Merge branch 'main' into fix-docs-import-os

Author: sekyondaMeta

advanced_source/dynamic_quantization_tutorial.py

@@ -134,10 +134,18 @@ def tokenize(self, path):
 # -----------------------------
 #
 # This is a tutorial on dynamic quantization, a quantization technique
-# that is applied after a model has been trained. Therefore, we'll simply load some
-# pretrained weights into this model architecture; these weights were obtained
-# by training for five epochs using the default settings in the word language model
-# example.
+# that is applied after a model has been trained. Therefore, we'll simply
+# load some pretrained weights into this model architecture; these
+# weights were obtained by training for five epochs using the default
+# settings in the word language model example.
+#
+# Before running this tutorial, download the required pre-trained model:
+#
+# .. code-block:: bash
+#
+#     wget https://s3.amazonaws.com/pytorch-tutorial-assets/word_language_model_quantize.pth
+#
+# Place the downloaded file in the data directory or update the model_data_filepath accordingly.
 
 ntokens = len(corpus.dictionary)
 

beginner_source/examples_autograd/polynomial_custom_function.py

@@ -33,8 +33,11 @@ def forward(ctx, input):
         """
         In the forward pass we receive a Tensor containing the input and return
         a Tensor containing the output. ctx is a context object that can be used
-        to stash information for backward computation. You can cache arbitrary
-        objects for use in the backward pass using the ctx.save_for_backward method.
+        to stash information for backward computation. You can cache tensors for
+        use in the backward pass using the ``ctx.save_for_backward`` method. Other
+        objects can be stored directly as attributes on the ctx object, such as
+        ``ctx.my_object = my_object``. Check out `Extending torch.autograd <https://docs.pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd>`_
+        for further details.
         """
         ctx.save_for_backward(input)
         return 0.5 * (5 * input ** 3 - 3 * input)

index.rst

@@ -806,21 +806,21 @@ Welcome to PyTorch Tutorials
    :header: Using the ExecuTorch SDK to Profile a Model
    :card_description: Explore how to use the ExecuTorch SDK to profile, debug, and visualize ExecuTorch models
    :image: _static/img/ExecuTorch-Logo-cropped.svg
-   :link: https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html
+   :link: https://docs.pytorch.org/executorch/main/tutorials/devtools-integration-tutorial.html
    :tags: Edge
 
 .. customcarditem::
    :header: Building an ExecuTorch iOS Demo App
    :card_description: Explore how to set up the ExecuTorch iOS Demo App, which uses the MobileNet v3 model to process live camera images leveraging three different backends: XNNPACK, Core ML, and Metal Performance Shaders (MPS).
    :image: _static/img/ExecuTorch-Logo-cropped.svg
-   :link: https://pytorch.org/executorch/stable/demo-apps-ios.html
+   :link: https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo
    :tags: Edge
 
 .. customcarditem::
    :header: Building an ExecuTorch Android Demo App
    :card_description: Learn how to set up the ExecuTorch Android Demo App for image segmentation tasks using the DeepLab v3 model and XNNPACK FP32 backend.
    :image: _static/img/ExecuTorch-Logo-cropped.svg
-   :link: https://pytorch.org/executorch/stable/demo-apps-android.html
+   :link: https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app
    :tags: Edge
 
 .. customcarditem::
@@ -1123,9 +1123,9 @@ Additional Resources
 
    Exporting to ExecuTorch Tutorial <https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html>
    Running an ExecuTorch Model in C++ Tutorial < https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html>
-   Using the ExecuTorch SDK to Profile a Model <https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html>
-   Building an ExecuTorch iOS Demo App <https://pytorch.org/executorch/stable/demo-apps-ios.html>
-   Building an ExecuTorch Android Demo App <https://pytorch.org/executorch/stable/demo-apps-android.html>
+   Using the ExecuTorch SDK to Profile a Model <https://docs.pytorch.org/executorch/main/tutorials/devtools-integration-tutorial.html>
+   Building an ExecuTorch iOS Demo App <https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo>
+   Building an ExecuTorch Android Demo App <https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app>
    Lowering a Model as a Delegate <https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html>
 
 .. toctree::

intermediate_source/tiatoolbox_tutorial.rst

@@ -348,7 +348,7 @@ The PatchPredictor class runs a CNN-based classifier written in PyTorch.
 -  Alternatively, you can pass ``pretrained_model`` as a string
    argument. This specifies the CNN model that performs the prediction,
    and it must be one of the models listed
-   `here <https://tia-toolbox.readthedocs.io/en/latest/usage.html?highlight=pretrained%20models#tiatoolbox.models.architecture.get_pretrained_model>`__.
+   `here <https://tia-toolbox.readthedocs.io/en/stable/_autosummary/tiatoolbox.models.architecture.get_pretrained_model.html#tiatoolbox.models.architecture.get_pretrained_model>`__.
    The command will look like this:
    ``predictor = PatchPredictor(pretrained_model='resnet18-kather100k', pretrained_weights=weights_path, batch_size=32)``.
 -  ``pretrained_weights``: When using a ``pretrained_model``, the
@@ -621,7 +621,7 @@ results. Here are the arguments and their descriptions:
    which is equivalent to level 0. In general, this is the level of
    greatest resolution. In this particular case, the image has only one
    level. More information can be found in the
-   `documentation <https://tia-toolbox.readthedocs.io/en/latest/usage.html?highlight=WSIReader.read_rect#tiatoolbox.wsicore.wsireader.WSIReader.read_rect>`__.
+   `documentation <https://tia-toolbox.readthedocs.io/en/stable/_autosummary/tiatoolbox.wsicore.wsireader.WSIReader.html#tiatoolbox.wsicore.wsireader.WSIReader.read_rect>`__.
 -  ``masks``: A list of paths corresponding to the masks of WSIs in the
    ``imgs`` list. These masks specify the regions in the original WSIs
    from which we want to extract patches. If the mask of a particular