Transforms
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packnet_sfm.datasets.transforms.get_transforms(mode, image_shape, jittering, **kwargs)[source] Get data augmentation transformations for each split
- Parameters
mode (str {'train', 'validation', 'test'}) – Mode from which we want the data augmentation transformations
image_shape (tuple (height, width)) – Image dimension to reshape
jittering (tuple (brightness, contrast, saturation, hue)) – Color jittering parameters
- Returns
XXX_transform – Data augmentation transformation for that mode
- Return type
Partial function
-
packnet_sfm.datasets.transforms.test_transforms(sample, image_shape)[source] Test data augmentation transformations
- Parameters
sample (dict) – Sample to be augmented
image_shape (tuple (height, width)) – Image dimension to reshape
- Returns
sample – Augmented sample
- Return type
dict
-
packnet_sfm.datasets.transforms.train_transforms(sample, image_shape, jittering)[source] Training data augmentation transformations
- Parameters
sample (dict) – Sample to be augmented
image_shape (tuple (height, width)) – Image dimension to reshape
jittering (tuple (brightness, contrast, saturation, hue)) – Color jittering parameters
- Returns
sample – Augmented sample
- Return type
dict
-
packnet_sfm.datasets.transforms.validation_transforms(sample, image_shape)[source] Validation data augmentation transformations
- Parameters
sample (dict) – Sample to be augmented
image_shape (tuple (height, width)) – Image dimension to reshape
- Returns
sample – Augmented sample
- Return type
dict