6 implement baseline models

.github/workflows/style.yml

@@ -0,0 +1,43 @@
+name: Style check
+
+on:
+  push:
+    branches:
+      - main
+      - master
+
+  pull_request:
+    branches:
+      - main
+      - master
+
+jobs:
+  flake8_py3:
+    permissions: write-all
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+
+      - name: Install flake8 and plugins
+        run: |
+          pip install flake8 flake8-docstrings flake8-annotations
+
+      - name: Configure Flake8
+        run: |
+          echo "[flake8]" > .flake8
+          echo "extend-ignore = E402,W504" >> .flake8
+          echo "exclude = .github,autoop/tests,tests/" >> .flake8
+          # exclude A101, A102, D100 and everything that starts with D2 and D4
+          echo "ignore = ANN101,ANN102,D100,D2,D4,ANN002,ANN003" >> .flake8
+
+      - name: Run flake8
+        uses: suo/flake8-github-action@releases/v1
+        with:
+          checkName: "flake8_py3"
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -1,6 +1,20 @@
-Pipfile.lock
 .idea
+.DS_Store
+.env
+.vscode/
+Pipfile.lock
 __pycache__/
 .DS_Store
 venv/
-.env
+project_name/data/full_data/
+project_name/data/subset_data/
+project_name/data/val/
+project_name/data/train/
+project_name/data/train_subset/
+project_name/data/__pycache__/
+project_name/data/val_subset/
+project_name/data/train_subset.7z
+project_name/data/val_subset.7z
+cnn_best.pth
+project_name/trained_linear_model.pkl
+/zoedepth/

FastAPI.py

@@ -0,0 +1,89 @@
+import io
+import torch
+
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import StreamingResponse
+from PIL import Image
+from project_name.models.cnn import CNNBackbone
+from project_name.models.Preprocessing_class import Preprocessing
+from numpy import array
+
+
+app = FastAPI(title="Depth Prediction API",
+              description="Uploads an image and "
+                          "returns a predicted depth map.")
+
+# Setup
+MODEL_PATH = "cnn_best.pth"
+model = CNNBackbone(pretrained=False)
+model.load_state_dict(torch.load(MODEL_PATH, map_location="cpu"))
+model.eval()
+preprocessor = Preprocessing(tile_size=(256, 256))
+
+
+def process_image(file_bytes: bytes,
+                  model: torch.nn.Module,
+                  preprocessor: Preprocessing) -> io.BytesIO:
+    """Function to process image
+
+    Args:
+        file_bytes (bytes): file in byte representation
+        model (torch.nn.Module): neural network model
+        preprocessor (Preprocessing): preprocessing class
+
+    Returns:
+        bytes: depth image in byte format
+    """
+    img_array = preprocessor.load_image(io.BytesIO(file_bytes))
+    tiles = preprocessor.tile_with_padding(img_array)
+    depth_tiles = []
+
+    for tile in tiles:
+        input_tensor = preprocessor.to_tensor(tile).unsqueeze(0)
+        with torch.no_grad():
+            output = model(input_tensor)
+        depth_tiles.append(output.squeeze().cpu().numpy())
+
+    depth_map = preprocessor.reconstruct_depth(array(depth_tiles))
+    depth_rgb = preprocessor.depth_to_rgb(depth_map, invert=True)
+
+    result_image = Image.fromarray(depth_rgb)
+    byte_io = io.BytesIO()
+    result_image.save(byte_io, format="PNG")
+    byte_io.seek(0)
+    return byte_io
+
+
+@app.post("/predict_depth/", summary="Predict depth from image")
+async def predict_depth(file: UploadFile = File(...)) -> StreamingResponse:
+    """Function to generate depth from image given
+
+    Args:
+        file (UploadFile, optional): image file that is uploaded.
+        Defaults to File(...).
+
+    Raises:
+        HTTPException: image format is not supported
+        HTTPException: when there is an error processing the image
+
+    Returns:
+        StreamingResponse: response with processed image
+    """
+    if not file.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="Invalid image format")
+    try:
+        contents = await file.read()
+        image_bytes = process_image(contents, model, preprocessor)
+        return StreamingResponse(image_bytes, media_type="image/png")
+    except Exception:
+        raise HTTPException(status_code=500, detail="Error processing image.")
+
+
+@app.get("/", summary="Health check")
+def read_root() -> dict:
+    """Health check function
+
+    Returns:
+        dict: dict of the status
+    """
+    return {"status": "healthy"}

README.md

@@ -1,77 +1,68 @@
-# Applied ML Template 🛠️
+# Applied ML Team 16
 
-**Welcome to Applied Machine Learning!** This template is designed to streamline the development process and boost the quality of your code.
+Welcome to our repo for our project for Applied machine learning project.
 
-Before getting started with your projects, we encourage you to carefully read the sections below and familiarise yourselves with the proposed tools.
+Our project is to train an model to approximate depth data from RGB images in a tilling pattern.
 
 ## Prerequisites
 Make sure you have the following software and tools installed:
 
-- **PyCharm**: We recommend using PyCharm as your IDE, since it offers a highly tailored experience for Python development. You can get a free student license [here](https://www.jetbrains.com/community/education/#students/).
-
-- **Pipenv**: Pipenv is used for dependency management. This tools enables users to easily create and manage virtual environments. To install Pipenv, use the following command:
-    ```bash
-    $ pip install --user pipenv
-    ```
-    For detailed installation instructions, [click here](https://pipenv.pypa.io/en/latest/installation.html).
-
-- **Git LFS**: Instead of committing large files to your repository, you should store and manage them using Git LFS. For installation information, [click here](https://github.com/git-lfs/git-lfs?utm_source=gitlfs_site&utm_medium=installation_link&utm_campaign=gitlfs#installing).
+- **Conda**: Conda is used for dependency management. This tools is a standard for a lot of machine learning libraries and has support for pip packages as well. "conda install --yes --file conda_requirements.txt"
+- **python 3.11**: a tested version of python that this repository works in.
 
 ## Getting Started
-### Setting up your own repository
-1. Fork this repository.
-2. Clone your fork locally.
-3. Configure a remote pointing to the upstream repository to sync changes between your fork and the original repository.
-   ```bash
-   git remote add upstream https://github.com/ivopascal/Applied-ML-Template
-   ```
-   **Don't skip this step.** We might update the original repository, so you should be able to easily pull our changes.
-
-   To update your forked repo follow these steps:
-   1. `git fetch upstream`
-   2. `git rebase upstream/main`
-   3. `git push origin main`
-
-      Sometimes you may need to use `git push --force origin main`. Only use this flag the first time you push after you rebased, and be careful as you might overwrite your teammates' changes.
-### Git LFS
-1. Set it up for your user account (only once, not each time you want to use it).
-    ```bash
-    git lfs install
-    ```
-2. Select the files that Git LFS should manage. To track all files of a certain type, you can use a wildcard as in the command below.
-    ```bash
-   git lfs track "*.psd"
-    ```
-3. Add _.gitattributes_ to the staging area.
-    ```bash
-    git add .gitattributes
-    ```
-That's all, you can commit and push as always. The tracked files will be automatically stored with Git LFS.
-
-### Pipenv
-This tool is incredibly easy to use. Let's **install** our first package, which you will all need in your projects.
-
+### general
+1. Clone this repository.
+2. Get the training and validation data and place in the data folder in the project_name folder.
+    * Data used to train our model
+        1. download from google drive. [drive](https://drive.google.com/drive/folders/1k_WsVOjaULgb3N2JebxjTVqJjVsw85dP)
+    * original data
+        1. download from original data. [website](https://diode-dataset.org/)
+        2. run subset_maker.py with amount sample and "val or "train" to get workable data folder.
+3. Create conda environment with python 3.11
+4. Install the packages from "conda_requirements.txt" using the command under.
 ```bash
-pipenv install pre-commit
+conda install --yes --file conda_requirements.txt
 ```
+5. Install pytorch using the command from their [website](https://pytorch.org/).
+6. Download the model from the release page and place it in the root folder of the repository
 
-After running this command, you will notice that two files were created, namely, _Pipfile_ and _Pipfile.lock_. _Pipfile_ is the configuration file that specifies all the dependencies in your virtual environment.
+### Train and validate
+Both the commands below should be ran in the root folder of the repository
 
-To **uninstall** a package, you can run the command:
+* #### Train
 ```bash
-pipenv uninstall <package-name>
+python main.py --epochs (amount epoch) --batch-size (batch size) --lr (learning rate) --freeze (amount before freeze) cnn
 ```
 
-To **activate** the virtual environment, run `pipenv shell`. You can now use the environment as you wish. To **deactivate** the environment run the command `exit`.
+* #### Validate
+```bash
+python main.py evaluate (model file name with extension) --batch-size (batch size)
+```
 
-If you **already have access to a Pipfile**, you can install the dependencies using `pipenv install`.
+### API
+1. Run the following command in the root directory of the repository.
+```bash
+uvicorn FastAPI:app --reload
+```
+2. In a new tab in the terminal, run the following, replacing image_path.jpg with the path and file name for the input image:
+```
+curl -X 'POST' \
+  'http://127.0.0.1:8000/predict_depth/' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: multipart/form-data' \
+  -F 'file=@image_path.jpg;type=image/jpeg'
+-- output output.png
+```
 
-For a comprehensive list of commands, consult the [official documentation](https://pipenv.pypa.io/en/latest/cli.html).
+### Streamlit
+1. Run the following command in the root directory of the repository.
+```bash
+streamlit run streamlit_main.py" when in the main folder of repository.
+```
+2. Follow instruction on the web demo.
 
 ### Unit testing
-You are expected to test your code using unit testing, which is a technique where small individual components of your code are tested in isolation.
-
-An **example** is given in _tests/test_main.py_, which uses the standard _unittest_ Python module to test whether the function _hello_world_ from _main.py_ works as expected.
 
 To run all the tests developed using _unittest_, simply use:
 ```bash
@@ -82,45 +73,36 @@ If you wish to see additional details, run it in verbose mode:
 python -m unittest discover -v tests
 ```
 
-### Pre-commit
-Another good coding practice is using pre-commit hooks. This is used to inspect the code before committing to ensure it matches your standards.
-
-In this course, we will be using two hooks (already configured in _.pre-commit-config.yaml_):
-- Unit testing
-- Flake8 (checks your code for errors, styling issues and complexity)
-
-Since we have already configured the hooks, all you need to do is run:
+repository map:
 ```bash
-pre-commit install
-```
-Now `pre-commit` will automatically run whenever you want to commit something to the repository.
-
-## Get Coding
-You are now ready to start working on your projects.
-
-We recommend following the same folder structure as in the original repository. This will make it easier for you to have cleaner and consistent code, and easier for us to follow your progress and help you.
-
-Your repository should look something like this:
-```bash
-├───data  # Stores .csv
-├───models  # Stores .pkl
-├───notebooks  # Contains experimental .ipynbs
+├───.github
+│   └────workflows
+│        └──── style.yml
 ├───project_name
-│   ├───data  # For data processing, not storing .csv
-│   ├───features
-│   └───models  # For model creation, not storing .pkl
-├───reports
+│   ├───data
+│   │   ├───val_subset # only there when validating
+│   │   ├───train_subset # only there when training
+│   │   ├───data_loader.py
+│   │   ├───data_test.py
+│   │   ├───path_grapper.py
+│   │   └───subset_maker.py
+│   ├───models
+│   │   ├───cnn.py
+│   │   └───Preprocessing_class.py
+│   └───Training
+│       ├───Evaluation
+│       │   ├───evaluate.py
+│       │   └───validation.py
+│       └───model_trainer.py
 ├───tests
 │   ├───data
 │   ├───features
 │   └───models
 ├───.gitignore
-├───.pre-commit-config.yaml
+├───cnn_best.pth # download from release page
 ├───main.py
-├───train_model.py
-├───Pipfile
-├───Pipfile.lock
-├───README.md
+├───streamlit_main.py
+├───FastAPI.py
+├───conda_requirements.txt
+└───README.md
 ```
-
-**Good luck and happy coding! 🚀**