Migration of Data Settings¶

This section introduces the migration of data settings:

Migration of Data Settings
- Data pipelines
- Dataloader

Data pipelines¶

We update data pipelines settings in MMagic 1.x. Important modifications are as following.

Remove normalization and color space transforms operations. They are moved from datasets transforms pipelines to data_preprocessor.
The original formatting transforms pipelines Collect and ToTensor are combined as PackInputs. More details of data pipelines are shown in transform guides.

Original

New

train_pipeline = [  # Training data processing pipeline
    dict(type='LoadImageFromFile',  # Load images from files
        io_backend='disk',  # io backend
        key='lq',  # Keys in results to find corresponding path
        flag='unchanged'),  # flag for reading images
    dict(type='LoadImageFromFile',  # Load images from files
        io_backend='disk',  # io backend
        key='gt',  # Keys in results to find corresponding path
        flag='unchanged'),  # flag for reading images
    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),  # Rescale images from [0, 255] to [0, 1]
    dict(type='Normalize',  # Augmentation pipeline that normalize the input images
        keys=['lq', 'gt'],  # Images to be normalized
        mean=[0, 0, 0],  # Mean values
        std=[1, 1, 1],  # Standard variance
        to_rgb=True),  # Change to RGB channel
    dict(type='PairedRandomCrop', gt_patch_size=96),  # Paired random crop
    dict(type='Flip',  # Flip images
        keys=['lq', 'gt'],  # Images to be flipped
        flip_ratio=0.5,  # Flip ratio
        direction='horizontal'),  # Flip direction
    dict(type='Flip',  # Flip images
        keys=['lq', 'gt'],  # Images to be flipped
        flip_ratio=0.5,  # Flip ratio
        direction='vertical'),  # Flip direction
    dict(type='RandomTransposeHW',  # Random transpose h and w for images
        keys=['lq', 'gt'],  # Images to be transposed
        transpose_ratio=0.5  # Transpose ratio
        ),
    dict(type='Collect',  # Pipeline that decides which keys in the data should be passed to the model
        keys=['lq', 'gt'],  # Keys to pass to the model
        meta_keys=['lq_path', 'gt_path']), # Meta information keys. In training, meta information is not needed
    dict(type='ToTensor',  # Convert images to tensor
        keys=['lq', 'gt'])  # Images to be converted to Tensor
]
test_pipeline = [  # Test pipeline
    dict(
        type='LoadImageFromFile',  # Load images from files
        io_backend='disk',  # io backend
        key='lq',  # Keys in results to find corresponding path
        flag='unchanged'),  # flag for reading images
    dict(
        type='LoadImageFromFile',  # Load images from files
        io_backend='disk',  # io backend
        key='gt',  # Keys in results to find corresponding path
        flag='unchanged'),  # flag for reading images
    dict(type='RescaleToZeroOne', keys=['lq', 'gt']),  # Rescale images from [0, 255] to [0, 1]
    dict(
        type='Normalize',  # Augmentation pipeline that normalize the input images
        keys=['lq', 'gt'],  # Images to be normalized
        mean=[0, 0, 0],  # Mean values
        std=[1, 1, 1],  # Standard variance
        to_rgb=True),  # Change to RGB channel
    dict(type='Collect',  # Pipeline that decides which keys in the data should be passed to the model
        keys=['lq', 'gt'],  # Keys to pass to the model
        meta_keys=['lq_path', 'gt_path']),  # Meta information keys
    dict(type='ToTensor',  # Convert images to tensor
        keys=['lq', 'gt'])  # Images to be converted to Tensor
]

train_pipeline = [  # Training data processing pipeline
    dict(type='LoadImageFromFile',  # Load images from files
        key='img',  # Keys in results to find corresponding path
        color_type='color',  # Color type of image
        channel_order='rgb',  # Channel order of image
        imdecode_backend='cv2'),  # decode backend
    dict(type='LoadImageFromFile',  # Load images from files
        key='gt',  # Keys in results to find corresponding path
        color_type='color',  # Color type of image
        channel_order='rgb',  # Channel order of image
        imdecode_backend='cv2'),  # decode backend
    dict(type='SetValues', dictionary=dict(scale=scale)),  # Set value to destination keys
    dict(type='PairedRandomCrop', gt_patch_size=96),  # Paired random crop
    dict(type='Flip',  # Flip images
        keys=['lq', 'gt'],  # Images to be flipped
        flip_ratio=0.5,  # Flip ratio
        direction='horizontal'),  # Flip direction
    dict(type='Flip',  # Flip images
        keys=['lq', 'gt'],  # Images to be flipped
        flip_ratio=0.5,  # Flip ratio
        direction='vertical'),  # Flip direction
    dict(type='RandomTransposeHW',  # Random transpose h and w for images
        keys=['lq', 'gt'],  # Images to be transposed
        transpose_ratio=0.5  # Transpose ratio
        ),
    dict(type='PackInputs')  # The config of collecting data from current pipeline
]
test_pipeline = [  # Test pipeline
    dict(type='LoadImageFromFile',  # Load images from files
        key='img',  # Keys in results to find corresponding path
        color_type='color',  # Color type of image
        channel_order='rgb',  # Channel order of image
        imdecode_backend='cv2'),  # decode backend
    dict(type='LoadImageFromFile',  # Load images from files
        key='gt',  # Keys in results to find corresponding path
        color_type='color',  # Color type of image
        channel_order='rgb',  # Channel order of image
        imdecode_backend='cv2'),  # decode backend
    dict(type='PackInputs')  # The config of collecting data from current pipeline
]

Dataloader¶

We update dataloader settings in MMagic 1.x. Important modifications are as following.

The original data field is split to train_dataloader, val_dataloader and test_dataloader. This allows us to configure them in fine-grained. For example, you can specify different sampler and batch size during training and test.
The samples_per_gpu is renamed to batch_size.
The workers_per_gpu is renamed to num_workers.

Original

New

data = dict(
    # train
    samples_per_gpu=16,  # Batch size of a single GPU
    workers_per_gpu=4,  # Worker to pre-fetch data for each single GPU
    drop_last=True,  # Use drop_last in data_loader
    train=dict(  # Train dataset config
        type='RepeatDataset',  # Repeated dataset for iter-based model
        times=1000,  # Repeated times for RepeatDataset
        dataset=dict(
            type=train_dataset_type,  # Type of dataset
            lq_folder='data/DIV2K/DIV2K_train_LR_bicubic/X2_sub',  # Path for lq folder
            gt_folder='data/DIV2K/DIV2K_train_HR_sub',  # Path for gt folder
            ann_file='data/DIV2K/meta_info_DIV2K800sub_GT.txt',  # Path for annotation file
            pipeline=train_pipeline,  # See above for train_pipeline
            scale=scale)),  # Scale factor for upsampling
    # val
    val_samples_per_gpu=1,  # Batch size of a single GPU for validation
    val_workers_per_gpu=4,  # Worker to pre-fetch data for each single GPU for validation
    val=dict(
        type=val_dataset_type,  # Type of dataset
        lq_folder='data/val_set5/Set5_bicLRx2',  # Path for lq folder
        gt_folder='data/val_set5/Set5_mod12',  # Path for gt folder
        pipeline=test_pipeline,  # See above for test_pipeline
        scale=scale,  # Scale factor for upsampling
        filename_tmpl='{}'),  # filename template
    # test
    test=dict(
        type=val_dataset_type,  # Type of dataset
        lq_folder='data/val_set5/Set5_bicLRx2',  # Path for lq folder
        gt_folder='data/val_set5/Set5_mod12',  # Path for gt folder
        pipeline=test_pipeline,  # See above for test_pipeline
        scale=scale,  # Scale factor for upsampling
        filename_tmpl='{}'))  # filename template

dataset_type = 'BasicImageDataset'  # The type of dataset
data_root = 'data'  # Root path of data
train_dataloader = dict(
    batch_size=16,
    num_workers=4,  # The number of workers to pre-fetch data for each single GPU
    persistent_workers=False,  # Whether maintain the workers Dataset instances alive
    sampler=dict(type='InfiniteSampler', shuffle=True),  # The type of data sampler
    dataset=dict(  # Train dataset config
        type=dataset_type,  # Type of dataset
        ann_file='meta_info_DIV2K800sub_GT.txt',  # Path of annotation file
        metainfo=dict(dataset_type='div2k', task_name='sisr'),
        data_root=data_root + '/DIV2K',  # Root path of data
        data_prefix=dict(  # Prefix of image path
            img='DIV2K_train_LR_bicubic/X2_sub', gt='DIV2K_train_HR_sub'),
        filename_tmpl=dict(img='{}', gt='{}'),  # Filename template
        pipeline=train_pipeline))
val_dataloader = dict(
    batch_size=1,
    num_workers=4,  # The number of workers to pre-fetch data for each single GPU
    persistent_workers=False,  # Whether maintain the workers Dataset instances alive
    drop_last=False,  # Whether drop the last incomplete batch
    sampler=dict(type='DefaultSampler', shuffle=False),  # The type of data sampler
    dataset=dict(  # Validation dataset config
        type=dataset_type,  # Type of dataset
        metainfo=dict(dataset_type='set5', task_name='sisr'),
        data_root=data_root + '/Set5',  # Root path of data
        data_prefix=dict(img='LRbicx2', gt='GTmod12'),  # Prefix of image path
        pipeline=test_pipeline))
test_dataloader = val_dataloader