( | ||
| type='RepeatDataset', | ||
| times=1000, | ||
| dataset=dict( | ||
| type=train_dataset_type, | ||
| lq_folder='data/DIV2K/DIV2K_train_LR_bicubic/X4_sub', | ||
| gt_folder='data/DIV2K/DIV2K_train_HR_sub', | ||
| ann_file='data/DIV2K/meta_info_DIV2K800sub_GT.txt', | ||
| pipeline=train_pipeline, | ||
| scale=scale)), | ||
| val=dict( | ||
| type=val_dataset_type, | ||
| lq_folder='data/val_set5/Set5_bicLRx4', | ||
| gt_folder='data/val_set5/Set5_mod12', | ||
| pipeline=test_pipeline, | ||
| scale=scale, | ||
| filename_tmpl='{}'), | ||
| test=dict( | ||
| type=val_dataset_type, | ||
| lq_folder='data/val_set5/Set5_bicLRx4', | ||
| gt_folder='data/val_set5/Set5_mod12', | ||
| pipeline=test_pipeline, | ||
| scale=scale, | ||
| filename_tmpl='{}')) | ||
|
|
||
| # optimizer | ||
| optimizers = dict(generator=dict(type='Adam', lr=1e-4, betas=(0.9, 0.999))) | ||
|
|
||
| # learning policy | ||
| total_iters = 1000000 | ||
| lr_config = dict( | ||
| policy='Step', | ||
| by_epoch=False, | ||
| step=[200000, 400000, 600000, 800000], | ||
| gamma=0.5) | ||
|
|
||
| checkpoint_config = dict(interval=5000, save_optimizer=True, by_epoch=False) | ||
| evaluation = dict(interval=5000, save_image=True, gpu_collect=True) | ||
| log_config = dict( | ||
| interval=100, hooks=[dict(type='TextLoggerHook', by_epoch=False)]) | ||
| visual_config = None | ||
|
|
||
| # runtime settings | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = f'./work_dirs/{exp_name}' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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Effect of 'unchanged': if it is grayscale image, you get 1 channel; if it has transparency, you get 4 channels; if it contains orientation metadata, it ignores.
Is this intended?
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No, this is not intended.