Merge branch 'master' into svhn-hwc

Author: augustoolucas

.circleci/regenerate.py

@@ -15,6 +15,7 @@
 """
 
 import jinja2
+from jinja2 import select_autoescape
 import yaml
 import os.path
 
@@ -295,7 +296,7 @@ def ios_workflows(indentation=6, nightly=False):
     env = jinja2.Environment(
         loader=jinja2.FileSystemLoader(d),
         lstrip_blocks=True,
-        autoescape=False,
+        autoescape=select_autoescape(enabled_extensions=('html', 'xml')),
         keep_trailing_newline=True,
     )
 

.circleci/unittest/linux/scripts/install.sh

@@ -26,5 +26,11 @@ fi
 printf "Installing PyTorch with %s\n" "${cudatoolkit}"
 conda install -y -c "pytorch-${UPLOAD_CHANNEL}" -c conda-forge "pytorch-${UPLOAD_CHANNEL}"::pytorch "${cudatoolkit}"
 
+if [ $PYTHON_VERSION == "3.6" ]; then
+    printf "Installing minimal PILLOW version\n"
+    # Install the minimal PILLOW version. Otherwise, let setup.py install the latest
+    pip install pillow==5.3.0
+fi
+
 printf "* Installing torchvision\n"
 python setup.py develop

.circleci/unittest/windows/scripts/install.sh

@@ -28,5 +28,11 @@ fi
 printf "Installing PyTorch with %s\n" "${cudatoolkit}"
 conda install -y -c "pytorch-${UPLOAD_CHANNEL}" -c conda-forge "pytorch-${UPLOAD_CHANNEL}"::pytorch "${cudatoolkit}"
 
+if [ $PYTHON_VERSION == "3.6" ]; then
+    printf "Installing minimal PILLOW version\n"
+    # Install the minimal PILLOW version. Otherwise, let setup.py install the latest
+    pip install pillow==5.3.0
+fi
+
 printf "* Installing torchvision\n"
 "$this_dir/vc_env_helper.bat" python setup.py develop

.github/workflows/bandit.yml

@@ -0,0 +1,23 @@
+# GitHub Actions Bandit Workflow
+
+name: Bandit
+
+on:
+  pull_request:
+    branches: [ master ]
+
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v2
+
+      # Task will fail if any high-severity issues are found
+      # Ignoring submodules
+      - name: Run Bandit Security Analysis
+        run: |
+              python -m pip install bandit
+              python -m bandit -r . -x ./third_party -lll

.github/workflows/codeql.yml

@@ -0,0 +1,43 @@
+# GitHub Actions CodeQL Workflow
+
+name: CodeQL
+
+on:
+  pull_request:
+    branches: [ master ]
+
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v2
+
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@v1
+        with:
+          languages: python, cpp
+
+      - name: Install Ninja
+        run: |
+              sudo apt-get update -y
+              sudo apt-get install -y ninja-build
+
+      - name: Update submodules
+        run: git submodule update --init --recursive
+
+      - name: Install Torch
+        run: |
+              python -m pip install cmake
+              python -m pip install torch==1.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+              sudo ln -s /usr/bin/ninja /usr/bin/ninja-build
+
+      - name: Build TorchVision
+        run: python setup.py develop --user
+
+      # If any code scanning alerts are found, they will be under Security -> CodeQL
+      # Link: https://github.com/pytorch/vision/security/code-scanning
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@v1

docs/source/transforms.rst

@@ -4,15 +4,34 @@ torchvision.transforms
 .. currentmodule:: torchvision.transforms
 
 Transforms are common image transformations. They can be chained together using :class:`Compose`.
-Additionally, there is the :mod:`torchvision.transforms.functional` module.
-Functional transforms give fine-grained control over the transformations.
+Most transform classes have a function equivalent: :ref:`functional
+transforms <functional_transforms>` give fine-grained control over the
+transformations.
 This is useful if you have to build a more complex transformation pipeline
 (e.g. in the case of segmentation tasks).
 
-All transformations accept PIL Image, Tensor Image or batch of Tensor Images as input. Tensor Image is a tensor with
-``(C, H, W)`` shape, where ``C`` is a number of channels, ``H`` and ``W`` are image height and width. Batch of
-Tensor Images is a tensor of ``(B, C, H, W)`` shape, where ``B`` is a number of images in the batch. Deterministic or
-random transformations applied on the batch of Tensor Images identically transform all the images of the batch.
+Most transformations accept both `PIL <https://pillow.readthedocs.io>`_
+images and tensor images, although some transformations are :ref:`PIL-only
+<transforms_pil_only>` and some are :ref:`tensor-only
+<transforms_tensor_only>`. The :ref:`conversion_transforms` may be used to
+convert to and from PIL images.
+
+The transformations that accept tensor images also accept batches of tensor
+images. A Tensor Image is a tensor with ``(C, H, W)`` shape, where ``C`` is a
+number of channels, ``H`` and ``W`` are image height and width. A batch of
+Tensor Images is a tensor of ``(B, C, H, W)`` shape, where ``B`` is a number
+of images in the batch.
+
+The expected range of the values of a tensor image is implicitely defined by
+the tensor dtype. Tensor images with a float dtype are expected to have
+values in ``[0, 1)``. Tensor images with an integer dtype are expected to
+have values in ``[0, MAX_DTYPE]`` where ``MAX_DTYPE`` is the largest value
+that can be represented in that dtype.
+
+Randomized transformations will apply the same transformation to all the
+images of a given batch, but they will produce different transformations
+across calls. For reproducible transformations across calls, you may use
+:ref:`functional transforms <functional_transforms>`.
 
 .. warning::
 
@@ -117,13 +136,16 @@ Transforms on PIL Image and torch.\*Tensor
 .. autoclass:: GaussianBlur
     :members:
 
+.. _transforms_pil_only:
+
 Transforms on PIL Image only
 ----------------------------
 
 .. autoclass:: RandomChoice
 
 .. autoclass:: RandomOrder
 
+.. _transforms_tensor_only:
 
 Transforms on torch.\*Tensor only
 ---------------------------------
@@ -139,6 +161,7 @@ Transforms on torch.\*Tensor only
 
 .. autoclass:: ConvertImageDtype
 
+.. _conversion_transforms:
 
 Conversion Transforms
 ---------------------
@@ -173,13 +196,16 @@ The new transform can be used standalone or mixed-and-matched with existing tran
     :members:
 
 
+.. _functional_transforms:
+
 Functional Transforms
 ---------------------
 
 Functional transforms give you fine-grained control of the transformation pipeline.
 As opposed to the transformations above, functional transforms don't contain a random number
 generator for their parameters.
-That means you have to specify/generate all parameters, but you can reuse the functional transform.
+That means you have to specify/generate all parameters, but the functional transform will give you
+reproducible results across calls.
 
 Example:
 you can apply a functional transform with the same parameters to multiple images like this:

mypy.ini

@@ -63,3 +63,7 @@ ignore_missing_imports = True
 [mypy-av.*]
 
 ignore_missing_imports = True
+
+[mypy-defusedxml.*]
+
+ignore_missing_imports = True

setup.py

@@ -67,7 +67,7 @@ def write_version_file():
     pytorch_dep,
 ]
 
-pillow_ver = ' >= 4.1.1'
+pillow_ver = ' >= 5.3.0'
 pillow_req = 'pillow-simd' if get_dist('pillow-simd') is not None else 'pillow'
 requirements.append(pillow_req + pillow_ver)
 

test/assets/fakedata/draw_boxes_util.png

@@ -299,6 +299,78 @@ def _test_forward(self, device, contiguous, x_dtype=None, rois_dtype=None, **kwa
         for aligned in (True, False):
             super()._test_forward(device, contiguous, x_dtype, rois_dtype, aligned=aligned)
 
+    def test_qroialign(self):
+        """Make sure quantized version of RoIAlign is close to float version"""
+        pool_size = 5
+        img_size = 10
+        n_channels = 2
+        num_imgs = 1
+        dtype = torch.float
+
+        def make_rois(num_rois=1000):
+            rois = torch.randint(0, img_size // 2, size=(num_rois, 5)).to(dtype)
+            rois[:, 0] = torch.randint(0, num_imgs, size=(num_rois,))  # set batch index
+            rois[:, 3:] += rois[:, 1:3]  # make sure boxes aren't degenerate
+            return rois
+
+        for aligned in (True, False):
+            for scale, zero_point in ((1, 0), (2, 10), (0.1, 50)):
+                for qdtype in (torch.qint8, torch.quint8, torch.qint32):
+
+                    x = torch.randint(50, 100, size=(num_imgs, n_channels, img_size, img_size)).to(dtype)
+                    qx = torch.quantize_per_tensor(x, scale=scale, zero_point=zero_point, dtype=qdtype)
+
+                    rois = make_rois()
+                    qrois = torch.quantize_per_tensor(rois, scale=scale, zero_point=zero_point, dtype=qdtype)
+
+                    x, rois = qx.dequantize(), qrois.dequantize()  # we want to pass the same inputs
+
+                    y = ops.roi_align(
+                        x,
+                        rois,
+                        output_size=pool_size,
+                        spatial_scale=1,
+                        sampling_ratio=-1,
+                        aligned=aligned,
+                    )
+                    qy = ops.roi_align(
+                        qx,
+                        qrois,
+                        output_size=pool_size,
+                        spatial_scale=1,
+                        sampling_ratio=-1,
+                        aligned=aligned,
+                    )
+
+                    # The output qy is itself a quantized tensor and there might have been a loss of info when it was
+                    # quantized. For a fair comparison we need to quantize y as well
+                    quantized_float_y = torch.quantize_per_tensor(y, scale=scale, zero_point=zero_point, dtype=qdtype)
+
+                    try:
+                        # Ideally, we would assert this, which passes with (scale, zero) == (1, 0)
+                        self.assertTrue((qy == quantized_float_y).all())
+                    except AssertionError:
+                        # But because the computation aren't exactly the same between the 2 RoIAlign procedures, some
+                        # rounding error may lead to a difference of 2 in the output.
+                        # For example with (scale, zero) = (2, 10), 45.00000... will be quantized to 44
+                        # but 45.00000001 will be rounded to 46. We make sure below that:
+                        # - such discrepancies between qy and quantized_float_y are very rare (less then 5%)
+                        # - any difference between qy and quantized_float_y is == scale
+                        diff_idx = torch.where(qy != quantized_float_y)
+                        num_diff = diff_idx[0].numel()
+                        self.assertTrue(num_diff / qy.numel() < .05)
+
+                        abs_diff = torch.abs(qy[diff_idx].dequantize() - quantized_float_y[diff_idx].dequantize())
+                        t_scale = torch.full_like(abs_diff, fill_value=scale)
+                        self.assertTrue(torch.allclose(abs_diff, t_scale, atol=1e-5))
+
+        x = torch.randint(50, 100, size=(2, 3, 10, 10)).to(dtype)
+        qx = torch.quantize_per_tensor(x, scale=1, zero_point=0, dtype=torch.qint8)
+        rois = make_rois(10)
+        qrois = torch.quantize_per_tensor(rois, scale=1, zero_point=0, dtype=torch.qint8)
+        with self.assertRaisesRegex(RuntimeError, "Only one image per batch is allowed"):
+            ops.roi_align(qx, qrois, output_size=pool_size)
+
 
 class PSRoIAlignTester(RoIOpTester, unittest.TestCase):
     def fn(self, x, rois, pool_h, pool_w, spatial_scale=1, sampling_ratio=-1, **kwargs):