Delete visual-quality-inspection

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

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

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

@@ -1,14 +0,0 @@
-<!-- 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

@@ -1,50 +0,0 @@
-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

@@ -1,34 +0,0 @@
-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

@@ -1,41 +0,0 @@
-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

@@ -1,25 +0,0 @@
-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

@@ -1,25 +0,0 @@
-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

@@ -1,41 +0,0 @@
-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

@@ -1,30 +0,0 @@
-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

@@ -1,30 +0,0 @@
-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

@@ -1,20 +0,0 @@
-_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

@@ -1,59 +0,0 @@
-# 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

@@ -1,30 +0,0 @@
-# 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

@@ -1,263 +0,0 @@
-# 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

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

@@ -1,34 +0,0 @@
-# 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

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

visual-quality-inspection/transfer-learning/Makefile

@@ -1,112 +0,0 @@
-#
-# 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

@@ -1,178 +0,0 @@
-# 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

@@ -1,98 +0,0 @@
-*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

@@ -1,12 +0,0 @@
-# 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

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

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

@@ -1,4 +0,0 @@
-# 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

@@ -1,11 +0,0 @@
-# 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

@@ -1,4 +0,0 @@
-# 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

@@ -1,143 +0,0 @@
-#
-# 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

@@ -1,73 +0,0 @@
-# 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

@@ -1,23 +0,0 @@
-# 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

@@ -1,24 +0,0 @@
-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

@@ -1,31 +0,0 @@
-# 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

@@ -1,92 +0,0 @@
-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

@@ -1,25 +0,0 @@
-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

@@ -1,28 +0,0 @@
-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

@@ -1,54 +0,0 @@
-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

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

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

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

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

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

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

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

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

@@ -1,43 +0,0 @@
-#
-# 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

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

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

@@ -1,58 +0,0 @@
-# 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

@@ -1,43 +0,0 @@
-/* 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

@@ -1,19 +0,0 @@
-/* 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

@@ -1,5 +0,0 @@
-{% 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

@@ -1,132 +0,0 @@
-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

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

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

@@ -1,111 +0,0 @@
-#!/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

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

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

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

visual-quality-inspection/transfer-learning/docs/examples/README.rst

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