Merge branch 'main' into transforms/mixupcutmix

Author: datumbox

.circleci/config.yml

@@ -45,14 +45,6 @@ commands:
               our_upload_channel=test
             fi
             echo "export UPLOAD_CHANNEL=${our_upload_channel}" >> ${BASH_ENV}
-  install_cuda_compatible_cmath:
-    description: "Install CUDA compatible cmath"
-    steps:
-      - run:
-          name: _HACK_ Install CUDA compatible cmath
-          no_output_timeout: 1m
-          command: |
-              powershell .circleci/scripts/vs_install_cmath.ps1
 
   brew_update:
     description: "Update Homebrew and install base formulae"
@@ -192,8 +184,9 @@ jobs:
       - checkout
       - run:
           command: |
-            pip install --user --progress-bar off flake8 typing
-            flake8 --config=setup.cfg .
+            pip install --user --progress-bar off pre-commit
+            pre-commit install-hooks
+      - run: pre-commit run --all-files
 
   python_type_check:
     docker:
@@ -300,7 +293,6 @@ jobs:
     steps:
       - checkout_merge
       - designate_upload_channel
-      - install_cuda_compatible_cmath
       - run:
           name: Build conda packages
           no_output_timeout: 20m
@@ -332,7 +324,6 @@ jobs:
     steps:
       - checkout_merge
       - designate_upload_channel
-      - install_cuda_compatible_cmath
       - run:
           name: Build wheel packages
           command: |
@@ -712,7 +703,6 @@ jobs:
     steps:
       - checkout
       - designate_upload_channel
-      - install_cuda_compatible_cmath
       - run:
           name: Generate cache key
           # This will refresh cache on Sundays, nightly build should generate new cache.
@@ -754,7 +744,6 @@ jobs:
     steps:
       - checkout
       - designate_upload_channel
-      - install_cuda_compatible_cmath
       - run:
           name: Generate cache key
           # This will refresh cache on Sundays, nightly build should generate new cache.
@@ -884,7 +873,6 @@ jobs:
     steps:
       - checkout_merge
       - designate_upload_channel
-      - install_cuda_compatible_cmath
       - run:
           command: |
             set -ex
@@ -898,7 +886,6 @@ jobs:
     steps:
       - checkout_merge
       - designate_upload_channel
-      - install_cuda_compatible_cmath
       - run:
           command: |
             set -ex

.circleci/config.yml.in

@@ -21,8 +21,8 @@
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 SOFTWARE.
-"""
-"""A wrapper script around clang-format, suitable for linting multiple files
+
+A wrapper script around clang-format, suitable for linting multiple files
 and to use for continuous integration.
 
 This is an alternative API for the clang-format command line.

.circleci/scripts/vs_install_cmath.ps1

@@ -1 +1 @@
-start /wait "" "%miniconda_exe%" /S /InstallationType=JustMe /RegisterPython=0 /AddToPath=0 /D=%tmp_conda%
+start /wait "" "%miniconda_exe%" /S /InstallationType=JustMe /RegisterPython=0 /AddToPath=0 /D=%tmp_conda%

.circleci/unittest/linux/scripts/run-clang-format.py

@@ -0,0 +1,14 @@
+repos:
+  - repo: https://gitlab.com/pycqa/flake8
+    rev: 3.9.2
+    hooks:
+      - id: flake8
+        args: [--config=setup.cfg]
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.0.1
+    hooks:
+      - id: check-docstring-first
+      - id: check-toml
+      - id: check-yaml
+        exclude: packaging/.*
+      - id: end-of-file-fixer

.circleci/unittest/windows/scripts/install_conda.bat

@@ -64,7 +64,7 @@ If you plan to modify the code or documentation, please follow the steps below:
 2. If you have modified the code (new feature or bug-fix), please add unit tests.
 3. If you have changed APIs, update the documentation. Make sure the documentation builds.
 4. Ensure the test suite passes.
-5. Make sure your code passes `flake8` formatting check.
+5. Make sure your code passes the formatting checks (see below).
 
 For more details about pull requests, 
 please read [GitHub's guides](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request). 
@@ -75,9 +75,19 @@ If you would like to contribute a new dataset, please see [here](#New-dataset).
 
 ### Code formatting and typing
 
-New code should be compatible with Python 3.X versions and be compliant with PEP8. To check the codebase, please run
+Contributions should be compatible with Python 3.X versions and be compliant with PEP8. To check the codebase, please 
+either run
 ```bash
-flake8 --config=setup.cfg .
+pre-commit run --all-files
+```
+or run
+```bash
+pre-commit install
+```
+once to perform these checks automatically before every `git commit`. If `pre-commit` is not available you can install 
+it with
+```
+pip install pre-commit
 ```
 
 The codebase has type annotations, please make sure to add type hints if required. We use `mypy` tool for type checking:

.pre-commit-config.yaml

@@ -106,9 +106,9 @@ otherwise, add the include and library paths in the environment variables ``TORC
 .. _libjpeg: http://ijg.org/
 .. _libjpeg-turbo: https://libjpeg-turbo.org/
 
-C++ API
-=======
-TorchVision also offers a C++ API that contains C++ equivalent of python models.
+Using the models on C++
+=======================
+TorchVision provides an example project for how to use the models on C++ using JIT Script.
 
 Installation From source:
 

CONTRIBUTING.md

@@ -39,4 +39,3 @@ allprojects {
 ext.deps = [
         jsr305: 'com.google.code.findbugs:jsr305:3.0.1',
 ]
-

README.rst

@@ -14,4 +14,4 @@
         android:background="@android:color/black"
         android:textColor="@android:color/white" />
 
-</FrameLayout>
+</FrameLayout>

android/build.gradle

@@ -9,4 +9,4 @@ torchvision it just hides the links. So we have to put them back here */
 article.pytorch-article .sphx-glr-download-link-note.admonition.note,
 article.pytorch-article .reference.download.internal, article.pytorch-article .sphx-glr-signature {
   display: block;
-}
+}

android/test_app/app/src/main/res/layout/activity_main.xml

@@ -147,6 +147,17 @@ KMNIST
 
 .. autoclass:: KMNIST
 
+LFW
+~~~~~
+
+.. autoclass:: LFWPeople
+  :members: __getitem__
+  :special-members:
+
+.. autoclass:: LFWPairs
+  :members: __getitem__
+  :special-members:
+
 LSUN
 ~~~~
 

docs/source/_static/css/custom_torchvision.css

@@ -126,4 +126,4 @@ API Reference
 
 .. autofunction:: create_feature_extractor
 
-.. autofunction:: get_graph_node_names
+.. autofunction:: get_graph_node_names

docs/source/_static/img/pytorch-logo-flame.svg

@@ -16,4 +16,4 @@ Once both dependencies are sorted, we can start the CMake fun:
 
 | That's it!
 | You should now have a ``hello-world`` executable in your ``build`` folder.
- Running it will output a (fairly long) tensor of random values to your terminal.
+ Running it will output a (fairly long) tensor of random values to your terminal.

docs/source/datasets.rst

@@ -1,25 +1,44 @@
 #include <iostream>
+#include <torch/script.h>
 #include <torch/torch.h>
 #include <torchvision/vision.h>
-#include <torchvision/models/resnet.h>
 
-int main()
-{
-  auto model = vision::models::ResNet18();
-  model->eval();
+int main() {
+  torch::DeviceType device_type;
+  device_type = torch::kCPU;
 
-  // Create a random input tensor and run it through the model.
-  auto in = torch::rand({1, 3, 10, 10});
-  auto out = model->forward(in);
+  torch::jit::script::Module model;
+  try {
+    std::cout << "Loading model\n";
+    // Deserialize the ScriptModule from a file using torch::jit::load().
+    model = torch::jit::load("resnet18.pt");
+    std::cout << "Model loaded\n";
+  } catch (const torch::Error& e) {
+    std::cout << "error loading the model\n";
+    return -1;
+  } catch (const std::exception& e) {
+    std::cout << "Other error: " << e.what() << "\n";
+    return -1;
+  }
 
-  std::cout << out.sizes();
+  // TorchScript models require a List[IValue] as input
+  std::vector<torch::jit::IValue> inputs;
+
+  // Create a random input tensor and run it through the model.
+  inputs.push_back(torch::rand({1, 3, 10, 10}));
+  auto out = model.forward(inputs);
+  std::cout << out << "\n";
 
   if (torch::cuda::is_available()) {
     // Move model and inputs to GPU
-    model->to(torch::kCUDA);
-    auto gpu_in = in.to(torch::kCUDA);
-    auto gpu_out = model->forward(gpu_in);
+    model.to(torch::kCUDA);
+
+    // Add GPU inputs
+    inputs.clear();
+    torch::TensorOptions options = torch::TensorOptions{torch::kCUDA};
+    inputs.push_back(torch::rand({1, 3, 10, 10}, options));
 
-    std::cout << gpu_out.sizes();
+    auto gpu_out = model.forward(inputs);
+    std::cout << gpu_out << "\n";
   }
 }

docs/source/feature_extraction.rst

@@ -0,0 +1,13 @@
+import os.path as osp
+
+import torch
+import torchvision
+
+HERE = osp.dirname(osp.abspath(__file__))
+ASSETS = osp.dirname(osp.dirname(HERE))
+
+model = torchvision.models.resnet18(pretrained=False)
+model.eval()
+
+traced_model = torch.jit.script(model)
+traced_model.save("resnet18.pt")

examples/cpp/hello_world/README.rst

@@ -1,4 +1,4 @@
 # Python examples
 
 The examples in this directory have been moved online in our [gallery
-page](https://pytorch.org/vision/stable/auto_examples/index.html).
+page](https://pytorch.org/vision/stable/auto_examples/index.html).

examples/cpp/hello_world/main.cpp

@@ -1,4 +1,4 @@
 Example gallery
 ===============
 
-Below is a gallery of examples
+Below is a gallery of examples

examples/cpp/hello_world/trace_model.py

@@ -5,4 +5,3 @@
 
 
 @end
-

examples/python/README.md

@@ -98,13 +98,18 @@ fi
 # Compile and run the CPP example
 popd
 cd examples/cpp/hello_world
-
 mkdir build
+
+# Trace model
+python trace_model.py
+cp resnet18.pt build
+
 cd build
 cmake .. -DTorch_DIR=$TORCH_PATH/share/cmake/Torch
 
 if [[ "$OSTYPE" == "msys" ]]; then
     "$script_dir/windows/internal/vc_env_helper.bat" "$script_dir/windows/internal/build_cpp_example.bat" $PARALLELISM
+    mv resnet18.pt Release
     cd Release
 else
     make -j$PARALLELISM