torch_mimicry.datasets¶
Contents
Dataset Loaders¶
Script for loading datasets.
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load_celeba_dataset
(root, transform_data=True, convert_tensor=True, download=True, split='all', size=64, **kwargs)[source]¶ Loads the CelebA dataset.
Parameters: - root (str) – Path to where datasets are stored.
- size (int) – Size to resize images to.
- transform_data (bool) – If True, preprocesses data.
- split (str) – The split of data to use.
- download (bool) – If True, downloads the dataset.
- convert_tensor (bool) – If True, converts image to tensor and preprocess to range [-1, 1].
Returns: Torch Dataset object.
Return type: Dataset
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load_cifar100_dataset
(root, split='train', download=True, transform_data=True, convert_tensor=True, **kwargs)[source]¶ Loads the CIFAR-100 dataset.
Parameters: Returns: Torch Dataset object.
Return type: Dataset
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load_cifar10_dataset
(root, split='train', download=True, transform_data=True, **kwargs)[source]¶ Loads the CIFAR-10 dataset.
Parameters: Returns: Torch Dataset object.
Return type: Dataset
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load_dataset
(root, name, **kwargs)[source]¶ Loads different datasets specifically for GAN training. By default, all images are normalized to values in the range [-1, 1].
Parameters: Returns: Torch Dataset object for a specific dataset.
Return type: Dataset
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load_fake_dataset
(root, transform_data=True, convert_tensor=True, image_size=(3, 32, 32), **kwargs)[source]¶ Loads fake dataset for testing.
Parameters: Returns: Torch Dataset object.
Return type: Dataset
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load_imagenet_dataset
(root, size=32, split='train', download=True, transform_data=True, convert_tensor=True, **kwargs)[source]¶ Loads the ImageNet dataset.
Parameters: - root (str) – Path to where datasets are stored.
- size (int) – Size to resize images to.
- transform_data (bool) – If True, preprocesses data.
- split (str) – The split of data to use.
- download (bool) – If True, downloads the dataset.
- convert_tensor (bool) – If True, converts image to tensor and preprocess to range [-1, 1].
Returns: Torch Dataset object.
Return type: Dataset
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load_lsun_bedroom_dataset
(root, size=128, transform_data=True, convert_tensor=True, **kwargs)[source]¶ Loads LSUN-Bedroom dataset.
Parameters: Returns: Torch Dataset object.
Return type: Dataset
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load_stl10_dataset
(root, size=48, split='unlabeled', download=True, transform_data=True, convert_tensor=True, **kwargs)[source]¶ Loads the STL10 dataset.
Parameters: - root (str) – Path to where datasets are stored.
- size (int) – Size to resize images to.
- transform_data (bool) – If True, preprocesses data.
- split (str) – The split of data to use.
- download (bool) – If True, downloads the dataset.
- convert_tensor (bool) – If True, converts image to tensor and preprocess to range [-1, 1].
Returns: Torch Dataset object.
Return type: Dataset
Image Loaders¶
Loads randomly sampled images from datasets for computing metrics.
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get_celeba_images
(num_samples, root='./datasets', size=128, **kwargs)[source]¶ Loads randomly sampled CelebA images.
Parameters: Returns: Batch of num_samples images in np array form.
Return type: ndarray
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get_cifar100_images
(num_samples, root='./datasets', **kwargs)[source]¶ Loads randomly sampled CIFAR-100 training images.
Parameters: Returns: Batch of num_samples images in np array form.
Return type: ndarray
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get_cifar10_images
(num_samples, root='./datasets', **kwargs)[source]¶ Loads randomly sampled CIFAR-10 training images.
Parameters: Returns: Batch of num_samples images in np array form.
Return type: ndarray
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get_dataset_images
(dataset, num_samples=50000, **kwargs)[source]¶ Randomly sample num_samples images based on input dataset name.
Parameters: - dataset (str/Dataset) – Dataset to load images from.
- num_samples (int) – The number of images to randomly sample.
Returns: - Batch of num_samples images from a dataset in np array form.
The final format is of (N, H, W, 3) shape for TF inference.
Return type: ndarray
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get_fake_data_images
(num_samples, root='./datasets', size=32, **kwargs)[source]¶ Loads fake images, especially for testing.
Parameters: Returns: Batch of num_samples images in np array form.
Return type: ndarray
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get_imagenet_images
(num_samples, root='./datasets', size=32)[source]¶ Directly reads the imagenet folder for obtaining random images sampled in equal proportion for each class.
Parameters: Returns: Batch of num_samples images in np array form.
Return type: ndarray
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get_lsun_bedroom_images
(num_samples, root='./datasets', size=128, **kwargs)[source]¶ Loads randomly sampled LSUN-Bedroom training images.
Parameters: Returns: Batch of num_samples images in np array form.
Return type: ndarray
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get_random_images
(dataset, num_samples)[source]¶ Randomly sample without replacement num_samples images.
Parameters: - dataset (Dataset) – Torch Dataset object for indexing elements.
- num_samples (int) – The number of images to randomly sample.
Returns: Batch of num_samples images in np array form.
Return type: ndarray
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get_stl10_images
(num_samples, root='./datasets', size=48, **kwargs)[source]¶ Loads randomly sampled STL-10 images.
Parameters: Returns: Batch of num_samples images in np array form.
Return type: ndarray
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sample_dataset_images
(dataset, num_samples)[source]¶ Randomly samples the dataset for images.
Parameters: - dataset (Dataset) – Torch dataset object to sample images from.
- num_samples (int) – The number of images to randomly sample.
Returns: Numpy array of images with first dim as batch size.
Return type: ndarray