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‎.github/workflows/build-wheels-aarch64-linux.yml

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- v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
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workflow_dispatch:
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permissions:
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id-token: write
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contents: read
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jobs:
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generate-matrix:
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uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
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trigger-event: ${{ github.event_name }}
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architecture: aarch64
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setup-miniconda: false
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secrets:
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AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
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AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

‎.github/workflows/build-wheels-linux.yml

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workflow_dispatch:
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permissions:
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id-token: write
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contents: read
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jobs:
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generate-matrix:
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uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
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package-name: ${{ matrix.package-name }}
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smoke-test-script: ${{ matrix.smoke-test-script }}
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trigger-event: ${{ github.event_name }}
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secrets:
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AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
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AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

‎.github/workflows/build-wheels-m1.yml

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workflow_dispatch:
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permissions:
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id-token: write
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contents: read
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jobs:
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generate-matrix:
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uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
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runner-type: macos-m1-12
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smoke-test-script: ${{ matrix.smoke-test-script }}
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trigger-event: ${{ github.event_name }}
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secrets:
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AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
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AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

‎.github/workflows/build-wheels-macos.yml

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workflow_dispatch:
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permissions:
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id-token: write
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contents: read
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jobs:
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generate-matrix:
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uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
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runner-type: macos-12
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smoke-test-script: ${{ matrix.smoke-test-script }}
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trigger-event: ${{ github.event_name }}
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secrets:
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AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
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AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

‎.github/workflows/build-wheels-windows.yml

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- v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
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workflow_dispatch:
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permissions:
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id-token: write
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contents: read
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jobs:
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generate-matrix:
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uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
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package-name: ${{ matrix.package-name }}
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smoke-test-script: ${{ matrix.smoke-test-script }}
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trigger-event: ${{ github.event_name }}
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secrets:
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AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
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AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}

‎docs/source/models/ssdlite.rst

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The SSDLite model is based on the `SSD: Single Shot MultiBox Detector
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<https://arxiv.org/abs/1512.02325>`__, `Searching for MobileNetV3
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<https://arxiv.org/abs/1905.02244>`__ and `MobileNetV2: Inverted Residuals and Linear
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Bottlenecks <https://arxiv.org/abs/1801.04381>__` papers.
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Bottlenecks <https://arxiv.org/abs/1801.04381>`__ papers.
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.. betastatus:: detection module
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‎torchvision/csrc/io/decoder/stream.cpp

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codecCtx_->thread_count = num_threads;
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} else {
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// otherwise set sensible defaults
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// with the special case for the different MPEG4 codecs
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// that don't have threading context functions
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if (codecCtx_->codec->capabilities & AV_CODEC_CAP_INTRA_ONLY) {
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codecCtx_->thread_type = FF_THREAD_FRAME;
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codecCtx_->thread_count = 2;
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} else {
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codecCtx_->thread_count = 8;
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codecCtx_->thread_type = FF_THREAD_SLICE;
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}
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codecCtx_->thread_count = 8;
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codecCtx_->thread_type = FF_THREAD_SLICE;
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}
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int ret;

‎torchvision/datasets/caltech.py

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target types. ``category`` represents the target class, and
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``annotation`` is a list of points from a hand-generated outline.
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Defaults to ``category``.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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Args:
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root (string): Root directory of dataset where directory
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``caltech256`` exists or will be saved to if download is set to True.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/celeba.py

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Defaults to ``attr``. If empty, ``None`` will be returned as target.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.PILToTensor``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/cifar.py

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``cifar-10-batches-py`` exists or will be saved to if download is set to True.
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train (bool, optional): If True, creates dataset from training set, otherwise
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creates from test set.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/clevr.py

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root (string): Root directory of dataset where directory ``root/clevr`` exists or will be saved to if download is
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set to True.
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split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
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transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in them target and transforms it.
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download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If

‎torchvision/datasets/coco.py

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Args:
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root (string): Root directory where images are downloaded to.
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annFile (string): Path to json annotation file.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.PILToTensor``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.
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Args:
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root (string): Root directory where images are downloaded to.
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annFile (string): Path to json annotation file.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.PILToTensor``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/country211.py

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Args:
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root (string): Root directory of the dataset.
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split (string, optional): The dataset split, supports ``"train"`` (default), ``"valid"`` and ``"test"``.
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transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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download (bool, optional): If True, downloads the dataset from the internet and puts it into

‎torchvision/datasets/dtd.py

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The partition only changes which split each image belongs to. Thus, regardless of the selected
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partition, combining all splits will result in all images.
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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download (bool, optional): If True, downloads the dataset from the internet and

‎torchvision/datasets/eurosat.py

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Args:
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/fakedata.py

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size (int, optional): Size of the dataset. Default: 1000 images
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image_size(tuple, optional): Size if the returned images. Default: (3, 224, 224)
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num_classes(int, optional): Number of classes in the dataset. Default: 10
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/fer2013.py

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``root/fer2013`` exists.
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split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
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transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``
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"""

‎torchvision/datasets/fgvc_aircraft.py

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``trainval`` and ``test``.
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annotation_level (str, optional): The annotation level, supports ``variant``,
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``family`` and ``manufacturer``.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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‎torchvision/datasets/flowers102.py

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transform (callable, optional): A function/transform that takes in an PIL image and returns a
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transform (callable, optional): A function/transform that takes in a PIL image and returns a
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transformed version. E.g, ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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‎torchvision/datasets/folder.py

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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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‎torchvision/datasets/food101.py

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Args:
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split (string, optional): The dataset split, supports ``"train"`` (default) and ``"test"``.
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transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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download (bool, optional): If True, downloads the dataset from the internet and

‎torchvision/datasets/gtsrb.py

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Args:
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root (string): Root directory of the dataset.
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split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
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transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version. E.g, ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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download (bool, optional): If True, downloads the dataset from the internet and

‎torchvision/datasets/imagenet.py

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Args:
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root (string): Root directory of the ImageNet Dataset.
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split (string, optional): The dataset split, supports ``train``, or ``val``.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/imagenette.py

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size (string, optional): The image size. Supports ``"full"`` (default), ``"320px"``, and ``"160px"``.
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download (bool, optional): If ``True``, downloads the dataset components and places them in ``root``. Already
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downloaded archives are not downloaded again.
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transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed
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transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
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version, e.g. ``transforms.RandomCrop``.
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target_transform (callable, optional): A function/transform that takes in the target and transforms it.
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‎torchvision/datasets/inaturalist.py

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Can also be a list to output a tuple with all specified target types.
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Defaults to ``full``.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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‎torchvision/datasets/kinetics.py

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split (str): split of the dataset to consider; supports ``"train"`` (default) ``"val"`` ``"test"``
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frame_rate (float): If omitted, interpolate different frame rate for each clip.
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step_between_clips (int): number of frames between each clip
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transform (callable, optional): A function/transform that takes in a TxHxWxC video
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transform (callable, optional): A function/transform that takes in a TxHxWxC video
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and returns a transformed version.
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download (bool): Download the official version of the dataset to root folder.
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num_workers (int): Use multiple workers for VideoClips creation

‎torchvision/datasets/lfw.py

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@@ -101,7 +101,7 @@ class LFWPeople(_LFW):
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``10fold`` (default).
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``deepfunneled``. Defaults to ``funneled``.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomRotation``
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target_transform (callable, optional): A function/transform that takes in the
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@@ -183,7 +183,7 @@ class LFWPairs(_LFW):
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``10fold``. Defaults to ``10fold``.
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``deepfunneled``. Defaults to ``funneled``.
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomRotation``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

‎torchvision/datasets/lsun.py

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root (string): Root directory for the database files.
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classes (string or list): One of {'train', 'val', 'test'} or a list of
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categories to load. e,g. ['bedroom_train', 'church_outdoor_train'].
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transform (callable, optional): A function/transform that takes in an PIL image
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transform (callable, optional): A function/transform that takes in a PIL image
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and returns a transformed version. E.g, ``transforms.RandomCrop``
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target_transform (callable, optional): A function/transform that takes in the
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target and transforms it.

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