Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+visual-quality-inspection/transfer-learning/workflows/vision_anomaly_detection/assets/Visual_quality_inspection_layered_architecture.JPG filter=lfs diff=lfs merge=lfs -text
+visual-quality-inspection/transfer-learning/workflows/vision_anomaly_detection/assets/visual_quality_inspection_pipeline.JPG filter=lfs diff=lfs merge=lfs -text

visual-quality-inspection/transfer-learning/.dockerignore

@@ -0,0 +1,4 @@
+.git*
+**__pycache__**
+docker
+Dockerfile*

visual-quality-inspection/transfer-learning/.github/pull_request_template.md

@@ -0,0 +1,14 @@
+<!-- Add a description of the changes made in the PR -->
+
+**Before requesting a review:**
+
+- [ ] I have ensured my PR title is accurate
+- [ ] I wrote a description of the changes being made, if it's not obvious
+- [ ] I have synced by branch with the base (i.e. `develop`)
+- [ ] I ran `make lint` on my branch and it passes
+- [ ] I ran the pytest tests that could reasonably be affected by my changes and they pass
+- [ ] I have performed a self code review of my own code on the "Files changed" tab of the pull request
+- [ ] I have commented my code in hard-to-understand areas
+- [ ] I have updated the documentation (in docstrings, notebooks, and .rst files)
+- [ ] I have added new tests that prove my fix is effective or that my feature works (or provide justification why not)
+- [ ] I have applied the appropriate labels to the PR (if your PR is not ready for review use "WIP")

visual-quality-inspection/transfer-learning/.github/workflows/build-container.yaml

@@ -0,0 +1,50 @@
+name: TLT Containers Weekly Builder
+on:
+  workflow_dispatch: # Can be manually executed
+  schedule: # 1/week Sunday at 11:00PM
+    - cron: "5 23 * * 0"
+jobs:
+  build:
+    container: # MLOps Dev container for Compose Automation
+      image: ${{ vars.GHA_CONTAINER_IMAGE }}
+      env: # Add ENVS to control compose building
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+      credentials: # CAAS Registry Creds
+        username: ${{ secrets.REGISTRY_USER }}
+        password: ${{ secrets.REGISTRY_TOKEN }}
+    runs-on: [aia-devops] # Runner Label
+    steps:
+    - uses: actions/checkout@v3
+      with:
+        submodules: true
+        set-safe-directory: true
+    - name: Build Container
+      run: docker compose build
+      working-directory: ./docker
+  push:
+    needs: [build]
+    strategy:
+      matrix:
+        container: ["tlt-devel", "tlt-prod", "tlt-dist-devel", "tlt-dist-prod"] # name of Compose container
+    container:
+      image: ${{ vars.GHA_CONTAINER_IMAGE }}
+      env: # Add ENVS to control compose building
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+      credentials: # CAAS Registry Creds
+        username: ${{ secrets.REGISTRY_USER }}
+        password: ${{ secrets.REGISTRY_TOKEN }}
+    runs-on: [aia-devops]
+    steps:
+    - uses: docker/login-action@v2
+      with: # CAAS Registry Creds
+        registry: ${{ vars.GHA_REGISTRY }}
+        username: ${{ secrets.REGISTRY_USER }}
+        password: ${{ secrets.REGISTRY_TOKEN }}
+    - name: Push Container # tlt-<num>-<container>
+      run: |
+        docker tag intel/ai-tools:${{ matrix.container }}-latest ${{ vars.GHA_REGISTRY_REPO }}:ww$(date +"%U")-${{ matrix.container }}
+        docker push ${{ vars.GHA_REGISTRY_REPO }}:ww$(date +"%U")-${{ matrix.container }}

visual-quality-inspection/transfer-learning/.github/workflows/docs-test.yaml

@@ -0,0 +1,34 @@
+name: Build and Test docs
+on:
+  pull_request:
+    types: [submitted]
+  # run the workflow if changes pushed to main or release branches
+  push:
+    branches:
+      - '**'
+    tags:
+      - '**'
+    paths:
+      - '**'
+
+# installs dependencies, build the docs and push it to `gh-pages`
+jobs:
+  docs-test:
+    runs-on: [ aia-devops ]
+    container:
+      image: ${{ vars.GHA_IMAGE }}
+      env:
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+      # credentials:
+      #   username: ${{ secrets.REGISTRY_USER }}
+      #   password: ${{ secrets.REGISTRY_TOKEN }}
+      volumes:
+        - /tf_dataset/dataset/transfer_learning:/tmp/data
+    steps:
+    - uses: actions/checkout@v3
+    # Test the docs
+    - name: Run documentation tests
+      run: |
+        make test_docs

visual-quality-inspection/transfer-learning/.github/workflows/integration.yaml

@@ -0,0 +1,41 @@
+name: Integration Test
+on:
+  pull_request_review:
+    types: [submitted]
+  # run the workflow if changes pushed to main or release branches
+  push:
+    branches:
+      - develop
+      - main
+      - r0.1
+      - r0.2
+      - r0.3
+      - r0.4
+      - r0.5
+    tags:
+      - '**'
+    paths:
+      - '**'
+jobs:
+  integration-test:
+    if: github.event.review.state == 'approved' ||
+        github.event.pull_request.merged == true ||
+        github.event_name == 'push'
+    runs-on: [ aia-devops ]
+    container:
+      image: ${{ vars.GHA_IMAGE }}
+      env:
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+      # credentials:
+      #   username: ${{ secrets.REGISTRY_USER }}
+      #   password: ${{ secrets.REGISTRY_TOKEN }}
+      volumes:
+        - /tf_dataset/dataset/transfer_learning:/tmp/data
+    steps:
+      - uses: actions/checkout@v3
+      - name: Run Integration Tests
+        shell: bash
+        continue-on-error: false
+        run: make integration

visual-quality-inspection/transfer-learning/.github/workflows/nightly-integration.yaml

@@ -0,0 +1,25 @@
+name: Nightly Integration Test
+on:
+  workflow_dispatch: # Can be manually executed
+  schedule: # nightly at 10:00PM
+    - cron: "0 22 * * *"
+jobs:
+  nightly-test:
+    runs-on: [ aia-devops ]
+    container:
+      image: ${{ vars.GHA_IMAGE }}
+      env:
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+      # credentials:
+      #   username: ${{ secrets.REGISTRY_USER }}
+      #   password: ${{ secrets.REGISTRY_TOKEN }}
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          ref: develop
+      - name: Run Integration Test
+        shell: bash
+        continue-on-error: false
+        run: make integration

visual-quality-inspection/transfer-learning/.github/workflows/nightly-notebook-test.yaml

@@ -0,0 +1,25 @@
+name: Nightly Notebooks Test
+on:
+  workflow_dispatch: # Can be manually executed
+  schedule: # nightly at 11:00PM
+    - cron: "0 23 * * *"
+jobs:
+  notebook-test:
+    runs-on: [ aia-devops ]
+    container:
+      image: ${{ vars.GHA_IMAGE }}
+      env:
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+        DATASET_DIR: /tmp/data
+        OUTPUT_DIR: /tmp/output
+      # credentials:
+      #   username: ${{ secrets.REGISTRY_USER }}
+      #   password: ${{ secrets.REGISTRY_TOKEN }}
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          ref: develop
+      - name: Run Notebook Tests
+        run: make test_notebook_catalog

visual-quality-inspection/transfer-learning/.github/workflows/notebook-test.yaml

@@ -0,0 +1,41 @@
+name: Notebooks Test
+on:
+  pull_request_review:
+    types: [submitted]
+  # run the workflow if changes pushed to main or release branches
+  push:
+    branches:
+      - develop
+      - main
+      - r0.1
+      - r0.2
+      - r0.3
+      - r0.4
+      - r0.5
+    tags:
+      - '**'
+    paths:
+      - '**'
+jobs:
+  notebook-test:
+    if: github.event.review.state == 'approved' ||
+        github.event.pull_request.merged == true ||
+        github.event_name == 'push'
+    runs-on: [ aia-devops ]
+    container:
+      image: ${{ vars.GHA_IMAGE }}
+      env:
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+        DATASET_DIR: /tmp/data
+        OUTPUT_DIR: /tmp/output
+      # credentials:
+      #   username: ${{ secrets.REGISTRY_USER }}
+      #   password: ${{ secrets.REGISTRY_TOKEN }}
+      volumes:
+        - /tf_dataset/dataset/transfer_learning:/tmp/data
+    steps:
+      - uses: actions/checkout@v3
+      - name: Run Notebook Tests
+        run: make test_notebook_custom

visual-quality-inspection/transfer-learning/.github/workflows/style-test.yaml

@@ -0,0 +1,30 @@
+name: Style Checks
+on:
+  pull_request:
+    types: [submitted]
+  # run the workflow if changes pushed to main or release branches
+  push:
+    branches:
+      - '**'
+    tags:
+      - '**'
+    paths:
+      - '**'
+# installs dependencies and runs the linter
+jobs:
+  style-test:
+    runs-on: [ aia-devops ]
+    container:
+      image: ${{ vars.GHA_IMAGE }}
+      env:
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+      # credentials:
+      #   username: ${{ secrets.REGISTRY_USER }}
+      #   password: ${{ secrets.REGISTRY_TOKEN }}
+    steps:
+    - uses: actions/checkout@v3
+    - name: Run linter
+      run: |
+        make lint

visual-quality-inspection/transfer-learning/.github/workflows/unittest.yaml

@@ -0,0 +1,30 @@
+name: Unit Test
+on:
+  pull_request:
+    types: [submitted]
+  # run the workflow if changes pushed to main or release branches
+  push:
+    branches:
+      - '**'
+    tags:
+      - '**'
+    paths:
+      - '**'
+jobs:
+  unit-test:
+    runs-on: [ aia-devops ]
+    container:
+      image: ${{ vars.GHA_IMAGE }}
+      env:
+        http_proxy: ${{ secrets.HTTP_PROXY }}
+        https_proxy: ${{ secrets.HTTPS_PROXY }}
+        no_proxy: ${{ secrets.NO_PROXY }}
+      # credentials:
+      #   username: ${{ secrets.REGISTRY_USER }}
+      #   password: ${{ secrets.REGISTRY_TOKEN }}
+    steps:
+      - uses: actions/checkout@v3
+      - name: Run Unit Test
+        shell: bash
+        continue-on-error: false
+        run: make unittest

visual-quality-inspection/transfer-learning/.gitignore

@@ -0,0 +1,20 @@
+_autosummary
+.coverage
+.DS_Store
+.idea*
+.ipynb_checkpoints
+.vscode
+*.egg-info/
+*.pyc
+**.log
+**/*.cache
+**/**.whl
+**/**/models/
+**/**venv
+**venv*
+build/
+data
+dist/
+docs/_build/
+nc_workspace
+output

visual-quality-inspection/transfer-learning/CODEOWNERS

@@ -0,0 +1,59 @@
+# This is a comment.
+# Each line is a file pattern followed by one or more owners.
+
+# These owners will be the default owners for everything in
+# the repo. Unless a later match takes precedence,
+# @global-owner1 and @global-owner2 will be requested for
+# review when someone opens a pull request.
+*       @ashahba @dmsuehir @etcylfleet @HarshaRamayanam @mhbuehler @okhleif-IL 
+
+# Order is important; the last matching pattern takes the most
+# precedence. When someone opens a pull request that only
+# modifies JS files, only @js-owner and not the global
+# owner(s) will be requested for a review.
+# *.js    @js-owner #This is an inline comment.
+
+# You can also use email addresses if you prefer. They'll be
+# used to look up users just like we do for commit author
+# emails.
+# *.go [email protected]
+
+# Teams can be specified as code owners as well. Teams should
+# be identified in the format @org/team-name. Teams must have
+# explicit write access to the repository. In this example,
+# the octocats team in the octo-org organization owns all .txt files.
+# *.txt @octo-org/octocats
+
+# In this example, @doctocat owns any files in the build/logs
+# directory at the root of the repository and any of its
+# subdirectories.
+# /build/logs/ @doctocat
+
+# The `docs/*` pattern will match files like
+# `docs/getting-started.md` but not further nested files like
+# `docs/build-app/troubleshooting.md`.
+# docs/*  [email protected]
+
+# In this example, @octocat owns any file in an apps directory
+# anywhere in your repository.
+# apps/ @octocat
+
+# In this example, @doctocat owns any file in the `/docs`
+# directory in the root of your repository and any of its
+# subdirectories.
+# /docs/ @doctocat
+
+# In this example, any change inside the `/scripts` directory
+# will require approval from @doctocat or @octocat.
+# /scripts/ @doctocat @octocat
+
+# In this example, @octocat owns any file in a `/logs` directory such as
+# `/build/logs`, `/scripts/logs`, and `/deeply/nested/logs`. Any changes
+# in a `/logs` directory will require approval from @octocat.
+# **/logs @octocat
+
+# In this example, @octocat owns any file in the `/apps`
+# directory in the root of your repository except for the `/apps/github`
+# subdirectory, as its owners are left empty.
+# /apps/ @octocat
+# /apps/github

visual-quality-inspection/transfer-learning/DATASETS.md

@@ -0,0 +1,30 @@
+# Datasets
+
+This is a comprehensive list of public datasets used by this repository.
+
+| Name (Link/Source) | Framework | Use Case |
+|--------------------| --------- | -------- |
+| [AG News (Hugging Face)](https://huggingface.co/datasets/ag_news) | PyTorch | Text Classification |
+| [AG News (TFDS)](https://www.tensorflow.org/datasets/catalog/ag_news_subset) | TensorFlow | Text Classification |
+| [Food101 (Torchvision)](https://pytorch.org/vision/stable/generated/torchvision.datasets.Food101.html#torchvision.datasets.Food101) | PyTorch | Image Classification |
+| [Food101 (TFDS)](https://www.tensorflow.org/datasets/catalog/food101) | TensorFlow | Image Classification |
+| [SMS Spam Collection](https://archive.ics.uci.edu/dataset/228/sms+spam+collection) | PyTorch & TensorFlow | Text Classification |
+| [TF Flowers (TFDS)](https://www.tensorflow.org/datasets/catalog/tf_flowers) |  PyTorch & TensorFlow | Image Classification |
+| [Cats vs. Dogs (TFDS)](https://www.tensorflow.org/datasets/catalog/cats_vs_dogs) |  TensorFlow | Image Classification |
+| [Country211 (Torchvision)](https://pytorch.org/vision/stable/generated/torchvision.datasets.Country211.html#torchvision.datasets.Country211) | PyTorch | Image Classification |
+| [DTD (Torchvision)](https://pytorch.org/vision/stable/generated/torchvision.datasets.DTD.html#torchvision.datasets.DTD) | PyTorch | Image Classification |
+| [FGVCAircraft (Torchvision)](https://pytorch.org/vision/stable/generated/torchvision.datasets.FGVCAircraft.html#torchvision.datasets.FGVCAircraft) | PyTorch | Image Classification |
+| [RenderedSST2 (Torchvision)](https://pytorch.org/vision/stable/generated/torchvision.datasets.RenderedSST2.html#torchvision.datasets.RenderedSST2) | PyTorch | Image Classification |
+| [Rock Paper Scissors (TFDS)](https://www.tensorflow.org/datasets/catalog/rock_paper_scissors) |  TensorFlow | Image Classification |
+| [Rotten_Tomatoes (Hugging Face)](https://huggingface.co/datasets/rotten_tomatoes) | PyTorch | Text Classification |
+| [TweetEval (Hugging Face)](https://huggingface.co/datasets/tweet_eval) | PyTorch | Text Classification |
+| [CIFAR10 (Torchvision)](https://pytorch.org/vision/stable/generated/torchvision.datasets.CIFAR10.html#torchvision.datasets.CIFAR10) |  PyTorch | Image Classification |
+| [IMDB Reviews (Hugging Face)](https://huggingface.co/datasets/imdb) | PyTorch | Text Classification |
+| [IMDB Reviews (TFDS)](https://www.tensorflow.org/datasets/catalog/imdb_reviews) | TensorFlow | Text Classification |
+| [GLUE/SST2 (TFDS)](https://www.tensorflow.org/datasets/catalog/glue#gluesst2) |  TensorFlow | Text Classification |
+| [GLUE/COLA (TFDS)](https://www.tensorflow.org/datasets/catalog/glue#gluecola_default_config) |  TensorFlow | Text Classification |
+| [Colorectal Histology (TFDS)](https://www.tensorflow.org/datasets/catalog/colorectal_histology) |  TensorFlow | Image Classification |
+| [RESISC45 (TFDS)](https://www.tensorflow.org/datasets/catalog/resisc45) |  TensorFlow | Image Classification |
+| [CDD-CESM](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=109379611) |  PyTorch & TensorFlow | Image & Text Classification |
+| [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) |  PyTorch & TensorFlow | Text Classification |
+| [MVTec](https://www.mvtec.com/company/research/datasets/mvtec-ad) |  PyTorch | Anomaly Detection |

visual-quality-inspection/transfer-learning/GetStarted.md

@@ -0,0 +1,263 @@
+# Get Started
+
+This is a guide for getting started with Intel® Transfer Learning Tool and will
+walk you through the steps to check system requirements, install, and then run
+the tool with a couple of examples showing no-code CLI and low-code API
+approaches.
+
+<p align="center"><b>Intel Transfer Learning Tool Get Started Flow</b></p>
+
+<img alt="Intel Transfer Learning Tool Get Started Flow" title="Intel Transfer Learning Tool Get Started Flow" src="images/TLT-GSG_flow.svg" width="800">
+
+## &#9312; Check System Requirements
+
+| Recommended Hardware         | Precision  |
+| ---------------------------- | ---------- |
+| Intel® 4th Gen Xeon® Scalable Performance processors | BF16 |
+| Intel® 1st, 2nd, 3rd, and 4th Gen Xeon® Scalable Performance processors | FP32 |
+
+| Resource         | Minimum  |
+| ---------------------------- | ---------- |
+| CPU Cores | 8  (16+ recommended) |
+| RAM | 16 GB (24-32+ GB recommended) |
+| Disk space | 10 GB minimum (can vary based on datasets downloaded) |
+
+| Required Software         |
+| ------------------------- |
+| Linux\* system (validated on Ubuntu\* 20.04/22.04 LTS) |
+| Python (3.8, 3.9, or 3.10) |
+| Pip |
+| Conda or Python virtualenv |
+| git (only required for advanced installation) |
+
+## &#9313; Install
+
+1. **Install Dependencies**
+
+   Install required packages using:
+
+   ```
+   sudo apt-get install build-essential python3-dev libgl1 libglib2.0-0
+   ```
+
+2. **Create and activate a Python3 virtual environment**
+
+   We encourage you to use a Python virtual environment (virtualenv or conda)
+   for consistent package management.  There are two ways to do this:
+
+   a. Use `virtualenv`:
+
+      ```
+      virtualenv -p python3 tlt_dev_venv
+      source tlt_dev_venv/bin/activate
+      ```
+
+   b. Or use `conda`:
+
+      ```
+      conda create --name tlt_dev_venv python=3.9
+      conda activate tlt_dev_venv
+      ```
+
+3. **Install Intel Transfer Learning Tool**
+
+   Use the Basic Installation instructions unless you plan on making code changes.
+
+   a. **Basic Installation**
+
+      ```
+      pip install intel-transfer-learning-tool
+      ```
+
+   b. **Advanced Installation**
+
+      Clone the repo:
+
+      ```
+      git clone https://github.com/IntelAI/transfer-learning.git
+      cd transfer-learning
+      ```
+
+      Then either do an editable install to avoid a rebuild and
+      install after each code change (preferred):
+
+      ```
+      pip install --editable .
+      ```
+
+      or build and install a wheel:
+
+      ```
+      python setup.py bdist_wheel
+      pip install dist/intel_transfer_learning_tool-0.5.0-py3-none-any.whl
+      ```
+
+
+4. **Additional Feature-Specific Steps**
+
+   * For distributed/multinode training, follow these additional
+     [distributed training instructions](tlt/distributed/README.md).
+
+5. **Verify Installation**
+
+   Verify that your installation was successful by using the following
+   command, which displays help information about the Intel Transfer Learning Tool:
+
+   ```
+   tlt --help
+   ```
+
+## &#9314; Run the Intel Transfer Learning Tool
+
+With the Intel Transfer Learning Tool, you can train AI models with TensorFlow or
+PyTorch using either no-code CLI commands at a bash prompt, or low-code API
+calls from a Python script. Both approaches provide the same opportunities for
+training, evaluation, optimization, and benchmarking. With the CLI, no
+programming experience is required, and you'll need basic Python knowledge to
+use the API. Choose the approach that works best for you.
+
+
+### Run Using the No-Code CLI
+
+Let's continue from the previous step where you prepared the dataset, and train
+a model using CLI commands.  This example uses the CLI to train an image
+classifier to identify different types of flowers. You can see a list of all
+available image classifier models using the command:
+
+```
+tlt list models --use-case image_classification
+```
+
+**Train a Model**
+
+In this example, we'll use the `tlt train` command to retrain the TensorFlow
+ResNet50v1.5 model using a flowers dataset from the
+[TensorFlow Datasets catalog](https://www.tensorflow.org/datasets/catalog/tf_flowers).
+The `--dataset-dir` and `--output-dir` paths need to point to writable folders on your system.
+```
+# Use the follow environment variable setting to reduce the warnings and log output from TensorFlow
+export TF_CPP_MIN_LOG_LEVEL="2"
+
+tlt train -f tensorflow --model-name resnet_v1_50 --dataset-name tf_flowers --dataset-dir "/tmp/data-${USER}" --output-dir "/tmp/output-${USER}"
+```
+```
+Model name: resnet_v1_50
+Framework: tensorflow
+Dataset name: tf_flowers
+Training epochs: 1
+Dataset dir: /tmp/data-user
+Output directory: /tmp/output-user
+...
+Model: "sequential"
+_________________________________________________________________
+Layer (type)                Output Shape              Param #
+=================================================================
+keras_layer (KerasLayer)    (None, 2048)              23561152
+dense (Dense)               (None, 5)                 10245
+=================================================================
+Total params: 23,571,397
+Trainable params: 10,245
+Non-trainable params: 23,561,152
+_________________________________________________________________
+Checkpoint directory: /tmp/output-user/resnet_v1_50_checkpoints
+86/86 [==============================] - 24s 248ms/step - loss: 0.4600 - acc: 0.8438
+Saved model directory: /tmp/output-user/resnet_v1_50/1
+```
+
+After training completes, the `tlt train` command evaluates the model. The loss and
+accuracy values are printed toward the end of the console output. The model is
+exported to the output directory you specified in a numbered folder created for
+each training run.
+
+**Next Steps**
+
+That ends this Get Started CLI example. As a next step, you can also follow the
+[Beyond Get Started CLI Example](examples/cli/README.md) for a complete example
+that includes evaluation, benchmarking, and quantization in the datasets.
+
+Read about all the CLI commands in the [CLI reference](/cli.md).
+Find more examples in our list of [Examples](examples/README.md).
+
+### Run Using the Low-Code API
+
+The following Python code example trains an image classification model with the TensorFlow
+flowers dataset using API calls from Python.  The model is
+benchmarked and quantized to INT8 precision for improved inference performance.
+
+You can run the API example using a Jupyter notebook. See the [notebook setup
+instructions](/notebooks/setup.md) for more details for preparing the Jupyter
+notebook environment.
+
+```python
+import os
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+
+from tlt.datasets import dataset_factory
+from tlt.models import model_factory
+from tlt.utils.types import FrameworkType, UseCaseType
+
+username = os.getenv('USER', 'user')
+
+# Specify a writable directory for the dataset to be downloaded
+dataset_dir = '/tmp/data-{}'.format(username)
+if not os.path.exists(dataset_dir):
+    os.makedirs(dataset_dir)
+
+# Specify a writeable directory for output (such as saved model files)
+output_dir = '/tmp/output-{}'.format(username)
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# Get the model
+model = model_factory.get_model(model_name="resnet_v1_50", framework=FrameworkType.TENSORFLOW)
+
+# Download and preprocess the flowers dataset from the TensorFlow datasets catalog
+dataset = dataset_factory.get_dataset(dataset_dir=dataset_dir,
+                                      dataset_name='tf_flowers',
+                                      use_case=UseCaseType.IMAGE_CLASSIFICATION,
+                                      framework=FrameworkType.TENSORFLOW,
+                                      dataset_catalog='tf_datasets')
+dataset.preprocess(image_size=model.image_size, batch_size=32)
+dataset.shuffle_split(train_pct=.75, val_pct=.25)
+
+# Train the model using the dataset
+model.train(dataset, output_dir=output_dir, epochs=1)
+
+# Evaluate the trained model
+metrics = model.evaluate(dataset)
+for metric_name, metric_value in zip(model._model.metrics_names, metrics):
+    print("{}: {}".format(metric_name, metric_value))
+
+# Export the model
+saved_model_dir = model.export(output_dir=output_dir)
+
+# Quantize the trained model
+quantization_output = os.path.join(output_dir, "quantized_model")
+model.quantize(quantization_output, dataset, overwrite_model=True)
+
+# Benchmark the trained model using the Intel Neural Compressor config file
+model.benchmark(dataset, saved_model_dir=quantization_output)
+
+# Do graph optimization on the trained model
+optimization_output = os.path.join(output_dir, "optimized_model")
+model.optimize_graph(optimization_output, overwrite_model=True)
+```
+
+For more information on the API, see the [API Documentation](/api.md).
+
+## Summary and Next Steps
+
+The Intel Transfer Learning Tool can be used to develop an AI model and export
+an Intel-optimized saved model for deployment. The sample CLI and API commands
+we've presented show how to execute end-to-end transfer learning workflows. 
+
+For the no-code CLI, you can follow a
+complete example that includes trainng, evaluation, benchmarking, and quantization
+in the datasets, as well as some additional models in the [Beyond Get Started
+CLI example](examples/cli/README.md) documentation. You can also read about all the
+CLI commands in the [CLI reference](/cli.md).
+
+For the low-code API, read about the API in the [API Documentation](/api.md).
+
+Find more CLI and API examples in our list of [Examples](examples/README.md).

visual-quality-inspection/transfer-learning/LICENSE

@@ -0,0 +1,202 @@
+
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visual-quality-inspection/transfer-learning/Legal.md

@@ -0,0 +1,34 @@
+# Legal Information
+
+## Disclaimer
+
+Intel® Transfer Learning Tool scripts are not intended for benchmarking Intel® platforms. For any
+performance and/or benchmarking information on specific Intel platforms, visit
+https://www.intel.ai/blog.
+
+Intel is committed to the respect of human rights and avoiding complicity in
+human rights abuses, a policy reflected in the Intel Global Human Rights
+Principles. Accordingly, by accessing the Intel material on this platform you
+agree that you will not use the material in a product or application that causes
+or contributes to a violation of an internationally recognized human right.
+
+## License
+
+Intel® Transfer Learning Tool, documentation, and example code are all licensed
+under Apache License Version 2.0.
+
+## Datasets
+
+To the extent that any [public datasets](DATASETS.md) are referenced by Intel or accessed using
+tools or code on this site those datasets are provided by the third party
+indicated as the data source. Intel does not create the data, or datasets, and
+does not warrant their accuracy or quality. By accessing the public dataset(s)
+you agree to the terms associated with those datasets and that your use complies
+with the applicable license.
+
+Intel expressly disclaims the accuracy, adequacy, or completeness of any public
+datasets, and is not liable for any errors, omissions, or defects in the data,
+or for any reliance on the data. Intel is not liable for any liability or
+damages relating to your use of public datasets.
+
+\*Other names and brands may be claimed as the property of others. [Trademarks](http://www.intel.com/content/www/us/en/legal/trademarks.html)

visual-quality-inspection/transfer-learning/MANIFEST.in

@@ -0,0 +1 @@
+recursive-include tlt *

visual-quality-inspection/transfer-learning/Makefile

@@ -0,0 +1,112 @@
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# 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,
+# 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.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+# Note: These are just placeholders for future additions to Makefile.
+# You can remove these comments later.
+ACTIVATE_TLT_VENV = "tlt_dev_venv/bin/activate"
+ACTIVATE_NOTEBOOK_VENV = "tlt_notebook_venv/bin/activate"
+ACTIVATE_TEST_VENV = "tlt_test_venv/bin/activate"
+ACTIVATE_DOCS_VENV = $(ACTIVATE_TEST_VENV)
+
+# Customize sample test run commands
+# PY_TEST_EXTRA_ARGS="'-vvv -k test_platform_util_with_no_args'" make test
+# PY_TEST_EXTRA_ARGS="'--collect-only'" make test
+PY_TEST_EXTRA_ARGS ?= "--durations=0"
+
+tlt_test_venv: $(CURDIR)/tests/requirements-test.txt
+	@echo "Creating a virtualenv tlt_test_venv..."
+	@test -d tlt_test_venv || virtualenv -p python3 tlt_test_venv
+
+	@echo "Building the TLT API in tlt_test_venv env..."
+	@. $(ACTIVATE_TEST_VENV) && pip install --editable .
+
+	@echo "Installing test dependencies..."
+	@. $(ACTIVATE_TEST_VENV) && pip install -r $(CURDIR)/tests/requirements-test.txt
+
+tlt_notebook_venv: $(CURDIR)/notebooks/requirements.txt
+	@echo "Creating a virtualenv tlt_notebook_venv..."
+	@test -d tlt_notebook_venv || virtualenv -p python3 tlt_notebook_venv
+
+	@echo "Installing TF & PYT notebook dependencies..."
+	@. $(ACTIVATE_NOTEBOOK_VENV) && pip install -r $(CURDIR)/notebooks/requirements.txt
+
+test: unittest integration
+
+unittest: tlt_test_venv
+	@echo "Testing unit test API..."
+	@. $(ACTIVATE_TEST_VENV) && PYTHONPATH=$(CURDIR)/tests py.test -vvv -s $(PY_TEST_EXTRA_ARGS) "-k not integration and not skip"
+
+integration: tlt_test_venv
+	@echo "Testing integration test API..."
+	@. $(ACTIVATE_TEST_VENV) && PYTHONPATH=$(CURDIR)/tests py.test -vvv -s $(PY_TEST_EXTRA_ARGS) "-k integration and not skip"
+
+lint: tlt_test_venv
+	@echo "Style checks..."
+	@. $(ACTIVATE_TEST_VENV) && flake8 tlt tests downloader
+
+clean:
+	rm -rf tlt_test_venv
+
+tlt_docs_venv: tlt_test_venv $(CURDIR)/docs/requirements-docs.txt
+	@echo "Installing docs dependencies..."
+	@. $(ACTIVATE_DOCS_VENV) && pip install -r $(CURDIR)/docs/requirements-docs.txt
+
+html: tlt_docs_venv
+	@echo "Building Sphinx documentation..."
+	@. $(ACTIVATE_DOCS_VENV) && $(MAKE) -C docs clean html
+
+test_docs: html
+	@echo "Testing Sphinx documentation..."
+	@. $(ACTIVATE_DOCS_VENV) && $(MAKE) -C docs doctest
+
+tlt_notebook_venv: tlt_test_venv
+	@echo "Installing notebook dependencies..."
+	@. $(ACTIVATE_TEST_VENV) && pip install -r $(CURDIR)/notebooks/requirements.txt
+
+test_notebook_custom: tlt_notebook_venv
+	@echo "Testing Jupyter notebooks with custom datasets..."
+	@. $(ACTIVATE_TEST_VENV) && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/image_classification/tlt_api_tf_image_classification/TLT_TF_Image_Classification_Transfer_Learning.ipynb remove_for_custom_dataset && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/image_classification/tlt_api_pyt_image_classification/TLT_PyTorch_Image_Classification_Transfer_Learning.ipynb remove_for_custom_dataset && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/text_classification/tlt_api_tf_text_classification/TLT_TF_Text_Classification.ipynb remove_for_custom_dataset && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/text_classification/tlt_api_pyt_text_classification/TLT_PYT_Text_Classification.ipynb remove_for_custom_dataset
+
+test_notebook_catalog: tlt_notebook_venv
+	@echo "Testing Jupyter notebooks with public catalog datasets..."
+	@. $(ACTIVATE_TEST_VENV) && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/image_classification/tlt_api_tf_image_classification/TLT_TF_Image_Classification_Transfer_Learning.ipynb remove_for_tf_dataset && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/image_classification/tlt_api_pyt_image_classification/TLT_PyTorch_Image_Classification_Transfer_Learning.ipynb remove_for_tv_dataset && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/text_classification/tlt_api_tf_text_classification/TLT_TF_Text_Classification.ipynb remove_for_tf_dataset && \
+	bash run_notebooks.sh $(CURDIR)/notebooks/text_classification/tlt_api_pyt_text_classification/TLT_PYT_Text_Classification.ipynb remove_for_hf_dataset
+
+test_tf_notebook: tlt_notebook_venv
+	@. $(ACTIVATE_TEST_VENV) && bash run_notebooks.sh tensorflow
+
+test_pyt_notebook: tlt_notebook_venv
+	@. $(ACTIVATE_TEST_VENV) && bash run_notebooks.sh pytorch
+
+dist: tlt_docs_venv
+	@echo "Create binary wheel..."
+	@. $(ACTIVATE_DOCS_VENV) && python setup.py bdist_wheel
+
+check_dist: dist
+	@echo "Testing the wheel..."
+	@. $(ACTIVATE_DOCS_VENV) && \
+	pip install twine && \
+	python setup.py bdist_wheel && \
+	twine check dist/*

visual-quality-inspection/transfer-learning/Models.md

@@ -0,0 +1,178 @@
+# Intel® Transfer Learning Tool Supported Models
+
+## Image Classification
+
+| Model name | Framework | Model Hub |
+|------------|-----------|-----------|
+| alexnet | PyTorch* | Torchvision* |
+| convnext_base | PyTorch | Torchvision |
+| convnext_large | PyTorch | Torchvision |
+| convnext_small | PyTorch | Torchvision |
+| convnext_tiny | PyTorch | Torchvision |
+| densenet121 | PyTorch | Torchvision |
+| densenet161 | PyTorch | Torchvision |
+| densenet169 | PyTorch | Torchvision |
+| densenet201 | PyTorch | Torchvision |
+| efficientnetv2-b0 | TensorFlow* | TensorFlow Hub* |
+| efficientnetv2-b1 | TensorFlow | TensorFlow Hub |
+| efficientnetv2-b2 | TensorFlow | TensorFlow Hub |
+| efficientnetv2-b3 | TensorFlow | TensorFlow Hub |
+| efficientnetv2-s | TensorFlow | TensorFlow Hub |
+| efficientnet_b0 | TensorFlow | TensorFlow Hub |
+| efficientnet_b0 | PyTorch | Torchvision |
+| efficientnet_b1 | TensorFlow | TensorFlow Hub |
+| efficientnet_b1 | PyTorch | Torchvision |
+| efficientnet_b2 | TensorFlow | TensorFlow Hub |
+| efficientnet_b2 | PyTorch | Torchvision |
+| efficientnet_b3 | TensorFlow | TensorFlow Hub |
+| efficientnet_b3 | PyTorch | Torchvision |
+| efficientnet_b4 | TensorFlow | TensorFlow Hub |
+| efficientnet_b4 | PyTorch | Torchvision |
+| efficientnet_b5 | TensorFlow | TensorFlow Hub |
+| efficientnet_b5 | PyTorch | Torchvision |
+| efficientnet_b6 | TensorFlow | TensorFlow Hub |
+| efficientnet_b6 | PyTorch | Torchvision |
+| efficientnet_b7 | TensorFlow | TensorFlow Hub |
+| efficientnet_b7 | PyTorch | Torchvision |
+| googlenet | PyTorch | Torchvision |
+| inception_v3 | TensorFlow | TensorFlow Hub |
+| mnasnet0_5 | PyTorch | Torchvision |
+| mnasnet1_0 | PyTorch | Torchvision |
+| mobilenet_v2 | PyTorch | Torchvision |
+| mobilenet_v2_100_224 | TensorFlow | TensorFlow Hub |
+| mobilenet_v3_large | PyTorch | Torchvision |
+| mobilenet_v3_small | PyTorch | Torchvision |
+| nasnet_large | TensorFlow | TensorFlow Hub |
+| proxyless_cpu | PyTorch | PyTorch Hub* |
+| regnet_x_16gf | PyTorch | Torchvision |
+| regnet_x_1_6gf | PyTorch | Torchvision |
+| regnet_x_32gf | PyTorch | Torchvision |
+| regnet_x_3_2gf | PyTorch | Torchvision |
+| regnet_x_400mf | PyTorch | Torchvision |
+| regnet_x_800mf | PyTorch | Torchvision |
+| regnet_x_8gf | PyTorch | Torchvision |
+| regnet_y_16gf | PyTorch | Torchvision |
+| regnet_y_1_6gf | PyTorch | Torchvision |
+| regnet_y_32gf | PyTorch | Torchvision |
+| regnet_y_3_2gf | PyTorch | Torchvision |
+| regnet_y_400mf | PyTorch | Torchvision |
+| regnet_y_800mf | PyTorch | Torchvision |
+| regnet_y_8gf | PyTorch | Torchvision |
+| resnet101 | PyTorch | Torchvision |
+| resnet152 | PyTorch | Torchvision |
+| resnet18 | PyTorch | Torchvision |
+| resnet18_ssl | PyTorch | PyTorch Hub |
+| resnet18_swsl | PyTorch | PyTorch Hub |
+| resnet34 | PyTorch | Torchvision |
+| resnet50 | PyTorch | Torchvision |
+| resnet50_ssl | PyTorch | PyTorch Hub |
+| resnet50_swsl | PyTorch | PyTorch Hub |
+| resnet_v1_50 | TensorFlow | TensorFlow Hub |
+| resnet_v2_101 | TensorFlow | TensorFlow Hub |
+| resnet_v2_50 | TensorFlow | TensorFlow Hub |
+| resnext101_32x16d_ssl | PyTorch | PyTorch Hub |
+| resnext101_32x16d_swsl | PyTorch | PyTorch Hub |
+| resnext101_32x16d_wsl | PyTorch | PyTorch Hub |
+| resnext101_32x32d_wsl | PyTorch | PyTorch Hub |
+| resnext101_32x48d_wsl | PyTorch | PyTorch Hub |
+| resnext101_32x4d_ssl | PyTorch | PyTorch Hub |
+| resnext101_32x4d_swsl | PyTorch | PyTorch Hub |
+| resnext101_32x8d | PyTorch | Torchvision |
+| resnext101_32x8d_ssl | PyTorch | PyTorch Hub |
+| resnext101_32x8d_swsl | PyTorch | PyTorch Hub |
+| resnext101_32x8d_wsl | PyTorch | PyTorch Hub |
+| resnext50_32x4d | PyTorch | Torchvision |
+| resnext50_32x4d_ssl | PyTorch | PyTorch Hub |
+| resnext50_32x4d_swsl | PyTorch | PyTorch Hub |
+| shufflenet_v2_x0_5 | PyTorch | Torchvision |
+| shufflenet_v2_x1_0 | PyTorch | Torchvision |
+| vgg11 | PyTorch | Torchvision |
+| vgg11_bn | PyTorch | Torchvision |
+| vgg13 | PyTorch | Torchvision |
+| vgg13_bn | PyTorch | Torchvision |
+| vgg16 | PyTorch | Torchvision |
+| vgg16_bn | PyTorch | Torchvision |
+| vgg19 | PyTorch | Torchvision |
+| vgg19_bn | PyTorch | Torchvision |
+| vit_b_16 | PyTorch | Torchvision |
+| vit_b_32 | PyTorch | Torchvision |
+| vit_l_16 | PyTorch | Torchvision |
+| vit_l_32 | PyTorch | Torchvision |
+| wide_resnet101_2 | PyTorch | Torchvision |
+| wide_resnet50_2 | PyTorch | Torchvision |
+| ConvNeXtBase | TensorFlow | Keras* |
+| ConvNeXtLarge | TensorFlow | Keras |
+| ConvNeXtSmall | TensorFlow | Keras |
+| ConvNeXtTiny | TensorFlow | Keras |
+| ConvNeXtXLarge | TensorFlow | Keras |
+| DenseNet121 | TensorFlow | Keras |
+| DenseNet169 | TensorFlow | Keras |
+| DenseNet201 | TensorFlow | Keras |
+| EfficientNetV2B0 | TensorFlow | Keras |
+| EfficientNetV2B1 | TensorFlow | Keras |
+| EfficientNetV2B2 | TensorFlow | Keras |
+| EfficientNetV2B3 | TensorFlow | Keras |
+| EfficientNetV2L | TensorFlow | Keras |
+| EfficientNetV2M | TensorFlow | Keras |
+| EfficientNetV2S | TensorFlow | Keras |
+| InceptionResNetV2 | TensorFlow | Keras |
+| InceptionV3 | TensorFlow | Keras |
+| MobileNet | TensorFlow | Keras |
+| MobileNetV2 | TensorFlow | Keras |
+| NASNetLarge | TensorFlow | Keras |
+| NASNetMobile | TensorFlow | Keras |
+| ResNet101 | TensorFlow | Keras |
+| ResNet101V2 | TensorFlow | Keras |
+| ResNet152 | TensorFlow | Keras |
+| ResNet152V2 | TensorFlow | Keras |
+| ResNet50 | TensorFlow | Keras |
+| ResNet50V2 | TensorFlow | Keras |
+| VGG16 | TensorFlow | Keras |
+| VGG19 | TensorFlow | Keras |
+| Xception | TensorFlow | Keras |
+
+## Text Classification
+
+| Model name | Framework | Model Hub |
+|------------|-----------|-----------|
+| bert-base-cased | PyTorch | Hugging Face* |
+| bert-base-uncased | TensorFlow | Hugging Face |
+| bert-large-uncased | TensorFlow | Hugging Face |
+| bert-large-uncased | PyTorch | Hugging Face |
+| clinical-bert | PyTorch | Hugging Face |
+| distilbert-base-uncased | PyTorch | Hugging Face |
+| google/bert_uncased_L-10_H-128_A-2 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-10_H-256_A-4 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-10_H-512_A-8 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-10_H-768_A-12 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-12_H-128_A-2 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-12_H-256_A-4 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-12_H-512_A-8 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-12_H-768_A-12 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-2_H-128_A-2 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-2_H-256_A-4 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-2_H-512_A-8 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-2_H-768_A-12 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-4_H-128_A-2 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-4_H-256_A-4 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-4_H-512_A-8 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-4_H-768_A-12 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-6_H-128_A-2 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-6_H-256_A-4 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-6_H-512_A-8 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-6_H-768_A-12 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-8_H-128_A-2 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-8_H-256_A-4 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-8_H-512_A-8 | TensorFlow | Hugging Face |
+| google/bert_uncased_L-8_H-768_A-12 | TensorFlow | Hugging Face |
+
+## Image Anomaly Detection
+
+| Model name | Framework | Model Hub |
+|------------|-----------|-----------|
+| resnet101 | PyTorch | Torchvision |
+| resnet152 | PyTorch | Torchvision |
+| resnet18 | PyTorch | Torchvision |
+| resnet34 | PyTorch | Torchvision |
+| resnet50 | PyTorch | Torchvision |
+

visual-quality-inspection/transfer-learning/README.md

@@ -0,0 +1,98 @@
+*Note: You may find it easier to read about Intel Transfer Learning tool, follow the Get
+Started guide, and browse the API material from our published documentation site
+https://intelai.github.io/transfer-learning.*
+
+<!-- SkipBadges -->
+
+# Intel® Transfer Learning Tool
+
+Transfer learning workflows use the knowledge learned by a pre-trained model on
+a large dataset to improve the performance of a related problem with a smaller
+dataset.
+
+## What is Intel® Transfer Learning Tool
+
+Intel® Transfer Learning Tool makes it easier and faster for you to
+create transfer learning workflows across a variety of AI use cases. Its
+open-source Python\* library leverages public pretrained model hubs,
+Intel-optimized deep learning frameworks, and your custom dataset to efficiently
+generate new models optimized for Intel hardware.
+
+This project documentation provides information, resource links, and instructions for the Intel
+Transfer Learning Tool as well as Jupyter\* notebooks and examples that
+demonstrate its usage.
+
+**Features:**
+* Supports PyTorch\* and TensorFlow\*
+* Select from over [100 image classification and text classification models](Models.md) from
+  Torchvision, PyTorch Hub, TensorFlow Hub, Keras, and Hugging Face
+* Use your own custom dataset or get started quickly with built-in datasets
+* Automatically create a trainable classification layer customized for your dataset
+* Pre-process your dataset using scaling, cropping, batching, and splitting
+* Use APIs for prediction, evaluation, and benchmarking
+* Export your model for deployment or resume training from checkpoints
+
+**Intel Optimizations:**
+* Boost performance with Intel® Optimization for TensorFlow and Intel® Extension for PyTorch
+* Quantize to INT8 to reduce model size and speed up inference using Intel® Neural Compressor
+* Optimize model for FP32 inference using Intel Neural Compressor
+* Reduce training time with auto-mixed precision for select hardware platforms
+* Further reduce training time with multinode training for PyTorch
+
+## How the Intel Transfer Learning Tool Works
+
+The Intel Transfer Learning Tool lets you train AI models with TensorFlow or
+PyTorch using either no-code command line interface (CLI) commands at a bash
+prompt, or low-code application programming interface (API) calls from a Python
+script.
+
+Use your own dataset or select an existing image or text classification dataset listed in the
+[public datasets](DATASETS.md) documentation. Construct your own CLI or API commands for training, evaluation,
+and optimization using the TensorFlow or PyTorch framework, and finally export
+your saved model optimized for inference on Intel CPUs.
+
+An overview of the Intel Transfer Learning Tool flow is shown in this
+figure:
+
+<p align="center"><b>Intel Transfer Learning Tool Flow</b></p>
+
+<img alt="Intel Transfer Learning Tool Flow" title="Intel Transfer Learing Tool Flow" src="images/TLT-tool_flow.svg" width="600">
+
+## Get Started
+
+The [Get Started](GetStarted.md) guide walks you through the steps to check
+system requirements, install, and then run the tool with a couple of examples
+showing no-code CLI and low-code API approaches. After that, you can check out
+these additional CLI and API [Examples](examples/README.md).
+
+<!-- ExpandGetStarted-Start -->
+As described in the [Get Started](GetStarted.md) guide, once you have a Python
+3.9 environment set up, you do a basic install of the Intel Transfer Learning
+Tool using:
+
+```
+pip install intel-transfer-learning-tool
+```
+
+Then you can use the Transfer Learning Tool CLI interface (tlt) to train a
+TensorFlow image classification model (resnet_v1_50), download and use an
+existing built-in dataset (tf_flowers), and save the trained model to
+`/tmp/output` using this one command:
+
+```
+tlt train --framework tensorflow --model-name resnet_v1_50 --dataset-name tf_flowers \
+   --output-dir /tmp/output --dataset-dir /tmp/data
+```
+
+Use `tlt --help` to see the list of CLI commands.  More detailed help for each
+command can be found using, for example, `tlt train --help`.
+
+<!-- ExpandGetStarted-End -->
+
+## Support
+
+The Intel Transfer Learning Tool team tracks bugs and enhancement requests using
+[GitHub issues](https://github.com/IntelAI/transfer-learning-tool/issues). Before submitting a
+suggestion or bug report, search the existing GitHub issues to see if your issue has already been reported.
+
+See [Legal Information](Legal.md) for Disclaimers, Trademark, and Licensing information.

visual-quality-inspection/transfer-learning/SECURITY.md

@@ -0,0 +1,12 @@
+# Security Policy
+
+## Report a Vulnerability
+
+Please report security issues or vulnerabilities to the [Intel® Security Center].
+
+For more information on how Intel® works to resolve security issues, see
+[Vulnerability Handling Guidelines].
+
+[Intel® Security Center]:https://www.intel.com/content/www/us/en/security-center/default.html
+
+[Vulnerability Handling Guidelines]:https://www.intel.com/content/www/us/en/security-center/vulnerability-handling-guidelines.html

visual-quality-inspection/transfer-learning/_config.yml

@@ -0,0 +1 @@
+theme: jekyll-theme-minimal

visual-quality-inspection/transfer-learning/api.md

@@ -0,0 +1,4 @@
+# API Reference
+
+Low-code Python\* API documentation is automatically generated from the code and
+appears in the Transfer Learning Tool documentation website's [API](https://intelai.github.io/transfer-learning/main/api.html) page.

visual-quality-inspection/transfer-learning/bandit.yaml

@@ -0,0 +1,11 @@
+# FILE: bandit.yaml
+exclude_dirs: [ '.venv', '.tox', 'tlt_test_venv', 'tlt_notebook_venv', 'tests' ]
+skips: [ 'B301', 'B311', 'B403', 'B404' ]
+# B301 - dill usage scoope is different from what's described in CWE-502
+#        dill is mostly used used for dumping/saving models to disk(serialization)
+#        When loading previously saved models from disk(descerialization),
+#        either Keras model loader or PyTorch loader used first to verify the model,
+#        and then create a copy to be passed to dill for loading.
+# B311 - random usage scope is different from what's described in CWE-330
+# B403 - this one is reported everytime 'dill' is imported, so it's actually covered by B301 justification
+# B404 - this one is reported everytime 'subprocess' is imported but this modules is not used as described in CWE-78

visual-quality-inspection/transfer-learning/cli.md

@@ -0,0 +1,4 @@
+# CLI Reference
+
+No-code bash CLI documentation is automatically generated from the code and
+appears in the Transfer Learning Tool documentation website's [CLI](https://intelai.github.io/transfer-learning/main/cli.html) page.

visual-quality-inspection/transfer-learning/docker/Dockerfile

@@ -0,0 +1,143 @@
+#
+# Copyright (c) 2023 Intel Corporation
+#
+# 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,
+# 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.
+#
+# SPDX-License-Identifier: Apache-2.0
+
+ARG IMAGE_NAME=ubuntu
+ARG IMAGE_TAG=22.04
+FROM ${IMAGE_NAME}:${IMAGE_TAG} as base
+
+# TLT base target
+FROM base as tlt-base
+
+ARG PYTHON=python3
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
+    libgl1 \
+    libglib2.0-0 \
+    ${PYTHON} \
+    python3-pip && \
+    apt-get clean autoclean && \
+    apt-get autoremove -y && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN ln -sf "$(which ${PYTHON})" /usr/bin/python
+
+# TLT target for GitHub actions
+FROM tlt-base as tlt-ci
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+ENV LANG C.UTF-8
+ARG PYTHON=python3
+
+RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
+    ${PYTHON}-dev \
+    ${PYTHON}-distutils \
+    build-essential \
+    ca-certificates \
+    make \
+    pandoc && \
+    apt-get clean autoclean && \
+    apt-get autoremove -y && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN ${PYTHON} -m pip install virtualenv
+
+# TLT target for development
+FROM tlt-ci as tlt-devel
+
+COPY . /tmp/intel-transfer-learning
+
+WORKDIR /tmp/intel-transfer-learning
+
+RUN ${PYTHON} setup.py bdist_wheel && \
+    pip install --no-cache-dir -f https://download.pytorch.org/whl/cpu/torch_stable.html dist/*.whl
+
+# TLT target for deployment
+FROM tlt-base as tlt-prod
+
+COPY --from=tlt-devel /usr/local/lib/python3.10/dist-packages /usr/local/lib/python3.10/dist-packages
+COPY --from=tlt-devel /usr/local/bin /usr/local/bin
+
+ENV DATASET_DIR=/tmp/data
+ENV OUTPUT_DIR=/tmp/output
+
+# TLT target for running with MPI
+FROM tlt-prod as tlt-mpi
+
+RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
+    libopenmpi-dev \
+    openmpi-bin \
+    openmpi-common \
+    openssh-client \
+    openssh-server && \
+    apt-get clean autoclean && \
+    apt-get autoremove -y && \
+    rm -rf /var/lib/apt/lists/*
+
+# Create a wrapper for OpenMPI to allow running as root by default
+RUN mv /usr/bin/mpirun /usr/bin/mpirun.real && \
+    echo '#!/bin/bash' > /usr/bin/mpirun && \
+    echo 'mpirun.real --allow-run-as-root "$@"' >> /usr/bin/mpirun && \
+    chmod a+x /usr/bin/mpirun
+
+# Configure OpenMPI to run good defaults:
+RUN echo "btl_tcp_if_exclude = lo,docker0" >> /etc/openmpi/openmpi-mca-params.conf
+
+# Install OpenSSH for MPI to communicate between containers and allow OpenSSH to
+# talk to containers without asking for confirmation
+RUN mkdir -p /var/run/sshd && \
+    cat /etc/ssh/ssh_config | grep -v StrictHostKeyChecking > /etc/ssh/ssh_config.new && \
+    echo "    StrictHostKeyChecking no" >> /etc/ssh/ssh_config.new && \
+    mv /etc/ssh/ssh_config.new /etc/ssh/ssh_config
+
+# TLT target for with MPI, Horovod and all development tools
+FROM tlt-mpi as tlt-dist-devel
+
+ARG HOROVOD_WITH_PYTORCH=1
+ARG HOROVOD_WITHOUT_MXNET=1
+ARG HOROVOD_WITH_TENSORFLOW=1
+ARG HOROVOD_VERSION
+
+ARG PYTHON=python3
+
+RUN apt-get update && apt-get install -y --no-install-recommends --fix-missing \
+    build-essential \
+    cmake \
+    g++ \
+    gcc \
+    git \
+    ${PYTHON}-dev && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+RUN python -m pip install --no-cache-dir horovod==${HOROVOD_VERSION}
+
+ARG ONECCL_VERSION
+ARG ONECCL_URL=https://developer.intel.com/ipex-whl-stable-cpu
+
+RUN python -m pip install --no-cache-dir oneccl_bind_pt==${ONECCL_VERSION} -f ${ONECCL_URL}
+
+COPY . /tmp/intel-transfer-learning
+
+WORKDIR /tmp/intel-transfer-learning
+
+FROM tlt-mpi as tlt-dist-prod
+
+COPY --from=tlt-dist-devel /usr/local/lib/${PYTHON}/dist-packages /usr/local/lib/python3.10/dist-packages
+COPY --from=tlt-dist-devel /usr/local/bin /usr/local/bin

visual-quality-inspection/transfer-learning/docker/README.md

@@ -0,0 +1,73 @@
+# Docker
+Follow these instructions to set up and run our provided Docker image.
+
+## Set Up Docker Engine and Docker Compose
+You'll need to install Docker Engine on your development system. Note that while **Docker Engine** is free to use, **Docker Desktop** may require you to purchase a license. See the [Docker Engine Server installation instructions](https://docs.docker.com/engine/install/#server) for details.
+
+To build and run this workload inside a Docker Container, ensure you have Docker Compose installed on your machine. If you don't have this tool installed, consult the official [Docker Compose installation documentation](https://docs.docker.com/compose/install/linux/#install-the-plugin-manually).
+
+```bash
+DOCKER_CONFIG=${DOCKER_CONFIG:-$HOME/.docker}
+mkdir -p $DOCKER_CONFIG/cli-plugins
+curl -SL https://github.com/docker/compose/releases/download/v2.7.0/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose
+chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose
+docker compose version
+```
+
+## Set Up Docker Image
+Build or Pull the provided docker images.
+
+```bash
+cd docker
+docker compose build
+```
+OR
+```bash
+docker pull intel/ai-tools:tlt-0.5.0
+docker pull intel/ai-tools:tlt-devel-0.5.0
+docker pull intel/ai-tools:tlt-dist-0.5.0
+docker pull intel/ai-tools:tlt-dist-devel-0.5.0
+```
+
+## Use Docker Image
+Utilize the TLT CLI without installation by using the provided docker image and docker compose.
+
+```bash
+docker compose run tlt-prod
+# OR
+docker compose run tlt-prod tlt --help
+```
+
+## Kubernetes
+### 1. Install Helm
+- Install [Helm](https://helm.sh/docs/intro/install/)
+```bash
+curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 && \
+chmod 700 get_helm.sh && \
+./get_helm.sh
+```
+### 2. Setting up Training Operator
+Install the standalone operator from GitHub or use a pre-existing Kubeflow configuration.
+```bash
+kubectl apply -k "github.com/kubeflow/training-operator/manifests/overlays/standalone"
+```
+OR
+```bash
+helm repo add cowboysysop https://cowboysysop.github.io/charts/
+helm install <release name> cowboysysop/training-operator
+```
+### 3. Deploy TLT Distributed Job
+For more customization information, see the chart [README](./docker/chart/README.md)
+```bash
+export NAMESPACE=kubeflow
+helm install --namespace ${NAMESPACE} --set ... tlt-distributed ./docker/chart
+```
+### 4. View 
+To view your workflow progress
+```bash
+kubectl get -o yaml mpijob tf-tlt-distributed -n ${NAMESPACE}
+```
+OR
+```bash
+kubectl logs tf-tlt-distributed-launcher -n ${NAMESPACE}
+```

visual-quality-inspection/transfer-learning/docker/chart/.helmignore

@@ -0,0 +1,23 @@
+# Patterns to ignore when building packages.
+# This supports shell glob matching, relative path matching, and
+# negation (prefixed with !). Only one pattern per line.
+.DS_Store
+# Common VCS dirs
+.git/
+.gitignore
+.bzr/
+.bzrignore
+.hg/
+.hgignore
+.svn/
+# Common backup files
+*.swp
+*.bak
+*.tmp
+*.orig
+*~
+# Various IDEs
+.project
+.idea/
+*.tmproj
+.vscode/

visual-quality-inspection/transfer-learning/docker/chart/Chart.yaml

@@ -0,0 +1,24 @@
+apiVersion: v2
+name: TLT TF Distributed Training
+description: A Helm chart for Kubernetes
+
+# A chart can be either an 'application' or a 'library' chart.
+#
+# Application charts are a collection of templates that can be packaged into versioned archives
+# to be deployed.
+#
+# Library charts provide useful utilities or functions for the chart developer. They're included as
+# a dependency of application charts to inject those utilities and functions into the rendering
+# pipeline. Library charts do not define any templates and therefore cannot be deployed.
+type: application
+
+# This is the chart version. This version number should be incremented each time you make changes
+# to the chart and its templates, including the app version.
+# Versions are expected to follow Semantic Versioning (https://semver.org/)
+version: 0.2.0
+
+# This is the version number of the application being deployed. This version number should be
+# incremented each time you make changes to the application. Versions are not expected to
+# follow Semantic Versioning. They should reflect the version the application is using.
+# It is recommended to use it with quotes.
+appVersion: "1.16.0"

visual-quality-inspection/transfer-learning/docker/chart/README.md

@@ -0,0 +1,31 @@
+# TLT TF Distributed Training
+
+![Version: 0.1.0](https://img.shields.io/badge/Version-0.1.0-informational?style=flat-square) ![Type: application](https://img.shields.io/badge/Type-application-informational?style=flat-square) ![AppVersion: 1.16.0](https://img.shields.io/badge/AppVersion-1.16.0-informational?style=flat-square)
+
+A Helm chart for Kubernetes
+
+## Values
+
+| Key | Type | Default | Description |
+|-----|------|---------|-------------|
+| batchDenom | int | `1` | Batch denominator to be used to divide global batch size |
+| batchSize | int | `128` | Global batch size to distributed data |
+| datasetName | string | `"cifar10"` | Dataset name to load from tfds |
+| epochs | int | `1` | Total epochs to train the model |
+| imageName | string | `"intel/ai-tools"` |  |
+| imageTag | string | `"0.5.0-dist-devel"` |  |
+| metadata.name | string | `"tlt-distributed"` |  |
+| metadata.namespace | string | `"kubeflow"` |  |
+| modelName | string | `"https://tfhub.dev/google/efficientnet/b1/feature-vector/1"` | TF Hub or HuggingFace model URL |
+| pvcName | string | `"tlt"` |  |
+| pvcResources.data | string | `"2Gi"` | Amount of Storage for Dataset |
+| pvcResources.output | string | `"1Gi"` | Amount of Storage for Output Directory |
+| pvcScn | string | `"nil"` | PVC `StorageClassName` |
+| resources.cpu | int | `2` | Number of Compute for Launcher |
+| resources.memory | string | `"4Gi"` | Amount of Memory for Launcher |
+| scaling | string | `"strong"` | For `weak` scaling, `lr` is scaled by a factor of `sqrt(batch_size/batch_denom)` and uses global batch size for all the processes. For `strong` scaling, lr is scaled by world size and divides global batch size by world size |
+| slotsPerWorker | int | `1` | Number of Processes Per Worker |
+| useCase | string | `"image_classification"` | Use case (`image_classification`|`text_classification`) |
+| workerResources.cpu | int | `4` | Number of Compute per Worker |
+| workerResources.memory | string | `"8Gi"` | Amount of Memory per Worker |
+| workers | int | `4` | Number of Workers |

visual-quality-inspection/transfer-learning/docker/chart/templates/mpijob.yaml

@@ -0,0 +1,92 @@
+apiVersion: kubeflow.org/v1
+kind: MPIJob
+metadata:
+  name: tf-{{ .Values.metadata.name }}
+  namespace: {{ .Values.metadata.namespace }}
+spec:
+  slotsPerWorker: {{ .Values.slotsPerWorker }}
+  runPolicy:
+    cleanPodPolicy: None
+  mpiReplicaSpecs:
+    Launcher:
+      replicas: 1
+      template:
+        spec:
+          hostIPC: true
+          containers:
+          - image: "{{ .Values.imageName }}:{{ .Values.imageTag }}"
+            name: mpi
+            command:
+            - horovodrun
+            args:
+            - --verbose
+            - -np
+            - {{ .Values.workers }}
+            - --hostfile
+            - /etc/mpi/hostfile
+            - python
+            - /tmp/intel-transfer-learning/tlt/distributed/tensorflow/run_train_tf.py
+            - --batch_denom
+            - "{{ .Values.batchDenom }}"
+            - --batch_size
+            - "{{ .Values.batchSize }}"
+            - --dataset-dir
+            - /tmp/data
+            - --dataset-name
+            - {{ .Values.datasetName }}
+            - --epochs
+            - "{{ .Values.epochs }}"
+            - --model-name
+            - {{ .Values.modelName }}
+            - --output-dir
+            - /tmp/output
+            - --scaling
+            - "{{ .Values.scaling }}"
+            - --shuffle
+            - --use-case
+            - {{ .Values.useCase }}
+            resources:
+              limits:
+                cpu: {{ .Values.resources.cpu }}
+                memory: {{ .Values.resources.memory }}
+            volumeMounts:
+            - name: dataset-dir
+              mountPath: /tmp/data
+            - name: output-dir
+              mountPath: /tmp/output
+          volumes:
+          - name: dshm
+            emptyDir:
+              medium: Memory
+          - name: dataset-dir
+            persistentVolumeClaim:
+              claimName: "{{ .Values.pvcName }}-data"
+          - name: output-dir
+            persistentVolumeClaim:
+              claimName: "{{ .Values.pvcName }}-output"
+    Worker:
+      replicas: {{ .Values.workers }}
+      template:
+        spec:
+          containers:
+          - image: "{{ .Values.imageName }}:{{ .Values.imageTag }}" 
+            name: mpi
+            resources:
+              limits:
+                cpu: {{ .Values.workerResources.cpu }}
+                memory: {{ .Values.workerResources.memory }}
+            volumeMounts:
+            - name: dataset-dir
+              mountPath: /tmp/data
+            - name: output-dir
+              mountPath: /tmp/output
+          volumes:
+          - name: dshm
+            emptyDir:
+              medium: Memory
+          - name: dataset-dir
+            persistentVolumeClaim:
+              claimName: "{{ .Values.pvcName }}-data"
+          - name: output-dir
+            persistentVolumeClaim:
+              claimName: "{{ .Values.pvcName }}-output"

visual-quality-inspection/transfer-learning/docker/chart/templates/pvc.yaml

@@ -0,0 +1,25 @@
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: {{ .Values.pvcName }}-data
+  namespace: {{ .Values.metadata.namespace }}
+spec:
+  storageClassName: {{ .Values.pvcScn }}
+  accessModes:
+    - "ReadWriteOnce"
+  resources: 
+    requests:
+      storage: {{ .Values.pvcResources.data }}
+---
+apiVersion: v1
+kind: PersistentVolumeClaim
+metadata:
+  name: {{ .Values.pvcName }}-output
+  namespace: {{ .Values.metadata.namespace }}
+spec:
+  storageClassName: {{ .Values.pvcScn }}
+  accessModes:
+    - "ReadWriteOnce"
+  resources: 
+    requests:
+      storage: {{ .Values.pvcResources.output }}

visual-quality-inspection/transfer-learning/docker/chart/values.yaml

@@ -0,0 +1,28 @@
+metadata:
+  name: tlt-distributed
+  namespace: kubeflow
+
+imageName: intel/ai-tools
+imageTag: 0.5.0-dist-devel
+
+batchDenom: 1
+batchSize: 128
+datasetName: cifar10
+epochs: 1
+modelName: https://tfhub.dev/google/efficientnet/b1/feature-vector/1
+scaling: strong
+slotsPerWorker: 1
+useCase: image_classification
+workers: 4
+
+pvcName: tlt
+pvcScn: nil
+pvcResources: 
+  data: 2Gi
+  output: 1Gi
+resources:
+  cpu: 2
+  memory: 4Gi
+workerResources:
+  cpu: 4
+  memory: 8Gi

visual-quality-inspection/transfer-learning/docker/docker-compose.yml

@@ -0,0 +1,54 @@
+version: "3"
+services:
+  tlt-devel:
+    build:
+      args:
+        http_proxy: ${http_proxy}
+        https_proxy: ${https_proxy}
+        no_proxy: ""
+        IMAGE_NAME: ubuntu
+        IMAGE_TAG: 22.04
+        PYTHON: python3.10 # Version must be specified for prod
+      context: ../
+      dockerfile: ./docker/Dockerfile
+      target: tlt-devel
+    image: intel/ai-tools:tlt-devel-latest
+    pull_policy: always
+  tlt-prod:
+    extends:
+      service: tlt-devel
+    build:
+      args:
+        DATASET_DIR: /tmp/data
+        OUTPUT_DIR: /tmp/output
+      target: tlt-prod
+    image: intel/ai-tools:tlt-prod-latest
+    volumes:
+      - /${DATASET_DIR:-$PWD/../data}:/tmp/data
+      - /${OUTPUT_DIR:-$PWD/../output}:/tmp/output
+  tlt-dist-devel:
+    extends:
+      service: tlt-prod
+    build:
+      args:
+        HOROVOD_VERSION: 0.28.0
+        ONECCL_VERSION: 2.0.0
+        ONECCL_URL: https://developer.intel.com/ipex-whl-stable-cpu
+      target: tlt-dist-devel
+    image: intel/ai-tools:tlt-dist-devel-latest
+  tlt-dist-prod:
+    extends:
+      service: tlt-dist-devel
+    build:
+      target: tlt-dist-prod
+    command: |
+      tlt train -f tensorflow
+      --dataset-name cifar10
+      --model-name resnet_v1_50
+      --dataset-dir /tmp/data
+      --output-dir /tmp/output
+    environment:
+      http_proxy: ${http_proxy}
+      https_proxy: ${https_proxy}
+      no_proxy: ${no_proxy}
+    image: intel/ai-tools:tlt-dist-prod-latest

visual-quality-inspection/transfer-learning/docs/.gitignore

@@ -0,0 +1 @@
+markdown

visual-quality-inspection/transfer-learning/docs/DATASETS.rst

@@ -0,0 +1,4 @@
+:orphan:
+
+.. include:: ../DATASETS.md
+   :parser: myst_parser.sphinx_

visual-quality-inspection/transfer-learning/docs/GetStarted.rst

@@ -0,0 +1,2 @@
+.. include:: ../GetStarted.md
+   :parser: myst_parser.sphinx_

visual-quality-inspection/transfer-learning/docs/Legal.rst

@@ -0,0 +1,2 @@
+.. include:: ../Legal.md
+   :parser: myst_parser.sphinx_

visual-quality-inspection/transfer-learning/docs/Makefile

@@ -0,0 +1,43 @@
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# 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,
+# 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.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+LISTEN_IP ?= 127.0.0.1
+LISTEN_PORT ?= 9999
+serve:
+	@python -m http.server --directory ./_build/html ${LISTEN_PORT} --bind ${LISTEN_IP}

visual-quality-inspection/transfer-learning/docs/Models.rst

@@ -0,0 +1,2 @@
+.. include:: ../Models.md
+   :parser: myst_parser.sphinx_

visual-quality-inspection/transfer-learning/docs/README.md

@@ -0,0 +1,58 @@
+# Building Documentation
+
+## Sphinx Documentation
+
+Install `tlt` and its dependencies for developers as described the [Get Started](/GetStarted) guide.
+```bash
+# Run these commands from root of the project
+python3 -m virtualenv tlt_dev_venv
+source tlt_dev_venv/bin/activate
+python -m pip install --editable .
+```
+
+Install Pandoc, Sphinx and a few other tools required to build docs
+```bash
+sudo apt-get install pandoc
+pip install -r docs/requirements-docs.txt
+```
+
+Navigate to the `docs` directory and run the doctests to ensure all tests pass:
+```bash
+# run this command from within docs directory
+make doctest
+```
+
+This should produce output similiar to:
+```bash
+Doctest summary
+===============
+    6 tests
+    0 failures in tests
+    0 failures in setup code
+    0 failures in cleanup code
+build succeeded.
+```
+
+Finally generate the html docs (from within `docs` directory):
+```bash
+make clean html
+```
+
+The output HTML files will be located in `transfer-learning/docs/_build/html`.
+
+To start a local HTTP server and view the docs locally, try:
+```bash
+make serve
+Serving HTTP on 127.0.1.1 port 9999 (http://127.0.1.1:9999/) ...
+```
+
+If you need to view the docs from another machine, please try either port forwarding or
+provide appropriate values for `LISTEN_IP/LISTEN_PORT` arguments.
+For example:
+```bash
+LISTEN_IP=0.0.0.0 make serve
+Serving HTTP on 0.0.0.0 port 9999 (http://0.0.0.0:9999/) ...
+```
+
+runs the docs server on the host while listening to all hosts.
+Now you can navigate to `HOSTNAME:9999` to view the docs.

visual-quality-inspection/transfer-learning/docs/_static/tlt-custom.css

@@ -0,0 +1,43 @@
+/* allow the page to use more of the window width */
+.wy-nav-content {
+       max-width: 1100px;
+   }
+
+/* allow text wrapping in a table to avoid table horizontal scrolling */
+.wy-table-responsive table td, .wy-table-responsive table th {
+        white-space: normal !important;
+    }
+
+/* center all images */
+.rst-content img {
+  margin-left: auto;
+  margin-right: auto;
+  display: block;
+}
+
+/* add an underline to title headings and wrap long API headings
+ * Note: we use JavaScript to add a ​ after the dot
+ * in really long H1 titles created automatically from the code */
+.rst-content h1,h2,h3,h4,h5 {
+    text-decoration: underline;
+    word-wrap: break-word;
+}
+
+/* add link color to module xref generated by autodoc */
+.rst-content a.internal code.xref span.pre {
+    color: #2980b9;
+}
+
+/* change red text color to dark gray in code literals */
+.rst-content code.literal, .rst-content tt.literal {
+    color: #404040;
+}
+
+/* change background color of search area/site title to increase contrast */
+.wy-side-nav-search {
+    background-color: #2f71b2;
+}
+/* change href link color to increase contrast */
+a {
+    color: #2f71b2;
+}

visual-quality-inspection/transfer-learning/docs/_static/tlt-custom.js

@@ -0,0 +1,19 @@
+/* Extra tlt-specific javascript */
+
+$(document).ready(function(){
+
+   /* open external links in a new tab */
+   $('a[class*=external]').attr({target: '_blank', rel: 'noopener'});
+
+   /* add word break points (zero-width space) after a period in really long titles */
+   $('h1').html(function(index, html){
+     return html.replace(/\./g, '.\u200B');
+   });
+
+   /* copy image alt tags as title so hover text tool tip by browser
+    * (Looks like the myst-parser isn't passing the title tag through to Sphinx,
+    * but is passing the alt tag) */
+   $("img[alt]").each(function(){
+       $(this).attr('title', $(this).attr('alt'));
+   });
+});

visual-quality-inspection/transfer-learning/docs/_templates/footer.html

@@ -0,0 +1,5 @@
+{% extends '!footer.html' %}
+{% block extrafooter %}
+*Other names and brands may be claimed as the property of others.
+<a href="http://www.intel.com/content/www/us/en/legal/trademarks.html">Trademarks</a>
+{% endblock %}

visual-quality-inspection/transfer-learning/docs/api.rst

@@ -0,0 +1,132 @@
+API Reference
+=============
+
+Datasets
+--------
+
+.. currentmodule:: tlt.datasets
+
+The simplest way to create datasets is with the dataset factory methods :meth:`load_dataset`, for using a
+custom dataset, and :meth:`get_dataset`, for downloading and using a third-party dataset from a catalog such as TensorFlow
+Datasets or Torchvision.
+
+Factory Methods
+***************
+
+.. automodule:: tlt.datasets.dataset_factory
+   :members: load_dataset, get_dataset
+
+Class Reference
+***************
+
+Image Classification
+^^^^^^^^^^^^^^^^^^^^
+
+.. currentmodule:: tlt.datasets.image_classification
+
+.. autosummary::
+   :toctree: _autosummary
+   :nosignatures:
+
+    tfds_image_classification_dataset.TFDSImageClassificationDataset
+    torchvision_image_classification_dataset.TorchvisionImageClassificationDataset
+    tf_custom_image_classification_dataset.TFCustomImageClassificationDataset
+    pytorch_custom_image_classification_dataset.PyTorchCustomImageClassificationDataset
+    image_classification_dataset.ImageClassificationDataset
+
+Text Classification
+^^^^^^^^^^^^^^^^^^^
+
+.. currentmodule:: tlt.datasets.text_classification
+
+.. autosummary::
+   :toctree: _autosummary
+   :nosignatures:
+
+    tfds_text_classification_dataset.TFDSTextClassificationDataset
+    hf_text_classification_dataset.HFTextClassificationDataset
+    tf_custom_text_classification_dataset.TFCustomTextClassificationDataset
+    hf_custom_text_classification_dataset.HFCustomTextClassificationDataset
+    text_classification_dataset.TextClassificationDataset
+
+Base Classes
+^^^^^^^^^^^^
+
+.. note:: Users should rarely need to interact directly with these.
+
+.. currentmodule:: tlt.datasets
+
+.. autosummary::
+   :toctree: _autosummary
+   :nosignatures:
+
+    pytorch_dataset.PyTorchDataset
+    tf_dataset.TFDataset
+    hf_dataset.HFDataset
+    dataset.BaseDataset
+
+Models
+------
+
+.. currentmodule:: tlt.models
+
+Discover and work with available models by using model factory methods. The :meth:`get_model` function will download
+third-party models, while the :meth:`load_model` function will load a custom model, from either a path location or a
+model object in memory. The model discovery and inspection methods are :meth:`get_supported_models` and
+:meth:`print_supported_models`.
+
+Factory Methods
+***************
+
+.. automodule:: tlt.models.model_factory
+   :members: get_model, load_model, get_supported_models, print_supported_models
+
+Class Reference
+***************
+
+Image Classification
+^^^^^^^^^^^^^^^^^^^^
+
+.. currentmodule:: tlt.models.image_classification
+
+.. autosummary::
+  :toctree: _autosummary
+  :nosignatures:
+
+   tfhub_image_classification_model.TFHubImageClassificationModel
+   tf_image_classification_model.TFImageClassificationModel
+   keras_image_classification_model.KerasImageClassificationModel
+   torchvision_image_classification_model.TorchvisionImageClassificationModel
+   pytorch_image_classification_model.PyTorchImageClassificationModel
+   pytorch_hub_image_classification_model.PyTorchHubImageClassificationModel
+   image_classification_model.ImageClassificationModel
+
+Text Classification
+^^^^^^^^^^^^^^^^^^^
+
+.. currentmodule:: tlt.models.text_classification
+
+.. autosummary::
+  :toctree: _autosummary
+  :nosignatures:
+
+   tf_text_classification_model.TFTextClassificationModel
+   pytorch_hf_text_classification_model.PyTorchHFTextClassificationModel
+   tf_hf_text_classification_model.TFHFTextClassificationModel
+   text_classification_model.TextClassificationModel
+
+Base Classes
+^^^^^^^^^^^^
+
+.. note:: Users should rarely need to interact directly with these.
+
+.. currentmodule:: tlt.models
+
+.. autosummary::
+   :toctree: _autosummary
+   :nosignatures:
+
+    pytorch_model.PyTorchModel
+    tf_model.TFModel
+    hf_model.HFModel
+    model.BaseModel

visual-quality-inspection/transfer-learning/docs/cli.rst

@@ -0,0 +1,7 @@
+CLI Reference
+=============
+
+.. click:: tlt.tools.cli.main:cli_group
+   :prog: tlt
+   :nested: full
+

visual-quality-inspection/transfer-learning/docs/conf.py

@@ -0,0 +1,111 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright (c) 2022 Intel Corporation
+#
+# 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,
+# 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.
+#
+# SPDX-License-Identifier: Apache-2.0
+#
+
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+import shutil
+import glob
+sys.path.insert(0, os.path.abspath('../..'))
+sys.setrecursionlimit(1500)
+import sphinx_rtd_theme
+from datetime import datetime
+
+# -- Project information -----------------------------------------------------
+
+project = 'Intel® Transfer Learning Tool'
+author = 'Intel Corporation'
+copyright = '2022-' + str(datetime.now().year) + u', ' + author
+
+# The full version, including alpha/beta/rc tags
+release = '0.2.0'
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'myst_parser',
+    'nbsphinx',
+    'nbsphinx_link',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'sphinx.ext.doctest',
+    'sphinx.ext.intersphinx',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.todo',
+    'sphinx.ext.viewcode',
+    'sphinx_click',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '.venv3', 'README.md']
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_rtd_theme'
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+html_last_updated_fmt = '%b %d, %Y'
+html_show_sphinx = False
+html_favicon = 'images/favicon-intel-32x32.png'
+
+html_static_path = ['_static']
+templates_path = ['_templates']
+
+def setup(app):
+   app.add_css_file("tlt-custom.css")
+   app.add_js_file("tlt-custom.js")
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+autodoc_member_order = 'bysource'
+nbsphinx_execute = 'never'
+nbsphinx_prolog = """
+:orphan:
+
+"""
+myst_heading_anchors = 2
+suppress_warnings = ["myst.xref_missing", "myst.header"]
+
+# ask the myst parser to process <img> tags so Sphinx can handle the properly
+myst_enable_extensions = [ "html_image" ]

visual-quality-inspection/transfer-learning/docs/distributed.rst

@@ -0,0 +1,4 @@
+:orphan:
+
+.. include:: ../tlt/distributed/README.md
+   :parser: myst_parser.sphinx_

visual-quality-inspection/transfer-learning/docs/docbuild.rst

@@ -0,0 +1,4 @@
+:orphan:
+
+.. include:: README.md
+   :parser: myst_parser.sphinx_