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How to use custom_gt method in pandera

Best Python code snippet using pandera_python

basicvsr_reds4_custom_load_lr_small.py

Source:basicvsr_reds4_custom_load_lr_small.py

1exp_name = 'basicvsr_reds4_custom_load_lr_small'2# model settings3model = dict(4    type='BasicVSR',5    generator=dict(6        type='BasicVSRNet',7        mid_channels=64,8        num_blocks=30,9        spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'10        'basicvsr/spynet_20210409-c6c1bd09.pth'),11    pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))12# model training and testing settings13train_cfg = dict(fix_iter=5000)14test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)15# dataset settings16train_dataset_type = 'SRREDSMultipleGTDataset_CUSTOM'17val_dataset_type = 'SRREDSMultipleGTDataset_CUSTOM'18train_pipeline = [19    dict(type='GenerateSegmentIndices', interval_list=[1]),20    dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),21    dict(22        type='LoadImageFromFileList',23        io_backend='disk',24        key='lq',25        channel_order='rgb'),26    dict(27        type='LoadImageFromFileList',28        io_backend='disk',29        key='gt',30        channel_order='rgb'),31    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),32    dict(type='PairedRandomCrop', gt_patch_size=256),33    dict(34        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,35        direction='horizontal'),36    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),37    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),38    dict(type='FramesToTensor', keys=['lq', 'gt']),39    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])40]41test_pipeline = [42    dict(type='GenerateSegmentIndices', interval_list=[1]),43    dict(44        type='LoadImageFromFileList',45        io_backend='disk',46        key='lq',47        channel_order='rgb'),48    dict(49        type='LoadImageFromFileList',50        io_backend='disk',51        key='gt',52        channel_order='rgb'),53    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),54    dict(type='FramesToTensor', keys=['lq', 'gt']),55    dict(56        type='Collect',57        keys=['lq', 'gt'],58        meta_keys=['lq_path', 'gt_path', 'key'])59]60demo_pipeline = [61    dict(type='GenerateSegmentIndices', interval_list=[1]),62    dict(63        type='LoadImageFromFileList',64        io_backend='disk',65        key='lq',66        channel_order='rgb'),67    dict(type='RescaleToZeroOne', keys=['lq']),68    dict(type='FramesToTensor', keys=['lq']),69    dict(type='Collect', keys=['lq'], meta_keys=['lq_path', 'key'])70]71data = dict(72    workers_per_gpu=6,73    train_dataloader=dict(samples_per_gpu=16, drop_last=True),  # 2 gpus74    val_dataloader=dict(samples_per_gpu=1),75    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),76    # train77    train=dict(78        type='RepeatDataset',79        times=1000,80        dataset=dict(81            type=train_dataset_type,82            lq_folder='/data/peiyuan/custom_train/custom_low_res',83            gt_folder='/data/peiyuan/custom_train/custom_gt',84            num_input_frames=15,85            pipeline=train_pipeline,86            scale=4,87            val_partition='REDS4',88            test_mode=False)),89    # val90    val=dict(91        type=val_dataset_type,92        lq_folder='/data/peiyuan/custom_train/custom_low_res',93        gt_folder='/data/peiyuan/custom_train/custom_gt',94        num_input_frames=100,95        pipeline=test_pipeline,96        scale=4,97        val_partition='REDS4',98        test_mode=True),99    # test100    test=dict(101        type=val_dataset_type,102        lq_folder='/data/peiyuan/custom_train/custom_low_res',103        gt_folder='/data/peiyuan/custom_train/custom_gt',104        num_input_frames=100,105        pipeline=test_pipeline,106        scale=4,107        val_partition='REDS4',108        test_mode=True),109)110# optimizer111optimizers = dict(112    generator=dict(113        type='Adam',114        lr=8e-5,115        betas=(0.9, 0.99),116        paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))117# learning policy118total_iters = 150000119lr_config = dict(120    policy='CosineRestart',121    by_epoch=False,122    periods=[150000],123    restart_weights=[1],124    min_lr=1e-7)125checkpoint_config = dict(interval=2500, save_optimizer=True, by_epoch=False)126# remove gpu_collect=True in non distributed training127evaluation = dict(interval=2500, save_image=False, gpu_collect=True)128log_config = dict(129    interval=100,130    hooks=[131        dict(type='TextLoggerHook', by_epoch=False),132        # dict(type='TensorboardLoggerHook'),133    ])134visual_config = None135# runtime settings136dist_params = dict(backend='nccl')137log_level = 'INFO'138work_dir = f'./work_dirs/{exp_name}'139load_from = None140resume_from = None141workflow = [('train', 1)]...

basicvsr_reds4_custom_load.py

Source:basicvsr_reds4_custom_load.py

1exp_name = 'basicvsr_reds4_custom_load'2# model settings3model = dict(4    type='BasicVSR',5    generator=dict(6        type='BasicVSRNet',7        mid_channels=64,8        num_blocks=30,9        spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'10        'basicvsr/spynet_20210409-c6c1bd09.pth'),11    pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))12# model training and testing settings13train_cfg = dict(fix_iter=5000)14test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)15# dataset settings16train_dataset_type = 'SRREDSMultipleGTDataset_CUSTOM'17val_dataset_type = 'SRREDSMultipleGTDataset_CUSTOM'18train_pipeline = [19    dict(type='GenerateSegmentIndices', interval_list=[1]),20    dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),21    dict(22        type='LoadImageFromFileList',23        io_backend='disk',24        key='lq',25        channel_order='rgb'),26    dict(27        type='LoadImageFromFileList',28        io_backend='disk',29        key='gt',30        channel_order='rgb'),31    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),32    dict(type='PairedRandomCrop', gt_patch_size=256),33    dict(34        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,35        direction='horizontal'),36    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),37    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),38    dict(type='FramesToTensor', keys=['lq', 'gt']),39    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])40]41test_pipeline = [42    dict(type='GenerateSegmentIndices', interval_list=[1]),43    dict(44        type='LoadImageFromFileList',45        io_backend='disk',46        key='lq',47        channel_order='rgb'),48    dict(49        type='LoadImageFromFileList',50        io_backend='disk',51        key='gt',52        channel_order='rgb'),53    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),54    dict(type='FramesToTensor', keys=['lq', 'gt']),55    dict(56        type='Collect',57        keys=['lq', 'gt'],58        meta_keys=['lq_path', 'gt_path', 'key'])59]60demo_pipeline = [61    dict(type='GenerateSegmentIndices', interval_list=[1]),62    dict(63        type='LoadImageFromFileList',64        io_backend='disk',65        key='lq',66        channel_order='rgb'),67    dict(type='RescaleToZeroOne', keys=['lq']),68    dict(type='FramesToTensor', keys=['lq']),69    dict(type='Collect', keys=['lq'], meta_keys=['lq_path', 'key'])70]71data = dict(72    workers_per_gpu=6,73    train_dataloader=dict(samples_per_gpu=16, drop_last=True),  # 2 gpus74    val_dataloader=dict(samples_per_gpu=1),75    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),76    # train77    train=dict(78        type='RepeatDataset',79        times=1000,80        dataset=dict(81            type=train_dataset_type,82            lq_folder='/data/peiyuan/custom_train/custom_low_res',83            gt_folder='/data/peiyuan/custom_train/custom_gt',84            num_input_frames=15,85            pipeline=train_pipeline,86            scale=4,87            val_partition='REDS4',88            test_mode=False)),89    # val90    val=dict(91        type=val_dataset_type,92        lq_folder='/data/peiyuan/custom_train/custom_low_res',93        gt_folder='/data/peiyuan/custom_train/custom_gt',94        num_input_frames=100,95        pipeline=test_pipeline,96        scale=4,97        val_partition='REDS4',98        test_mode=True),99    # test100    test=dict(101        type=val_dataset_type,102        lq_folder='/data/peiyuan/custom_train/custom_low_res',103        gt_folder='/data/peiyuan/custom_train/custom_gt',104        num_input_frames=100,105        pipeline=test_pipeline,106        scale=4,107        val_partition='REDS4',108        test_mode=True),109)110# optimizer111optimizers = dict(112    generator=dict(113        type='Adam',114        lr=4e-4,115        betas=(0.9, 0.99),116        paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))117# learning policy118total_iters = 150000119lr_config = dict(120    policy='CosineRestart',121    by_epoch=False,122    periods=[150000],123    restart_weights=[1],124    min_lr=1e-7)125checkpoint_config = dict(interval=1000, save_optimizer=True, by_epoch=False)126# remove gpu_collect=True in non distributed training127evaluation = dict(interval=1000, save_image=False, gpu_collect=True)128log_config = dict(129    interval=100,130    hooks=[131        dict(type='TextLoggerHook', by_epoch=False),132        # dict(type='TensorboardLoggerHook'),133    ])134visual_config = None135# runtime settings136dist_params = dict(backend='nccl')137log_level = 'INFO'138work_dir = f'./work_dirs/{exp_name}'139load_from = None140resume_from = None141workflow = [('train', 1)]...

basicvsr_reds4_custom.py

Source:basicvsr_reds4_custom.py

1exp_name = 'basicvsr_reds4_custom'2# model settings3model = dict(4    type='BasicVSR',5    generator=dict(6        type='BasicVSRNet',7        mid_channels=64,8        num_blocks=30,9        spynet_pretrained='https://download.openmmlab.com/mmediting/restorers/'10        'basicvsr/spynet_20210409-c6c1bd09.pth'),11    pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))12# model training and testing settings13train_cfg = dict(fix_iter=5000)14test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)15# dataset settings16train_dataset_type = 'SRREDSMultipleGTDataset_CUSTOM'17val_dataset_type = 'SRREDSMultipleGTDataset_CUSTOM'18train_pipeline = [19    dict(type='GenerateSegmentIndices', interval_list=[1]),20    dict(type='TemporalReverse', keys='lq_path', reverse_ratio=0),21    dict(22        type='LoadImageFromFileList',23        io_backend='disk',24        key='lq',25        channel_order='rgb'),26    dict(27        type='LoadImageFromFileList',28        io_backend='disk',29        key='gt',30        channel_order='rgb'),31    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),32    dict(type='PairedRandomCrop', gt_patch_size=256),33    dict(34        type='Flip', keys=['lq', 'gt'], flip_ratio=0.5,35        direction='horizontal'),36    dict(type='Flip', keys=['lq', 'gt'], flip_ratio=0.5, direction='vertical'),37    dict(type='RandomTransposeHW', keys=['lq', 'gt'], transpose_ratio=0.5),38    dict(type='FramesToTensor', keys=['lq', 'gt']),39    dict(type='Collect', keys=['lq', 'gt'], meta_keys=['lq_path', 'gt_path'])40]41test_pipeline = [42    dict(type='GenerateSegmentIndices', interval_list=[1]),43    dict(44        type='LoadImageFromFileList',45        io_backend='disk',46        key='lq',47        channel_order='rgb'),48    dict(49        type='LoadImageFromFileList',50        io_backend='disk',51        key='gt',52        channel_order='rgb'),53    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),54    dict(type='FramesToTensor', keys=['lq', 'gt']),55    dict(56        type='Collect',57        keys=['lq', 'gt'],58        meta_keys=['lq_path', 'gt_path', 'key'])59]60demo_pipeline = [61    dict(type='GenerateSegmentIndices', interval_list=[1]),62    dict(63        type='LoadImageFromFileList',64        io_backend='disk',65        key='lq',66        channel_order='rgb'),67    dict(type='RescaleToZeroOne', keys=['lq']),68    dict(type='FramesToTensor', keys=['lq']),69    dict(type='Collect', keys=['lq'], meta_keys=['lq_path', 'key'])70]71data = dict(72    workers_per_gpu=6,73    train_dataloader=dict(samples_per_gpu=16, drop_last=True),  # 2 gpus74    val_dataloader=dict(samples_per_gpu=1),75    test_dataloader=dict(samples_per_gpu=1, workers_per_gpu=1),76    # train77    train=dict(78        type='RepeatDataset',79        times=1000,80        dataset=dict(81            type=train_dataset_type,82            lq_folder='/data/peiyuan/custom_train/custom_low_res',83            gt_folder='/data/peiyuan/custom_train/custom_gt',84            num_input_frames=15,85            pipeline=train_pipeline,86            scale=4,87            val_partition='REDS4',88            test_mode=False)),89    # val90    val=dict(91        type=val_dataset_type,92        lq_folder='/data/peiyuan/custom_train/custom_low_res',93        gt_folder='/data/peiyuan/custom_train/custom_gt',94        num_input_frames=100,95        pipeline=test_pipeline,96        scale=4,97        val_partition='REDS4',98        test_mode=True),99    # test100    test=dict(101        type=val_dataset_type,102        lq_folder='/data/peiyuan/custom_train/custom_low_res',103        gt_folder='/data/peiyuan/custom_train/custom_gt',104        num_input_frames=100,105        pipeline=test_pipeline,106        scale=4,107        val_partition='REDS4',108        test_mode=True),109)110# optimizer111optimizers = dict(112    generator=dict(113        type='Adam',114        lr=4e-4,115        betas=(0.9, 0.99),116        paramwise_cfg=dict(custom_keys={'spynet': dict(lr_mult=0.125)})))117# learning policy118total_iters = 150000119lr_config = dict(120    policy='CosineRestart',121    by_epoch=False,122    periods=[150000],123    restart_weights=[1],124    min_lr=1e-7)125checkpoint_config = dict(interval=1000, save_optimizer=True, by_epoch=False)126# remove gpu_collect=True in non distributed training127evaluation = dict(interval=1000, save_image=False, gpu_collect=True)128log_config = dict(129    interval=100,130    hooks=[131        dict(type='TextLoggerHook', by_epoch=False),132        # dict(type='TensorboardLoggerHook'),133    ])134visual_config = None135# runtime settings136dist_params = dict(backend='nccl')137log_level = 'INFO'138work_dir = f'./work_dirs/{exp_name}'139load_from = None140resume_from = None141workflow = [('train', 1)]...

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