migrate prototype CI to Nova

.github/workflows/prototype-tests-linux-gpu.yml

@@ -0,0 +1,95 @@
+name: Prototype tests on Linux
+
+on:
+  pull_request:
+
+jobs:
+  tests:
+    strategy:
+      matrix:
+        python-version:
+          - "3.7"
+          - "3.8"
+          - "3.9"
+          - "3.10"
+        gpu-arch-type: ["cpu"]
+        gpu-arch-version: [""]
+        runner: ["linux.2xlarge"]
+        include:
+          - python-version: "3.8"
+            gpu-arch-type: cuda
+            gpu-arch-version: "11.6"
+            runner: linux.4xlarge.nvidia.gpu
+      fail-fast: false
+    uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+    with:
+      job-name: Python ${{ matrix.python-version }}, ${{ matrix.gpu-arch-type }}
+      repository: pytorch/vision
+      gpu-arch-type: ${{ matrix.gpu-arch-type }}
+      gpu-arch-version: ${{ matrix.gpu-arch-version }}
+      runner: ${{ matrix.runner }}
+      timeout: 45
+      script: |
+        # Mark Build Directory Safe
+
+        echo '::group::Set PyTorch conda channel'
+        if [[ (${GITHUB_EVENT_NAME} = 'pull_request' && (${GITHUB_BASE_REF} = 'release'*)) || (${GITHUB_REF} = 'refs/heads/release'*) ]]; then
+          POSTFIX=test
+        else
+          POSTFIX=nightly
+        fi
+        PYTORCH_CHANNEL=pytorch-"${POSTFIX}"
+        echo "${PYTORCH_CHANNEL}"
+        echo '::endgroup::'
+
+        echo '::group::Set PyTorch conda mutex'
+        if [[ ${{ matrix.gpu-arch-type }} = 'cuda' ]]; then
+          PYTORCH_MUTEX="pytorch-cuda=${{ matrix.gpu-arch-version }}"
+        else
+          PYTORCH_MUTEX=cpuonly
+        fi
+        echo "${PYTORCH_MUTEX}"
+        echo '::endgroup::'
+
+        echo '::group::Create conda environment'
+        conda create --prefix $PWD/ci \
+          --quiet --yes \
+          python=${{ matrix.python-version }} \
+          numpy libpng jpeg scipy
+        conda activate $PWD/ci
+        echo '::endgroup::'
+
+        echo '::group::Install PyTorch'
+        conda install \
+          --quiet --yes \
+          -c "${PYTORCH_CHANNEL}" \
+          -c nvidia \
+          pytorch \
+          "${PYTORCH_MUTEX}"
+        if [[ ${{ matrix.gpu-arch-type }} = 'cuda' ]]; then
+          python3 -c "import torch; exit(not torch.cuda.is_available())"
+        fi
+        echo '::endgroup::'
+
+        echo '::group::Install TorchVision'
+        python setup.py develop
+        echo '::endgroup::'
+
+        echo '::group::Collect PyTorch environment information'
+        python -m torch.utils.collect_env
+        echo '::endgroup::'
+
+        echo '::group::Install testing utilities'
+        pip install --progress-bar=off pytest pytest-mock pytest-cov
+        echo '::endgroup::'
+
+        echo '::group::Run prototype tests'
+        # We don't want to run the prototype datasets tests. Since the positional glob into `pytest`, i.e. 
+        # `test/test_prototype*.py` takes the highest priority, neither `--ignore` nor `--ignore-glob` can help us here.
+        rm test/test_prototype_datasets*.py
+        pytest \
+          --durations=25 \
+          --cov=torchvision/prototype \
+          --cov-report=term-missing \
+          test/test_prototype*.py
+        echo '::endgroup::'

test/test_prototype_transforms_consistency.py

@@ -1,9 +1,10 @@
 import enum
+import importlib.machinery
+import importlib.util
 import inspect
 import random
 import re
 from collections import defaultdict
-from importlib.machinery import SourceFileLoader
 from pathlib import Path
 
 import numpy as np
@@ -890,8 +891,16 @@ def test_aa(self, inpt, interpolation):
 
 
 def import_transforms_from_references(reference):
-    ref_det_filepath = Path(__file__).parent.parent / "references" / reference / "transforms.py"
-    return SourceFileLoader(ref_det_filepath.stem, ref_det_filepath.as_posix()).load_module()
+    HERE = Path(__file__).parent
+    PROJECT_ROOT = HERE.parent
+
+    loader = importlib.machinery.SourceFileLoader(
+        "transforms", str(PROJECT_ROOT / "references" / reference / "transforms.py")
+    )
+    spec = importlib.util.spec_from_loader("transforms", loader)
+    module = importlib.util.module_from_spec(spec)
+    loader.exec_module(module)
+    return module
 
 
 det_transforms = import_transforms_from_references("detection")

test/test_prototype_transforms_functional.py

@@ -625,10 +625,10 @@ def _compute_expected_bbox(bbox, angle_, expand_, center_):
         )
         transformed_points = np.matmul(points, affine_matrix.T)
         out_bbox = [
-            np.min(transformed_points[:4, 0]),
-            np.min(transformed_points[:4, 1]),
-            np.max(transformed_points[:4, 0]),
-            np.max(transformed_points[:4, 1]),
+            float(np.min(transformed_points[:4, 0])),
+            float(np.min(transformed_points[:4, 1])),
+            float(np.max(transformed_points[:4, 0])),
+            float(np.max(transformed_points[:4, 1])),
         ]
         if expand_:
             tr_x = np.min(transformed_points[4:, 0])