Initial commit

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/actions/install_windows_dep/action.yml

+name: "Install Dependencies"
+inputs:
+  torch-version:
+    description: torch version to install
+  torchvision-version:
+    description: torch vision version to install, version number or "master"
+  pytorch-index:
+    description: where to install torch from
+    required: false
+    default: "https://download.pytorch.org/whl/torch_stable.html"
+    # use test wheels index to have access to RC wheels
+    # https://download.pytorch.org/whl/test/torch_test.html
+runs:
+  using: composite
+  steps:
+  - name: Install Dependencies
+    shell: bash
+    run: |
+      pip install certifi --ignore-installed  # required on windows to workaround some cert issue
+      pip install numpy cython  # required on windows before pycocotools
+      pip install opencv-python-headless pytest-xdist pycocotools tensorboard onnx
+      pip install -U git+https://github.com/facebookresearch/iopath
+      pip install -U git+https://github.com/facebookresearch/fvcore
+      pip install torch==${{inputs.torch-version}} torchvision==${{inputs.torchvision-version}} -f ${{inputs.pytorch-index}}

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/actions/run_unittests/action.yml

+name: "Run Unit Tests"
+runs:
+  using: composite
+  steps:
+  - name: Run Unit Tests
+    shell: bash
+    run: |
+      python -m pytest -sv --durations=15 tests  # parallel causes some random failures

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/actions/run_unittests_win/action.yml

+name: "Run Unit Tests"
+runs:
+  using: composite
+  steps:
+  - name: Run Unit Tests
+    shell: bash
+    run: |
+      echo TODO: unittest fails for now

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/actions/uninstall_tests/action.yml

+name: "Run Tests After Uninstalling"
+runs:
+  using: composite
+  steps:
+  - name: "Run Tests After Uninstalling"
+    shell: bash
+    run: |
+      pip uninstall -y detectron2
+      # Remove built binaries
+      rm -rf build/ detectron2/*.so
+      # Tests that code is importable without installation
+      . ./.github/import-tests.sh

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/import-tests.sh

+#!/bin/bash -e
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+# Test that import works without building detectron2.
+
+# Check that _C is not importable
+python -c "from detectron2 import _C" > /dev/null 2>&1 && {
+  echo "This test should be run without building detectron2."
+  exit 1
+}
+
+# Check that other modules are still importable, even when _C is not importable
+python -c "from detectron2 import modeling"
+python -c "from detectron2 import modeling, data"
+python -c "from detectron2 import evaluation, export, checkpoint"
+python -c "from detectron2 import utils, engine"

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/pull_request_template.md

+Thanks for your contribution!
+
+If you're sending a large PR (e.g., >100 lines),
+please open an issue first about the feature / bug, and indicate how you want to contribute.
+
+We do not always accept features.
+See https://detectron2.readthedocs.io/notes/contributing.html#pull-requests about how we handle PRs.
+
+Before submitting a PR, please run `dev/linter.sh` to lint the code.

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/workflows/check-template.yml

+name: Check issue template
+
+on:
+  issues:
+    types: [opened]
+
+jobs:
+  check-template:
+    runs-on: ubuntu-latest
+    # comment this out when testing with https://github.com/nektos/act
+    if: ${{ github.repository_owner == 'facebookresearch' }}
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/github-script@v3
+        with:
+          github-token: ${{secrets.GITHUB_TOKEN}}
+          script: |
+            // Arguments available:
+            // - github: A pre-authenticated octokit/rest.js client
+            // - context: An object containing the context of the workflow run
+            // - core: A reference to the @actions/core package
+            // - io: A reference to the @actions/io package
+            const fs = require('fs');
+            const editDistance = require(`${process.env.GITHUB_WORKSPACE}/.github/workflows/levenshtein.js`).getEditDistance
+            issue = await github.issues.get({
+              owner: context.issue.owner,
+              repo: context.issue.repo,
+              issue_number: context.issue.number,
+            });
+            const hasLabel = issue.data.labels.length > 0;
+            if (hasLabel || issue.state === "closed") {
+              // don't require template on them
+              core.debug("Issue " + issue.data.title + " was skipped.");
+              return;
+            }
+
+            sameAsTemplate = function(filename, body) {
+              let tmpl = fs.readFileSync(`.github/ISSUE_TEMPLATE/${filename}`, 'utf8');
+              tmpl = tmpl.toLowerCase().split("---").slice(2).join("").trim();
+              tmpl = tmpl.replace(/(\r\n|\n|\r)/gm, "");
+              let bodyr = body.replace(/(\r\n|\n|\r)/gm, "");
+              let dist = editDistance(tmpl, bodyr);
+              return dist < 8;
+            };
+
+            checkFail = async function(msg) {
+              core.info("Processing '" + issue.data.title + "' with message: " + msg);
+              await github.issues.addLabels({
+                owner: context.issue.owner,
+                repo: context.issue.repo,
+                issue_number: context.issue.number,
+                labels: ["needs-more-info"],
+              });
+              await github.issues.createComment({
+                owner: context.issue.owner,
+                repo: context.issue.repo,
+                issue_number: context.issue.number,
+                body: msg,
+              });
+            };
+
+            const body = issue.data.body.toLowerCase().trim();
+
+            if (sameAsTemplate("bugs.md", body) || sameAsTemplate("unexpected-problems-bugs.md", body)) {
+              await checkFail(`
+            We found that not enough information is provided about this issue.
+            Please provide details following the [issue template](https://github.com/facebookresearch/detectron2/issues/new/choose).`)
+              return;
+            }
+
+            const hasInstructions = body.indexOf("reproduce") != -1;
+            const hasEnvironment = (body.indexOf("environment") != -1) || (body.indexOf("colab") != -1) || (body.indexOf("docker") != -1);
+            if (hasInstructions && hasEnvironment) {
+              core.debug("Issue " + issue.data.title + " follows template.");
+              return;
+            }
+
+            let message = "You've chosen to report an unexpected problem or bug. Unless you already know the root cause of it, please include details about it by filling the [issue template](https://github.com/facebookresearch/detectron2/issues/new/choose).\n";
+            message += "The following information is missing: ";
+            if (!hasInstructions) {
+              message += "\"Instructions To Reproduce the Issue and __Full__ Logs\"; ";
+            }
+            if (!hasEnvironment) {
+              message += "\"Your Environment\"; ";
+            }
+            await checkFail(message);

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/workflows/levenshtein.js

+/*
+Copyright (c) 2011 Andrei Mackenzie
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 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.
+*/
+
+// Compute the edit distance between the two given strings
+exports.getEditDistance = function(a, b){
+  if(a.length == 0) return b.length;
+  if(b.length == 0) return a.length;
+
+  var matrix = [];
+
+  // increment along the first column of each row
+  var i;
+  for(i = 0; i <= b.length; i++){
+    matrix[i] = [i];
+  }
+
+  // increment each column in the first row
+  var j;
+  for(j = 0; j <= a.length; j++){
+    matrix[0][j] = j;
+  }
+
+  // Fill in the rest of the matrix
+  for(i = 1; i <= b.length; i++){
+    for(j = 1; j <= a.length; j++){
+      if(b.charAt(i-1) == a.charAt(j-1)){
+        matrix[i][j] = matrix[i-1][j-1];
+      } else {
+        matrix[i][j] = Math.min(matrix[i-1][j-1] + 1, // substitution
+                                Math.min(matrix[i][j-1] + 1, // insertion
+                                         matrix[i-1][j] + 1)); // deletion
+      }
+    }
+  }
+
+  return matrix[b.length][a.length];
+};

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/workflows/needs-reply.yml

+name: Close/Lock issues after inactivity
+
+on:
+  schedule:
+    - cron: "42 1 * * *"
+
+jobs:
+  close-issues-needs-more-info:
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'facebookresearch' }}
+    steps:
+      - name: Close old issues that need reply
+        uses: actions/github-script@v3
+        with:
+          github-token: ${{secrets.GITHUB_TOKEN}}
+          # Modified from https://github.com/dwieeb/needs-reply
+          script: |
+            // Arguments available:
+            // - github: A pre-authenticated octokit/rest.js client
+            // - context: An object containing the context of the workflow run
+            // - core: A reference to the @actions/core package
+            // - io: A reference to the @actions/io package
+            const kLabelToCheck = "needs-more-info";
+            const kInvalidLabel = "invalid/unrelated";
+            const kDaysBeforeClose = 7;
+            const kMessage = "Requested information was not provided in 7 days, so we're closing this issue.\n\nPlease open new issue if information becomes available. Otherwise, use [github discussions](https://github.com/facebookresearch/detectron2/discussions) for free-form discussions."
+
+            issues = await github.issues.listForRepo({
+              owner: context.repo.owner,
+              repo: context.repo.repo,
+              state: 'open',
+              labels: kLabelToCheck,
+              sort: 'updated',
+              direction: 'asc',
+              per_page: 30,
+              page: 1,
+            });
+            issues = issues.data;
+            if (issues.length === 0) {
+              core.info('No more issues found to process. Exiting.');
+              return;
+            }
+            for (const issue of issues) {
+              if (!!issue.pull_request)
+                continue;
+              core.info(`Processing issue #${issue.number}`);
+
+              let updatedAt = new Date(issue.updated_at).getTime();
+              const numComments = issue.comments;
+              const comments = await github.issues.listComments({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: issue.number,
+                per_page: 30,
+                page: Math.floor((numComments - 1) / 30) + 1, // the last page
+              });
+              const lastComments = comments.data
+                .map(l => new Date(l.created_at).getTime())
+                .sort();
+              if (lastComments.length > 0) {
+                updatedAt = lastComments[lastComments.length - 1];
+              }
+
+              const now = new Date().getTime();
+              const daysSinceUpdated = (now - updatedAt) / 1000 / 60 / 60 / 24;
+
+              if (daysSinceUpdated < kDaysBeforeClose) {
+                core.info(`Skipping #${issue.number} because it has been updated in the last ${daysSinceUpdated} days`);
+                continue;
+              }
+              core.info(`Closing #${issue.number} because it has not been updated in the last ${daysSinceUpdated} days`);
+              await github.issues.createComment({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  issue_number: issue.number,
+                  body: kMessage,
+              });
+              const newLabels = numComments <= 2 ? [kInvalidLabel, kLabelToCheck] : issue.labels;
+              await github.issues.update({
+                owner: context.repo.owner,
+                repo: context.repo.repo,
+                issue_number: issue.number,
+                labels: newLabels,
+                state: 'closed',
+              });
+            }
+
+  lock-issues-after-closed:
+    runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'facebookresearch' }}
+    steps:
+      - name: Lock closed issues that have no activity for a while
+        uses: dessant/lock-threads@v2
+        with:
+          github-token: ${{ github.token }}
+          issue-lock-inactive-days: '300'
+          process-only: 'issues'
+          issue-exclude-labels: 'enhancement,bug,documentation'

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/workflows/remove-needs-reply.yml

+name: Remove needs-more-info label
+
+on:
+  issue_comment:
+    types: [created]
+  issues:
+    types: [edited]
+
+jobs:
+  remove-needs-more-info-label:
+    runs-on: ubuntu-latest
+    # 1. issue_comment events could include PR comment, filter them out
+    # 2. Only trigger action if event was produced by the original author
+    if: ${{ !github.event.issue.pull_request && github.event.sender.login == github.event.issue.user.login }}
+    steps:
+      - name: Remove needs-more-info label
+        uses: octokit/request-action@v2.x
+        continue-on-error: true
+        with:
+          route: DELETE /repos/:repository/issues/:issue/labels/:label
+          repository: ${{ github.repository }}
+          issue: ${{ github.event.issue.number }}
+          label: needs-more-info
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

hygon-hub/common/base/autodrive/3rdparty/detectron2/.github/workflows/workflow.yml

+name: CI
+on:
+  push:
+  pull_request:
+  schedule:
+    - cron: "0 0 * * *"  # @daily
+
+# Run linter with github actions for quick feedbacks.
+jobs:
+  linter:
+    runs-on: ubuntu-latest
+    # run on PRs, or commits to facebookresearch (not internal)
+    if: ${{ github.repository_owner == 'facebookresearch' || github.event_name == 'pull_request' }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.9
+      - name: Install dependencies
+        # flake8-bugbear flake8-comprehensions are useful but not available internally
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install flake8==6.1.0 isort==4.3.21
+          python -m pip install black==24.4.2
+          flake8 --version
+      - name: Lint
+        run: |
+          echo "Running isort"
+          isort -c -sp .
+          echo "Running black"
+          black -l 100 --check .
+          echo "Running flake8"
+          flake8 .
+
+  macos_tests:
+    runs-on: macos-latest
+    # run on PRs, or commits to facebookresearch (not internal)
+    if: ${{ github.repository_owner == 'facebookresearch' || github.event_name == 'pull_request' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        torch: ["1.13.1", "2.2.2"]
+        include:
+          - torch: "1.13.1"
+            torchvision: "0.14.1"
+          - torch: "2.2.2"
+            torchvision: "0.17.2"
+    env:
+      # point datasets to ~/.torch so it's cached by CI
+      DETECTRON2_DATASETS: ~/.torch/datasets
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.8
+      - name: Cache dependencies
+        uses: actions/cache@v4
+        with:
+          path: |
+            ${{ env.pythonLocation }}/lib/python3.8/site-packages
+            ~/.torch
+          key: ${{ runner.os }}-torch${{ matrix.torch }}-${{ hashFiles('setup.py') }}-20220119
+
+      - name: Install dependencies
+        run: |
+          python -m pip install -U pip
+          python -m pip install wheel ninja opencv-python-headless onnx pytest-xdist
+          python -m pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
+          # install from github to get latest; install iopath first since fvcore depends on it
+          python -m pip install -U 'git+https://github.com/facebookresearch/iopath'
+          python -m pip install -U 'git+https://github.com/facebookresearch/fvcore'
+          wget https://raw.githubusercontent.com/pytorch/pytorch/v${{matrix.torch}}/torch/utils/collect_env.py
+          python collect_env.py
+
+      - name: Build and install
+        run: |
+          CC=clang CXX=clang++ python -m pip install -e .[all]
+          python -m detectron2.utils.collect_env
+          ./datasets/prepare_for_tests.sh
+      - name: Run unittests
+        run: python -m pytest -n 4 --durations=15 -sv tests/
+
+  linux_gpu_tests:
+    runs-on: 4-core-ubuntu-gpu-t4
+    # run on PRs, or commits to facebookresearch (not internal)
+    if: ${{ github.repository_owner == 'facebookresearch' || github.event_name == 'pull_request' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        torch: ["2.4.1"]
+        include:
+          - torch: "2.4.1"
+            torchvision: "0.19.1"
+            cuda: "12-8"
+    env:
+      PYTORCH_INDEX: "https://download.pytorch.org/whl/cu128"
+      DETECTRON2_DATASETS: ~/.torch/datasets
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.8
+
+      - name: Cache dependencies
+        uses: actions/cache@v4
+        id: load-cache
+        with:
+          path: |
+            ${{ env.pythonLocation }}/lib/python3.8/site-packages
+            ~/.torch
+          key: ${{ runner.os }}-torch-gpu${{ matrix.torch }}-${{ hashFiles('setup.py') }}-20210827
+
+      - name: Install GPU Dependencies
+        uses: ./.github/actions/install_linux_gpu_dep
+        with:
+          cuda-version: ${{matrix.cuda}}
+
+      - name: Install Dependencies
+        uses: ./.github/actions/install_linux_dep
+        if: steps.load-cache.outputs.cache-hit != 'true'
+        with:
+          torch-version: ${{matrix.torch}}
+          torchvision-version: ${{matrix.torchvision}}
+          pytorch-index: $PYTORCH_INDEX
+
+      - name: Install Detectron2
+        uses: ./.github/actions/install_detectron2
+
+      - name: Run Unit Tests
+        uses: ./.github/actions/run_unittests
+
+      - name: Run Tests After Uninstalling
+        uses: ./.github/actions/uninstall_tests
+
+  linux_cpu_tests:
+    runs-on: ubuntu-latest
+    # run on PRs, or commits to facebookresearch (not internal)
+    if: ${{ github.repository_owner == 'facebookresearch' || github.event_name == 'pull_request' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        torch: ["1.13.1", "2.2.2"]
+        include:
+          - torch: "1.13.1"
+            torchvision: "0.14.1"
+          - torch: "2.2.2"
+            torchvision: "0.17.2"
+    env:
+      PYTORCH_INDEX: "https://download.pytorch.org/whl/cu118"
+      DETECTRON2_DATASETS: ~/.torch/datasets
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.8
+
+      - name: Cache dependencies
+        uses: actions/cache@v4
+        id: load-cache
+        with:
+          path: |
+            ${{ env.pythonLocation }}/lib/python3.8/site-packages
+            ~/.torch
+          key: ${{ runner.os }}-torch${{ matrix.torch }}-${{ hashFiles('setup.py') }}-20210827
+
+      - name: Install Dependencies
+        uses: ./.github/actions/install_linux_dep
+        if: steps.load-cache.outputs.cache-hit != 'true'
+        with:
+          torch-version: ${{matrix.torch}}
+          torchvision-version: ${{matrix.torchvision}}
+          pytorch-index: $PYTORCH_INDEX
+
+      - name: Install Detectron2
+        uses: ./.github/actions/install_detectron2
+
+      - name: Run Unit Tests
+        uses: ./.github/actions/run_unittests
+
+      - name: Run Tests After Uninstalling
+        uses: ./.github/actions/uninstall_tests
+
+  windows_cpu_tests:
+    runs-on: windows-latest
+    # run on PRs, or commits to facebookresearch (not internal)
+    if: ${{ github.repository_owner == 'facebookresearch' || github.event_name == 'pull_request' }}
+    strategy:
+      fail-fast: false
+      matrix:
+        torch: ["1.13.1", "2.2.2"]
+        include:
+          - torch: "1.13.1"
+            torchvision: "0.14.1"
+          - torch: "2.2.2"
+            torchvision: "0.17.2"
+    env:
+      PYTORCH_INDEX: "https://download.pytorch.org/whl/cu118"
+      DETECTRON2_DATASETS: ~/.torch/datasets
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.8
+
+      # NOTE: Cache is disabled because it's not working on Windows.
+      # - name: Cache dependencies
+      #   uses: actions/cache@v4
+      #   id: load-cache
+      #   with:
+      #     path: |
+      #       ${{ env.pythonLocation }}\Lib\site-packages
+      #       ~\.torch
+      #     key: ${{ runner.os }}-torch${{ matrix.torch }}-${{ hashFiles('setup.py') }}-20210404
+
+      - name: Install Dependencies
+        uses: ./.github/actions/install_windows_dep
+        if: steps.load-cache.outputs.cache-hit != 'true'
+        with:
+          torch-version: ${{matrix.torch}}
+          torchvision-version: ${{matrix.torchvision}}
+          pytorch-index: $PYTORCH_INDEX
+
+      - name: Install Detectron2
+        uses: ./.github/actions/install_detectron2_win
+
+      - name: Run Unit Tests
+        uses: ./.github/actions/run_unittests_win

hygon-hub/common/base/autodrive/3rdparty/detectron2/.gitignore

+# output dir
+output
+instant_test_output
+inference_test_output
+
+
+*.png
+*.json
+*.diff
+*.jpg
+!/projects/DensePose/doc/images/*.jpg
+
+# compilation and distribution
+__pycache__
+_ext
+*.pyc
+*.pyd
+*.so
+*.dll
+*.egg-info/
+build/
+dist/
+wheels/
+
+# pytorch/python/numpy formats
+*.pth
+*.pkl
+*.npy
+*.ts
+model_ts*.txt
+
+# ipython/jupyter notebooks
+*.ipynb
+**/.ipynb_checkpoints/
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# editor settings
+.idea
+.vscode
+_darcs
+
+# project dirs
+/detectron2/model_zoo/configs
+/datasets/*
+!/datasets/*.*
+/projects/*/datasets
+/models
+/snippet

hygon-hub/common/base/autodrive/3rdparty/detectron2/GETTING_STARTED.md

+## Getting Started with Detectron2
+
+This document provides a brief intro of the usage of builtin command-line tools in detectron2.
+
+For a tutorial that involves actual coding with the API,
+see our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+which covers how to run inference with an
+existing model, and how to train a builtin model on a custom dataset.
+
+
+### Inference Demo with Pre-trained Models
+
+1. Pick a model and its config file from
+  [model zoo](MODEL_ZOO.md),
+  for example, `mask_rcnn_R_50_FPN_3x.yaml`.
+2. We provide `demo.py` that is able to demo builtin configs. Run it with:
+```
+cd demo/
+python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \
+  --input input1.jpg input2.jpg \
+  [--other-options]
+  --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
+```
+The configs are made for training, therefore we need to specify `MODEL.WEIGHTS` to a model from model zoo for evaluation.
+This command will run the inference and show visualizations in an OpenCV window.
+
+For details of the command line arguments, see `demo.py -h` or look at its source code
+to understand its behavior. Some common arguments are:
+* To run __on your webcam__, replace `--input files` with `--webcam`.
+* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
+* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
+* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
+
+
+### Training & Evaluation in Command Line
+
+We provide two scripts in "tools/plain_train_net.py" and "tools/train_net.py",
+that are made to train all the configs provided in detectron2. You may want to
+use it as a reference to write your own training script.
+
+Compared to "train_net.py", "plain_train_net.py" supports fewer default
+features. It also includes fewer abstraction, therefore is easier to add custom
+logic.
+
+To train a model with "train_net.py", first
+setup the corresponding datasets following
+[datasets/README.md](./datasets/README.md),
+then run:
+```
+cd tools/
+./train_net.py --num-gpus 8 \
+  --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
+```
+
+The configs are made for 8-GPU training.
+To train on 1 GPU, you may need to [change some parameters](https://arxiv.org/abs/1706.02677), e.g.:
+```
+./train_net.py \
+  --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
+  --num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
+```
+
+To evaluate a model's performance, use
+```
+./train_net.py \
+  --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \
+  --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
+```
+For more options, see `./train_net.py -h`.
+
+### Use Detectron2 APIs in Your Code
+
+See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+to learn how to use detectron2 APIs to:
+1. run inference with an existing model
+2. train a builtin model on a custom dataset
+
+See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/main/projects)
+for more ways to build your project on detectron2.

hygon-hub/common/base/autodrive/3rdparty/detectron2/INSTALL.md

+## Installation
+
+### Requirements
+- Linux or macOS with Python ≥ 3.7
+- PyTorch ≥ 1.8 and [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.
+  Install them together at [pytorch.org](https://pytorch.org) to make sure of this
+- OpenCV is optional but needed by demo and visualization
+
+
+### Build Detectron2 from Source
+
+gcc & g++ ≥ 5.4 are required. [ninja](https://ninja-build.org/) is optional but recommended for faster build.
+After having them, run:
+```
+python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
+# (add --user if you don't have permission)
+
+# Or, to install it from a local clone:
+git clone https://github.com/facebookresearch/detectron2.git
+python -m pip install -e detectron2
+
+# On macOS, you may need to prepend the above commands with a few environment variables:
+CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ...
+```
+
+To __rebuild__ detectron2 that's built from a local clone, use `rm -rf build/ **/*.so` to clean the
+old build first. You often need to rebuild detectron2 after reinstalling PyTorch.
+
+### Common Installation Issues
+
+Click each issue for its solutions:
+
+<details>
+<summary>
+Undefined symbols that looks like "TH..","at::Tensor...","torch..."
+</summary>
+<br/>
+
+This usually happens when detectron2 or torchvision is not
+compiled with the version of PyTorch you're running.
+
+If the error comes from a pre-built torchvision, uninstall torchvision and pytorch and reinstall them
+following [pytorch.org](http://pytorch.org). So the versions will match.
+
+If the error comes from a pre-built detectron2, check [release notes](https://github.com/facebookresearch/detectron2/releases),
+uninstall and reinstall the correct pre-built detectron2 that matches pytorch version.
+
+If the error comes from detectron2 or torchvision that you built manually from source,
+remove files you built (`build/`, `**/*.so`) and rebuild it so it can pick up the version of pytorch currently in your environment.
+
+If the above instructions do not resolve this problem, please provide an environment (e.g. a dockerfile) that can reproduce the issue.
+</details>
+
+<details>
+<summary>
+Missing torch dynamic libraries, OR segmentation fault immediately when using detectron2.
+</summary>
+This usually happens when detectron2 or torchvision is not
+compiled with the version of PyTorch you're running. See the previous common issue for the solution.
+</details>
+
+<details>
+<summary>
+Undefined C++ symbols (e.g. "GLIBCXX..") or C++ symbols not found.
+</summary>
+<br/>
+Usually it's because the library is compiled with a newer C++ compiler but run with an old C++ runtime.
+
+This often happens with old anaconda.
+It may help to run `conda update libgcc` to upgrade its runtime.
+
+The fundamental solution is to avoid the mismatch, either by compiling using older version of C++
+compiler, or run the code with proper C++ runtime.
+To run the code with a specific C++ runtime, you can use environment variable `LD_PRELOAD=/path/to/libstdc++.so`.
+
+</details>
+
+<details>
+<summary>
+"nvcc not found" or "Not compiled with GPU support" or "Detectron2 CUDA Compiler: not available".
+</summary>
+<br/>
+CUDA is not found when building detectron2.
+You should make sure
+
+```
+python -c 'import torch; from torch.utils.cpp_extension import CUDA_HOME; print(torch.cuda.is_available(), CUDA_HOME)'
+```
+
+print `(True, a directory with cuda)` at the time you build detectron2.
+
+Most models can run inference (but not training) without GPU support. To use CPUs, set `MODEL.DEVICE='cpu'` in the config.
+</details>
+
+<details>
+<summary>
+"invalid device function" or "no kernel image is available for execution".
+</summary>
+<br/>
+Two possibilities:
+
+* You build detectron2 with one version of CUDA but run it with a different version.
+
+  To check whether it is the case,
+  use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
+  In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
+  to contain cuda libraries of the same version.
+
+  When they are inconsistent,
+  you need to either install a different build of PyTorch (or build by yourself)
+  to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
+
+* PyTorch/torchvision/Detectron2 is not built for the correct GPU SM architecture (aka. compute capability).
+
+  The architecture included by PyTorch/detectron2/torchvision is available in the "architecture flags" in
+  `python -m detectron2.utils.collect_env`. It must include
+  the architecture of your GPU, which can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
+
+  If you're using pre-built PyTorch/detectron2/torchvision, they have included support for most popular GPUs already.
+  If not supported, you need to build them from source.
+
+  When building detectron2/torchvision from source, they detect the GPU device and build for only the device.
+  This means the compiled code may not work on a different GPU device.
+  To recompile them for the correct architecture, remove all installed/compiled files,
+  and rebuild them with the `TORCH_CUDA_ARCH_LIST` environment variable set properly.
+  For example, `export TORCH_CUDA_ARCH_LIST="6.0;7.0"` makes it compile for both P100s and V100s.
+</details>
+
+<details>
+<summary>
+Undefined CUDA symbols; Cannot open libcudart.so
+</summary>
+<br/>
+The version of NVCC you use to build detectron2 or torchvision does
+not match the version of CUDA you are running with.
+This often happens when using anaconda's CUDA runtime.
+
+Use `python -m detectron2.utils.collect_env` to find out inconsistent CUDA versions.
+In the output of this command, you should expect "Detectron2 CUDA Compiler", "CUDA_HOME", "PyTorch built with - CUDA"
+to contain cuda libraries of the same version.
+
+When they are inconsistent,
+you need to either install a different build of PyTorch (or build by yourself)
+to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
+</details>
+
+
+<details>
+<summary>
+C++ compilation errors from NVCC / NVRTC, or "Unsupported gpu architecture"
+</summary>
+<br/>
+A few possibilities:
+
+1. Local CUDA/NVCC version has to match the CUDA version of your PyTorch. Both can be found in `python collect_env.py`
+   (download from [here](./detectron2/utils/collect_env.py)).
+   When they are inconsistent, you need to either install a different build of PyTorch (or build by yourself)
+   to match your local CUDA installation, or install a different version of CUDA to match PyTorch.
+
+2. Local CUDA/NVCC version shall support the SM architecture (a.k.a. compute capability) of your GPU.
+   The capability of your GPU can be found at [developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus).
+   The capability supported by NVCC is listed at [here](https://gist.github.com/ax3l/9489132).
+   If your NVCC version is too old, this can be workaround by setting environment variable
+   `TORCH_CUDA_ARCH_LIST` to a lower, supported capability.
+
+3. The combination of NVCC and GCC you use is incompatible. You need to change one of their versions.
+   See [here](https://gist.github.com/ax3l/9489132) for some valid combinations.
+   Notably, CUDA<=10.1.105 doesn't support GCC>7.3.
+
+   The CUDA/GCC version used by PyTorch can be found by `print(torch.__config__.show())`.
+
+</details>
+
+
+<details>
+<summary>
+"ImportError: cannot import name '_C'".
+</summary>
+<br/>
+Please build and install detectron2 following the instructions above.
+
+Or, if you are running code from detectron2's root directory, `cd` to a different one.
+Otherwise you may not import the code that you installed.
+</details>
+
+
+<details>
+<summary>
+Any issue on windows.
+</summary>
+<br/>
+
+Detectron2 is continuously built on windows with [CircleCI](https://app.circleci.com/pipelines/github/facebookresearch/detectron2?branch=main).
+However we do not provide official support for it.
+PRs that improves code compatibility on windows are welcome.
+</details>
+
+<details>
+<summary>
+ONNX conversion segfault after some "TraceWarning".
+</summary>
+<br/>
+The ONNX package is compiled with a too old compiler.
+
+Please build and install ONNX from its source code using a compiler
+whose version is closer to what's used by PyTorch (available in `torch.__config__.show()`).
+</details>
+
+
+<details>
+<summary>
+"library not found for -lstdc++" on older version of MacOS
+</summary>
+<br/>
+
+See [this stackoverflow answer](https://stackoverflow.com/questions/56083725/macos-build-issues-lstdc-not-found-while-building-python-package).
+
+</details>
+
+
+### Installation inside specific environments:
+
+* __Colab__: see our [Colab Tutorial](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+  which has step-by-step instructions.
+
+* __Docker__: The official [Dockerfile](docker) installs detectron2 with a few simple commands.

hygon-hub/common/base/autodrive/3rdparty/detectron2/LICENSE

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hygon-hub/common/base/autodrive/3rdparty/detectron2/README.md

+<img src=".github/Detectron2-Logo-Horz.svg" width="300" >
+
+Detectron2 is Facebook AI Research's next generation library
+that provides state-of-the-art detection and segmentation algorithms.
+It is the successor of
+[Detectron](https://github.com/facebookresearch/Detectron/)
+and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).
+It supports a number of computer vision research projects and production applications in Facebook.
+
+<div align="center">
+  <img src="https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png"/>
+</div>
+<br>
+
+## Learn More about Detectron2
+
+* Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend,
+  DeepLab, ViTDet, MViTv2 etc.
+* Used as a library to support building [research projects](projects/) on top of it.
+* Models can be exported to TorchScript format or Caffe2 format for deployment.
+* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).
+
+See our [blog post](https://ai.meta.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)
+to see more demos.
+See this [interview](https://ai.meta.com/blog/detectron-everingham-prize/) to learn more about the stories behind detectron2.
+
+## Installation
+
+See [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html).
+
+## Getting Started
+
+See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html),
+and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
+to learn about basic usage.
+
+Learn more at our [documentation](https://detectron2.readthedocs.org).
+And see [projects/](projects/) for some projects that are built on top of detectron2.
+
+## Model Zoo and Baselines
+
+We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).
+
+## License
+
+Detectron2 is released under the [Apache 2.0 license](LICENSE).
+
+## Citing Detectron2
+
+If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.
+
+```BibTeX
+@misc{wu2019detectron2,
+  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
+                  Wan-Yen Lo and Ross Girshick},
+  title =        {Detectron2},
+  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
+  year =         {2019}
+}
+```

hygon-hub/common/base/autodrive/3rdparty/detectron2/configs/Base-RCNN-C4.yaml

+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  RPN:
+    PRE_NMS_TOPK_TEST: 6000
+    POST_NMS_TOPK_TEST: 1000
+  ROI_HEADS:
+    NAME: "Res5ROIHeads"
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2

hygon-hub/common/base/autodrive/3rdparty/detectron2/configs/Base-RCNN-DilatedC5.yaml

+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  RESNETS:
+    OUT_FEATURES: ["res5"]
+    RES5_DILATION: 2
+  RPN:
+    IN_FEATURES: ["res5"]
+    PRE_NMS_TOPK_TEST: 6000
+    POST_NMS_TOPK_TEST: 1000
+  ROI_HEADS:
+    NAME: "StandardROIHeads"
+    IN_FEATURES: ["res5"]
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_FC: 2
+    POOLER_RESOLUTION: 7
+  ROI_MASK_HEAD:
+    NAME: "MaskRCNNConvUpsampleHead"
+    NUM_CONV: 4
+    POOLER_RESOLUTION: 14
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2

hygon-hub/common/base/autodrive/3rdparty/detectron2/configs/Base-RCNN-FPN.yaml

+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  BACKBONE:
+    NAME: "build_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+  FPN:
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+  ANCHOR_GENERATOR:
+    SIZES: [[32], [64], [128], [256], [512]]  # One size for each in feature map
+    ASPECT_RATIOS: [[0.5, 1.0, 2.0]]  # Three aspect ratios (same for all in feature maps)
+  RPN:
+    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
+    PRE_NMS_TOPK_TRAIN: 2000  # Per FPN level
+    PRE_NMS_TOPK_TEST: 1000  # Per FPN level
+    # Detectron1 uses 2000 proposals per-batch,
+    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
+    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
+    POST_NMS_TOPK_TRAIN: 1000
+    POST_NMS_TOPK_TEST: 1000
+  ROI_HEADS:
+    NAME: "StandardROIHeads"
+    IN_FEATURES: ["p2", "p3", "p4", "p5"]
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_FC: 2
+    POOLER_RESOLUTION: 7
+  ROI_MASK_HEAD:
+    NAME: "MaskRCNNConvUpsampleHead"
+    NUM_CONV: 4
+    POOLER_RESOLUTION: 14
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2