merge conflict

2 years ago · d3c548fe14
--- a/.dev_scripts/build_docs.sh
+++ b/.dev_scripts/build_docs.sh
@@ -0,0 +1,8 @@
 pip install -r requirements/docs.txt
 cd docs
 rm -rf build

 # update api rst
 #rm -rf source/api/
 #sphinx-apidoc --module-first -o source/api/ ../modelscope/
 make html
--- a/.dev_scripts/build_image.sh
+++ b/.dev_scripts/build_image.sh
@@ -0,0 +1,169 @@
 #!/bin/bash
 # default values.
 BASE_CPU_IMAGE=reg.docker.alibaba-inc.com/modelscope/ubuntu:20.04
 BASE_GPU_IMAGE=reg.docker.alibaba-inc.com/modelscope/ubuntu:20.04-cuda11.3.0-cudnn8-devel
 MODELSCOPE_REPO_ADDRESS=reg.docker.alibaba-inc.com/modelscope/modelscope
 python_version=3.7.13
 torch_version=1.11.0
 cudatoolkit_version=11.3
 tensorflow_version=1.15.5
 modelscope_version=None
 is_ci_test=False
 is_dsw=False
 is_cpu=False
 run_ci_test=False
 function usage(){
    echo "usage: build.sh "
    echo "       --python=python_version set python version, default: $python_version"
    echo "       --torch=torch_version set pytorch version, fefault: $torch_version"
    echo "       --cudatoolkit=cudatoolkit_version set cudatoolkit version used for pytorch, default: $cudatoolkit_version"
    echo "       --tensorflow=tensorflow_version set tensorflow version, default: $tensorflow_version"
    echo "       --modelscope=modelscope_version set modelscope version, default: $modelscope_version"
    echo "       --test option for run test before push image, only push on ci test pass"
    echo "       --cpu option for build cpu version"
    echo "       --dsw option for build dsw version"
    echo "       --ci  option for build ci version"
    echo "       --push option for push image to remote repo"
 }
 for i in "$@"; do
  case $i in
    --python=*)
      python_version="${i#*=}"
      shift
      ;;
    --torch=*)
      torch_version="${i#*=}"
      shift # pytorch version
      ;;
    --tensorflow=*)
      tensorflow_version="${i#*=}"
      shift # tensorflow version
      ;;
    --cudatoolkit=*)
      cudatoolkit_version="${i#*=}"
      shift # cudatoolkit for pytorch
      ;;
    --modelscope=*)
      modelscope_version="${i#*=}"
      shift # cudatoolkit for pytorch
      ;;
    --test)
      run_ci_test=True
      shift # will run ci test
      ;;
    --cpu)
      is_cpu=True
      shift # is cpu image
      ;;
    --ci)
      is_ci_test=True
      shift # is ci, will not install modelscope
      ;;
    --dsw)
      is_dsw=True
      shift # is dsw, will set dsw cache location
      ;;
    --push)
      is_push=True
      shift # is dsw, will set dsw cache location
      ;;
    --help)
      usage
      exit 0
      ;;
    -*|--*)
      echo "Unknown option $i"
      usage
      exit 1
      ;;
    *)
      ;;
  esac
 done

 if [ "$modelscope_version" == "None" ]; then
    echo "ModelScope version must specify!"
    exit 1
 fi
 if [ "$is_cpu" == "True" ]; then
    export BASE_IMAGE=$BASE_CPU_IMAGE
    base_tag=ubuntu20.04
    export USE_GPU=False
 else
    export BASE_IMAGE=$BASE_GPU_IMAGE
    base_tag=ubuntu20.04-cuda11.3.0
    export USE_GPU=True
 fi
 if [[ $python_version == 3.7* ]]; then
    base_tag=$base_tag-py37
 elif [[ $python_version == z* ]]; then
    base_tag=$base_tag-py38
 elif [[ $python_version == z* ]]; then
    base_tag=$base_tag-py39
 else
    echo "Unsupport python version: $python_version"
    exit 1
 fi

 target_image_tag=$base_tag-torch$torch_version-tf$tensorflow_version
 if [ "$is_ci_test" == "True" ]; then
    target_image_tag=$target_image_tag-$modelscope_version-ci
 else
    target_image_tag=$target_image_tag-$modelscope_version-test
 fi
 export IMAGE_TO_BUILD=$MODELSCOPE_REPO_ADDRESS:$target_image_tag
 export PYTHON_VERSION=$python_version
 export TORCH_VERSION=$torch_version
 export CUDATOOLKIT_VERSION=$cudatoolkit_version
 export TENSORFLOW_VERSION=$tensorflow_version
 echo -e "Building image with:\npython$python_version\npytorch$torch_version\ntensorflow:$tensorflow_version\ncudatoolkit:$cudatoolkit_version\ncpu:$is_cpu\nis_ci:$is_ci_test\nis_dsw:$is_dsw\n"
 docker_file_content=`cat docker/Dockerfile.ubuntu`
 if [ "$is_ci_test" != "True" ]; then
    echo "Building ModelScope lib, will install ModelScope lib to image"
    docker_file_content="${docker_file_content} \nRUN pip install --no-cache-dir  modelscope==$modelscope_version -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html"
 fi
 echo "$is_dsw"
 if [ "$is_dsw" == "False" ]; then
    echo "Not DSW image"
 else
    echo "Building dsw image well need set ModelScope lib cache location."
    docker_file_content="${docker_file_content} \nENV MODELSCOPE_CACHE=/mnt/workspace/.cache/modelscope"
 fi
 printf "$docker_file_content" > Dockerfile
 docker build -t $IMAGE_TO_BUILD  \
             --build-arg USE_GPU \
             --build-arg BASE_IMAGE \
             --build-arg PYTHON_VERSION \
             --build-arg TORCH_VERSION \
             --build-arg CUDATOOLKIT_VERSION \
             --build-arg TENSORFLOW_VERSION \
             -f Dockerfile .

 if [ $? -ne 0 ]; then
  echo "Running docker build command error, please check the log!"
  exit -1
 fi
 if [ "$run_ci_test" == "True" ]; then
    echo "Running ci case."
    export MODELSCOPE_CACHE=/home/mulin.lyh/model_scope_cache
    export MODELSCOPE_HOME_CACHE=/home/mulin.lyh/ci_case_home # for credential
    export IMAGE_NAME=$MODELSCOPE_REPO_ADDRESS
    export IMAGE_VERSION=$target_image_tag
    export MODELSCOPE_DOMAIN=www.modelscope.cn
    export HUB_DATASET_ENDPOINT=http://www.modelscope.cn
    export CI_TEST=True
    export TEST_LEVEL=1
    if [ "$is_ci_test" != "True" ]; then
        echo "Testing for dsw image or MaaS-lib image"
        export CI_COMMAND="python tests/run.py"
    fi
    bash .dev_scripts/dockerci.sh
    if [ $? -ne 0 ]; then
       echo "Running unittest failed, please check the log!"
       exit -1
    fi
 fi
 if [ "$is_push" == "True" ]; then
    echo "Pushing image: $IMAGE_TO_BUILD"
    docker push $IMAGE_TO_BUILD
 fi
--- a/.dev_scripts/ci_container_test.sh
+++ b/.dev_scripts/ci_container_test.sh
@@ -0,0 +1,36 @@
 echo "Testing envs"
 printenv
 echo "ENV END"
 if [ "$MODELSCOPE_SDK_DEBUG" == "True" ]; then
    awk -F: '/^[^#]/ { print $1 }' requirements/framework.txt | xargs -n 1 pip install -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
    awk -F: '/^[^#]/ { print $1 }' requirements/audio.txt | xargs -n 1 pip install -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
    awk -F: '/^[^#]/ { print $1 }' requirements/cv.txt | xargs -n 1 pip install -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
    awk -F: '/^[^#]/ { print $1 }' requirements/multi-modal.txt | xargs -n 1 pip install -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
    awk -F: '/^[^#]/ { print $1 }' requirements/nlp.txt | xargs -n 1 pip install -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
    pip install -r requirements/tests.txt

    git config --global --add safe.directory /Maas-lib
    git config --global user.email tmp
    git config --global user.name tmp.com

    # linter test
    # use internal project for pre-commit due to the network problem
    if [ `git remote -v | grep alibaba  | wc -l` -gt 1 ]; then
        pre-commit run -c .pre-commit-config_local.yaml --all-files
        if [ $? -ne 0 ]; then
            echo "linter test failed, please run 'pre-commit run --all-files' to check"
            exit -1
        fi
    fi
    # test with install
    python setup.py install
 else
    echo "Running case in release image, run case directly!"
 fi
 if [ $# -eq 0 ]; then
    ci_command="python tests/run.py --subprocess"
 else
    ci_command="$@"
 fi
 echo "Running case with command: $ci_command"
 $ci_command
--- a/.dev_scripts/dockerci.sh
+++ b/.dev_scripts/dockerci.sh
@@ -0,0 +1,80 @@
 #!/bin/bash
 MODELSCOPE_CACHE_DIR_IN_CONTAINER=/modelscope_cache
 CODE_DIR=$PWD
 CODE_DIR_IN_CONTAINER=/Maas-lib
 echo "$USER"
 gpus='7 6 5 4 3 2 1 0'
 cpu_sets='0-7 8-15 16-23 24-30 31-37 38-44 45-51 52-58'
 cpu_sets_arr=($cpu_sets)
 is_get_file_lock=false
 # export RUN_CASE_COMMAND='python tests/run.py --run_config tests/run_config.yaml'
 CI_COMMAND=${CI_COMMAND:-bash .dev_scripts/ci_container_test.sh $RUN_CASE_BASE_COMMAND}
 echo "ci command: $CI_COMMAND"
 for gpu in $gpus
 do
  exec {lock_fd}>"/tmp/gpu$gpu" || exit 1
  flock -n "$lock_fd" || { echo "WARN: gpu $gpu is in use!" >&2; continue; }
  echo "get gpu lock $gpu"
  CONTAINER_NAME="modelscope-ci-$gpu"
  let is_get_file_lock=true

  # pull image if there are update
  docker pull ${IMAGE_NAME}:${IMAGE_VERSION}
  if [ "$MODELSCOPE_SDK_DEBUG" == "True" ]; then
    docker run --rm --name $CONTAINER_NAME --shm-size=16gb \
              --cpuset-cpus=${cpu_sets_arr[$gpu]} \
              --gpus="device=$gpu" \
              -v $CODE_DIR:$CODE_DIR_IN_CONTAINER \
              -v $MODELSCOPE_CACHE:$MODELSCOPE_CACHE_DIR_IN_CONTAINER \
              -v $MODELSCOPE_HOME_CACHE/$gpu:/root \
              -v /home/admin/pre-commit:/home/admin/pre-commit \
              -e CI_TEST=True \
              -e TEST_LEVEL=$TEST_LEVEL \
              -e MODELSCOPE_CACHE=$MODELSCOPE_CACHE_DIR_IN_CONTAINER \
              -e MODELSCOPE_DOMAIN=$MODELSCOPE_DOMAIN \
              -e MODELSCOPE_SDK_DEBUG=True \
              -e HUB_DATASET_ENDPOINT=$HUB_DATASET_ENDPOINT \
              -e TEST_ACCESS_TOKEN_CITEST=$TEST_ACCESS_TOKEN_CITEST \
              -e TEST_ACCESS_TOKEN_SDKDEV=$TEST_ACCESS_TOKEN_SDKDEV \
              -e TEST_LEVEL=$TEST_LEVEL \
              -e MODELSCOPE_ENVIRONMENT='ci' \
              -e TEST_UPLOAD_MS_TOKEN=$TEST_UPLOAD_MS_TOKEN \
              -e MODEL_TAG_URL=$MODEL_TAG_URL \
              --workdir=$CODE_DIR_IN_CONTAINER \
              --net host  \
              ${IMAGE_NAME}:${IMAGE_VERSION} \
              $CI_COMMAND
  else
    docker run --rm --name $CONTAINER_NAME --shm-size=16gb \
              --cpuset-cpus=${cpu_sets_arr[$gpu]} \
              --gpus="device=$gpu" \
              -v $CODE_DIR:$CODE_DIR_IN_CONTAINER \
              -v $MODELSCOPE_CACHE:$MODELSCOPE_CACHE_DIR_IN_CONTAINER \
              -v $MODELSCOPE_HOME_CACHE/$gpu:/root \
              -v /home/admin/pre-commit:/home/admin/pre-commit \
              -e CI_TEST=True \
              -e TEST_LEVEL=$TEST_LEVEL \
              -e MODELSCOPE_CACHE=$MODELSCOPE_CACHE_DIR_IN_CONTAINER \
              -e MODELSCOPE_DOMAIN=$MODELSCOPE_DOMAIN \
              -e HUB_DATASET_ENDPOINT=$HUB_DATASET_ENDPOINT \
              -e TEST_ACCESS_TOKEN_CITEST=$TEST_ACCESS_TOKEN_CITEST \
              -e TEST_ACCESS_TOKEN_SDKDEV=$TEST_ACCESS_TOKEN_SDKDEV \
              -e TEST_LEVEL=$TEST_LEVEL \
              -e MODELSCOPE_ENVIRONMENT='ci' \
              -e TEST_UPLOAD_MS_TOKEN=$TEST_UPLOAD_MS_TOKEN \
              -e MODEL_TAG_URL=$MODEL_TAG_URL \
              --workdir=$CODE_DIR_IN_CONTAINER \
              --net host  \
              ${IMAGE_NAME}:${IMAGE_VERSION} \
              $CI_COMMAND
  fi
  if [ $? -ne 0 ]; then
    echo "Running test case failed, please check the log!"
    exit -1
  fi
  break
 done
 if [ "$is_get_file_lock" = false ] ; then
    echo 'No free GPU!'
    exit 1
 fi
--- a/.dev_scripts/linter.sh
+++ b/.dev_scripts/linter.sh
@@ -0,0 +1,3 @@
 yapf -r -i modelscope/ configs/ tests/ setup.py
 isort -rc modelscope/ configs/ tests/ setup.py
 flake8 modelscope/ configs/ tests/ setup.py
--- a/.dev_scripts/run_docker.sh
+++ b/.dev_scripts/run_docker.sh
@@ -0,0 +1,7 @@
 #sudo docker run --name zwm_maas -v /home/wenmeng.zwm/workspace:/home/wenmeng.zwm/workspace   --net host -ti reg.docker.alibaba-inc.com/pai-dlc/tensorflow-training:2.3-gpu-py36-cu101-ubuntu18.04 bash
 #sudo docker run --name zwm_maas_pytorch -v /home/wenmeng.zwm/workspace:/home/wenmeng.zwm/workspace --net host  -ti reg.docker.alibaba-inc.com/pai-dlc/pytorch-training:1.10PAI-gpu-py36-cu113-ubuntu18.04 bash
 CONTAINER_NAME=modelscope-dev
 IMAGE_NAME=registry.cn-shanghai.aliyuncs.com/modelscope/modelscope
 IMAGE_VERSION=v0.1.1-16-g62856fa-devel
 MOUNT_DIR=/home/wenmeng.zwm/workspace
 sudo docker run --name  $CONTAINER_NAME -v $MOUNT_DIR:$MOUNT_DIR --net host  -ti ${IMAGE_NAME}:${IMAGE_VERSION} bash
--- a/.dockerignore
+++ b/.dockerignore
@@ -0,0 +1,11 @@
 .gitignore
 tests
 data
 .dev_scripts
 .dockerignore
 .git
 .gitattributes
 .pre-commit-config.yaml
 .pre-commit-config_local.yaml
 .readthedocs.yaml
 Dockfile
--- a/.gitattributes
+++ b/.gitattributes
@@ -0,0 +1,9 @@
 *.png filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
 *.JPEG filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.pickle filter=lfs diff=lfs merge=lfs -text
 *.avi filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
--- a/.github/workflows/citest.yaml
+++ b/.github/workflows/citest.yaml
@@ -0,0 +1,64 @@
 name: citest

 on:
  push:
    branches:
      - master
      - "release/**"
    paths-ignore:
      - "setup.*"
      - "requirements.txt"
      - "requirements/**"
      - "docs/**"
      - "tools/**"
      - ".dev_scripts/**"
      - "README.md"
      - "README_zh-CN.md"
      - "NOTICE"
      - ".github/workflows/lint.yaml"
      - ".github/workflows/publish.yaml"

  pull_request:
    paths-ignore:
      - "setup.*"
      - "requirements.txt"
      - "requirements/**"
      - "docs/**"
      - "tools/**"
      - ".dev_scripts/**"
      - "README.md"
      - "README_zh-CN.md"
      - "NOTICE"
      - ".github/workflows/lint.yaml"
      - ".github/workflows/publish.yaml"

 concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

 jobs:
  unittest:
    # The type of runner that the job will run on
    runs-on: [modelscope-self-hosted]
    steps:
      - name: ResetFileMode
        shell: bash
        run: |
          # reset filemode to allow action runner to delete files
          # generated by root in docker
          set -e
          source ~/.bashrc
          sudo chown -R $USER:$USER  $ACTION_RUNNER_DIR

      - name: Checkout
        uses: actions/checkout@v2
        with:
            lfs: 'true'
      - name: Checkout LFS objects
        run: git lfs checkout
      - name: Run unittest
        shell: bash
        run: |
          set -e
          source /mnt/modelscope/ci_env.sh
          bash .dev_scripts/dockerci.sh
--- a/.github/workflows/lint.yaml
+++ b/.github/workflows/lint.yaml
@@ -0,0 +1,22 @@
 name: Lint test

 on: [push, pull_request]

 concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
  cancel-in-progress: true

 jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Set up Python 3.7
        uses: actions/setup-python@v2
        with:
          python-version: 3.7
      - name: Install pre-commit hook
        run: |
          pip install pre-commit
      - name: Linting
        run: pre-commit run --all-files
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,128 @@
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
 *$py.class

 # C extensions
 *.so

 # Distribution / packaging
 .Python
 build/
 develop-eggs/
 dist/
 downloads/
 eggs/
 .eggs/
 lib/
 lib64/
 parts/
 sdist/
 var/
 wheels/
 *.egg-info/
 .installed.cfg
 *.egg
 /package
 /temp
 MANIFEST

 # PyInstaller
 #  Usually these files are written by a python script from a template
 #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 *.manifest
 *.spec

 # Installer logs
 pip-log.txt
 pip-delete-this-directory.txt

 # Unit test / coverage reports
 htmlcov/
 .tox/
 .coverage
 .coverage.*
 .cache
 nosetests.xml
 coverage.xml
 *.cover
 .hypothesis/
 .pytest_cache/

 # Translations
 *.mo
 *.pot

 # Django stuff:
 *.log
 local_settings.py
 db.sqlite3

 # Flask stuff:
 instance/
 .webassets-cache

 # Scrapy stuff:
 .scrapy

 # Sphinx documentation
 docs/_build/

 # PyBuilder
 target/

 # Jupyter Notebook
 .ipynb_checkpoints

 # pyenv
 .python-version

 # celery beat schedule file
 celerybeat-schedule

 # SageMath parsed files
 *.sage.py

 # Environments
 .env
 .venv
 env/
 venv/
 ENV/
 env.bak/
 venv.bak/

 # Spyder project settings
 .spyderproject
 .spyproject

 # Rope project settings
 .ropeproject

 # mkdocs documentation
 /site

 # mypy
 .mypy_cache/

 .vscode
 .idea

 # custom
 *.pkl
 *.pkl.json
 *.log.json
 *.whl
 *.tar.gz
 *.swp
 *.log
 *.tar.gz
 source.sh
 tensorboard.sh
 .DS_Store
 replace.sh
 result.png

 # Pytorch
 *.pth
 *.pt
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,37 @@
 repos:
  - repo: https://gitlab.com/pycqa/flake8.git
    rev: 4.0.0
    hooks:
      - id: flake8
        exclude: thirdparty/|examples/
  - repo: https://github.com/PyCQA/isort.git
    rev: 4.3.21
    hooks:
      - id: isort
        exclude: examples
  - repo: https://github.com/pre-commit/mirrors-yapf.git
    rev: v0.30.0
    hooks:
      - id: yapf
        exclude: thirdparty/|examples/
  - repo: https://github.com/pre-commit/pre-commit-hooks.git
    rev: v3.1.0
    hooks:
      - id: trailing-whitespace
        exclude: thirdparty/
      - id: check-yaml
        exclude: thirdparty/
      - id: end-of-file-fixer
        exclude: thirdparty/
      - id: requirements-txt-fixer
        exclude: thirdparty/
      - id: double-quote-string-fixer
        exclude: thirdparty/
      - id: check-merge-conflict
        exclude: thirdparty/
      - id: fix-encoding-pragma
        exclude: thirdparty/
        args: ["--remove"]
      - id: mixed-line-ending
        exclude: thirdparty/
        args: ["--fix=lf"]
--- a/.pre-commit-config_local.yaml
+++ b/.pre-commit-config_local.yaml
@@ -0,0 +1,37 @@
 repos:
  - repo: /home/admin/pre-commit/flake8
    rev: 4.0.0
    hooks:
      - id: flake8
        exclude: thirdparty/|examples/
  - repo: /home/admin/pre-commit/isort
    rev: 4.3.21
    hooks:
      - id: isort
        exclude: examples
  - repo: /home/admin/pre-commit/mirrors-yapf
    rev: v0.30.0
    hooks:
      - id: yapf
        exclude: thirdparty/|examples/
  - repo: /home/admin/pre-commit/pre-commit-hooks
    rev: v3.1.0
    hooks:
      - id: trailing-whitespace
        exclude: thirdparty/
      - id: check-yaml
        exclude: thirdparty/
      - id: end-of-file-fixer
        exclude: thirdparty/
      - id: requirements-txt-fixer
        exclude: thirdparty/
      - id: double-quote-string-fixer
        exclude: thirdparty/
      - id: check-merge-conflict
        exclude: thirdparty/
      - id: fix-encoding-pragma
        exclude: thirdparty/
        args: ["--remove"]
      - id: mixed-line-ending
        exclude: thirdparty/
        args: ["--fix=lf"]
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -0,0 +1,28 @@
 version: 2

 # Set the version of Python and other tools you might need
 build:
  os: ubuntu-20.04
  tools:
    python: "3.7"
    # You can also specify other tool versions:
    # nodejs: "16"
    # rust: "1.55"
    # golang: "1.17"
  jobs:
    post_checkout:
      - echo "dummy"

 # Build documentation in the docs/ directory with Sphinx
 sphinx:
   configuration: docs/source/conf.py

 # If using Sphinx, optionally build your docs in additional formats such as PDF
 # formats:
 formats: all

 python:
  install:
    - requirements: requirements/docs.txt
    - requirements: requirements/readthedocs.txt
    - requirements: requirements/framework.txt
--- a/+ 204
+++ b/+ 204
@@ -71,3 +71,207 @@ distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 =======
 Copyright 2022-2023 Alibaba ModelScope. All rights reserved.

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
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   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
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      file or class name and description of purpose be included on the
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   Copyright 2020-2022 Alibaba ModelScope.

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
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--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -0,0 +1 @@
 recursive-include modelscope/configs *.py
--- a/+ 25
+++ b/+ 25
@@ -0,0 +1,25 @@
 WHL_BUILD_DIR :=package
 DOC_BUILD_DIR :=docs/build/

 # default rule
 default: whl docs

 .PHONY: docs
 docs:
 	bash .dev_scripts/build_docs.sh

 .PHONY: linter
 linter:
 	bash .dev_scripts/linter.sh

 .PHONY: test
 test:
 	bash .dev_scripts/citest.sh

 .PHONY: whl
 whl:
 	python setup.py sdist bdist_wheel

 .PHONY: clean
 clean:
 	rm -rf  $(WHL_BUILD_DIR) $(DOC_BUILD_DIR)
--- a/Makefile.docker
+++ b/Makefile.docker
@@ -0,0 +1,67 @@
 DOCKER_REGISTRY           = registry.cn-shanghai.aliyuncs.com
 DOCKER_ORG                = modelscope
 DOCKER_IMAGE              = modelscope
 DOCKER_FULL_NAME          = $(DOCKER_REGISTRY)/$(DOCKER_ORG)/$(DOCKER_IMAGE)

 # CUDA_VERSION              = 11.3
 # CUDNN_VERSION             = 8
 BASE_RUNTIME              = reg.docker.alibaba-inc.com/pai-dlc/pytorch-training:1.10PAI-gpu-py36-cu113-ubuntu18.04
 # BASE_DEVEL                = reg.docker.alibaba-inc.com/pai-dlc/pytorch-training:1.10PAI-gpu-py36-cu113-ubuntu18.04
 BASE_DEVEL                = pytorch/pytorch:1.10.0-cuda11.3-cudnn8-devel


 MODELSCOPE_VERSION           = $(shell git describe --tags --always)

 # Can be either official / dev
 BUILD_TYPE                = dev
 BUILD_PROGRESS            = auto
 BUILD_ARGS                = --build-arg BASE_IMAGE=$(BASE_IMAGE)

 EXTRA_DOCKER_BUILD_FLAGS ?= --network=host
 # DOCKER_BUILD              = DOCKER_BUILDKIT=1 \
 # 							docker build \
 # 								--progress=$(BUILD_PROGRESS) \
 # 								$(EXTRA_DOCKER_BUILD_FLAGS) \
 # 								--target $(BUILD_TYPE) \
 # 								-t $(DOCKER_FULL_NAME):$(DOCKER_TAG) \
 # 								$(BUILD_ARGS) \
 #								-f docker/pytorch.dockerfile .
 DOCKER_BUILD              = DOCKER_BUILDKIT=1 \
 							docker build \
 								$(EXTRA_DOCKER_BUILD_FLAGS) \
 								-t $(DOCKER_FULL_NAME):$(DOCKER_TAG) \
 								$(BUILD_ARGS)  \
 								-f docker/pytorch.dockerfile .
 DOCKER_PUSH               = docker push $(DOCKER_FULL_NAME):$(DOCKER_TAG)

 .PHONY: all
 all: devel-image

 .PHONY: devel-image
 devel-image: BASE_IMAGE := $(BASE_DEVEL)
 devel-image: DOCKER_TAG := $(MODELSCOPE_VERSION)-devel
 devel-image:
 	$(DOCKER_BUILD)

 .PHONY: devel-push
 devel-push: BASE_IMAGE := $(BASE_DEVEL)
 devel-push: DOCKER_TAG := $(MODELSCOPE_VERSION)-devel
 devel-push:
 	$(DOCKER_PUSH)

 .PHONY: runtime-image
 runtime-image: BASE_IMAGE := $(BASE_RUNTIME)
 runtime-image: DOCKER_TAG := $(MODELSCOPE_VERSION)-runtime
 runtime-image:
 	$(DOCKER_BUILD)
 	docker tag $(DOCKER_FULL_NAME):$(DOCKER_TAG) $(DOCKER_FULL_NAME):latest

 .PHONY: runtime-push
 runtime-push: BASE_IMAGE := $(BASE_RUNTIME)
 runtime-push: DOCKER_TAG := $(MODELSCOPE_VERSION)-runtime
 runtime-push:
 	$(DOCKER_PUSH)

 .PHONY: clean
 clean:
 	-docker rmi -f $(shell docker images -q $(DOCKER_FULL_NAME))
--- a/README.md
+++ b/README.md
@@ -1,2 +1,29 @@
 # ModelScope

 =======
 # Introduction

 [ModelScope]( https://www.modelscope.cn) is a “Model-as-a-Service” (MaaS) platform that seeks to bring together most advanced machine learning models from the AI community, and to streamline the process of leveraging AI models in real applications. The core ModelScope library enables developers to perform inference, training and evaluation, through rich layers of API designs that facilitate a unified experience across state-of-the-art models from different AI domains.

 The Python library offers the layered-APIs necessary for model contributors to integrate models from CV, NLP, Speech, Multi-Modality, as well as Scientific-computation, into the ModelScope ecosystem. Implementations for all these different models are encapsulated within the library in a way that allows easy and unified access. With such integration, model inference, finetuning, and evaluations can be done with only a few lines of codes. In the meantime, flexibilities are provided so that different components in the model applications can be customized as well, where necessary.

 Apart from harboring implementations of various models, ModelScope library also enables the necessary interactions with ModelScope backend services, particularly with the Model-Hub and Dataset-Hub. Such interactions facilitate management of  various entities (models and datasets) to be performed seamlessly under-the-hood, including entity lookup, version control, cache management, and many others.

 # Installation

 Please refer to [installation](https://modelscope.cn/docs/%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85).

 # Get Started

 You can refer to [quick_start](https://modelscope.cn/docs/%E5%BF%AB%E9%80%9F%E5%BC%80%E5%A7%8B) for quick start.

 We also provide other documentations including:
 * [Introduction to tasks](https://modelscope.cn/docs/%E4%BB%BB%E5%8A%A1%E7%9A%84%E4%BB%8B%E7%BB%8D)
 * [Use pipeline for model inference](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E6%8E%A8%E7%90%86Pipeline)
 * [Finetune example](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AE%AD%E7%BB%83Train)
 * [Preprocessing of data](https://modelscope.cn/docs/%E6%95%B0%E6%8D%AE%E7%9A%84%E9%A2%84%E5%A4%84%E7%90%86)
 * [Evaluation metrics](https://modelscope.cn/docs/%E6%A8%A1%E5%9E%8B%E7%9A%84%E8%AF%84%E4%BC%B0)

 # License

 This project is licensed under the [Apache License (Version 2.0)](https://github.com/modelscope/modelscope/blob/master/LICENSE).
--- a/configs/README.md
+++ b/configs/README.md
@@ -0,0 +1 @@
 Each model should be associated with a configuration.json file hosted on modelscope model-hub, together with the model binaries. This folder serves the purpose of hosting example configuration, for reference.
--- a/configs/cv/configuration.json
+++ b/configs/cv/configuration.json
@@ -0,0 +1,178 @@
 {
    "framework": "pytorch",

    "task": "image_classification",

    "model": {
        "type": "classification",
        "pretrained": null,
        "backbone": {
            "type": "ResNet",
            "depth": 50,
            "out_indices": [
                4
            ],
            "norm_cfg": {
                "type": "BN"
            }
        },
        "head": {
            "type": "ClsHead",
            "with_avg_pool": true,
            "in_channels": 2048,
            "loss_config": {
                "type": "CrossEntropyLossWithLabelSmooth",
                "label_smooth": 0
            },
            "num_classes": 1000
        }
    },

    "dataset": {
        "train": {
            "type": "ClsDataset",
            "data_source": {
                "list_file": "data/imagenet_raw/meta/train_labeled.txt",
                "root": "data/imagenet_raw/train/",
                "type": "ClsSourceImageList"
            }
        },
        "val": {
            "type": "ClsDataset",
            "data_source": {
                "list_file": "data/imagenet_raw/meta/val_labeled.txt",
                "root": "data/imagenet_raw/validation/",
                "type": "ClsSourceImageList"
            }
        },
        "test": {}
    },


    "preprocessor":{
        "train": [
                {
                    "type": "RandomResizedCrop",
                    "size": 224
                },
                {
                    "type": "RandomHorizontalFlip"
                },
                {
                    "type": "ToTensor"
                },
                {
                    "type": "Normalize",
                    "mean": [
                        0.485,
                        0.456,
                        0.406
                    ],
                    "std": [
                        0.229,
                        0.224,
                        0.225
                    ]
                },
                {
                    "type": "Collect",
                    "keys": [
                        "img",
                        "gt_labels"
                    ]
                }
       ],
       "val": [
                {
                    "type": "Resize",
                    "size": 256
                },
                {
                    "type": "CenterCrop",
                    "size": 224
                },
                {
                    "type": "ToTensor"
                },
                {
                    "type": "Normalize",
                    "mean": [
                        0.485,
                        0.456,
                        0.406
                    ],
                    "std": [
                        0.229,
                        0.224,
                        0.225
                    ]
                },
                {
                    "type": "Collect",
                    "keys": [
                        "img",
                        "gt_labels"
                    ]
                }
       ]
    },

    "train": {
        "work_dir": "./work_dir",
        "dataloader": {
            "batch_size_per_gpu": 2,
            "workers_per_gpu": 1
        },
        "optimizer": {
            "type": "SGD",
            "lr": 0.01,
            "options": {
                "grad_clip": {
                    "max_norm": 2.0
                }
            }
        },
        "lr_scheduler": {
            "type": "StepLR",
            "step_size": 2,
            "options": {
                "warmup": {
                    "type": "LinearWarmup",
                    "warmup_iters": 2

                }
            }
        },
        "hooks":
            [
                {
                    "type": "CheckpointHook",
                    "interval": 2
                },
                {
                    "type": "TextLoggerHook",
                    "interval": 1
                },
                {
                    "type": "IterTimerHook"
                },
                {
                    "type": "EvaluationHook",
                    "interval": 1
                }
            ]
    },

    "evaluation": {
        "dataloader": {
            "batch_size_per_gpu": 2,
            "workers_per_gpu": 1,
            "shuffle": false
        },
        "metrics": ["accuracy", "precision", "recall"]
    },
    "pipeline": {
        "type": "dummy"
    }

 }
--- a/configs/examples/configuration.json
+++ b/configs/examples/configuration.json
@@ -0,0 +1,7 @@
 {
    "a": 1,
    "b" : {
        "c": [1,2,3],
        "d" : "dd"
    }
 }
--- a/configs/examples/configuration.py
+++ b/configs/examples/configuration.py
@@ -0,0 +1,2 @@
 a = 1
 b = dict(c=[1, 2, 3], d='dd')
--- a/configs/examples/configuration.yaml
+++ b/configs/examples/configuration.yaml
@@ -0,0 +1,4 @@
 a: 1
 b:
  c: [1,2,3]
  d: dd
--- a/configs/examples/plain_args.yaml
+++ b/configs/examples/plain_args.yaml
@@ -0,0 +1,5 @@
 model_dir: path/to/model
 lr: 0.01
 optimizer: Adam
 weight_decay: 1e-6
 save_checkpoint_epochs: 20
--- a/configs/examples/train.json
+++ b/configs/examples/train.json
@@ -0,0 +1,131 @@
 {
    "framework": "pytorch",

    "task": "image_classification",

    "model": {
        "type": "Resnet50ForImageClassification",
        "pretrained": null,
        "backbone": {
            "type": "ResNet",
            "depth": 50,
            "out_indices": [
                4
            ],
            "norm_cfg": {
                "type": "BN"
            }
        },
        "head": {
            "type": "ClsHead",
            "with_avg_pool": true,
            "in_channels": 2048,
            "loss_config": {
                "type": "CrossEntropyLossWithLabelSmooth",
                "label_smooth": 0
            },
            "num_classes": 1000
        }
    },

    "dataset": {
        "train": {
            "type": "ClsDataset",
            "data_source": {
                "list_file": "data/imagenet_raw/meta/train_labeled.txt",
                "root": "data/imagenet_raw/train/",
                "type": "ClsSourceImageList"
            }
        },
        "val": {
            "type": "ClsDataset",
            "data_source": {
                "list_file": "data/imagenet_raw/meta/val_labeled.txt",
                "root": "data/imagenet_raw/validation/",
                "type": "ClsSourceImageList"
            }
        }
    },


    "preprocessor":{
        "train": [
                {
                    "type": "RandomResizedCrop",
                    "size": 224
                },
                {
                    "type": "RandomHorizontalFlip"
                },
                {
                    "type": "ToTensor"
                },
                {
                    "type": "Normalize",
                    "mean": [
                        0.485,
                        0.456,
                        0.406
                    ],
                    "std": [
                        0.229,
                        0.224,
                        0.225
                    ]
                },
                {
                    "type": "Collect",
                    "keys": [
                        "img",
                        "gt_labels"
                    ]
                }
       ],
       "val": [
                {
                    "type": "Resize",
                    "size": 256
                },
                {
                    "type": "CenterCrop",
                    "size": 224
                },
                {
                    "type": "ToTensor"
                },
                {
                    "type": "Normalize",
                    "mean": [
                        0.485,
                        0.456,
                        0.406
                    ],
                    "std": [
                        0.229,
                        0.224,
                        0.225
                    ]
                },
                {
                    "type": "Collect",
                    "keys": [
                        "img",
                        "gt_labels"
                    ]
                }
       ]
    },

    "train": {
        "batch_size": 32,
        "learning_rate": 0.00001,
        "lr_scheduler_type": "cosine",
        "num_epochs": 20
    },

    "evaluation": {
        "batch_size": 32,
        "metrics": ["accuracy", "precision", "recall"]
    }

 }
--- a/configs/nlp/sbert_sentence_similarity.json
+++ b/configs/nlp/sbert_sentence_similarity.json
@@ -0,0 +1,87 @@
 {
    "framework": "pytorch",
    "task": "sentence-similarity",
    "preprocessor": {
      "type": "sen-sim-tokenizer",
      "first_sequence": "sentence1",
      "second_sequence": "sentence2"
    },
    "model": {
        "type": "text-classification",
        "backbone": {
            "type": "structbert",
            "prefix": "encoder",
            "attention_probs_dropout_prob": 0.1,
            "easynlp_version": "0.0.3",
            "gradient_checkpointing": false,
            "hidden_act": "gelu",
            "hidden_dropout_prob": 0.1,
            "hidden_size": 768,
            "initializer_range": 0.02,
            "intermediate_size": 3072,
            "layer_norm_eps": 1e-12,
            "max_position_embeddings": 512,
            "num_attention_heads": 12,
            "num_hidden_layers": 12,
            "pad_token_id": 0,
            "position_embedding_type": "absolute",
            "transformers_version": "4.6.0.dev0",
            "type_vocab_size": 2,
            "use_cache": true,
            "vocab_size": 21128
        },
        "head": {
            "type": "text-classification",
            "hidden_dropout_prob": 0.1,
            "hidden_size": 768
        }
    },
    "pipeline": {
       "type": "sentence-similarity"
    },
    "train": {
        "work_dir": "/tmp",
        "dataloader": {
            "batch_size_per_gpu": 2,
            "workers_per_gpu": 1
        },
        "optimizer": {
            "type": "SGD",
            "lr": 0.01,
            "options": {
                "grad_clip": {
                    "max_norm": 2.0
                }
            }
        },
        "lr_scheduler": {
            "type": "StepLR",
            "step_size": 2,
            "options": {
                "warmup": {
                    "type": "LinearWarmup",
                    "warmup_iters": 2
                }
            }
        },
        "hooks": [{
            "type": "CheckpointHook",
            "interval": 1
        }, {
            "type": "TextLoggerHook",
            "interval": 1
        }, {
            "type": "IterTimerHook"
        }, {
            "type": "EvaluationHook",
            "interval": 1
        }]
    },
    "evaluation": {
        "dataloader": {
            "batch_size_per_gpu": 2,
            "workers_per_gpu": 1,
            "shuffle": false
        }
    }
  }
--- a/configs/nlp/sequence_classification_trainer.yaml
+++ b/configs/nlp/sequence_classification_trainer.yaml
@@ -0,0 +1,62 @@
 # In current version, many arguments are not used in pipelines, so,
 # a tag `[being used]` will indicate which argument is being used
 version: v0.1
 framework: pytorch
 task: text-classification

 model:
  path: bert-base-sst2
  backbone:
    type: bert
    prefix: bert
  attention_probs_dropout_prob: 0.1
  bos_token_id: 0
  eos_token_id: 2
  hidden_act: elu
  hidden_dropout_prob: 0.1
  hidden_size: 768
  initializer_range: 0.02
  intermediate_size: 3072
  layer_norm_eps: 1e-05
  max_position_embeddings: 514
  model_type: roberta
  num_attention_heads: 12
  num_hidden_layers: 12
  pad_token_id: 1
  type_vocab_size: 1
  vocab_size: 50265
  num_classes: 5


 col_index: &col_indexs
  text_col: 0
  label_col: 1

 dataset:
  train:
    <<: *col_indexs
    file: ~
  valid:
    <<: *col_indexs
    file: glue/sst2  # [being used]
  test:
    <<: *col_indexs
    file: ~

 preprocessor:
  type: Tokenize
  tokenizer_name: /workspace/bert-base-sst2

 train:
  batch_size: 256
  learning_rate: 0.00001
  lr_scheduler_type: cosine
  num_steps: 100000

 evaluation: # [being used]
  model_path: .cache/easynlp/
  max_sequence_length: 128
  batch_size: 32
  metrics:
    - accuracy
    - f1
--- a/data/test/audios/1ch_nihaomiya.wav
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