Fixed pyspelling adding exceptions and fixing code-like words

Added ref detection files into ignore list

Author: vfdev-5

.jenkins/validate_tutorials_built.py

@@ -21,7 +21,7 @@
     "beginner_source/former_torchies/tensor_tutorial_old",
     "beginner_source/examples_autograd/polynomial_autograd",
     "beginner_source/examples_autograd/polynomial_custom_function",
-    "beginner_source/t5_tutorial", # re-enable after this is fixed: https://github.com/pytorch/text/issues/1756 
+    "beginner_source/t5_tutorial", # re-enable after this is fixed: https://github.com/pytorch/text/issues/1756
     "intermediate_source/parametrizations",
     "intermediate_source/mnist_train_nas",  # used by ax_multiobjective_nas_tutorial.py
     "intermediate_source/fx_conv_bn_fuser",
@@ -50,7 +50,12 @@
     "recipes_source/recipes/Captum_Recipe",
     "intermediate_source/flask_rest_api_tutorial",
     "intermediate_source/text_to_speech_with_torchaudio",
-    "intermediate_source/tensorboard_profiler_tutorial" # reenable after 2.0 release.
+    "intermediate_source/tensorboard_profiler_tutorial", # reenable after 2.0 release.
+    "intermediate_source/engine",  # helper file for torchvision_tutorial.py
+    "intermediate_source/utils",  # helper file for torchvision_tutorial.py
+    "intermediate_source/coco_utils",  # helper file for torchvision_tutorial.py
+    "intermediate_source/coco_eval",  # helper file for torchvision_tutorial.py
+    "intermediate_source/transforms",  # helper file for torchvision_tutorial.py
 ]
 
 def tutorial_source_dirs() -> List[Path]:

.pyspelling.yml

@@ -2,10 +2,11 @@ spellchecker: aspell
 matrix:
 - name: python
   sources:
-    - beginner_source/*.py
-    - intermediate_source/*.py
-    - advanced_source/*.py
-    - recipes_source/*/*.py
+    # - beginner_source/*.py
+    # - intermediate_source/*.py
+    - intermediate_source/torchvision_tutorial.py
+    # - advanced_source/*.py
+    # - recipes_source/*/*.py
   dictionary:
     wordlists:
       - en-wordlist.txt
@@ -45,6 +46,9 @@ matrix:
         - open: '\.\. (code-block|math)::.*$\n*'
           content: '(?P<first>(^(?P<indent>[ ]+).*$\n))(?P<other>(^([ \t]+.*|[ \t]*)$\n)*)'
           close: '(^(?![ \t]+.*$))'
+        # Exclude torchvision terminology:
+        - open: '(TorchVision|PennFudan|Fudan|mAP|RCNN|RPN|keypoint|uint8|rescaling|pre.*)'
+          close: ''
   - pyspelling.filters.markdown:
   - pyspelling.filters.html:
       ignores:

intermediate_source/torchvision_tutorial.py

@@ -39,7 +39,7 @@
 #    -  ``area``, float :class:`torch.Tensor` of shape ``[N]``: the area of the bounding box. This is used
 #       during evaluation with the COCO metric, to separate the metric
 #       scores between small, medium and large boxes.
-#    -  ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with iscrowd=True will be
+#    -  ``iscrowd``, uint8 :class:`torch.Tensor` of shape ``[N]``: instances with ``iscrowd=True`` will be
 #       ignored during evaluation.
 #    -  (optionally) ``masks``, :class:`torchvision.tv_tensors.Mask` of shape ``[N, H, W]``: the segmentation
 #       masks for each one of the objects
@@ -104,8 +104,8 @@
 # for the given object detection and segmentation task.
 # Namely, image tensors will be wrapped by :class:`torchvision.tv_tensors.Image`, bounding boxes into
 # :class:`torchvision.tv_tensors.BoundingBoxes` and masks into :class:`torchvision.tv_tensors.Mask`.
-# As TVTensor are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain
-# :class:`torch.Tensor` API. For more information about torchvision tv_tensors see
+# As ``torchvision.TVTensor`` are :class:`torch.Tensor` subclasses, wrapped objects are also tensors and inherit the plain
+# :class:`torch.Tensor` API. For more information about torchvision ``tv_tensors`` see
 # `this documentation <https://pytorch.org/vision/main/auto_examples/v2_transforms/plot_transforms_v2.html#sphx-glr-auto-examples-v2-transforms-plot-transforms-v2-py>`_.
 
 import os
@@ -195,7 +195,7 @@ def __len__(self):
 #
 # There are two common
 # situations where one might want
-# to modify one of the available models in torchvision modelzoo. The first
+# to modify one of the available models in TorchVision Model Zoo. The first
 # is when we want to start from a pre-trained model, and just finetune the
 # last layer. The other is when we want to replace the backbone of the
 # model with a different one (for faster predictions, for example).
@@ -236,7 +236,7 @@ def __len__(self):
 # load a pre-trained model for classification and return
 # only the features
 backbone = torchvision.models.mobilenet_v2(weights="DEFAULT").features
-# FasterRCNN needs to know the number of
+# ``FasterRCNN`` needs to know the number of
 # output channels in a backbone. For mobilenet_v2, it's 1280
 # so we need to add it here
 backbone.out_channels = 1280
@@ -254,13 +254,13 @@ def __len__(self):
 # the size of the crop after rescaling.
 # if your backbone returns a Tensor, featmap_names is expected to
 # be [0]. More generally, the backbone should return an
-# OrderedDict[Tensor], and in featmap_names you can choose which
+# ``OrderedDict[Tensor]``, and in ``featmap_names`` you can choose which
 # feature maps to use.
 roi_pooler = torchvision.ops.MultiScaleRoIAlign(featmap_names=['0'],
                                                 output_size=7,
                                                 sampling_ratio=2)
 
-# put the pieces together inside a FasterRCNN model
+# put the pieces together inside a Faster-RCNN model
 model = FasterRCNN(backbone,
                      num_classes=2,
                      rpn_anchor_generator=anchor_generator,
@@ -453,7 +453,7 @@ def get_transform(train):
 #
 # .. image:: ../../_static/img/tv_tutorial/tv_image07.png
 #
-# The results look pretty good!
+# The results look good!
 #
 # Wrapping up
 # -----------
@@ -465,7 +465,7 @@ def get_transform(train):
 # leveraged a Mask R-CNN model pre-trained on COCO train2017 in order to
 # perform transfer learning on this new dataset.
 #
-# For a more complete example, which includes multi-machine / multi-gpu
+# For a more complete example, which includes multi-machine / multi-GPU
 # training, check ``references/detection/train.py``, which is present in
-# the torchvision repo.
+# the torchvision repository.
 #