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Update model url and add three more setting See merge request open-mmlab/mmediting!349
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configs/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,125 @@ | ||
| # model settings | ||
| model = dict( | ||
| type='GCA', | ||
| backbone=dict( | ||
| type='SimpleEncoderDecoder', | ||
| encoder=dict( | ||
| type='ResShortcutEnc', | ||
| block='BasicBlock', | ||
| layers=[3, 4, 4, 2], | ||
| in_channels=4, | ||
| with_spectral_norm=True), | ||
| decoder=dict( | ||
| type='ResShortcutDec', | ||
| block='BasicBlockDec', | ||
| layers=[2, 3, 3, 2], | ||
| with_spectral_norm=True)), | ||
| loss_alpha=dict(type='L1Loss'), | ||
| pretrained='open-mmlab://mmedit/res34_en_nomixup') | ||
| train_cfg = dict(train_backbone=True) | ||
| test_cfg = dict(metrics=['SAD', 'MSE', 'GRAD', 'CONN']) | ||
|
|
||
| # dataset settings | ||
| dataset_type = 'AdobeComp1kDataset' | ||
| data_root = './data/adobe_composition-1k/' | ||
| bg_dir = './data/coco/train2017' | ||
| img_norm_cfg = dict( | ||
| mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], to_rgb=True) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile', key='alpha', flag='grayscale'), | ||
| dict(type='LoadImageFromFile', key='merged'), | ||
| dict( | ||
| type='CropAroundUnknown', | ||
| keys=['alpha', 'merged'], | ||
| crop_sizes=[320, 480, 640]), | ||
| dict(type='Flip', keys=['alpha', 'merged']), | ||
| dict( | ||
| type='Resize', | ||
| keys=['alpha', 'merged'], | ||
| scale=(320, 320), | ||
| keep_ratio=False), | ||
| dict(type='GenerateTrimap', kernel_size=(1, 30)), | ||
| dict(type='RescaleToZeroOne', keys=['merged', 'alpha']), | ||
| dict(type='Normalize', keys=['merged'], **img_norm_cfg), | ||
| dict(type='Collect', keys=['merged', 'alpha', 'trimap'], meta_keys=[]), | ||
| dict(type='ImageToTensor', keys=['merged', 'alpha', 'trimap']), | ||
| dict(type='FormatTrimap', to_onehot=False), | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| key='alpha', | ||
| flag='grayscale', | ||
| save_original_img=True), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| key='trimap', | ||
| flag='grayscale', | ||
| save_original_img=True), | ||
| dict(type='LoadImageFromFile', key='merged'), | ||
| dict(type='Pad', keys=['trimap', 'merged'], mode='reflect'), | ||
| dict(type='RescaleToZeroOne', keys=['merged']), | ||
| dict(type='Normalize', keys=['merged'], **img_norm_cfg), | ||
| dict( | ||
| type='Collect', | ||
| keys=['merged', 'trimap'], | ||
| meta_keys=[ | ||
| 'merged_path', 'pad', 'merged_ori_shape', 'ori_alpha', 'ori_trimap' | ||
| ]), | ||
| dict(type='ImageToTensor', keys=['merged', 'trimap']), | ||
| dict(type='FormatTrimap', to_onehot=False), | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=10, | ||
| workers_per_gpu=4, | ||
| val_samples_per_gpu=1, | ||
| val_workers_per_gpu=4, | ||
| drop_last=True, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'training_list.json', | ||
| data_prefix=data_root, | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'test_list.json', | ||
| data_prefix=data_root, | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'test_list.json', | ||
| data_prefix=data_root, | ||
| pipeline=test_pipeline)) | ||
|
|
||
| # optimizer | ||
| optimizers = dict(type='Adam', lr=4e-4, betas=[0.5, 0.999]) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='CosineAnealing', | ||
| min_lr=0, | ||
| by_epoch=False, | ||
| warmup='linear', | ||
| warmup_iters=5000, | ||
| warmup_ratio=0.001) | ||
|
|
||
| # checkpoint saving | ||
| checkpoint_config = dict(interval=2000, by_epoch=False) | ||
| evaluation = dict(interval=2000, save_image=False, gpu_collect=False) | ||
| # yapf:disable | ||
| log_config = dict( | ||
| interval=10, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook', by_epoch=False), | ||
| # dict(type='TensorboardLoggerHook'), | ||
| # dict(type='PaviLoggerHook', init_kwargs=dict(project='gca')) | ||
| ]) | ||
| # yapf:enable | ||
|
|
||
| # runtime settings | ||
| total_iters = 200000 | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = './work_dirs/shortcut' | ||
| 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 +1,125 @@ | ||
| # model settings | ||
| model = dict( | ||
| type='GCA', | ||
| backbone=dict( | ||
| type='SimpleEncoderDecoder', | ||
| encoder=dict( | ||
| type='ResGCAEncoder', | ||
| block='BasicBlock', | ||
| layers=[3, 4, 4, 2], | ||
| in_channels=4, | ||
| with_spectral_norm=True), | ||
| decoder=dict( | ||
| type='ResGCADecoder', | ||
| block='BasicBlockDec', | ||
| layers=[2, 3, 3, 2], | ||
| with_spectral_norm=True)), | ||
| loss_alpha=dict(type='L1Loss'), | ||
| pretrained='open-mmlab://mmedit/res34_en_nomixup') | ||
| train_cfg = dict(train_backbone=True) | ||
| test_cfg = dict(metrics=['SAD', 'MSE', 'GRAD', 'CONN']) | ||
|
|
||
| # dataset settings | ||
| dataset_type = 'AdobeComp1kDataset' | ||
| data_root = './data/adobe_composition-1k/' | ||
| bg_dir = './data/coco/train2017' | ||
| img_norm_cfg = dict( | ||
| mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], to_rgb=True) | ||
| train_pipeline = [ | ||
| dict(type='LoadImageFromFile', key='alpha', flag='grayscale'), | ||
| dict(type='LoadImageFromFile', key='merged'), | ||
| dict( | ||
| type='CropAroundUnknown', | ||
| keys=['alpha', 'merged'], | ||
| crop_sizes=[320, 480, 640]), | ||
| dict(type='Flip', keys=['alpha', 'merged']), | ||
| dict( | ||
| type='Resize', | ||
| keys=['alpha', 'merged'], | ||
| scale=(320, 320), | ||
| keep_ratio=False), | ||
| dict(type='GenerateTrimap', kernel_size=(1, 30)), | ||
| dict(type='RescaleToZeroOne', keys=['merged', 'alpha']), | ||
| dict(type='Normalize', keys=['merged'], **img_norm_cfg), | ||
| dict(type='Collect', keys=['merged', 'alpha', 'trimap'], meta_keys=[]), | ||
| dict(type='ImageToTensor', keys=['merged', 'alpha', 'trimap']), | ||
| dict(type='FormatTrimap', to_onehot=False), | ||
| ] | ||
| test_pipeline = [ | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| key='alpha', | ||
| flag='grayscale', | ||
| save_original_img=True), | ||
| dict( | ||
| type='LoadImageFromFile', | ||
| key='trimap', | ||
| flag='grayscale', | ||
| save_original_img=True), | ||
| dict(type='LoadImageFromFile', key='merged'), | ||
| dict(type='Pad', keys=['trimap', 'merged'], mode='reflect'), | ||
| dict(type='RescaleToZeroOne', keys=['merged']), | ||
| dict(type='Normalize', keys=['merged'], **img_norm_cfg), | ||
| dict( | ||
| type='Collect', | ||
| keys=['merged', 'trimap'], | ||
| meta_keys=[ | ||
| 'merged_path', 'pad', 'merged_ori_shape', 'ori_alpha', 'ori_trimap' | ||
| ]), | ||
| dict(type='ImageToTensor', keys=['merged', 'trimap']), | ||
| dict(type='FormatTrimap', to_onehot=False), | ||
| ] | ||
| data = dict( | ||
| samples_per_gpu=10, | ||
| workers_per_gpu=4, | ||
| val_samples_per_gpu=1, | ||
| val_workers_per_gpu=4, | ||
| drop_last=True, | ||
| train=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'training_list.json', | ||
| data_prefix=data_root, | ||
| pipeline=train_pipeline), | ||
| val=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'test_list.json', | ||
| data_prefix=data_root, | ||
| pipeline=test_pipeline), | ||
| test=dict( | ||
| type=dataset_type, | ||
| ann_file=data_root + 'test_list.json', | ||
| data_prefix=data_root, | ||
| pipeline=test_pipeline)) | ||
|
|
||
| # optimizer | ||
| optimizers = dict(type='Adam', lr=4e-4, betas=[0.5, 0.999]) | ||
| # learning policy | ||
| lr_config = dict( | ||
| policy='CosineAnealing', | ||
| min_lr=0, | ||
| by_epoch=False, | ||
| warmup='linear', | ||
| warmup_iters=5000, | ||
| warmup_ratio=0.001) | ||
|
|
||
| # checkpoint saving | ||
| checkpoint_config = dict(interval=2000, by_epoch=False) | ||
| evaluation = dict(interval=2000, save_image=False, gpu_collect=False) | ||
| # yapf:disable | ||
| log_config = dict( | ||
| interval=10, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook', by_epoch=False), | ||
| # dict(type='TensorboardLoggerHook'), | ||
| # dict(type='PaviLoggerHook', init_kwargs=dict(project='gca')) | ||
| ]) | ||
| # yapf:enable | ||
|
|
||
| # runtime settings | ||
| total_iters = 200000 | ||
| dist_params = dict(backend='nccl') | ||
| log_level = 'INFO' | ||
| work_dir = './work_dirs/gca' | ||
| load_from = None | ||
| resume_from = None | ||
| workflow = [('train', 1)] |
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