Samplers¶
Samplers are used during the training phase and are especially useful when your training data is not uniformly distributed among all of your classes.
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class
pywick.samplers.
ImbalancedDatasetSampler
(dataset, indices=None, num_samples=None)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObject
Samples elements randomly from a given list of indices for imbalanced dataset
Parameters: - indices – (list, optional): a list of indices
- num_samples – (int, optional): number of samples to draw
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class
pywick.samplers.
MultiSampler
(nb_samples, desired_samples, shuffle=False)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObject
Samples elements more than once in a single pass through the data.
This allows the number of samples per epoch to be larger than the number of samples itself, which can be useful when training on 2D slices taken from 3D images, for instance.
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class
pywick.samplers.
StratifiedSampler
(class_vector, batch_size)[source]¶ Bases:
sphinx.ext.autodoc.importer._MockObject
Stratified Sampling
Provides equal representation of target classes in each batch
Parameters: - class_vector – (torch tensor): a vector of class labels
- batch_size – (int): size of the batch