diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000..379ec66 --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,57 @@ +name: CI + +on: + pull_request: + push: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ci-${{ github.ref }} + cancel-in-progress: true + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v7 + - run: pipx install poetry + - uses: actions/setup-python@v7 + with: + python-version: "3.14" + cache: poetry + - run: poetry install --with dev + - run: poetry run ruff check . + - run: poetry run ruff format --check . + - run: poetry run mypy src + + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.12", "3.13", "3.14"] + steps: + - uses: actions/checkout@v7 + - run: pipx install poetry + - uses: actions/setup-python@v7 + with: + python-version: ${{ matrix.python-version }} + cache: poetry + - run: poetry install --with dev + - run: poetry run pytest -q + + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v7 + - run: pipx install poetry + - uses: actions/setup-python@v7 + with: + python-version: "3.14" + cache: poetry + - run: poetry install --with docs + - run: poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md + - run: poetry run mkdocs build --strict diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..d2cb234 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,36 @@ +name: Docs + +on: + push: + branches: [main] + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + deploy: + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - uses: actions/checkout@v7 + - run: pipx install poetry + - uses: actions/setup-python@v7 + with: + python-version: "3.14" + cache: poetry + - run: poetry install --with docs + - run: poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md + - run: poetry run mkdocs build --strict + - uses: actions/upload-pages-artifact@v5 + with: + path: site + - id: deployment + uses: actions/deploy-pages@v5 diff --git a/.github/workflows/pr-title.yml b/.github/workflows/pr-title.yml new file mode 100644 index 0000000..e050db2 --- /dev/null +++ b/.github/workflows/pr-title.yml @@ -0,0 +1,16 @@ +name: PR title + +on: + pull_request: + types: [opened, edited, synchronize, reopened] + +permissions: + pull-requests: read + +jobs: + conventional-title: + runs-on: ubuntu-latest + steps: + - uses: amannn/action-semantic-pull-request@v6 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml new file mode 100644 index 0000000..89d0785 --- /dev/null +++ b/.github/workflows/release.yml @@ -0,0 +1,39 @@ +name: Release + +on: + push: + branches: [main] + +permissions: + contents: write + id-token: write # needed only for PyPI trusted publishing + +concurrency: + group: release + cancel-in-progress: false + +jobs: + release: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v7 + with: + fetch-depth: 0 + ref: ${{ github.ref_name }} + + - name: Semantic release + id: release + uses: python-semantic-release/python-semantic-release@v10 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + + - name: Attach distributions to the GitHub release + if: steps.release.outputs.released == 'true' + uses: python-semantic-release/publish-action@v10 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + tag: ${{ steps.release.outputs.tag }} + + - name: Publish to PyPI (enable with repo variable PUBLISH_PYPI=true) + if: steps.release.outputs.released == 'true' && vars.PUBLISH_PYPI == 'true' + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/.gitignore b/.gitignore index 3ec4bec..e288f3e 100644 --- a/.gitignore +++ b/.gitignore @@ -102,4 +102,16 @@ venv.bak/ # mypy .mypy_cache/ -./dataset/TRAIN \ No newline at end of file +./dataset/TRAIN +# Project +/data/* +!/data/README.md +runs/ +site/ +docs/reference/cli.md +.venv/ +.ruff_cache/ +.mypy_cache/ +.pytest_cache/ +segmentation/ +metrics.json diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..4f7c2ee --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,3 @@ +# CHANGELOG + + diff --git a/CNN/README.md b/CNN/README.md deleted file mode 100644 index 8e8818e..0000000 --- a/CNN/README.md +++ /dev/null @@ -1,2 +0,0 @@ -Wiki -# includeamin diff --git a/CNN/datasets/DatasetLoader.py b/CNN/datasets/DatasetLoader.py deleted file mode 100644 index 296ae5c..0000000 --- a/CNN/datasets/DatasetLoader.py +++ /dev/null @@ -1,33 +0,0 @@ -import numpy as np -import cv2 -import os - - -# datasets_name/class/image.jpg -class DatasetLoader: - def __init__(self, preprocessors=None): - self.preprocessors = preprocessors - if self.preprocessors is None: - self.preprocessors = [] - - def load(self, image_paths, verbose=-1): - data = [] - labels = [] - for (i, image_paths) in enumerate(image_paths): - # try: - # print(image_paths) - # raise Exception(image_paths) - image = cv2.imread(image_paths) - label = image_paths.split(os.path.sep)[-2] - if self.preprocessors is not None: - for p in self.preprocessors: - image = p.preprocess(image) - data.append(image) - labels.append(label) - if verbose > 0 and i > 0 and (i + 1) % verbose == 0: - print("[INFO] processed {}/{}".format(i + 1, len(image_paths))) - # - # except Exception as identifier: - # print(identifier) - - return np.array(data), np.array(labels) diff --git a/CNN/datasets/TEST/EOSINOPHIL/_0_1845.jpeg b/CNN/datasets/TEST/EOSINOPHIL/_0_1845.jpeg deleted file mode 100755 index ea53997..0000000 Binary files a/CNN/datasets/TEST/EOSINOPHIL/_0_1845.jpeg and /dev/null differ diff --git a/CNN/datasets/TEST/EOSINOPHIL/_0_187.jpeg 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mode 100644 index 28808a7..0000000 --- a/CNN/nn/conv/IncludeNet.py +++ /dev/null @@ -1,41 +0,0 @@ -from keras.models import Sequential -from keras.layers.convolutional import Conv2D -from keras.layers.core import Activation -from keras.layers.core import Flatten -from keras.layers.core import Dense, Dropout -from keras import backend as K -from keras.layers import MaxPooling2D - -size = 50 - - -class IncludeNet: - @staticmethod - def build(width, height, depth, classes): - model = Sequential() - input_shape = (height, width, depth) - if K.image_data_format() == "channels_first": - input_shape = (depth, height, width) - # model.add(Dropout(0.2,input_shape=inputShape)) - model.add(Conv2D(size, (3, 3), padding="same", input_shape=input_shape)) - model.add(Activation("relu")) - model.add(MaxPooling2D(pool_size=(3, 3))) - - model.add(Conv2D(size, (3, 3), padding="same", input_shape=input_shape)) - model.add(Activation("relu")) - model.add(MaxPooling2D(pool_size=(3, 3))) - - model.add(Conv2D(size, (3, 3), padding="same", input_shape=input_shape)) - model.add(Activation("relu")) - model.add(MaxPooling2D(pool_size=(3, 3))) - model.add(Dropout(0.5)) - - model.add(Conv2D(size, (3, 3), padding="same", input_shape=input_shape)) - model.add(Activation("relu")) - - model.add(Flatten()) - model.add(Activation("relu")) - - model.add(Dense(classes)) - model.add(Activation("softmax")) - return model diff --git a/CNN/preprocessing/ImageToArray.py b/CNN/preprocessing/ImageToArray.py deleted file mode 100644 index c99148a..0000000 --- a/CNN/preprocessing/ImageToArray.py +++ /dev/null @@ -1,9 +0,0 @@ -from keras.preprocessing.image import img_to_array - - -class ImageToArrayPreprocessor: - def __init__(self, data_format=None): - self.dataFormat = data_format - - def preprocess(self, image): - return img_to_array(image, data_format=self.dataFormat) diff --git a/CNN/preprocessing/PreProcessor.py b/CNN/preprocessing/PreProcessor.py deleted file mode 100644 index 4c4260e..0000000 --- a/CNN/preprocessing/PreProcessor.py +++ /dev/null @@ -1,73 +0,0 @@ -import cv2 -import numpy as np - - -class PreProcessor: - def __init__(self, width, height, inter=cv2.INTER_AREA): - self.width = width - self.height = height - self.inter = inter - - def preprocess(self, input_image): - try: - # if in train time with 100 epoch and size 50 get bad result remove all this line und just resize , convert all dataset befor train network - # input_image = cv2.imread(image_paths) - # Type = image_paths.split(os.path.sep)[-2] - image_blur = cv2.GaussianBlur(input_image, (7, 7), 0) - image_blur_hsv = cv2.cvtColor(image_blur, cv2.COLOR_RGB2HSV) - min_red = np.array([80, 60, 140]) - max_red = np.array([255, 255, 255]) - image_red1 = cv2.inRange(image_blur_hsv, min_red, max_red) - big_contour, mask = self.find_biggest_contour(image_red1) - (x, y), radius = cv2.minEnclosingCircle(big_contour) - center = (int(x), int(y)) - radius = int(radius) - imCircle = input_image.copy() - cv2.circle(imCircle, center, radius, (0, 255, 0), 1) - height, width, channels = imCircle.shape - border = [0, 0, 0, 0] - if center[0] + radius > width: - extera = (center[0] + radius) - width - border[3] = extera + 1 - - if (center[0] - radius < 0): - extera = width - (center[0] + radius) - border[2] = extera + 1 - - if center[1] + radius > height: - extera = (center[1] + radius) - height - border[1] = extera + 1 - - if center[1] + radius < 0: - extera = height - (center[1] + radius) - border[0] = extera + 1 - - y = center[1] - radius - if y < 0: - y = 0 - y2 = center[1] + radius - x = center[0] - radius - if x < 0: - x = 0 - - x2 = center[0] + radius - - cropped_image = input_image[y:y2, x:x2] - - return cv2.resize(cropped_image, (self.width, self.height), - interpolation=self.inter) - except Exception as a: - print("preprocessor", a) - - def find_biggest_contour(self, image): - image = image.copy() - s, contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) - biggest_contour = max(contours, key=cv2.contourArea) - mask = np.zeros(image.shape, np.uint8) - cv2.drawContours(mask, [biggest_contour], -1, 255, -1) - return biggest_contour, mask - - def overlay_mask(self, mask, image): - rgb_mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) - img = cv2.addWeighted(rgb_mask, 0.5, image, 0.5, 0) - # show(img) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..abb8822 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,55 @@ +# Contributing + +Thanks for helping! This project uses [Poetry](https://python-poetry.org/) and Python 3.12+. + +## Setup + +```bash +make install-docs # = poetry install --with dev,docs --extras kaggle +``` + +Run `make help` to list every shortcut (`make check`, `make format`, `make docs`, `make train`, ...). + +## Checks (all run in CI) + +`make check` runs lint, type-check and tests. `make docs` builds the docs site. Without `make`: + +```bash +poetry run ruff check . && poetry run ruff format --check . +poetry run mypy src +poetry run pytest +poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md +poetry run mkdocs build --strict +``` + +Tests use the small fixture dataset in `tests/fixtures/`; they never download data or pretrained weights. +Mark long-running or network tests with `@pytest.mark.slow` (they are skipped by default). + +## Commit messages and PR titles + +Releases are automated from [Conventional Commits](https://www.conventionalcommits.org/). PRs are +squash-merged, so the **PR title** must follow the format: + +| Prefix | Effect | +|---|---| +| `feat:` | minor release | +| `fix:`, `perf:` | patch release | +| `feat!:` or a `BREAKING CHANGE:` footer | major release (minor while version is 0.x) | +| `docs:`, `chore:`, `ci:`, `test:`, `refactor:` | no release | + +Do not edit `CHANGELOG.md` or the version in `pyproject.toml` by hand; the release workflow does it on merge to `main`. + +## Adding a model + +Add an entry to `models/registry.py`, a config in `configs/`, and a shape test in `tests/test_models.py`. + +## Releases + +Every push to `main` runs the `Release` workflow (python-semantic-release). It computes the next +version from the Conventional Commits, updates `project.version` in `pyproject.toml` and +`CHANGELOG.md`, tags `vX.Y.Z` and creates a GitHub Release with the built wheel and sdist. + +- The workflow pushes the version-bump commit and tag to `main`, so branch protection must allow + `github-actions[bot]` to push (or use a PAT / GitHub App token instead of `GITHUB_TOKEN`). +- PyPI publishing is optional: register the project on PyPI with a trusted publisher for this + repository and the `release.yml` workflow, then set the repository variable `PUBLISH_PYPI` to `true`. diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..9acbfe0 --- /dev/null +++ b/Makefile @@ -0,0 +1,64 @@ +.DEFAULT_GOAL := help + +RUN := poetry run +CONFIG ?= configs/resnet18.yaml +CKPT ?= +IMAGES ?= + +.PHONY: help install install-docs lint format typecheck test check \ + docs-cli docs docs-serve build data train evaluate predict clean + +help: ## Show this help + @awk 'BEGIN {FS = ":.*## "} /^[a-zA-Z_-]+:.*## / {printf " \033[36m%-13s\033[0m %s\n", $$1, $$2}' $(MAKEFILE_LIST) + +install: ## Install the project with dev tools and the Kaggle extra + poetry install --with dev --extras kaggle + +install-docs: ## Also install the docs tooling + poetry install --with dev,docs --extras kaggle + +lint: ## Run ruff (lint + format check) + $(RUN) ruff check . + $(RUN) ruff format --check . + +format: ## Auto-fix lint issues and format the code + $(RUN) ruff check --fix . + $(RUN) ruff format . + +typecheck: ## Run mypy + $(RUN) mypy src + +test: ## Run the test suite + $(RUN) pytest -q + +check: lint typecheck test ## Run everything CI runs (lint, mypy, tests) + +docs-cli: ## Generate the CLI reference page + $(RUN) typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md + +docs: docs-cli ## Build the docs site (strict) into site/ + $(RUN) mkdocs build --strict + +docs-serve: docs-cli ## Serve the docs locally with live reload + $(RUN) mkdocs serve + +build: ## Build the wheel and sdist into dist/ + poetry build + +data: ## Download the Kaggle dataset into data/ + $(RUN) wbc download-data --dest data + +train: ## Train a model: make train [CONFIG=configs/resnet18.yaml] + $(RUN) wbc -v train -c $(CONFIG) + +evaluate: ## Evaluate on TEST: make evaluate CKPT=runs//best.pt + @test -n "$(CKPT)" || { echo "Usage: make evaluate CKPT=runs//best.pt"; exit 1; } + $(RUN) wbc evaluate $(CKPT) --output $(dir $(CKPT))test_metrics.json + +predict: ## Predict images: make predict CKPT=runs//best.pt IMAGES=path/to/cell.jpeg + @test -n "$(CKPT)" -a -n "$(IMAGES)" || { echo "Usage: make predict CKPT=runs//best.pt IMAGES="; exit 1; } + $(RUN) wbc predict $(IMAGES) -c $(CKPT) + +clean: ## Remove build outputs and caches + rm -rf dist site .pytest_cache .mypy_cache .ruff_cache docs/reference/cli.md + find . -name __pycache__ -type d -not -path './.git/*' -prune -exec rm -rf {} + diff --git a/README.md b/README.md index da711f9..3183059 100644 --- a/README.md +++ b/README.md @@ -1,39 +1,46 @@ -# WBC-Classification -Classification of WBC ( White Blood Cells ) with CNN . (Convectional Neural Network) ---- -[![Donate](https://img.shields.io/badge/Donate-PayPal-green.svg)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=FEFTEUJT3YPDJ) ---- +# WBC Classification +[![CI](https://github.com/includeamin/WBC-Classification/actions/workflows/ci.yml/badge.svg)](https://github.com/includeamin/WBC-Classification/actions/workflows/ci.yml) +[![Release](https://img.shields.io/github/v/release/includeamin/WBC-Classification)](https://github.com/includeamin/WBC-Classification/releases) +[![Python](https://img.shields.io/badge/python-3.12%2B-blue)](pyproject.toml) +[![License](https://img.shields.io/github/license/includeamin/WBC-Classification)](LICENSE) +[![Docs](https://img.shields.io/badge/docs-mkdocs-informational)](https://includeamin.github.io/WBC-Classification/) -[![HitCount](http://hits.dwyl.com/includeamin/WBC-Classification.svg)](http://hits.dwyl.com/includeamin/WBC-Classification) +Classify white blood cell images — **eosinophil, lymphocyte, monocyte, neutrophil** — with PyTorch. +Fine-tune a pretrained ResNet / EfficientNet or train the small `baseline` CNN, all from one CLI. -### DATASET -- [Dataset](https://www.kaggle.com/paultimothymooney/blood-cells/kernels?sortBy=relevance&group=everyone&search=includeamin&page=1&pageSize=20&datasetId=9232) -### Requirements -``` -Keras==2.1.5 -numpy==1.18.1 -matplotlib==3.1.1 -imutils==0.5.3 -scikit_learn==0.23.1 -``` +## Quick start -### Train and save trained model -Use this command to train the model and save model ```bash -git clone +git clone https://github.com/includeamin/WBC-Classification.git cd WBC-Classification -python3 learning.py -d ./CNN/datasets/TRAIN/ -m ./TrainedModel/model_epoch_100.hdf5 -``` -after train you will see result plot: -![](image.png) +poetry install --extras kaggle -### Test the model -```bash -python3 load_test_model.py -d ./CNN/datasets/TEST -m ./TrainedModel/model_epoch_100.hdf5 +poetry run wbc download-data --dest data +poetry run wbc train -c configs/resnet18.yaml +poetry run wbc evaluate runs//best.pt --output runs//test_metrics.json +poetry run wbc predict path/to/cell.jpeg -c runs//best.pt ``` -## NOTE -default epoch count is 100. for change it, just edit `cp` variable in [`learning.py`](learning.py) -# Todo -- more customizable +Shortcuts are available through `make` (`make data`, `make train`, `make check`, ...); run `make help` for the list. + +Requires Python 3.12+ and [Poetry](https://python-poetry.org/). Training pretrained models is best done on a GPU +(see the [training guide](https://includeamin.github.io/WBC-Classification/training/) and `notebooks/train_colab.ipynb`). + +## Documentation + +Full docs: — data setup, configuration reference, +CLI and API reference, architecture, results. + +## Dataset + +[Blood Cell Images](https://www.kaggle.com/datasets/paultimothymooney/blood-cells) on Kaggle (not stored in this repository). + +## Contributing + +See [CONTRIBUTING.md](CONTRIBUTING.md). Releases and the changelog are automated from +[Conventional Commits](https://www.conventionalcommits.org/); see [CHANGELOG.md](CHANGELOG.md). + +## License + +BSD 3-Clause — see [LICENSE](LICENSE). diff --git a/TrainedModel/model_epoch_100.hdf5 b/TrainedModel/model_epoch_100.hdf5 deleted file mode 100644 index e01f3e0..0000000 Binary files a/TrainedModel/model_epoch_100.hdf5 and /dev/null differ diff --git a/WBC-Detection/4.jpeg b/WBC-Detection/4.jpeg deleted file mode 100755 index 848db72..0000000 Binary files a/WBC-Detection/4.jpeg and /dev/null differ diff --git a/WBC-Detection/README.md b/WBC-Detection/README.md deleted file mode 100644 index c207a55..0000000 --- a/WBC-Detection/README.md +++ /dev/null @@ -1,2 +0,0 @@ -Descriptions are in WIKI -https://github.com/includeamin/WBC-Classification/wiki/WBC-Detection-in-Blood-cell diff --git a/WBC-Detection/WBC-Detection.py b/WBC-Detection/WBC-Detection.py deleted file mode 100644 index bc9f931..0000000 --- a/WBC-Detection/WBC-Detection.py +++ /dev/null @@ -1,62 +0,0 @@ -import cv2 -import matplotlib -from matplotlib import colors -from matplotlib import pyplot as plt -import numpy as np - -def find_biggest_contour(image): - image = image.copy() - s, contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) - biggest_contour = max(contours, key=cv2.contourArea) - mask = np.zeros(image.shape, np.uint8) - cv2.drawContours(mask, [biggest_contour], -1, 255, -1) - return biggest_contour, mask -def overlay_mask(mask, image): - rgb_mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) - img = cv2.addWeighted(rgb_mask, 0.5, image, 0.5, 0) -def show_mask(mask): - plt.figure(figsize=(10, 10)) - plt.imshow(mask, cmap='gray') -def show(image): - plt.figure(figsize=(15, 15)) - plt.imshow(image, interpolation='nearest') - plt.show() - -im = cv2.imread("4.jpeg") -im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) - -image_blur = cv2.GaussianBlur(im, (7, 7), 0) -image_blur_hsv = cv2.cvtColor(image_blur, cv2.COLOR_RGB2HSV) -min_red = np.array([80, 60, 140]) -max_red = np.array([255, 255, 255]) -image_red1 = cv2.inRange(image_blur_hsv, min_red, max_red) -show(image_blur) -show(image_blur_hsv) -show(image_red1) -big_contour, mask = find_biggest_contour(image_red1) -overlay_mask(mask,im) - -moments = cv2.moments(mask) -centre_of_mass = ( - int(moments['m10'] / moments['m00']), - int(moments['m01'] / moments['m00']) -) -image_with_com = im.copy() - -cv2.circle(image_with_com, centre_of_mass, 10, (0, 255, 0), -1, cv2.LINE_AA) -image_with_ellipse = im.copy() -ellipse = cv2.fitEllipse(big_contour) - - - - -img =cv2.ellipse(image_with_ellipse, ellipse, (0, 255, 0), 2) -dst= cv2.bitwise_and(im,im,mask=mask) - -r = 100.0 / dst.shape[1] -dim = (100, int(dst.shape[0] * r)) - - -resized = cv2.resize(dst, dim, interpolation = cv2.INTER_AREA) -show(image_with_ellipse) - diff --git a/_config.yml b/_config.yml deleted file mode 100644 index c419263..0000000 --- a/_config.yml +++ /dev/null @@ -1 +0,0 @@ -theme: jekyll-theme-cayman \ No newline at end of file diff --git a/configs/baseline.yaml b/configs/baseline.yaml new file mode 100644 index 0000000..dca06a5 --- /dev/null +++ b/configs/baseline.yaml @@ -0,0 +1,16 @@ +# Lightweight baseline: the original IncludeNet idea, rewritten with BatchNorm + GAP. +preprocessing: + crop: false +data: + root: data + image_size: 64 + batch_size: 64 + val_fraction: 0.2 +model: + name: baseline + pretrained: false +train: + name: baseline + epochs: 60 + lr: 0.001 + patience: 10 diff --git a/configs/efficientnet_b0.yaml b/configs/efficientnet_b0.yaml new file mode 100644 index 0000000..197e819 --- /dev/null +++ b/configs/efficientnet_b0.yaml @@ -0,0 +1,16 @@ +# Fine-tune an ImageNet-pretrained EfficientNet-B0. +preprocessing: + crop: false +data: + root: data + image_size: 160 + batch_size: 32 +model: + name: efficientnet_b0 + pretrained: true + freeze_backbone_epochs: 2 +train: + name: efficientnet_b0 + epochs: 25 + lr: 0.0005 + patience: 6 diff --git a/configs/resnet18.yaml b/configs/resnet18.yaml new file mode 100644 index 0000000..005e3e9 --- /dev/null +++ b/configs/resnet18.yaml @@ -0,0 +1,16 @@ +# Fine-tune an ImageNet-pretrained ResNet-18. +preprocessing: + crop: false +data: + root: data + image_size: 128 + batch_size: 32 +model: + name: resnet18 + pretrained: true + freeze_backbone_epochs: 2 +train: + name: resnet18 + epochs: 25 + lr: 0.0005 + patience: 6 diff --git a/data/README.md b/data/README.md new file mode 100644 index 0000000..3f1abdd --- /dev/null +++ b/data/README.md @@ -0,0 +1,32 @@ +# Data + +The dataset is **not** stored in git. It is the public Kaggle +[Blood Cell Images](https://www.kaggle.com/datasets/paultimothymooney/blood-cells) dataset +(four classes: EOSINOPHIL, LYMPHOCYTE, MONOCYTE, NEUTROPHIL; 320×240 JPEG). + +## Download + +```bash +poetry install --extras kaggle +poetry run wbc download-data --dest data +``` + +If the download fails because Kaggle requires authentication, create an API token at + and expose it as `KAGGLE_USERNAME` / `KAGGLE_KEY` +(or `~/.kaggle/kaggle.json`), then retry. You can also download and unzip the dataset +manually and copy its `TRAIN/` and `TEST/` folders here. + +## Expected layout + +``` +data/ +├── TRAIN/ +│ ├── EOSINOPHIL/*.jpeg +│ ├── LYMPHOCYTE/*.jpeg +│ ├── MONOCYTE/*.jpeg +│ └── NEUTROPHIL/*.jpeg +└── TEST/ # same four class folders; used only by `wbc evaluate` +``` + +Class names are taken from the folder names. Files that are not `.jpg/.jpeg/.png` are ignored. +Everything in this folder except this README is git-ignored. diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000..16af6a8 --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,68 @@ +# Architecture + +The package is organised in layers. Each layer only imports from the layers below it, which keeps +every module small, testable on its own and easy to replace. + +```mermaid +flowchart TB + CLI["cli.py
wbc train · evaluate · predict · segment · download-data"] + ENGINE["engine/
train · evaluate · predict · metrics · checkpoint · runtime"] + DATA["data/
labels · datasets · transforms · download"] + MODELS["models/
baseline · backbone · registry"] + PRE["preprocessing/
segmentation (HSV detect + crop)"] + FOUND["config.py · errors.py
pydantic config, WBCError types"] + + CLI --> ENGINE + CLI --> DATA + CLI --> PRE + ENGINE --> DATA + ENGINE --> MODELS + DATA --> PRE + DATA --> FOUND + ENGINE --> FOUND + MODELS --> FOUND + PRE --> FOUND +``` + +## Layers + +| Layer | Modules | Responsibility | +|---|---|---| +| Interface | `cli.py` | Parse arguments, build a `Config`, call the engine, print results. Turns any `WBCError` into `Error: …` and exit code 1. | +| Engine | `engine/train.py`, `evaluate.py`, `predict.py` | The three workflows. Orchestrate data and models; own the training loop, device handling and run folders. | +| Engine support | `engine/metrics.py`, `checkpoint.py`, `runtime.py` | Metrics and plots, single-file checkpoints, seeding and device selection. | +| Data | `data/labels.py`, `datasets.py`, `transforms.py`, `download.py` | Find classes from folders, list samples, stratified split, load images as RGB, build transforms and `DataLoader`s, fetch the Kaggle dataset. | +| Models | `models/baseline.py`, `backbone.py`, `registry.py` | The networks and a name → factory registry. See [Models](models.md). | +| Preprocessing | `preprocessing/segmentation.py` | Classical OpenCV cell detection and crop. Optional. See [Pipeline](pipeline.md). | +| Foundation | `config.py`, `errors.py` | Validated configuration and the shared error types. No dependencies on other layers. | + +## Design rules + +- **One direction.** Lower layers never import upper layers. Nothing runs at import time. +- **Config in, artifacts out.** Every workflow takes a validated `Config` (YAML + CLI overrides) and writes + to a run folder; the saved `config.yaml` reproduces the run. +- **One checkpoint file.** A `.pt` file holds the weights, the class names and the full config, so + `evaluate` and `predict` need nothing but the file. The model is rebuilt from the stored config. +- **Errors at the boundaries.** Config loading, dataset discovery, image loading and checkpoint loading + validate their input and raise `ConfigError`, `DataError`, `SegmentationError` or `CheckpointError` with an + actionable message. Inner code trusts validated inputs. +- **Extensible by registry.** A new model is one entry in `models/registry.py`, a config in `configs/` and a + shape test. + +## Repository layout + +``` +src/wbc_classification/ +├── config.py # pydantic config + YAML loading + overrides +├── errors.py # WBCError and subclasses +├── data/ # labels, datasets/loaders, transforms, Kaggle download +├── preprocessing/ # optional HSV cell segmentation and crop +├── models/ # baseline (IncludeNet), torchvision backbones, registry +├── engine/ # train, evaluate, predict, metrics, checkpoints, runtime +└── cli.py # `wbc` Typer application +configs/ # one YAML per experiment +tests/ # unit, CLI and end-to-end smoke tests (fixture dataset) +docs/ # this site +``` + +See the generated [Python API](reference/api.md) for signatures. diff --git a/WBC-Detection/blur.png b/docs/assets/detection/blur.png similarity index 100% rename from WBC-Detection/blur.png rename to docs/assets/detection/blur.png diff --git a/WBC-Detection/color filtering.png b/docs/assets/detection/color-filtering.png similarity index 100% rename from WBC-Detection/color filtering.png rename to docs/assets/detection/color-filtering.png diff --git a/WBC-Detection/final.png b/docs/assets/detection/final.png similarity index 100% rename from WBC-Detection/final.png rename to docs/assets/detection/final.png diff --git a/WBC-Detection/hsv.png b/docs/assets/detection/hsv.png similarity index 100% rename from WBC-Detection/hsv.png rename to docs/assets/detection/hsv.png diff --git a/WBC-Detection/mask.png b/docs/assets/detection/mask.png similarity index 100% rename from WBC-Detection/mask.png rename to docs/assets/detection/mask.png diff --git a/docs/changelog.md b/docs/changelog.md new file mode 100644 index 0000000..786b75d --- /dev/null +++ b/docs/changelog.md @@ -0,0 +1 @@ +--8<-- "CHANGELOG.md" diff --git a/docs/configuration.md b/docs/configuration.md new file mode 100644 index 0000000..ade78b2 --- /dev/null +++ b/docs/configuration.md @@ -0,0 +1,35 @@ +# Configuration + +Experiments are described by a YAML file (see `configs/`). Unknown keys are rejected so typos fail fast. +Common values have dedicated flags (`wbc train -c cfg.yaml --epochs 5 --lr 0.0003`). +Any field can be overridden with the repeatable `--set section.field=value` option; values are parsed as YAML, +and `--set` takes precedence over the dedicated flags: + +```bash +wbc train -c configs/resnet18.yaml --set data.num_workers=4 --set train.seed=1 +``` + +```yaml +preprocessing: + crop: false # crop to the detected cell first (HSV segmentation) +data: + root: data # folder with TRAIN/ and TEST/ + image_size: 224 # square input size + batch_size: 32 + val_fraction: 0.2 # stratified split from TRAIN, 0 < x < 0.5 + num_workers: 0 +model: + name: resnet18 # baseline | resnet18 | resnet50 | efficientnet_b0 + pretrained: true # ImageNet weights (backbones only) + freeze_backbone_epochs: 0 +train: + name: run # run folder prefix + epochs: 30 + lr: 0.001 + weight_decay: 0.0001 + patience: 7 # early-stopping patience (epochs) + amp: true # mixed precision (CUDA only) + seed: 42 + device: auto # auto | cpu | cuda | mps + output_dir: runs +``` diff --git a/docs/contributing.md b/docs/contributing.md new file mode 100644 index 0000000..ea38c9b --- /dev/null +++ b/docs/contributing.md @@ -0,0 +1 @@ +--8<-- "CONTRIBUTING.md" diff --git a/docs/data.md b/docs/data.md new file mode 100644 index 0000000..1881bd3 --- /dev/null +++ b/docs/data.md @@ -0,0 +1,26 @@ +# Data + +The project uses the public Kaggle +[Blood Cell Images](https://www.kaggle.com/datasets/paultimothymooney/blood-cells) dataset +(320×240 JPEG, four classes). It is not stored in git. + +```bash +poetry run wbc download-data --dest data # add --force to overwrite +``` + +Expected layout: + + data/ + ├── TRAIN/{EOSINOPHIL,LYMPHOCYTE,MONOCYTE,NEUTROPHIL}/*.jpeg + └── TEST/{EOSINOPHIL,LYMPHOCYTE,MONOCYTE,NEUTROPHIL}/*.jpeg + +- Class names come from the folder names; non-image files are ignored. +- A stratified, seeded validation split is carved from `TRAIN`. `TEST` is only used by `wbc evaluate`. +- If Kaggle asks for authentication, create an API token and set `KAGGLE_USERNAME` / `KAGGLE_KEY` + (or use `~/.kaggle/kaggle.json`). You can also download the dataset manually and copy `TRAIN/` and `TEST/` into `data/`. + +!!! warning "Validation split caveat" + The Kaggle `TRAIN` folder contains augmented copies of a smaller set of original images, so the + stratified validation split drawn from `TRAIN` can contain near-duplicates of training images and + validation accuracy may look optimistic. The held-out `TEST` accuracy from `wbc evaluate` is the + number to report. diff --git a/docs/getting-started.md b/docs/getting-started.md new file mode 100644 index 0000000..83bfb7f --- /dev/null +++ b/docs/getting-started.md @@ -0,0 +1,30 @@ +# Getting started + +## Install + +Requires Python 3.12+ and [Poetry](https://python-poetry.org/). + +```bash +git clone https://github.com/includeamin/WBC-Classification.git +cd WBC-Classification +poetry install --extras kaggle +``` + +## Get the data + +```bash +poetry run wbc download-data --dest data +``` + +See [Data](data.md) for the expected layout and Kaggle credentials. + +## Train, evaluate, predict + +```bash +poetry run wbc train -c configs/resnet18.yaml +poetry run wbc evaluate runs/resnet18-/best.pt --output runs/resnet18-/test_metrics.json +poetry run wbc predict path/to/cell.jpeg -c runs/resnet18-/best.pt +``` + +Use `poetry run wbc --help` or the [CLI reference](reference/cli.md) for all options. +Training on a CPU is slow for the pretrained backbones; see [Training](training.md) for GPU and Colab. diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..1541f26 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,10 @@ +# WBC Classification + +Classify white blood cell images into four types — **eosinophil, lymphocyte, monocyte, neutrophil** — with PyTorch. + +- Fine-tune a pretrained backbone (ResNet, EfficientNet) or train the small `baseline` CNN (IncludeNet). +- Config-driven, reproducible training: every run saves its config, metrics, plots and checkpoints. +- An optional OpenCV step crops the cell before classification. +- One command-line tool: `wbc train | evaluate | predict | segment | download-data`. + +Start with [Getting started](getting-started.md). diff --git a/docs/models.md b/docs/models.md new file mode 100644 index 0000000..4a7128c --- /dev/null +++ b/docs/models.md @@ -0,0 +1,85 @@ +# Models + +Models are built by name through one function, `build_model(name, num_classes, pretrained)` in +`models/registry.py`. Every model takes a batch of RGB images `(N, 3, H, W)` and returns raw class scores +(logits) `(N, num_classes)`; the loss applies softmax internally and `predict` applies it explicitly. + +```mermaid +flowchart LR + REG["registry.build_model(name, num_classes, pretrained)"] + REG -->|"baseline"| B["IncludeNet
models/baseline.py"] + REG -->|"resnet18 · resnet50 · efficientnet_b0"| T["torchvision backbone
+ new linear head
models/backbone.py"] +``` + +| Name | Type | Parameters (4 classes) | Typical input | Pretrained weights | +|---|---|---|---|---| +| `baseline` | small custom CNN (IncludeNet) | 241,700 | 64 × 64 | no | +| `resnet18` | ResNet-18 | 11,178,564 | 128 × 128 | ImageNet | +| `resnet50` | ResNet-50 | 23,516,228 | 128 × 128 or larger | ImageNet | +| `efficientnet_b0` | EfficientNet-B0 | 4,012,672 | 160 × 160 | ImageNet | + +The "typical input" is what the shipped configs use (there is no shipped `resnet50` config; copy +`configs/resnet18.yaml` and change `model.name`). Any size ≥ 16 px works because every model ends in +global average pooling. Parameter counts are for the 4-class head. + +## `baseline`: IncludeNet layer by layer + +The original project's small CNN, rewritten in PyTorch with BatchNorm and global average pooling (the +legacy Keras version used a fixed-size flatten and no normalisation). Shapes below are for a 64 × 64 input. + +| # | Layer | Output shape | Parameters | +|---|---|---|---| +| – | Input (RGB, normalised) | 3 × 64 × 64 | – | +| 1 | Conv 3×3, 3 → 32, pad 1, no bias · BatchNorm · ReLU · MaxPool 2 | 32 × 32 × 32 | 928 | +| 2 | Conv 3×3, 32 → 64 · BatchNorm · ReLU · MaxPool 2 | 64 × 16 × 16 | 18,560 | +| 3 | Conv 3×3, 64 → 128 · BatchNorm · ReLU · MaxPool 2 | 128 × 8 × 8 | 73,984 | +| 4 | Conv 3×3, 128 → 128 · BatchNorm · ReLU | 128 × 8 × 8 | 147,712 | +| 5 | Global average pooling | 128 | – | +| 6 | Dropout (p = 0.5) | 128 | – | +| 7 | Linear 128 → 4 | 4 | 516 | + +```mermaid +flowchart LR + X["image
3×H×W"] --> C1["Conv-BN-ReLU
32 + MaxPool"] --> C2["Conv-BN-ReLU
64 + MaxPool"] --> C3["Conv-BN-ReLU
128 + MaxPool"] --> C4["Conv-BN-ReLU
128"] --> GAP["Global avg pool"] --> D["Dropout 0.5"] --> FC["Linear → 4 logits"] +``` + +The width (32) and dropout (0.5) are constructor arguments of `IncludeNet`; the registry uses the defaults. + +## Backbones + +`build_backbone` loads a torchvision model (`weights="DEFAULT"` = ImageNet when `model.pretrained: true`) +and replaces only the final classification layer with a fresh `Linear(…, num_classes)`. Everything else is +the stock architecture. + +| Model | Feature extractor (unchanged) | Head that is replaced | +|---|---|---| +| `resnet18` | 7×7 stride-2 conv stem → max-pool → 4 stages of 2 BasicBlocks (64, 128, 256, 512 ch) → global avg pool | `fc`: Linear 512 → 4 | +| `resnet50` | same stem → 4 stages of 3, 4, 6, 3 Bottleneck blocks (256, 512, 1024, 2048 ch) → global avg pool | `fc`: Linear 2048 → 4 | +| `efficientnet_b0` | 3×3 stride-2 stem → 7 MBConv stages → 1×1 conv to 1280 ch (SiLU, BatchNorm) → global avg pool | `classifier[-1]`: Linear 1280 → 4 (the preceding Dropout 0.2 is kept) | + +### Freezing the backbone + +With `model.freeze_backbone_epochs: N`, only the new head is trained for the first N epochs, then all +layers are unfrozen and fine-tuned together. This protects the pretrained features while the random head +settles. Notes: + +- Only backbones support it; `baseline` raises a `ConfigError` if you set it. +- Frozen layers keep `requires_grad = False`, but BatchNorm layers inside the frozen backbone still update + their running statistics because the model stays in train mode. +- Early-stopping patience also counts the frozen epochs. + +## Choosing a model + +- **No GPU, quick experiments, or a lightweight baseline:** `baseline`. +- **Best accuracy for the effort:** `resnet18` or `efficientnet_b0` fine-tuned from ImageNet weights. +- **Larger capacity:** `resnet50`, at roughly twice the compute of `resnet18`. + +No accuracy numbers are published yet; see [Results](results.md) for how to produce them. + +## Adding a model + +1. Implement it (or wrap a torchvision model) in `models/` so it maps `(N, 3, H, W)` to `(N, num_classes)`. +2. Add it to `MODEL_NAMES` and `build_model` in `models/registry.py`. +3. Add a YAML in `configs/` and a shape test in `tests/test_models.py`. + +If it is a backbone with a replaceable head, also teach `set_backbone_frozen` which attribute is the head. diff --git a/docs/pipeline.md b/docs/pipeline.md new file mode 100644 index 0000000..8e123e5 --- /dev/null +++ b/docs/pipeline.md @@ -0,0 +1,123 @@ +# Pipeline: detection and classification, end to end + +This page follows one image from a file on disk to a predicted cell type, and the same stages during +training and evaluation. Every stage is a small module with a matching config key. + +!!! note "Scope: one cell per image" + The pipeline classifies **one white blood cell per image**, like the Kaggle dataset it is trained on. + The optional detection step finds the single largest cell-coloured region. It does **not** detect or + count several cells in a wide smear image, and no cell counting is implemented. See + [Limits](#limits-and-what-is-not-included). + +## Inference (`wbc predict`) + +```mermaid +flowchart LR + F["image file
.jpg · .jpeg · .png"] --> L["load_image
decode, convert to RGB"] + L --> Q{"preprocessing.crop?"} + Q -- yes --> S["detect cell
HSV threshold, largest contour"] + S -- found --> CR["crop to enclosing square"] + S -- not found --> W["warn, keep full image"] + Q -- no --> T + CR --> T["transform
resize to data.image_size, normalise"] + W --> T + T --> M["model
rebuilt from checkpoint"] + M --> P["softmax → probabilities"] + P --> O["label + confidence
(optional annotated PNG)"] +``` + +1. **Load** (`data/datasets.py: load_image`): decode the file and convert it to RGB. Grayscale, RGBA and + palette images work; an unreadable file raises `DataError` naming the file. +2. **Detect and crop** (`preprocessing/segmentation.py`), only when `preprocessing.crop: true` in the checkpoint's + config: see [Detection stage](#detection-stage). If no cell is found the full image is used and a + warning is logged, so one bad image never stops a batch. +3. **Transform** (`data/transforms.py`): resize to the square `data.image_size`, convert to a float tensor in + [0, 1] and normalise with the ImageNet mean/std. (The same transform is used for evaluation.) +4. **Model** (`models/`): rebuilt from the architecture name and class names stored in the checkpoint, set to + eval mode, run without gradients on the chosen device. +5. **Output**: softmax gives one probability per class; the highest is the predicted label. `wbc predict` prints + `path: LABEL (confidence)` per image and can save labelled copies with `--annotate-dir`. + +The crop setting and image size come from the checkpoint, so inference always matches how the model was trained. + +## Detection stage + +`wbc segment` and the optional crop use classical computer vision, no learned weights: + +```mermaid +flowchart LR + A["RGB image"] --> B["Gaussian blur 7×7"] --> C["RGB → HSV"] --> D["threshold
H≥80, S≥60, V≥140"] + D --> E["find contours"] --> F["keep the largest
(must cover ≥ 1% of the image)"] + F --> G["mask · centre · enclosing circle"] + G --> H["crop square around the circle
(clamped to the image)"] +``` + +- The thresholds were tuned for the stained images in this dataset. On other stains, scanners or magnifications + they may need adjusting (`HSV_LOWER` / `HSV_UPPER` in `preprocessing/segmentation.py`); check with + `wbc segment` before enabling `preprocessing.crop`. +- Only the **largest** region is kept. If nothing passes the threshold, or the region is smaller than 1% of + the image, a `SegmentationError` is raised (`wbc segment` reports it; training and prediction fall back to the + full image). +- `wbc segment image.jpeg --output-dir out/` writes `*_crop.png`, `*_overlay.png` (contour and circle drawn) + and `*_mask.png` so you can inspect what the detector did. The step-by-step images are in the + [detection walkthrough](wbc-detection.md). + +## Training (`wbc train`) + +```mermaid +flowchart TB + D["data/TRAIN/CLASS/*.jpeg"] --> S["discover classes, list samples"] + S --> SP["stratified, seeded split
train / validation (data.val_fraction)"] + SP --> TR["train transforms
crop (optional) · resize · flips · rotation · colour jitter · normalise"] + SP --> VA["eval transforms
crop (optional) · resize · normalise"] + TR --> LOOP["training loop
AdamW · cosine LR · AMP on CUDA"] + VA --> LOOP + M["build_model(name)"] --> LOOP + LOOP --> ES{"validation accuracy
improved?"} + ES -- yes --> BEST["save best.pt"] + ES -- patience exceeded --> STOP["early stop"] + LOOP --> LAST["save last.pt every epoch"] + BEST --> ART + STOP --> ART["run folder: config.yaml · metrics.csv
curves.png · confusion_matrix.png"] +``` + +- **Split:** the validation set is carved from `TRAIN` with a seeded, stratified split; `TEST` is never used + during training. +- **Augmentation** (training only): random horizontal and vertical flips, rotation up to 180°, and mild colour + jitter to cover stain variation. Cells have no preferred orientation, so these are label-preserving. +- **Reproducibility:** the seed fixes Python, NumPy and PyTorch randomness, and the run folder keeps the exact + config. +- **Output:** `runs/-/` with `config.yaml`, `metrics.csv`, `curves.png`, + `confusion_matrix.png` (validation, best epoch), `best.pt` and `last.pt`. + +## Evaluation (`wbc evaluate`) + +`evaluate` loads a checkpoint and runs it over `TEST/` with the eval transform (and the crop setting stored in +the checkpoint). It reports per-class precision, recall and F1 plus accuracy and a confusion matrix, and with +`--output` writes them as JSON and a PNG. This is the number to report: the validation split comes from the +training folder, which in the Kaggle dataset contains augmented copies of the same originals (see +[Data](data.md)). + +## From stage to code + +| Stage | Config key | Code | +|---|---|---| +| Find classes and samples | `data.root` | `data/labels.py`, `data/datasets.py` | +| Split | `data.val_fraction`, `train.seed` | `data/datasets.py: stratified_split` | +| Detect and crop | `preprocessing.crop` | `preprocessing/segmentation.py` | +| Resize, augment, normalise | `data.image_size` | `data/transforms.py` | +| Model | `model.name`, `model.pretrained` | `models/registry.py` | +| Optimise | `train.lr`, `train.epochs`, `train.patience` | `engine/train.py` | +| Metrics | – | `engine/metrics.py` | +| Save and reload | – | `engine/checkpoint.py` | + +## Limits and what is not included + +- **One cell per image.** Detection keeps only the largest region. A smear image with many white cells would be + classified as if it contained one. +- **No counting.** There is no multi-cell detection, no per-cell classification across an image and no cell + counts. These would need a different detector (for example connected components or a learned object + detector) feeding each cell crop to the classifier; none of that exists in this project. +- **Four classes.** Eosinophil, lymphocyte, monocyte, neutrophil only; other cell types and non-cell images are + not recognised, and the model always picks one of the four. +- **Research use only.** This is an educational/research project, not a validated medical device. diff --git a/docs/reference/api.md b/docs/reference/api.md new file mode 100644 index 0000000..cf18c90 --- /dev/null +++ b/docs/reference/api.md @@ -0,0 +1,9 @@ +# Python API + +::: wbc_classification.config +::: wbc_classification.data.datasets +::: wbc_classification.preprocessing.segmentation +::: wbc_classification.models.registry +::: wbc_classification.engine.train +::: wbc_classification.engine.evaluate +::: wbc_classification.engine.predict diff --git a/docs/results.md b/docs/results.md new file mode 100644 index 0000000..809e0a4 --- /dev/null +++ b/docs/results.md @@ -0,0 +1,18 @@ +# Results + +!!! note + No pretrained checkpoints or benchmark numbers are published yet. Train a model with + `wbc train` and `wbc evaluate`, then fill in the table below. + +!!! warning "Validation split caveat" + The Kaggle `TRAIN` folder contains augmented copies of a smaller set of original images, so the + stratified validation split drawn from `TRAIN` can contain near-duplicates of training images and + validation accuracy may look optimistic. The held-out `TEST` accuracy from `wbc evaluate` is the + number to report. + +| Model | Image size | Crop | Epochs | TEST accuracy | Macro F1 | Notes | +|---|---|---|---|---|---|---| +| baseline | 64 | no | – | – | – | – | +| resnet18 | 128 | no | – | – | – | – | +| resnet18 | 128 | yes | – | – | – | crop vs no-crop comparison | +| efficientnet_b0 | 160 | no | – | – | – | – | diff --git a/docs/superpowers/plans/2026-10-04-wbc-modernization.md b/docs/superpowers/plans/2026-10-04-wbc-modernization.md new file mode 100644 index 0000000..c28c955 --- /dev/null +++ b/docs/superpowers/plans/2026-10-04-wbc-modernization.md @@ -0,0 +1,3297 @@ +# WBC-Classification Modernization Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking. + +**Goal:** Replace the legacy Keras scripts with a Poetry-managed, PyTorch-based, documented, tested and auto-released open-source package (`wbc-classification`) with a `wbc` CLI. + +**Architecture:** A `src/wbc_classification` package with small single-purpose modules (config, labels/data, optional OpenCV segmentation, models + registry, engine for train/evaluate/predict, Typer CLI). Everything is config-driven (pydantic + YAML); a checkpoint file carries weights, class names and config so inference needs nothing else. Errors are raised as `WBCError` subclasses at boundaries and rendered by the CLI. + +**Tech Stack:** Python 3.14 (floor 3.12), Poetry, PyTorch + torchvision, OpenCV (headless), pydantic, Typer, scikit-learn (metrics/split), pytest, ruff, mypy, MkDocs Material + mkdocstrings, python-semantic-release, GitHub Actions. + +**Spec:** `docs/superpowers/specs/2026-10-04-wbc-modernization-design.md` + +## Global Constraints + +- Python: `requires-python = ">=3.12,<3.15"`; develop on 3.14; CI matrix 3.12 / 3.13 / 3.14. +- Poetry with a committed `poetry.lock`; `src/` layout; console script `wbc = "wbc_classification.cli:app"`. +- Dependencies are added with `poetry add` (latest at install time, caret ranges); never hand-write version numbers from memory. +- Dependency groups: main (torch, torchvision, numpy, opencv-python-headless, pillow, scikit-learn, matplotlib, typer, pydantic, pyyaml); dev (pytest, ruff, mypy, types-pyyaml); docs (mkdocs, mkdocs-material, mkdocstrings[python]); release (python-semantic-release); optional extra `kaggle` (kagglehub). +- Nothing runs at import time (no argparse / `test()` / file IO at module level). +- Errors: raise `WBCError` subclasses (`ConfigError`, `DataError`, `SegmentationError`, `CheckpointError`) with actionable messages; no silent `print` + `return None`. +- Data layout is the Kaggle layout: `/TRAIN//*.jpeg`, `/TEST//*.jpeg`; TEST is used only by `evaluate`. +- Cell crop is optional via `preprocessing.crop` (default `false`). +- Conventional Commits for every commit (`feat:`, `fix:`, `docs:`, `chore:`, `ci:`, `test:`, `refactor:`), each commit message ending with the trailer `Co-Authored-By: Claude Sonnet 5.5 `. +- Before every commit run: `poetry run ruff check --fix . && poetry run ruff format . && poetry run mypy src && poetry run pytest -q` (once those tools exist, i.e. from Task 2 on; mypy/pytest as applicable). +- Work happens on branch `refactor/modernization`; never push or rewrite git history. The `master` → `main` rename and GitHub settings are owner steps (see final task). +- No pretrained checkpoint or metrics are published; docs ship with a results template. + +## Review Focus + +Inputs/conditions the spec implies but whose behaviour a user would expect, each pinned by a test in the owning task: + +1. Non-RGB images (grayscale, RGBA) are loaded as RGB, and an unreadable/corrupt image raises `DataError` naming the file — Task 5. +2. Stray non-image files (`.DS_Store`, `notes.txt`) in class folders are ignored; an empty class folder or missing data dir raises `DataError` with a `wbc download-data` hint — Task 5. +3. A dataset too small for a stratified validation split raises `DataError`, not a raw sklearn `ValueError` — Task 5. +4. `predict` on a directory with no images, on a missing path, or on a corrupt image raises `DataError`, never a traceback from deep inside PIL/torch — Task 9. +5. A corrupt, non-checkpoint, or class-count-mismatched checkpoint file raises `CheckpointError`; a typo'd config key raises `ConfigError` naming the key — Tasks 3 and 7. + +--- + +### Task 1: Remove dataset images from the tree, keep test fixtures + +**Files:** +- Create: `tests/fixtures/data/{TRAIN,TEST}//*.jpeg` (moved), `tests/fixtures/cell.jpeg` (moved), `data/README.md` +- Modify: `.gitignore` +- Delete: `CNN/datasets/TRAIN`, `CNN/datasets/TEST`, `CNN/datasets/TEST_SIMPLE` + +**Interfaces:** +- Produces: fixture dataset `tests/fixtures/data` with 6 train + 2 test images per class (EOSINOPHIL, LYMPHOCYTE, MONOCYTE, NEUTROPHIL), 320×240 RGB JPEG; `tests/fixtures/cell.jpeg` (a real cell image the HSV segmentation is known to work on). + +- [ ] **Step 1: Move fixture images with git** + +```bash +set -e +src=CNN/datasets; dst=tests/fixtures/data +for cls in EOSINOPHIL LYMPHOCYTE MONOCYTE NEUTROPHIL; do + mkdir -p $dst/TRAIN/$cls $dst/TEST/$cls + git ls-files $src/TRAIN/$cls | sort | head -6 | while read -r f; do git mv "$f" $dst/TRAIN/$cls/; done + git ls-files $src/TEST/$cls | sort | head -2 | while read -r f; do git mv "$f" $dst/TEST/$cls/; done +done +git mv WBC-Detection/4.jpeg tests/fixtures/cell.jpeg +``` + +- [ ] **Step 2: Verify counts** + +Run: `find tests/fixtures/data/TRAIN -name '*.jpeg' | wc -l; find tests/fixtures/data/TEST -name '*.jpeg' | wc -l` +Expected: `24` then `8` + +- [ ] **Step 3: Remove the rest of the dataset from the tree** + +```bash +git rm -r -q CNN/datasets/TRAIN CNN/datasets/TEST CNN/datasets/TEST_SIMPLE +git ls-files | grep -ciE '\.(jpe?g|png)$' +``` +Expected: a small number (fixtures + `WBC-Detection/*.png` + `image.png`, about 39); the old dataset is gone. + +- [ ] **Step 4: Write `data/README.md`** + +````markdown +# Data + +The dataset is **not** stored in git. It is the public Kaggle +[Blood Cell Images](https://www.kaggle.com/datasets/paultimothymooney/blood-cells) dataset +(four classes: EOSINOPHIL, LYMPHOCYTE, MONOCYTE, NEUTROPHIL; 320×240 JPEG). + +## Download + +```bash +poetry install --extras kaggle +poetry run wbc download-data --dest data +``` + +If the download fails because Kaggle requires authentication, create an API token at + and expose it as `KAGGLE_USERNAME` / `KAGGLE_KEY` +(or `~/.kaggle/kaggle.json`), then retry. You can also download and unzip the dataset +manually and copy its `TRAIN/` and `TEST/` folders here. + +## Expected layout + +``` +data/ +├── TRAIN/ +│ ├── EOSINOPHIL/*.jpeg +│ ├── LYMPHOCYTE/*.jpeg +│ ├── MONOCYTE/*.jpeg +│ └── NEUTROPHIL/*.jpeg +└── TEST/ # same four class folders; used only by `wbc evaluate` +``` + +Class names are taken from the folder names. Files that are not `.jpg/.jpeg/.png` are ignored. +Everything in this folder except this README is git-ignored. +```` + +- [ ] **Step 5: Update `.gitignore`** + +Append: + +```gitignore + +# Project +/data/* +!/data/README.md +runs/ +site/ +docs/reference/cli.md +.venv/ +.ruff_cache/ +.mypy_cache/ +.pytest_cache/ +``` + +- [ ] **Step 6: Commit** + +```bash +git add -A +git commit -m "chore: remove dataset images from tree, keep test fixtures + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 2: Poetry scaffold, tooling, package skeleton + +**Files:** +- Create: `pyproject.toml`, `src/wbc_classification/__init__.py`, `src/wbc_classification/py.typed`, `tests/__init__.py` (empty), `tests/test_package.py`, `CHANGELOG.md` +- Generated: `poetry.lock` + +**Interfaces:** +- Produces: importable package `wbc_classification` with `__version__: str`; a working Poetry environment on Python 3.14 with all groups installed. + +- [ ] **Step 1: Write the failing test** — `tests/test_package.py` + +```python +import wbc_classification + + +def test_version_is_a_string(): + assert isinstance(wbc_classification.__version__, str) + assert wbc_classification.__version__.count(".") == 2 +``` + +- [ ] **Step 2: Write `pyproject.toml` skeleton (no dependencies yet)** + +```toml +[project] +name = "wbc-classification" +version = "0.0.0" +description = "Classify white blood cell images (eosinophil, lymphocyte, monocyte, neutrophil) with PyTorch." +readme = "README.md" +requires-python = ">=3.12,<3.15" +license = { text = "BSD-3-Clause" } +authors = [{ name = "Amin Jamal" }] +keywords = ["white blood cells", "image classification", "pytorch", "medical imaging"] +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: BSD License", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Image Recognition", +] +dependencies = [] + +[project.urls] +Homepage = "https://github.com/includeamin/WBC-Classification" +Documentation = "https://includeamin.github.io/WBC-Classification/" +Changelog = "https://github.com/includeamin/WBC-Classification/blob/main/CHANGELOG.md" + +[project.scripts] +wbc = "wbc_classification.cli:app" + +[project.optional-dependencies] +kaggle = [] + +[tool.poetry] +packages = [{ include = "wbc_classification", from = "src" }] + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.ruff] +line-length = 100 +src = ["src", "tests"] + +[tool.ruff.lint] +select = ["E", "F", "I", "UP", "B", "SIM"] + +[tool.mypy] +python_version = "3.12" +ignore_missing_imports = true +check_untyped_defs = true +warn_unused_ignores = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-m 'not slow'" +markers = ["slow: long-running tests (full training, network); not run in CI"] +``` + +- [ ] **Step 3: Create the environment and add dependencies (latest versions)** + +```bash +poetry env use python3.14 +poetry add torch torchvision numpy opencv-python-headless pillow scikit-learn matplotlib typer pydantic pyyaml +poetry add --group dev pytest ruff mypy types-pyyaml +poetry add --group docs mkdocs mkdocs-material "mkdocstrings[python]" +poetry add --group release python-semantic-release +``` +Expected: each command resolves and installs. If one fails because a package has no Python 3.14 support, STOP and report which package (the spec's fallback is dev on 3.13); do not pin old versions silently. + +- [ ] **Step 4: Add the `kaggle` extra** + +Edit `pyproject.toml`: set `kaggle = ["kagglehub"]` under `[project.optional-dependencies]`, then: + +```bash +poetry lock +poetry install --with dev,docs,release --extras kaggle +``` +Then replace `"kagglehub"` with `"kagglehub>="` (read it with `poetry show kagglehub | head -3`) and run `poetry lock` again. + +- [ ] **Step 5: Create the package** + +`src/wbc_classification/__init__.py`: + +```python +"""Classification of white blood cell images with PyTorch.""" + +from importlib.metadata import version + +__version__ = version("wbc-classification") + +__all__ = ["__version__"] +``` + +`src/wbc_classification/py.typed`: empty file. +`CHANGELOG.md`: + +```markdown +# CHANGELOG + + +``` +`README.md` currently exists (legacy); leave it for Task 14. + +- [ ] **Step 6: Run the test** + +Run: `poetry run pytest tests/test_package.py -v` +Expected: PASS. Also run `poetry run ruff check . && poetry run mypy src`; expected clean. + +- [ ] **Step 7: Verify OpenCV 5 imports** + +Run: `poetry run python -c "import cv2, torch, torchvision; print(cv2.__version__, torch.__version__, torchvision.__version__)"` +Expected: prints three versions. If OpenCV 5 fails to import, run `poetry add "opencv-python-headless<5"` and note it in the commit message. + +- [ ] **Step 8: Commit** + +```bash +git add pyproject.toml poetry.lock src tests CHANGELOG.md +git commit -m "build: scaffold Poetry project with src layout and tooling + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 3: Errors and configuration + +**Files:** +- Create: `src/wbc_classification/errors.py`, `src/wbc_classification/config.py`, `tests/test_config.py` + +**Interfaces:** +- Produces: + - `errors.WBCError(Exception)`, `ConfigError`, `DataError`, `SegmentationError`, `CheckpointError` (all subclass `WBCError`). + - `config.Config` with sections `preprocessing: PreprocessingConfig(crop: bool=False)`, `data: DataConfig(root: Path="data", image_size: int=224, batch_size: int=32, val_fraction: float=0.2, num_workers: int=0)`, `model: ModelConfig(name: str="resnet18", pretrained: bool=True, freeze_backbone_epochs: int=0)`, `train: TrainConfig(name: str="run", epochs: int=30, lr: float=1e-3, weight_decay: float=1e-4, patience: int=7, amp: bool=True, seed: int=42, device: str="auto", output_dir: Path="runs")`. + - `load_config(path: Path | None = None, overrides: dict[str, Any] | None = None) -> Config` — override keys are `"section.field"`, `None` values are skipped. + - `dump_config(config: Config) -> str` (YAML). + +- [ ] **Step 1: Write the failing tests** — `tests/test_config.py` + +```python +from pathlib import Path + +import pytest + +from wbc_classification.config import Config, dump_config, load_config +from wbc_classification.errors import ConfigError + + +def test_defaults(): + cfg = Config() + assert cfg.model.name == "resnet18" + assert cfg.preprocessing.crop is False + assert cfg.data.root == Path("data") + + +def test_load_yaml_with_overrides(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("model:\n name: baseline\ntrain:\n epochs: 3\n") + cfg = load_config(path, {"train.epochs": 5, "data.image_size": None, "data.batch_size": 8}) + assert cfg.model.name == "baseline" + assert cfg.train.epochs == 5 + assert cfg.data.image_size == 224 # None override ignored + assert cfg.data.batch_size == 8 + + +def test_missing_file(tmp_path): + with pytest.raises(ConfigError, match="not found"): + load_config(tmp_path / "nope.yaml") + + +def test_typo_in_section_is_rejected(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("trian:\n epochs: 3\n") + with pytest.raises(ConfigError, match="trian"): + load_config(path) + + +def test_typo_in_field_names_the_key(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("train:\n epohcs: 3\n") + with pytest.raises(ConfigError, match="epohcs"): + load_config(path) + + +def test_out_of_range_value(): + with pytest.raises(ConfigError, match="val_fraction"): + load_config(None, {"data.val_fraction": 0.9}) + + +def test_bad_override_key(): + with pytest.raises(ConfigError, match="section.field"): + load_config(None, {"epochs": 3}) + + +def test_invalid_yaml(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("train: [unclosed") + with pytest.raises(ConfigError, match="Invalid YAML"): + load_config(path) + + +def test_dump_roundtrip(tmp_path): + cfg = load_config(None, {"train.epochs": 7, "data.root": "somewhere"}) + path = tmp_path / "out.yaml" + path.write_text(dump_config(cfg)) + assert load_config(path) == cfg +``` + +- [ ] **Step 2: Run to verify failure** + +Run: `poetry run pytest tests/test_config.py -v` +Expected: FAIL (`ModuleNotFoundError: wbc_classification.config`) + +- [ ] **Step 3: Implement** — `src/wbc_classification/errors.py` + +```python +"""Exception types raised for user-facing problems.""" + + +class WBCError(Exception): + """Base class for errors the CLI reports without a traceback.""" + + +class ConfigError(WBCError): + """Invalid or unreadable configuration.""" + + +class DataError(WBCError): + """Missing, empty or unreadable dataset / image.""" + + +class SegmentationError(WBCError): + """No cell could be located in an image.""" + + +class CheckpointError(WBCError): + """Checkpoint is missing, corrupt or inconsistent.""" +``` + +`src/wbc_classification/config.py`: + +```python +"""Experiment configuration: pydantic models, YAML loading and CLI overrides.""" + +from pathlib import Path +from typing import Any + +import yaml +from pydantic import BaseModel, ConfigDict, Field, ValidationError + +from wbc_classification.errors import ConfigError + + +class _Section(BaseModel): + model_config = ConfigDict(extra="forbid") + + +class PreprocessingConfig(_Section): + crop: bool = False + + +class DataConfig(_Section): + root: Path = Path("data") + image_size: int = Field(224, ge=16) + batch_size: int = Field(32, ge=1) + val_fraction: float = Field(0.2, gt=0, lt=0.5) + num_workers: int = Field(0, ge=0) + + +class ModelConfig(_Section): + name: str = "resnet18" + pretrained: bool = True + freeze_backbone_epochs: int = Field(0, ge=0) + + +class TrainConfig(_Section): + name: str = "run" + epochs: int = Field(30, ge=1) + lr: float = Field(1e-3, gt=0) + weight_decay: float = Field(1e-4, ge=0) + patience: int = Field(7, ge=1) + amp: bool = True + seed: int = 42 + device: str = "auto" + output_dir: Path = Path("runs") + + +class Config(_Section): + preprocessing: PreprocessingConfig = PreprocessingConfig() + data: DataConfig = DataConfig() + model: ModelConfig = ModelConfig() + train: TrainConfig = TrainConfig() + + +def load_config(path: Path | None = None, overrides: dict[str, Any] | None = None) -> Config: + """Load a YAML config (optional) and apply ``{"section.field": value}`` overrides.""" + raw: dict[str, Any] = {} + if path is not None: + if not path.is_file(): + raise ConfigError(f"Config file not found: {path}") + try: + loaded = yaml.safe_load(path.read_text()) + except yaml.YAMLError as exc: + raise ConfigError(f"Invalid YAML in {path}: {exc}") from exc + if loaded is None: + loaded = {} + if not isinstance(loaded, dict): + raise ConfigError(f"Config {path} must be a mapping of sections") + raw = loaded + + for key, value in (overrides or {}).items(): + if value is None: + continue + section, _, field = key.partition(".") + if not field: + raise ConfigError(f"Override key must look like 'section.field', got {key!r}") + target = raw.setdefault(section, {}) + if not isinstance(target, dict): + raise ConfigError(f"Config section {section!r} must be a mapping") + target[field] = value + + try: + return Config.model_validate(raw) + except ValidationError as exc: + details = "; ".join( + f"{'.'.join(str(p) for p in err['loc'])}: {err['msg']}" for err in exc.errors() + ) + raise ConfigError(f"Invalid configuration: {details}") from exc + + +def dump_config(config: Config) -> str: + """Serialise a config to YAML (paths become strings).""" + return yaml.safe_dump(config.model_dump(mode="json"), sort_keys=False) +``` + +- [ ] **Step 4: Run tests** + +Run: `poetry run pytest tests/test_config.py -v` +Expected: all PASS + +- [ ] **Step 5: Commit** + +```bash +git add src tests +git commit -m "feat: add error types and validated YAML configuration + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 4: Optional cell segmentation / crop + +**Files:** +- Create: `src/wbc_classification/preprocessing/__init__.py` (empty docstring), `src/wbc_classification/preprocessing/segmentation.py`, `tests/test_segmentation.py` + +**Interfaces:** +- Consumes: `errors.SegmentationError`. +- Produces (all take an **RGB** `np.ndarray` of shape `(H, W, 3)`, dtype `uint8`): + - `CellSegmentation(mask: np.ndarray, contour: np.ndarray, center: tuple[int, int], radius: int)` (frozen dataclass) + - `segment_cell(rgb) -> CellSegmentation` (raises `SegmentationError`) + - `crop_cell(rgb) -> np.ndarray` (square-ish crop around the minimum enclosing circle, clamped to the image) + - `draw_overlay(rgb, segmentation) -> np.ndarray` + +- [ ] **Step 1: Write the failing tests** — `tests/test_segmentation.py` + +```python +import cv2 +import numpy as np +import pytest +from PIL import Image + +from wbc_classification.errors import SegmentationError +from wbc_classification.preprocessing.segmentation import crop_cell, draw_overlay, segment_cell + + +def _synthetic_cell() -> np.ndarray: + image = np.full((200, 300, 3), 255, dtype=np.uint8) + cv2.circle(image, (150, 100), 40, (120, 60, 160), -1) # purple disc on white + return image + + +def test_segments_synthetic_cell(): + seg = segment_cell(_synthetic_cell()) + assert abs(seg.center[0] - 150) <= 3 and abs(seg.center[1] - 100) <= 3 + assert abs(seg.radius - 40) <= 4 + assert seg.mask.shape == (200, 300) + + +def test_crop_is_around_the_cell(): + crop = crop_cell(_synthetic_cell()) + assert 70 <= crop.shape[0] <= 90 and 70 <= crop.shape[1] <= 90 + + +def test_crop_is_clamped_at_image_border(): + image = np.full((100, 100, 3), 255, dtype=np.uint8) + cv2.circle(image, (5, 5), 30, (120, 60, 160), -1) # cell hanging off the corner + crop = crop_cell(image) + assert crop.size > 0 and crop.shape[0] <= 100 and crop.shape[1] <= 100 + + +def test_no_cell_raises(): + with pytest.raises(SegmentationError): + segment_cell(np.zeros((240, 320, 3), dtype=np.uint8)) + + +def test_bad_shape_raises(): + with pytest.raises(SegmentationError): + segment_cell(np.zeros((10, 10), dtype=np.uint8)) + + +def test_real_image_fixture(cell_image): + rgb = np.asarray(Image.open(cell_image).convert("RGB")) + crop = crop_cell(rgb) + assert crop.ndim == 3 and crop.size > 0 + assert crop.shape[0] <= rgb.shape[0] and crop.shape[1] <= rgb.shape[1] + + +def test_overlay_keeps_shape(): + image = _synthetic_cell() + assert draw_overlay(image, segment_cell(image)).shape == image.shape +``` + +- [ ] **Step 2: Create `tests/conftest.py`** (shared by later tasks) + +```python +from pathlib import Path + +import pytest +from PIL import Image + +FIXTURES = Path(__file__).parent / "fixtures" + + +@pytest.fixture(scope="session") +def data_root() -> Path: + return FIXTURES / "data" + + +@pytest.fixture(scope="session") +def cell_image() -> Path: + return FIXTURES / "cell.jpeg" + + +def _write_image(path: Path, mode: str = "RGB", size: tuple[int, int] = (64, 48)) -> Path: + path.parent.mkdir(parents=True, exist_ok=True) + Image.new(mode, size).save(path) + return path + + +@pytest.fixture +def write_image(): + return _write_image +``` + +- [ ] **Step 3: Run to verify failure** + +Run: `poetry run pytest tests/test_segmentation.py -v` +Expected: FAIL (module not found) + +- [ ] **Step 4: Implement** — `src/wbc_classification/preprocessing/segmentation.py` + +```python +"""Classical HSV-threshold segmentation used for the optional cell crop.""" + +from dataclasses import dataclass + +import cv2 +import numpy as np + +from wbc_classification.errors import SegmentationError + +HSV_LOWER = np.array([80, 60, 140], dtype=np.uint8) +HSV_UPPER = np.array([255, 255, 255], dtype=np.uint8) +MIN_AREA_FRACTION = 0.01 + + +@dataclass(frozen=True) +class CellSegmentation: + mask: np.ndarray + contour: np.ndarray + center: tuple[int, int] + radius: int + + +def segment_cell(rgb: np.ndarray) -> CellSegmentation: + """Locate the largest cell-coloured region in an RGB image.""" + if rgb.ndim != 3 or rgb.shape[2] != 3: + raise SegmentationError(f"expected an (H, W, 3) RGB image, got shape {rgb.shape}") + blurred = cv2.GaussianBlur(rgb, (7, 7), 0) + hsv = cv2.cvtColor(blurred, cv2.COLOR_RGB2HSV) + threshold = cv2.inRange(hsv, HSV_LOWER, HSV_UPPER) + contours, _ = cv2.findContours(threshold, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + raise SegmentationError("no cell-coloured region found") + biggest = max(contours, key=cv2.contourArea) + if cv2.contourArea(biggest) < MIN_AREA_FRACTION * rgb.shape[0] * rgb.shape[1]: + raise SegmentationError("detected region is too small to be a cell") + mask = np.zeros(threshold.shape, dtype=np.uint8) + cv2.drawContours(mask, [biggest], -1, 255, -1) + (cx, cy), radius = cv2.minEnclosingCircle(biggest) + return CellSegmentation(mask, biggest, (int(cx), int(cy)), int(radius)) + + +def crop_cell(rgb: np.ndarray) -> np.ndarray: + """Crop the image to the cell's enclosing square, clamped to the image bounds.""" + segmentation = segment_cell(rgb) + height, width = rgb.shape[:2] + cx, cy = segmentation.center + radius = max(segmentation.radius, 1) + return rgb[max(cy - radius, 0) : min(cy + radius, height), max(cx - radius, 0) : min(cx + radius, width)] + + +def draw_overlay(rgb: np.ndarray, segmentation: CellSegmentation) -> np.ndarray: + """Return a copy of the image with the cell contour and enclosing circle drawn.""" + overlay = rgb.copy() + cv2.drawContours(overlay, [segmentation.contour], -1, (255, 0, 0), 2) + cv2.circle(overlay, segmentation.center, segmentation.radius, (0, 255, 0), 2) + return overlay +``` + +`src/wbc_classification/preprocessing/__init__.py`: `"""Image preprocessing."""` + +- [ ] **Step 5: Run tests** + +Run: `poetry run pytest tests/test_segmentation.py -v` +Expected: all PASS. If `test_real_image_fixture` raises "too small", lower `MIN_AREA_FRACTION` until the fixture passes and keep `test_no_cell_raises` green; record the new value in the commit message. + +- [ ] **Step 6: Commit** + +```bash +git add src tests +git commit -m "feat: add optional HSV cell segmentation and crop + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 5: Labels, datasets, transforms, loaders, dataset download helper + +**Files:** +- Create: `src/wbc_classification/data/__init__.py`, `labels.py`, `datasets.py`, `transforms.py`, `download.py`; `tests/test_data.py` + +**Interfaces:** +- Consumes: `Config`, `errors.*`, `segmentation.crop_cell`. +- Produces: + - `labels.IMAGE_EXTENSIONS: frozenset[str]`, `is_image_file(path: Path) -> bool`, `discover_classes(split_dir: Path) -> list[str]` (sorted, raises `DataError`). + - `transforms.build_transforms(image_size: int, train: bool) -> torchvision.transforms.v2.Compose`. + - `datasets.list_samples(split_dir: Path, class_names: Sequence[str]) -> list[tuple[Path, int]]`; `stratified_split(samples, val_fraction: float, seed: int) -> tuple[list, list]`; `load_image(path: Path, crop: bool = False) -> PIL.Image.Image` (always RGB); `WBCDataset(samples, transform, crop=False)` yielding `(Tensor, int)`; `Loaders(train: DataLoader, val: DataLoader, class_names: list[str])`; `build_train_val_loaders(cfg: Config) -> Loaders`; `build_test_loader(cfg: Config, class_names: Sequence[str]) -> DataLoader`. + - `download.find_dataset_root(base: Path) -> Path`, `install_dataset(source: Path, dest: Path, force: bool = False) -> Path`, `download_dataset(dest: Path, force: bool = False) -> Path`. + +- [ ] **Step 1: Write the failing tests** — `tests/test_data.py` + +```python +import logging + +import pytest +import torch +from PIL import Image + +from wbc_classification.config import Config, DataConfig +from wbc_classification.data.datasets import ( + WBCDataset, + build_test_loader, + build_train_val_loaders, + list_samples, + load_image, + stratified_split, +) +from wbc_classification.data.download import find_dataset_root, install_dataset +from wbc_classification.data.labels import discover_classes +from wbc_classification.data.transforms import build_transforms +from wbc_classification.errors import DataError + +CLASSES = ["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"] + + +def _cfg(data_root): + return Config(data=DataConfig(root=data_root, image_size=32, batch_size=4, val_fraction=0.25)) + + +def test_discover_classes_sorted(data_root): + assert discover_classes(data_root / "TRAIN") == CLASSES + + +def test_missing_dir_hints_download(tmp_path): + with pytest.raises(DataError, match="wbc download-data"): + discover_classes(tmp_path / "nope") + + +def test_empty_class_folder_raises(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + (tmp_path / "B").mkdir() + with pytest.raises(DataError, match="B"): + discover_classes(tmp_path) + + +def test_hidden_dirs_ignored(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + (tmp_path / ".ipynb_checkpoints").mkdir() + assert discover_classes(tmp_path) == ["A"] + + +def test_list_samples_ignores_non_images(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + (tmp_path / "A" / ".DS_Store").write_bytes(b"x") + (tmp_path / "A" / "notes.txt").write_text("hi") + assert list_samples(tmp_path, ["A"]) == [(tmp_path / "A" / "1.png", 0)] + + +def test_list_samples_missing_class(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + with pytest.raises(DataError, match="B"): + list_samples(tmp_path, ["A", "B"]) + + +def test_stratified_split_keeps_all_classes(data_root): + samples = list_samples(data_root / "TRAIN", CLASSES) + train, val = stratified_split(samples, 0.25, seed=0) + assert len(train) + len(val) == len(samples) == 24 + assert {label for _, label in val} == {0, 1, 2, 3} + + +def test_stratified_split_is_seeded(data_root): + samples = list_samples(data_root / "TRAIN", CLASSES) + assert stratified_split(samples, 0.25, 1) == stratified_split(samples, 0.25, 1) + + +def test_stratified_split_too_small_raises(tmp_path, write_image): + for cls in "AB": + write_image(tmp_path / cls / "1.png") # one image per class + with pytest.raises(DataError, match="validation split"): + stratified_split(list_samples(tmp_path, ["A", "B"]), 0.25, 0) + + +@pytest.mark.parametrize("mode", ["L", "RGBA", "RGB"]) +def test_load_image_always_rgb(tmp_path, write_image, mode): + path = write_image(tmp_path / "x.png", mode=mode) + assert load_image(path).mode == "RGB" + + +def test_load_image_corrupt_names_file(tmp_path): + bad = tmp_path / "broken.jpeg" + bad.write_bytes(b"not an image") + with pytest.raises(DataError, match="broken.jpeg"): + load_image(bad) + + +def test_crop_falls_back_to_full_image(tmp_path, caplog): + path = tmp_path / "black.png" + Image.new("RGB", (64, 48)).save(path) # black: no cell + with caplog.at_level(logging.WARNING): + image = load_image(path, crop=True) + assert image.size == (64, 48) + assert "No cell found" in caplog.text + + +def test_transforms_shape_and_dtype(data_root): + sample = list_samples(data_root / "TRAIN", CLASSES)[0][0] + for train in (True, False): + out = build_transforms(32, train)(load_image(sample)) + assert out.shape == (3, 32, 32) and out.dtype == torch.float32 + + +def test_dataset_item(data_root): + samples = list_samples(data_root / "TRAIN", CLASSES) + tensor, label = WBCDataset(samples, build_transforms(32, False))[0] + assert tensor.shape == (3, 32, 32) and isinstance(label, int) + + +def test_build_loaders(data_root): + loaders = build_train_val_loaders(_cfg(data_root)) + images, labels = next(iter(loaders.train)) + assert images.shape == (4, 3, 32, 32) and labels.shape == (4,) + assert loaders.class_names == CLASSES + test_loader = build_test_loader(_cfg(data_root), loaders.class_names) + assert len(test_loader.dataset) == 8 + + +def test_install_dataset_from_nested_kaggle_layout(tmp_path, write_image): + source = tmp_path / "cache" / "dataset2-master" / "images" + for split in ("TRAIN", "TEST", "TEST_SIMPLE"): + write_image(source / split / "A" / "1.png") + assert find_dataset_root(tmp_path / "cache") == source + dest = tmp_path / "data" + install_dataset(tmp_path / "cache", dest) + assert (dest / "TRAIN" / "A" / "1.png").is_file() and (dest / "TEST").is_dir() + assert not (dest / "TEST_SIMPLE").exists() + with pytest.raises(DataError, match="--force"): + install_dataset(tmp_path / "cache", dest) + install_dataset(tmp_path / "cache", dest, force=True) + + +def test_find_dataset_root_missing(tmp_path): + with pytest.raises(DataError, match="TRAIN"): + find_dataset_root(tmp_path) +``` + +- [ ] **Step 2: Run to verify failure** + +Run: `poetry run pytest tests/test_data.py -v` +Expected: FAIL (modules not found) + +- [ ] **Step 3: Implement** — `src/wbc_classification/data/__init__.py`: `"""Datasets, transforms and data download."""` + +`labels.py`: + +```python +"""Class discovery from the dataset folder layout.""" + +from pathlib import Path + +from wbc_classification.errors import DataError + +IMAGE_EXTENSIONS = frozenset({".jpg", ".jpeg", ".png"}) + + +def is_image_file(path: Path) -> bool: + return path.is_file() and path.suffix.lower() in IMAGE_EXTENSIONS + + +def discover_classes(split_dir: Path) -> list[str]: + """Class names are the sorted sub-folder names of a split directory.""" + if not split_dir.is_dir(): + raise DataError( + f"Dataset directory not found: {split_dir}. " + "Run `wbc download-data` or point data.root at your dataset." + ) + class_dirs = sorted( + p for p in split_dir.iterdir() if p.is_dir() and not p.name.startswith(".") + ) + if not class_dirs: + raise DataError(f"No class folders found in {split_dir}") + for class_dir in class_dirs: + if not any(is_image_file(f) for f in class_dir.iterdir()): + raise DataError(f"Class folder {class_dir} contains no images") + return [p.name for p in class_dirs] +``` + +`transforms.py`: + +```python +"""Train / eval image transforms.""" + +import torch +from torchvision.transforms import v2 + +IMAGENET_MEAN = (0.485, 0.456, 0.406) +IMAGENET_STD = (0.229, 0.224, 0.225) + + +def build_transforms(image_size: int, train: bool) -> v2.Compose: + steps: list[v2.Transform] = [v2.Resize((image_size, image_size))] + if train: + steps += [ + v2.RandomHorizontalFlip(), + v2.RandomVerticalFlip(), + v2.RandomRotation(180), + v2.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.02), + ] + steps += [ + v2.ToImage(), + v2.ToDtype(torch.float32, scale=True), + v2.Normalize(IMAGENET_MEAN, IMAGENET_STD), + ] + return v2.Compose(steps) +``` + +`datasets.py`: + +```python +"""Dataset listing, splitting, loading and DataLoader construction.""" + +import logging +import math +from collections import Counter +from collections.abc import Callable, Sequence +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from PIL import Image +from sklearn.model_selection import train_test_split +from torch.utils.data import DataLoader, Dataset + +from wbc_classification.config import Config +from wbc_classification.data.labels import discover_classes, is_image_file +from wbc_classification.data.transforms import build_transforms +from wbc_classification.errors import DataError, SegmentationError +from wbc_classification.preprocessing.segmentation import crop_cell + +logger = logging.getLogger(__name__) + +Sample = tuple[Path, int] + + +def list_samples(split_dir: Path, class_names: Sequence[str]) -> list[Sample]: + samples: list[Sample] = [] + for index, name in enumerate(class_names): + class_dir = split_dir / name + if not class_dir.is_dir(): + raise DataError(f"Missing class folder {class_dir}") + files = sorted(p for p in class_dir.iterdir() if is_image_file(p)) + if not files: + raise DataError(f"Class folder {class_dir} contains no images") + samples.extend((p, index) for p in files) + return samples + + +def stratified_split( + samples: list[Sample], val_fraction: float, seed: int +) -> tuple[list[Sample], list[Sample]]: + labels = [label for _, label in samples] + counts = Counter(labels) + n_classes = len(counts) + n_val = math.ceil(val_fraction * len(samples)) + n_train = len(samples) - n_val + if min(counts.values()) < 2 or n_val < n_classes or n_train < n_classes: + raise DataError( + f"Not enough images to make a stratified validation split " + f"({len(samples)} images, {n_classes} classes, val_fraction={val_fraction}); " + "add more images or raise data.val_fraction." + ) + train, val = train_test_split( + samples, test_size=val_fraction, stratify=labels, random_state=seed + ) + return train, val + + +def load_image(path: Path, crop: bool = False) -> Image.Image: + """Load an image as RGB, optionally cropped to the cell (full image if none found).""" + try: + with Image.open(path) as handle: + image = handle.convert("RGB") + except OSError as exc: + raise DataError(f"Cannot read image {path}: {exc}") from exc + if crop: + try: + image = Image.fromarray(crop_cell(np.asarray(image))) + except SegmentationError as exc: + logger.warning("No cell found in %s (%s); using the full image", path, exc) + return image + + +class WBCDataset(Dataset[tuple[torch.Tensor, int]]): + def __init__( + self, samples: list[Sample], transform: Callable[[Image.Image], torch.Tensor], crop: bool = False + ) -> None: + self.samples = samples + self.transform = transform + self.crop = crop + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: + path, label = self.samples[index] + return self.transform(load_image(path, self.crop)), label + + +@dataclass(frozen=True) +class Loaders: + train: DataLoader + val: DataLoader + class_names: list[str] + + +def build_train_val_loaders(cfg: Config) -> Loaders: + train_dir = cfg.data.root / "TRAIN" + class_names = discover_classes(train_dir) + train_samples, val_samples = stratified_split( + list_samples(train_dir, class_names), cfg.data.val_fraction, cfg.train.seed + ) + crop = cfg.preprocessing.crop + train_ds = WBCDataset(train_samples, build_transforms(cfg.data.image_size, True), crop) + val_ds = WBCDataset(val_samples, build_transforms(cfg.data.image_size, False), crop) + common = { + "batch_size": cfg.data.batch_size, + "num_workers": cfg.data.num_workers, + "pin_memory": torch.cuda.is_available(), + } + generator = torch.Generator().manual_seed(cfg.train.seed) + return Loaders( + train=DataLoader(train_ds, shuffle=True, generator=generator, **common), + val=DataLoader(val_ds, shuffle=False, **common), + class_names=class_names, + ) + + +def build_test_loader(cfg: Config, class_names: Sequence[str]) -> DataLoader: + samples = list_samples(cfg.data.root / "TEST", class_names) + dataset = WBCDataset( + samples, build_transforms(cfg.data.image_size, False), cfg.preprocessing.crop + ) + return DataLoader( + dataset, + batch_size=cfg.data.batch_size, + shuffle=False, + num_workers=cfg.data.num_workers, + pin_memory=torch.cuda.is_available(), + ) +``` + +`download.py`: + +```python +"""Download and install the Kaggle blood-cells dataset.""" + +import shutil +from pathlib import Path + +from wbc_classification.errors import DataError + +DATASET_HANDLE = "paultimothymooney/blood-cells" +SPLITS = ("TRAIN", "TEST") + + +def find_dataset_root(base: Path) -> Path: + """Find the folder that contains both TRAIN/ and TEST/ below ``base``.""" + for train_dir in sorted(base.rglob("TRAIN")): + if train_dir.is_dir() and (train_dir.parent / "TEST").is_dir(): + return train_dir.parent + raise DataError(f"Could not find TRAIN/ and TEST/ folders under {base}") + + +def install_dataset(source: Path, dest: Path, force: bool = False) -> Path: + root = find_dataset_root(source) + for split in SPLITS: + target = dest / split + if target.exists(): + if not force: + raise DataError(f"{target} already exists; pass --force to overwrite") + shutil.rmtree(target) + shutil.copytree(root / split, target) + return dest + + +def download_dataset(dest: Path, force: bool = False) -> Path: + try: + import kagglehub + except ImportError as exc: + raise DataError( + "kagglehub is not installed. Run `poetry install --extras kaggle` " + "(or `pip install 'wbc-classification[kaggle]'`)." + ) from exc + try: + source = Path(kagglehub.dataset_download(DATASET_HANDLE)) + except Exception as exc: # network / auth errors from kagglehub vary widely + raise DataError( + f"Download failed: {exc}. If Kaggle requires authentication see data/README.md." + ) from exc + return install_dataset(source, dest, force) +``` + +- [ ] **Step 4: Run tests** + +Run: `poetry run pytest tests/test_data.py -v` +Expected: all PASS + +- [ ] **Step 5: Commit** + +```bash +git add src tests +git commit -m "feat: add dataset discovery, splits, transforms, loaders and download helper + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 6: Models and registry + +**Files:** +- Create: `src/wbc_classification/models/__init__.py`, `baseline.py`, `backbone.py`, `registry.py`; `tests/test_models.py` + +**Interfaces:** +- Consumes: `errors.ConfigError`. +- Produces: + - `baseline.IncludeNet(num_classes: int, width: int = 32, dropout: float = 0.5)` (`nn.Module`; any input size ≥16). + - `backbone.SUPPORTED_BACKBONES = ("resnet18", "resnet50", "efficientnet_b0")`, `build_backbone(name: str, num_classes: int, pretrained: bool) -> nn.Module`, `set_backbone_frozen(model: nn.Module, frozen: bool) -> None` (only the classification head stays trainable when frozen). + - `registry.MODEL_NAMES = ("baseline", *SUPPORTED_BACKBONES)`, `build_model(name: str, num_classes: int, pretrained: bool = False) -> nn.Module` (raises `ConfigError` for unknown), `supports_freezing(name: str) -> bool`. + +- [ ] **Step 1: Write the failing tests** — `tests/test_models.py` + +```python +import pytest +import torch + +from wbc_classification.errors import ConfigError +from wbc_classification.models.backbone import set_backbone_frozen +from wbc_classification.models.registry import MODEL_NAMES, build_model, supports_freezing + + +@pytest.mark.parametrize("name", MODEL_NAMES) +def test_forward_shape(name): + model = build_model(name, num_classes=4).eval() + with torch.no_grad(): + assert model(torch.zeros(2, 3, 64, 64)).shape == (2, 4) + + +def test_baseline_accepts_tiny_and_odd_sizes(): + model = build_model("baseline", num_classes=4).eval() + with torch.no_grad(): + assert model(torch.zeros(1, 3, 16, 16)).shape == (1, 4) + assert model(torch.zeros(1, 3, 50, 70)).shape == (1, 4) + + +def test_unknown_model_lists_choices(): + with pytest.raises(ConfigError, match="resnet18"): + build_model("vgg", num_classes=4) + + +@pytest.mark.parametrize("name", ["resnet18", "efficientnet_b0"]) +def test_freeze_leaves_only_head_trainable(name): + model = build_model(name, num_classes=4) + set_backbone_frozen(model, True) + trainable = [n for n, p in model.named_parameters() if p.requires_grad] + assert trainable and all(n.startswith(("fc", "classifier")) for n in trainable) + set_backbone_frozen(model, False) + assert all(p.requires_grad for p in model.parameters()) + + +def test_supports_freezing(): + assert supports_freezing("resnet18") and not supports_freezing("baseline") +``` + +- [ ] **Step 2: Run to verify failure** + +Run: `poetry run pytest tests/test_models.py -v` +Expected: FAIL (module not found) + +- [ ] **Step 3: Implement** — `models/__init__.py`: `"""Model definitions and registry."""` + +`baseline.py`: + +```python +"""IncludeNet: the project's original small CNN, rewritten in PyTorch.""" + +import torch +from torch import nn + + +def _block(in_channels: int, out_channels: int, pool: bool) -> nn.Sequential: + layers: list[nn.Module] = [ + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ] + if pool: + layers.append(nn.MaxPool2d(2)) + return nn.Sequential(*layers) + + +class IncludeNet(nn.Module): + """Four conv blocks, global average pooling and a linear head; input-size independent.""" + + def __init__(self, num_classes: int, width: int = 32, dropout: float = 0.5) -> None: + super().__init__() + self.features = nn.Sequential( + _block(3, width, pool=True), + _block(width, 2 * width, pool=True), + _block(2 * width, 4 * width, pool=True), + _block(4 * width, 4 * width, pool=False), + ) + self.pool = nn.AdaptiveAvgPool2d(1) + self.dropout = nn.Dropout(dropout) + self.classifier = nn.Linear(4 * width, num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.pool(self.features(x)).flatten(1) + return self.classifier(self.dropout(x)) +``` + +`backbone.py`: + +```python +"""Pretrained torchvision backbones with a replaced classification head.""" + +from typing import Any, cast + +from torch import nn +from torchvision import models as tv_models + +SUPPORTED_BACKBONES = ("resnet18", "resnet50", "efficientnet_b0") + + +def build_backbone(name: str, num_classes: int, pretrained: bool) -> nn.Module: + model = cast(Any, tv_models.get_model(name, weights="DEFAULT" if pretrained else None)) + if name.startswith("resnet"): + model.fc = nn.Linear(model.fc.in_features, num_classes) + else: + model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, num_classes) + return cast(nn.Module, model) + + +def set_backbone_frozen(model: nn.Module, frozen: bool) -> None: + """Freeze everything except the classification head (or unfreeze everything).""" + m = cast(Any, model) + head = m.fc if hasattr(m, "fc") else m.classifier + head_ids = {id(p) for p in head.parameters()} + for param in model.parameters(): + param.requires_grad = (not frozen) or id(param) in head_ids +``` + +`registry.py`: + +```python +"""Name -> model factory.""" + +from torch import nn + +from wbc_classification.errors import ConfigError +from wbc_classification.models.backbone import SUPPORTED_BACKBONES, build_backbone +from wbc_classification.models.baseline import IncludeNet + +MODEL_NAMES = ("baseline", *SUPPORTED_BACKBONES) + + +def build_model(name: str, num_classes: int, pretrained: bool = False) -> nn.Module: + if name == "baseline": + return IncludeNet(num_classes) # no pretrained weights exist for the baseline + if name in SUPPORTED_BACKBONES: + return build_backbone(name, num_classes, pretrained) + raise ConfigError(f"Unknown model {name!r}. Available: {', '.join(MODEL_NAMES)}") + + +def supports_freezing(name: str) -> bool: + return name in SUPPORTED_BACKBONES +``` + +- [ ] **Step 4: Run tests** + +Run: `poetry run pytest tests/test_models.py -v` +Expected: all PASS (no network needed; `pretrained=False`) + +- [ ] **Step 5: Commit** + +```bash +git add src tests +git commit -m "feat: add PyTorch baseline model, backbones and registry + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 7: Runtime helpers, metrics, checkpoints + +**Files:** +- Create: `src/wbc_classification/engine/__init__.py`, `runtime.py`, `metrics.py`, `checkpoint.py`; `tests/test_engine_basics.py` + +**Interfaces:** +- Consumes: `Config`, `build_model`, `errors.*`. +- Produces: + - `runtime.set_seed(seed: int) -> None`; `runtime.resolve_device(name: str) -> torch.device` (`"auto"` → cuda, then mps, then cpu; invalid → `ConfigError`). + - `metrics.compute_metrics(y_true: Sequence[int], y_pred: Sequence[int], class_names: Sequence[str]) -> dict` with keys `accuracy: float`, `report: dict`, `confusion_matrix: list[list[int]]`, `class_names: list[str]` (JSON-serialisable); `metrics.save_confusion_matrix(matrix, class_names, path: Path) -> None`; `metrics.save_curves(history: list[dict[str, float]], path: Path) -> None` (history rows have keys `epoch, train_loss, train_acc, val_loss, val_acc, lr`); `metrics.format_report(metrics: dict) -> str`. + - `checkpoint.save_checkpoint(path: Path, model: nn.Module, class_names: Sequence[str], config: Config, epoch: int, val_accuracy: float) -> None`; `checkpoint.load_checkpoint(path: Path) -> tuple[nn.Module, list[str], Config]` (CPU, eval-mode-agnostic; raises `CheckpointError`). + +- [ ] **Step 1: Write the failing tests** — `tests/test_engine_basics.py` + +```python +import json + +import pytest +import torch + +from wbc_classification.config import Config, ModelConfig +from wbc_classification.engine.checkpoint import load_checkpoint, save_checkpoint +from wbc_classification.engine.metrics import ( + compute_metrics, + format_report, + save_confusion_matrix, + save_curves, +) +from wbc_classification.engine.runtime import resolve_device, set_seed +from wbc_classification.errors import CheckpointError, ConfigError +from wbc_classification.models.registry import build_model + +NAMES = ["A", "B", "C", "D"] + + +def test_resolve_device(): + assert resolve_device("cpu").type == "cpu" + assert resolve_device("auto").type in {"cpu", "cuda", "mps"} + with pytest.raises(ConfigError, match="bogus"): + resolve_device("bogus") + + +def test_set_seed_makes_torch_deterministic(): + set_seed(3) + first = torch.rand(3) + set_seed(3) + assert torch.equal(first, torch.rand(3)) + + +def test_compute_metrics_perfect(): + m = compute_metrics([0, 1, 2, 3], [0, 1, 2, 3], NAMES) + assert m["accuracy"] == 1.0 + assert m["confusion_matrix"] == [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]] + json.dumps(m) # must be JSON-serialisable + assert "A" in format_report(m) + + +def test_compute_metrics_handles_missing_class_in_predictions(): + m = compute_metrics([0, 0, 1, 1], [0, 0, 0, 0], NAMES) + assert m["accuracy"] == 0.5 and len(m["confusion_matrix"]) == 4 + json.dumps(m) + + +def test_plots_are_written(tmp_path): + save_confusion_matrix([[1, 0], [0, 1]], ["A", "B"], tmp_path / "cm.png") + history = [ + {"epoch": 1, "train_loss": 1.0, "train_acc": 0.5, "val_loss": 1.1, "val_acc": 0.4, "lr": 0.1}, + {"epoch": 2, "train_loss": 0.8, "train_acc": 0.6, "val_loss": 0.9, "val_acc": 0.5, "lr": 0.05}, + ] + save_curves(history, tmp_path / "curves.png") + assert (tmp_path / "cm.png").stat().st_size > 0 and (tmp_path / "curves.png").stat().st_size > 0 + + +def _baseline(): + return build_model("baseline", num_classes=4) + + +def test_checkpoint_roundtrip(tmp_path): + model = _baseline().eval() + cfg = Config(model=ModelConfig(name="baseline", pretrained=False)) + path = tmp_path / "m.pt" + save_checkpoint(path, model, NAMES, cfg, epoch=3, val_accuracy=0.9) + loaded, names, loaded_cfg = load_checkpoint(path) + assert names == NAMES and loaded_cfg == cfg + x = torch.rand(1, 3, 32, 32) + assert torch.allclose(model(x), loaded.eval()(x)) + + +def test_checkpoint_missing(tmp_path): + with pytest.raises(CheckpointError, match="not found"): + load_checkpoint(tmp_path / "nope.pt") + + +def test_checkpoint_corrupt_file(tmp_path): + bad = tmp_path / "bad.pt" + bad.write_bytes(b"junk") + with pytest.raises(CheckpointError, match="bad.pt"): + load_checkpoint(bad) + + +def test_checkpoint_not_ours(tmp_path): + path = tmp_path / "other.pt" + torch.save({"hello": 1}, path) + with pytest.raises(CheckpointError, match="not a wbc-classification checkpoint"): + load_checkpoint(path) + + +def test_checkpoint_class_count_mismatch(tmp_path): + cfg = Config(model=ModelConfig(name="baseline", pretrained=False)) + path = tmp_path / "m.pt" + save_checkpoint(path, _baseline(), ["A", "B", "C"], cfg, epoch=1, val_accuracy=0.1) # 4 outputs vs 3 names + with pytest.raises(CheckpointError, match="do not match"): + load_checkpoint(path) +``` + +- [ ] **Step 2: Run to verify failure** + +Run: `poetry run pytest tests/test_engine_basics.py -v` +Expected: FAIL (module not found) + +- [ ] **Step 3: Implement** — `engine/__init__.py`: `"""Training, evaluation and inference."""` + +`runtime.py`: + +```python +"""Seeding and device selection.""" + +import random + +import numpy as np +import torch + +from wbc_classification.errors import ConfigError + + +def set_seed(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +def resolve_device(name: str) -> torch.device: + if name == "auto": + if torch.cuda.is_available(): + return torch.device("cuda") + if torch.backends.mps.is_available(): + return torch.device("mps") + return torch.device("cpu") + try: + return torch.device(name) + except RuntimeError as exc: + raise ConfigError(f"Unknown device {name!r} (use auto, cpu, cuda, cuda:0, mps)") from exc +``` + +`metrics.py`: + +```python +"""Classification metrics and plots.""" + +import json +from collections.abc import Sequence +from pathlib import Path +from typing import Any + +from matplotlib.figure import Figure +from sklearn.metrics import accuracy_score, classification_report, confusion_matrix + + +def compute_metrics( + y_true: Sequence[int], y_pred: Sequence[int], class_names: Sequence[str] +) -> dict[str, Any]: + labels = list(range(len(class_names))) + report = classification_report( + y_true, y_pred, labels=labels, target_names=list(class_names), output_dict=True, zero_division=0 + ) + matrix = confusion_matrix(y_true, y_pred, labels=labels) + result = { + "accuracy": float(accuracy_score(y_true, y_pred)), + "report": report, + "confusion_matrix": matrix.tolist(), + "class_names": list(class_names), + } + return json.loads(json.dumps(result, default=float)) # plain, JSON-safe types + + +def format_report(metrics: dict[str, Any]) -> str: + lines = [f"{'class':<14}{'precision':>10}{'recall':>10}{'f1':>10}{'support':>10}"] + for name in metrics["class_names"]: + row = metrics["report"][name] + lines.append( + f"{name:<14}{row['precision']:>10.3f}{row['recall']:>10.3f}" + f"{row['f1-score']:>10.3f}{int(row['support']):>10}" + ) + lines.append(f"{'accuracy':<14}{metrics['accuracy']:>40.3f}") + return "\n".join(lines) + + +def save_confusion_matrix(matrix: Sequence[Sequence[int]], class_names: Sequence[str], path: Path) -> None: + fig = Figure(figsize=(6, 5)) + ax = fig.subplots() + image = ax.imshow(matrix, cmap="Blues") + ax.set_xticks(range(len(class_names)), labels=class_names, rotation=45, ha="right") + ax.set_yticks(range(len(class_names)), labels=class_names) + ax.set_xlabel("Predicted") + ax.set_ylabel("True") + for i, row in enumerate(matrix): + for j, value in enumerate(row): + ax.text(j, i, str(value), ha="center", va="center") + fig.colorbar(image, ax=ax) + fig.savefig(path, dpi=150, bbox_inches="tight") + + +def save_curves(history: Sequence[dict[str, float]], path: Path) -> None: + epochs = [row["epoch"] for row in history] + fig = Figure(figsize=(10, 4)) + loss_ax, acc_ax = fig.subplots(1, 2) + loss_ax.plot(epochs, [r["train_loss"] for r in history], label="train") + loss_ax.plot(epochs, [r["val_loss"] for r in history], label="val") + loss_ax.set(title="Loss", xlabel="Epoch") + acc_ax.plot(epochs, [r["train_acc"] for r in history], label="train") + acc_ax.plot(epochs, [r["val_acc"] for r in history], label="val") + acc_ax.set(title="Accuracy", xlabel="Epoch") + loss_ax.legend() + acc_ax.legend() + fig.savefig(path, dpi=150, bbox_inches="tight") +``` + +`checkpoint.py`: + +```python +"""Single-file checkpoints: weights + class names + config.""" + +import pickle +from collections.abc import Sequence +from pathlib import Path + +import torch +from torch import nn + +from wbc_classification.config import Config +from wbc_classification.errors import CheckpointError, ConfigError +from wbc_classification.models.registry import build_model + +_REQUIRED_KEYS = {"state_dict", "class_names", "config"} + + +def save_checkpoint( + path: Path, + model: nn.Module, + class_names: Sequence[str], + config: Config, + epoch: int, + val_accuracy: float, +) -> None: + torch.save( + { + "state_dict": {k: v.detach().cpu() for k, v in model.state_dict().items()}, + "class_names": list(class_names), + "config": config.model_dump(mode="json"), + "epoch": epoch, + "val_accuracy": val_accuracy, + }, + path, + ) + + +def load_checkpoint(path: Path) -> tuple[nn.Module, list[str], Config]: + if not path.is_file(): + raise CheckpointError(f"Checkpoint not found: {path}") + try: + payload = torch.load(path, map_location="cpu", weights_only=True) + except (RuntimeError, pickle.UnpicklingError, EOFError, OSError) as exc: + raise CheckpointError(f"Cannot read checkpoint {path}: {exc}") from exc + if not isinstance(payload, dict) or not _REQUIRED_KEYS <= payload.keys(): + raise CheckpointError(f"{path} is not a wbc-classification checkpoint") + try: + config = Config.model_validate(payload["config"]) + class_names = list(payload["class_names"]) + model = build_model(config.model.name, num_classes=len(class_names), pretrained=False) + model.load_state_dict(payload["state_dict"]) + except ConfigError as exc: + raise CheckpointError(f"{path} stores an unusable model config: {exc}") from exc + except ValueError as exc: # pydantic.ValidationError subclasses ValueError + raise CheckpointError(f"{path} stores an invalid config: {exc}") from exc + except RuntimeError as exc: + raise CheckpointError( + f"Weights in {path} do not match its stored class names / model ({exc})" + ) from exc + return model, class_names, config +``` + +- [ ] **Step 4: Run tests** + +Run: `poetry run pytest tests/test_engine_basics.py -v` +Expected: all PASS + +- [ ] **Step 5: Commit** + +```bash +git add src tests +git commit -m "feat: add runtime helpers, metrics and checkpoint IO + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 8: Training engine + +**Files:** +- Create: `src/wbc_classification/engine/train.py`; `tests/test_train.py` +- Modify: `tests/conftest.py` (add shared tiny-config helpers and a session-scoped trained run) + +**Interfaces:** +- Consumes: `Config`, `build_train_val_loaders`, `build_model`, `set_backbone_frozen`, `supports_freezing`, `save_checkpoint`, `save_curves`, `dump_config`, `set_seed`, `resolve_device`. +- Produces: `train.TrainResult(run_dir: Path, best_checkpoint: Path, last_checkpoint: Path, best_val_accuracy: float, history: list[dict[str, float]])` (frozen dataclass); `train.train(cfg: Config) -> TrainResult`. Run dir layout: `/-/{config.yaml, metrics.csv, curves.png, best.pt, last.pt}`. +- conftest produces: `tiny_config(out_dir: Path, data_root: Path) -> Config` helper (module-level function `make_tiny_config`), fixture `tiny_config` (function scope), fixture `trained` (session scope → `TrainResult`). + +- [ ] **Step 1: Extend `tests/conftest.py`** + +Add below the existing code: + +```python +from wbc_classification.config import Config, DataConfig, ModelConfig, TrainConfig + + +def make_tiny_config(output_dir: Path, data_root: Path) -> Config: + return Config( + data=DataConfig(root=data_root, image_size=32, batch_size=4, val_fraction=0.25), + model=ModelConfig(name="baseline", pretrained=False), + train=TrainConfig(name="test", epochs=1, amp=False, device="cpu", output_dir=output_dir), + ) + + +@pytest.fixture +def tiny_config(tmp_path: Path, data_root: Path) -> Config: + return make_tiny_config(tmp_path / "runs", data_root) + + +@pytest.fixture(scope="session") +def trained(tmp_path_factory: pytest.TempPathFactory, data_root: Path): + from wbc_classification.engine.train import train + + return train(make_tiny_config(tmp_path_factory.mktemp("runs"), data_root)) +``` + +- [ ] **Step 2: Write the failing tests** — `tests/test_train.py` + +```python +import csv + +import pytest + +from wbc_classification.engine.checkpoint import load_checkpoint +from wbc_classification.engine.train import train +from wbc_classification.errors import ConfigError + + +def test_train_writes_all_artifacts(trained): + run = trained.run_dir + for name in ("config.yaml", "metrics.csv", "curves.png", "best.pt", "last.pt"): + assert (run / name).is_file(), name + assert len(trained.history) == 1 + assert 0.0 <= trained.best_val_accuracy <= 1.0 + with (run / "metrics.csv").open() as handle: + rows = list(csv.DictReader(handle)) + assert rows[0]["epoch"] == "1" and "val_acc" in rows[0] + + +def test_trained_checkpoint_loads(trained): + model, class_names, cfg = load_checkpoint(trained.best_checkpoint) + assert len(class_names) == 4 and cfg.model.name == "baseline" + + +def test_freeze_on_baseline_is_rejected(tiny_config): + tiny_config.model.freeze_backbone_epochs = 1 + with pytest.raises(ConfigError, match="baseline"): + train(tiny_config) + + +def test_early_stopping(tiny_config): + tiny_config.train.epochs = 6 + tiny_config.train.patience = 1 + result = train(tiny_config) + assert 1 <= len(result.history) <= 6 + + +def test_same_seed_same_first_epoch(tiny_config, tmp_path): + first = train(tiny_config).history[0]["train_loss"] + tiny_config.train.output_dir = tmp_path / "again" + assert train(tiny_config).history[0]["train_loss"] == pytest.approx(first, rel=1e-4) +``` + +- [ ] **Step 3: Run to verify failure** + +Run: `poetry run pytest tests/test_train.py -v` +Expected: FAIL (module not found) + +- [ ] **Step 4: Implement** — `engine/train.py` + +```python +"""Training loop.""" + +import csv +import logging +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path + +import torch +from torch import nn +from torch.optim import AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader + +from wbc_classification.config import Config, dump_config +from wbc_classification.data.datasets import build_train_val_loaders +from wbc_classification.engine.checkpoint import save_checkpoint +from wbc_classification.engine.metrics import save_curves +from wbc_classification.engine.runtime import resolve_device, set_seed +from wbc_classification.errors import ConfigError +from wbc_classification.models.backbone import set_backbone_frozen +from wbc_classification.models.registry import build_model, supports_freezing + +logger = logging.getLogger(__name__) + +HISTORY_FIELDS = ["epoch", "train_loss", "train_acc", "val_loss", "val_acc", "lr"] + + +@dataclass(frozen=True) +class TrainResult: + run_dir: Path + best_checkpoint: Path + last_checkpoint: Path + best_val_accuracy: float + history: list[dict[str, float]] + + +def _run_epoch( + model: nn.Module, + loader: DataLoader, + device: torch.device, + criterion: nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scaler: torch.amp.GradScaler | None = None, + amp: bool = False, +) -> tuple[float, float]: + training = optimizer is not None + model.train(training) + total_loss, correct, count = 0.0, 0, 0 + with torch.set_grad_enabled(training): + for images, labels in loader: + images, labels = images.to(device), labels.to(device) + with torch.autocast(device_type=device.type, enabled=amp): + logits = model(images) + loss = criterion(logits, labels) + if optimizer is not None and scaler is not None: + optimizer.zero_grad(set_to_none=True) + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + total_loss += loss.item() * labels.size(0) + correct += (logits.argmax(dim=1) == labels).sum().item() + count += labels.size(0) + return total_loss / count, correct / count + + +def train(cfg: Config) -> TrainResult: + if cfg.model.freeze_backbone_epochs and not supports_freezing(cfg.model.name): + raise ConfigError( + f"model.freeze_backbone_epochs is not supported for {cfg.model.name!r} " + "(it has no pretrained backbone)" + ) + set_seed(cfg.train.seed) + device = resolve_device(cfg.train.device) + loaders = build_train_val_loaders(cfg) + model = build_model(cfg.model.name, len(loaders.class_names), cfg.model.pretrained).to(device) + + run_dir = cfg.train.output_dir / f"{cfg.train.name}-{datetime.now():%Y%m%d-%H%M%S}" + run_dir.mkdir(parents=True, exist_ok=True) + (run_dir / "config.yaml").write_text(dump_config(cfg)) + + criterion = nn.CrossEntropyLoss() + optimizer = AdamW(model.parameters(), lr=cfg.train.lr, weight_decay=cfg.train.weight_decay) + scheduler = CosineAnnealingLR(optimizer, T_max=cfg.train.epochs) + amp = cfg.train.amp and device.type == "cuda" + scaler = torch.amp.GradScaler("cuda", enabled=amp) + + best_path, last_path = run_dir / "best.pt", run_dir / "last.pt" + best_acc, bad_epochs = -1.0, 0 + history: list[dict[str, float]] = [] + + for epoch in range(1, cfg.train.epochs + 1): + if cfg.model.freeze_backbone_epochs: + set_backbone_frozen(model, epoch <= cfg.model.freeze_backbone_epochs) + train_loss, train_acc = _run_epoch( + model, loaders.train, device, criterion, optimizer, scaler, amp + ) + val_loss, val_acc = _run_epoch(model, loaders.val, device, criterion, amp=amp) + history.append( + { + "epoch": epoch, + "train_loss": train_loss, + "train_acc": train_acc, + "val_loss": val_loss, + "val_acc": val_acc, + "lr": optimizer.param_groups[0]["lr"], + } + ) + scheduler.step() + logger.info( + "epoch %d/%d train_loss=%.4f train_acc=%.3f val_loss=%.4f val_acc=%.3f", + epoch, cfg.train.epochs, train_loss, train_acc, val_loss, val_acc, + ) + save_checkpoint(last_path, model, loaders.class_names, cfg, epoch, val_acc) + if val_acc > best_acc: + best_acc, bad_epochs = val_acc, 0 + save_checkpoint(best_path, model, loaders.class_names, cfg, epoch, val_acc) + else: + bad_epochs += 1 + if bad_epochs >= cfg.train.patience: + logger.info("early stopping at epoch %d", epoch) + break + + with (run_dir / "metrics.csv").open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=HISTORY_FIELDS) + writer.writeheader() + writer.writerows(history) + save_curves(history, run_dir / "curves.png") + return TrainResult(run_dir, best_path, last_path, best_acc, history) +``` + +- [ ] **Step 5: Run tests** + +Run: `poetry run pytest tests/test_train.py -v` +Expected: all PASS (each training run takes a few seconds on CPU). If `test_same_seed_same_first_epoch` is flaky due to nondeterministic CPU ops, relax `rel` to `1e-2` and say so in the commit message. + +- [ ] **Step 6: Commit** + +```bash +git add src tests +git commit -m "feat: add training loop with run artifacts and early stopping + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 9: Evaluation and prediction + +**Files:** +- Create: `src/wbc_classification/engine/evaluate.py`, `src/wbc_classification/engine/predict.py`; `tests/test_evaluate_predict.py` + +**Interfaces:** +- Consumes: `load_checkpoint`, `build_test_loader`, `compute_metrics`, `save_confusion_matrix`, `resolve_device`, `build_transforms`, `load_image`, `is_image_file`, `DataError`. +- Produces: + - `evaluate.evaluate_checkpoint(checkpoint: Path, data_root: Path | None = None, device: str = "auto", output: Path | None = None) -> dict` (the `compute_metrics` dict; if `output` is given writes `` JSON and `_confusion.png` next to it). + - `predict.Prediction(path: Path, label: str, probabilities: dict[str, float])` (frozen dataclass); `predict.collect_images(inputs: Sequence[Path]) -> list[Path]`; `predict.predict_images(checkpoint: Path, inputs: Sequence[Path], device: str = "auto") -> list[Prediction]`; `predict.annotate_image(prediction: Prediction, output_dir: Path) -> Path`. + +- [ ] **Step 1: Write the failing tests** — `tests/test_evaluate_predict.py` + +```python +import json + +import pytest + +from wbc_classification.engine.evaluate import evaluate_checkpoint +from wbc_classification.engine.predict import annotate_image, collect_images, predict_images +from wbc_classification.errors import CheckpointError, DataError + + +def test_evaluate_reports_per_class_and_writes_outputs(trained, data_root, tmp_path): + out = tmp_path / "metrics.json" + metrics = evaluate_checkpoint(trained.best_checkpoint, data_root=data_root, device="cpu", output=out) + assert 0.0 <= metrics["accuracy"] <= 1.0 + assert set(metrics["class_names"]) == {"EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"} + assert sum(sum(row) for row in metrics["confusion_matrix"]) == 8 # the 8 TEST fixtures + assert json.loads(out.read_text())["accuracy"] == metrics["accuracy"] + assert (tmp_path / "metrics_confusion.png").is_file() + + +def test_evaluate_missing_checkpoint(tmp_path): + with pytest.raises(CheckpointError): + evaluate_checkpoint(tmp_path / "nope.pt") + + +def test_evaluate_missing_data_dir(trained, tmp_path): + with pytest.raises(DataError, match="wbc download-data"): + evaluate_checkpoint(trained.best_checkpoint, data_root=tmp_path, device="cpu") + + +def test_predict_directory(trained, data_root): + preds = predict_images(trained.best_checkpoint, [data_root / "TEST"], device="cpu") + assert len(preds) == 8 + for pred in preds: + assert pred.label in pred.probabilities + assert sum(pred.probabilities.values()) == pytest.approx(1.0, abs=1e-4) + + +def test_predict_single_file(trained, cell_image): + (pred,) = predict_images(trained.best_checkpoint, [cell_image], device="cpu") + assert pred.path == cell_image + + +def test_predict_empty_dir_raises(trained, tmp_path): + with pytest.raises(DataError, match="No images"): + predict_images(trained.best_checkpoint, [tmp_path], device="cpu") + + +def test_predict_missing_path_raises(trained, tmp_path): + with pytest.raises(DataError, match="not found"): + predict_images(trained.best_checkpoint, [tmp_path / "ghost.jpg"], device="cpu") + + +def test_predict_corrupt_image_raises(trained, tmp_path): + bad = tmp_path / "bad.jpeg" + bad.write_bytes(b"nope") + with pytest.raises(DataError, match="bad.jpeg"): + predict_images(trained.best_checkpoint, [bad], device="cpu") + + +def test_collect_images_ignores_non_images(tmp_path, write_image): + write_image(tmp_path / "a.png") + (tmp_path / "notes.txt").write_text("x") + assert collect_images([tmp_path]) == [tmp_path / "a.png"] + + +def test_annotate_writes_file(trained, cell_image, tmp_path): + (pred,) = predict_images(trained.best_checkpoint, [cell_image], device="cpu") + assert annotate_image(pred, tmp_path).is_file() +``` + +- [ ] **Step 2: Run to verify failure** + +Run: `poetry run pytest tests/test_evaluate_predict.py -v` +Expected: FAIL (module not found) + +- [ ] **Step 3: Implement** — `engine/evaluate.py` + +```python +"""Evaluate a checkpoint on the TEST split.""" + +import json +from pathlib import Path +from typing import Any + +import torch + +from wbc_classification.data.datasets import build_test_loader +from wbc_classification.engine.checkpoint import load_checkpoint +from wbc_classification.engine.metrics import compute_metrics, save_confusion_matrix +from wbc_classification.engine.runtime import resolve_device + + +def evaluate_checkpoint( + checkpoint: Path, + data_root: Path | None = None, + device: str = "auto", + output: Path | None = None, +) -> dict[str, Any]: + model, class_names, cfg = load_checkpoint(checkpoint) + if data_root is not None: + cfg.data.root = data_root + torch_device = resolve_device(device) + model.to(torch_device).eval() + loader = build_test_loader(cfg, class_names) + + y_true: list[int] = [] + y_pred: list[int] = [] + with torch.no_grad(): + for images, labels in loader: + logits = model(images.to(torch_device)) + y_pred.extend(logits.argmax(dim=1).cpu().tolist()) + y_true.extend(labels.tolist()) + + metrics = compute_metrics(y_true, y_pred, class_names) + if output is not None: + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(metrics, indent=2)) + save_confusion_matrix( + metrics["confusion_matrix"], class_names, output.with_name(f"{output.stem}_confusion.png") + ) + return metrics +``` + +`engine/predict.py`: + +```python +"""Predict classes for images or folders of images.""" + +from collections.abc import Sequence +from dataclasses import dataclass +from pathlib import Path + +import torch +from PIL import ImageDraw + +from wbc_classification.data.datasets import load_image +from wbc_classification.data.labels import is_image_file +from wbc_classification.data.transforms import build_transforms +from wbc_classification.engine.checkpoint import load_checkpoint +from wbc_classification.engine.runtime import resolve_device +from wbc_classification.errors import DataError + + +@dataclass(frozen=True) +class Prediction: + path: Path + label: str + probabilities: dict[str, float] + + +def collect_images(inputs: Sequence[Path]) -> list[Path]: + images: list[Path] = [] + for item in inputs: + if item.is_dir(): + images.extend(sorted(p for p in item.rglob("*") if is_image_file(p))) + elif item.is_file(): + images.append(item) + else: + raise DataError(f"Path not found: {item}") + if not images: + raise DataError("No images found in the given inputs") + return images + + +def predict_images( + checkpoint: Path, inputs: Sequence[Path], device: str = "auto" +) -> list[Prediction]: + model, class_names, cfg = load_checkpoint(checkpoint) + torch_device = resolve_device(device) + model.to(torch_device).eval() + transform = build_transforms(cfg.data.image_size, train=False) + + predictions: list[Prediction] = [] + with torch.no_grad(): + for path in collect_images(inputs): + image = load_image(path, crop=cfg.preprocessing.crop) + batch = transform(image).unsqueeze(0).to(torch_device) + probs = torch.softmax(model(batch), dim=1)[0].cpu().tolist() + best = max(range(len(class_names)), key=probs.__getitem__) + predictions.append( + Prediction(path, class_names[best], dict(zip(class_names, probs, strict=True))) + ) + return predictions + + +def annotate_image(prediction: Prediction, output_dir: Path) -> Path: + """Write a copy of the image with the predicted label drawn on it.""" + image = load_image(prediction.path) + confidence = prediction.probabilities[prediction.label] + ImageDraw.Draw(image).text((8, 8), f"{prediction.label} {confidence:.0%}", fill=(0, 255, 0)) + output_dir.mkdir(parents=True, exist_ok=True) + out = output_dir / f"{prediction.path.stem}_pred.png" + image.save(out) + return out +``` + +- [ ] **Step 4: Run tests** + +Run: `poetry run pytest tests/test_evaluate_predict.py -v` +Expected: all PASS + +- [ ] **Step 5: Commit** + +```bash +git add src tests +git commit -m "feat: add checkpoint evaluation and prediction + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 10: CLI + +**Files:** +- Create: `src/wbc_classification/cli.py`, `src/wbc_classification/__main__.py`; `tests/test_cli.py` + +**Interfaces:** +- Consumes: everything above. +- Produces: Typer `app` with commands `train`, `evaluate`, `predict`, `segment`, `download-data`; any `WBCError` → `Error: ` on stderr, exit code 1. `python -m wbc_classification` runs the app. + - `wbc train [-c CONFIG] [--model M] [--epochs N] [--batch-size N] [--image-size N] [--lr X] [--data-root P] [--output-dir P] [--device D] [--name S] [--pretrained/--no-pretrained] [--crop/--no-crop]` + - `wbc evaluate CHECKPOINT [--data-root P] [--device D] [--output FILE.json]` + - `wbc predict INPUT... -c CHECKPOINT [--device D] [--annotate-dir DIR]` + - `wbc segment IMAGE [--output-dir DIR]` writes `_crop.png`, `_overlay.png`, `_mask.png` + - `wbc download-data [--dest DIR] [--force]` + +- [ ] **Step 1: Write the failing tests** — `tests/test_cli.py` + +```python +from typer.testing import CliRunner + +from wbc_classification.cli import app + +runner = CliRunner() + + +def test_help_lists_commands(): + result = runner.invoke(app, ["--help"]) + assert result.exit_code == 0 + for command in ("train", "evaluate", "predict", "segment", "download-data"): + assert command in result.output + + +def test_train_command(data_root, tmp_path): + result = runner.invoke( + app, + [ + "train", "--data-root", str(data_root), "--output-dir", str(tmp_path), + "--model", "baseline", "--no-pretrained", "--epochs", "1", "--batch-size", "4", + "--image-size", "32", "--device", "cpu", "--name", "cli", + ], + ) + assert result.exit_code == 0, result.output + assert "best validation accuracy" in result.output.lower() + assert list(tmp_path.glob("cli-*/best.pt")) + + +def test_evaluate_command(trained, data_root, tmp_path): + out = tmp_path / "m.json" + result = runner.invoke( + app, ["evaluate", str(trained.best_checkpoint), "--data-root", str(data_root), + "--device", "cpu", "--output", str(out)] + ) + assert result.exit_code == 0, result.output + assert "accuracy" in result.output and out.is_file() + + +def test_predict_command(trained, cell_image, tmp_path): + result = runner.invoke( + app, ["predict", str(cell_image), "-c", str(trained.best_checkpoint), + "--device", "cpu", "--annotate-dir", str(tmp_path)] + ) + assert result.exit_code == 0, result.output + assert "cell.jpeg" in result.output + assert (tmp_path / "cell_pred.png").is_file() + + +def test_segment_command(cell_image, tmp_path): + result = runner.invoke(app, ["segment", str(cell_image), "--output-dir", str(tmp_path)]) + assert result.exit_code == 0, result.output + for suffix in ("crop", "overlay", "mask"): + assert (tmp_path / f"cell_{suffix}.png").is_file() + + +def test_segment_no_cell_is_a_clean_error(tmp_path, write_image): + black = write_image(tmp_path / "black.png") + result = runner.invoke(app, ["segment", str(black), "--output-dir", str(tmp_path)]) + assert result.exit_code == 1 + assert "Error:" in result.output and "Traceback" not in result.output + + +def test_missing_checkpoint_is_a_clean_error(tmp_path): + result = runner.invoke(app, ["evaluate", str(tmp_path / "nope.pt")]) + assert result.exit_code == 1 and "Checkpoint not found" in result.output + + +def test_bad_config_is_a_clean_error(tmp_path): + cfg = tmp_path / "c.yaml" + cfg.write_text("train:\n epohcs: 2\n") + result = runner.invoke(app, ["train", "-c", str(cfg)]) + assert result.exit_code == 1 and "epohcs" in result.output +``` + +(Typer's `CliRunner` mixes stderr into `result.output` by default; if the installed Typer separates them, use `result.output` + `result.stderr` or construct `CliRunner(mix_stderr=...)` per its current API.) + +- [ ] **Step 2: Run to verify failure** + +Run: `poetry run pytest tests/test_cli.py -v` +Expected: FAIL (module not found) + +- [ ] **Step 3: Implement** — `src/wbc_classification/cli.py` + +```python +"""`wbc` command-line interface.""" + +import functools +import logging +from collections.abc import Callable +from pathlib import Path +from typing import Annotated, Any + +import numpy as np +import typer +from PIL import Image + +from wbc_classification import __version__ +from wbc_classification.config import load_config +from wbc_classification.data.datasets import load_image +from wbc_classification.data.download import download_dataset +from wbc_classification.engine.evaluate import evaluate_checkpoint +from wbc_classification.engine.metrics import format_report +from wbc_classification.engine.predict import annotate_image, predict_images +from wbc_classification.engine.train import train as run_training +from wbc_classification.errors import WBCError +from wbc_classification.preprocessing.segmentation import crop_cell, draw_overlay, segment_cell + +app = typer.Typer( + help="Classify white blood cell images (eosinophil, lymphocyte, monocyte, neutrophil).", + no_args_is_help=True, + add_completion=False, +) + + +def _version_callback(value: bool) -> None: + if value: + typer.echo(__version__) + raise typer.Exit() + + +@app.callback() +def main( + version: Annotated[ + bool, typer.Option("--version", callback=_version_callback, is_eager=True, help="Show version.") + ] = False, + verbose: Annotated[bool, typer.Option("--verbose", "-v", help="Log progress per epoch.")] = False, +) -> None: + logging.basicConfig(level=logging.INFO if verbose else logging.WARNING, format="%(message)s") + + +def _handle_errors(func: Callable[..., Any]) -> Callable[..., Any]: + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + try: + return func(*args, **kwargs) + except WBCError as exc: + typer.secho(f"Error: {exc}", fg=typer.colors.RED, err=True) + raise typer.Exit(code=1) from exc + + return wrapper + + +@app.command() +@_handle_errors +def train( + config: Annotated[Path | None, typer.Option("--config", "-c", help="YAML config file.")] = None, + model: Annotated[str | None, typer.Option(help="baseline, resnet18, resnet50, efficientnet_b0.")] = None, + epochs: Annotated[int | None, typer.Option(help="Number of epochs.")] = None, + batch_size: Annotated[int | None, typer.Option(help="Batch size.")] = None, + image_size: Annotated[int | None, typer.Option(help="Square input size in pixels.")] = None, + lr: Annotated[float | None, typer.Option(help="Learning rate.")] = None, + data_root: Annotated[Path | None, typer.Option(help="Folder containing TRAIN/ and TEST/.")] = None, + output_dir: Annotated[Path | None, typer.Option(help="Where run folders are written.")] = None, + device: Annotated[str | None, typer.Option(help="auto, cpu, cuda, mps.")] = None, + name: Annotated[str | None, typer.Option(help="Run name prefix.")] = None, + pretrained: Annotated[bool | None, typer.Option("--pretrained/--no-pretrained")] = None, + crop: Annotated[bool | None, typer.Option("--crop/--no-crop", help="Crop to the cell first.")] = None, +) -> None: + """Train a model and write a run folder with checkpoints, metrics and plots.""" + cfg = load_config( + config, + { + "model.name": model, + "train.epochs": epochs, + "data.batch_size": batch_size, + "data.image_size": image_size, + "train.lr": lr, + "data.root": data_root, + "train.output_dir": output_dir, + "train.device": device, + "train.name": name, + "model.pretrained": pretrained, + "preprocessing.crop": crop, + }, + ) + result = run_training(cfg) + typer.echo(f"Best validation accuracy: {result.best_val_accuracy:.4f}") + typer.echo(f"Run folder: {result.run_dir}") + typer.echo(f"Best checkpoint: {result.best_checkpoint}") + + +@app.command() +@_handle_errors +def evaluate( + checkpoint: Annotated[Path, typer.Argument(help="Checkpoint (.pt) written by `wbc train`.")], + data_root: Annotated[Path | None, typer.Option(help="Folder containing TEST/.")] = None, + device: Annotated[str, typer.Option(help="auto, cpu, cuda, mps.")] = "auto", + output: Annotated[Path | None, typer.Option(help="Write metrics JSON (+ confusion PNG) here.")] = None, +) -> None: + """Evaluate a checkpoint on the TEST split.""" + metrics = evaluate_checkpoint(checkpoint, data_root, device, output) + typer.echo(format_report(metrics)) + + +@app.command() +@_handle_errors +def predict( + inputs: Annotated[list[Path], typer.Argument(help="Image files and/or folders of images.")], + checkpoint: Annotated[Path, typer.Option("--checkpoint", "-c", help="Checkpoint (.pt).")], + device: Annotated[str, typer.Option(help="auto, cpu, cuda, mps.")] = "auto", + annotate_dir: Annotated[Path | None, typer.Option(help="Save labelled copies of the images here.")] = None, +) -> None: + """Predict the cell type of one or more images.""" + for prediction in predict_images(checkpoint, inputs, device): + confidence = prediction.probabilities[prediction.label] + typer.echo(f"{prediction.path}: {prediction.label} ({confidence:.1%})") + if annotate_dir is not None: + annotate_image(prediction, annotate_dir) + + +@app.command() +@_handle_errors +def segment( + image: Annotated[Path, typer.Argument(help="Image to segment.")], + output_dir: Annotated[Path, typer.Option(help="Where to write crop/overlay/mask PNGs.")] = Path("segmentation"), +) -> None: + """Locate the cell with HSV segmentation and save the crop, overlay and mask.""" + rgb = np.asarray(load_image(image)) + segmentation = segment_cell(rgb) + output_dir.mkdir(parents=True, exist_ok=True) + Image.fromarray(crop_cell(rgb)).save(output_dir / f"{image.stem}_crop.png") + Image.fromarray(draw_overlay(rgb, segmentation)).save(output_dir / f"{image.stem}_overlay.png") + Image.fromarray(segmentation.mask).save(output_dir / f"{image.stem}_mask.png") + typer.echo(f"Saved crop, overlay and mask to {output_dir}") + + +@app.command("download-data") +@_handle_errors +def download_data( + dest: Annotated[Path, typer.Option(help="Destination folder.")] = Path("data"), + force: Annotated[bool, typer.Option(help="Overwrite existing TRAIN/TEST folders.")] = False, +) -> None: + """Download the Kaggle blood-cells dataset into DEST (needs the `kaggle` extra).""" + path = download_dataset(dest, force) + typer.echo(f"Dataset installed in {path}") +``` + +`src/wbc_classification/__main__.py`: + +```python +from wbc_classification.cli import app + +app() +``` + +- [ ] **Step 4: Run tests** + +Run: `poetry run pytest tests/test_cli.py -v && poetry run wbc --version && poetry run wbc --help` +Expected: tests PASS; version prints; help lists the five commands. + +- [ ] **Step 5: Commit** + +```bash +git add src tests +git commit -m "feat: add wbc command-line interface + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 11: Experiment configs and end-to-end smoke test + +**Files:** +- Create: `configs/baseline.yaml`, `configs/resnet18.yaml`, `configs/efficientnet_b0.yaml`, `tests/test_configs_and_smoke.py` + +**Interfaces:** +- Consumes: `load_config`, `app`, `trained` fixture. +- Produces: three experiment YAMLs that load cleanly; one end-to-end smoke test (train → evaluate → predict via the CLI, through a reloaded checkpoint). + +- [ ] **Step 1: Write the configs** + +`configs/baseline.yaml`: + +```yaml +# Lightweight baseline: the original IncludeNet idea, rewritten with BatchNorm + GAP. +preprocessing: + crop: false +data: + root: data + image_size: 64 + batch_size: 64 + val_fraction: 0.2 +model: + name: baseline + pretrained: false +train: + name: baseline + epochs: 60 + lr: 0.001 + patience: 10 +``` + +`configs/resnet18.yaml`: + +```yaml +# Fine-tune an ImageNet-pretrained ResNet-18. +preprocessing: + crop: false +data: + root: data + image_size: 128 + batch_size: 32 +model: + name: resnet18 + pretrained: true + freeze_backbone_epochs: 2 +train: + name: resnet18 + epochs: 25 + lr: 0.0005 + patience: 6 +``` + +`configs/efficientnet_b0.yaml`: + +```yaml +# Fine-tune an ImageNet-pretrained EfficientNet-B0. +preprocessing: + crop: false +data: + root: data + image_size: 160 + batch_size: 32 +model: + name: efficientnet_b0 + pretrained: true + freeze_backbone_epochs: 2 +train: + name: efficientnet_b0 + epochs: 25 + lr: 0.0005 + patience: 6 +``` + +- [ ] **Step 2: Write the tests** — `tests/test_configs_and_smoke.py` + +```python +from pathlib import Path + +import pytest +from typer.testing import CliRunner + +from wbc_classification.cli import app +from wbc_classification.config import load_config +from wbc_classification.models.registry import MODEL_NAMES + +CONFIGS = sorted((Path(__file__).parent.parent / "configs").glob("*.yaml")) + + +def test_configs_exist(): + assert len(CONFIGS) >= 3 + + +@pytest.mark.parametrize("path", CONFIGS, ids=lambda p: p.stem) +def test_shipped_configs_are_valid(path): + cfg = load_config(path) + assert cfg.model.name in MODEL_NAMES + + +def test_end_to_end_smoke(data_root, tmp_path): + """train -> evaluate -> predict through the CLI, reloading the saved checkpoint.""" + runner = CliRunner() + train = runner.invoke( + app, + ["train", "-c", str(CONFIGS[0].parent / "baseline.yaml"), "--data-root", str(data_root), + "--output-dir", str(tmp_path), "--epochs", "1", "--batch-size", "4", + "--image-size", "32", "--device", "cpu"], + ) + assert train.exit_code == 0, train.output + (checkpoint,) = tmp_path.glob("baseline-*/best.pt") + evaluate = runner.invoke( + app, ["evaluate", str(checkpoint), "--data-root", str(data_root), "--device", "cpu"] + ) + assert evaluate.exit_code == 0, evaluate.output + predict = runner.invoke( + app, ["predict", str(data_root / "TEST"), "-c", str(checkpoint), "--device", "cpu"] + ) + assert predict.exit_code == 0, predict.output + assert predict.output.count("(") == 8 # one confidence per TEST image +``` + +- [ ] **Step 3: Run** + +Run: `poetry run pytest -q` +Expected: the whole suite passes. Also time it: `poetry run pytest -q --durations=5` — total should be well under two minutes on CPU. + +- [ ] **Step 4: Commit** + +```bash +git add configs tests +git commit -m "feat: add experiment configs and end-to-end smoke test + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 12: CI workflows + +**Files:** +- Create: `.github/workflows/ci.yml`, `.github/workflows/pr-title.yml` + +**Interfaces:** +- Produces: CI jobs `lint`, `test` (matrix 3.12/3.13/3.14), `docs` (builds the docs once Task 13 exists) triggered on PRs and pushes to `main`; PR-title check. + +- [ ] **Step 1: Check current action major versions** + +Run: + +```bash +for r in actions/checkout actions/setup-python actions/upload-pages-artifact actions/deploy-pages amannn/action-semantic-pull-request python-semantic-release/python-semantic-release python-semantic-release/publish-action pypa/gh-action-pypi-publish; do printf '%s ' $r; gh api repos/$r/releases/latest --jq .tag_name; done +``` +Use the major tag of each result below instead of the versions written here if they differ. (`pypa/gh-action-pypi-publish` is conventionally pinned to `release/v1`.) + +- [ ] **Step 2: Write `.github/workflows/ci.yml`** + +```yaml +name: CI + +on: + pull_request: + push: + branches: [main] + +permissions: + contents: read + +concurrency: + group: ci-${{ github.ref }} + cancel-in-progress: true + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v5 + - run: pipx install poetry + - uses: actions/setup-python@v6 + with: + python-version: "3.14" + cache: poetry + - run: poetry install --with dev + - run: poetry run ruff check . + - run: poetry run ruff format --check . + - run: poetry run mypy src + + test: + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.12", "3.13", "3.14"] + steps: + - uses: actions/checkout@v5 + - run: pipx install poetry + - uses: actions/setup-python@v6 + with: + python-version: ${{ matrix.python-version }} + cache: poetry + - run: poetry install --with dev + - run: poetry run pytest -q + + docs: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v5 + - run: pipx install poetry + - uses: actions/setup-python@v6 + with: + python-version: "3.14" + cache: poetry + - run: poetry install --with docs + - run: poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md + - run: poetry run mkdocs build --strict +``` + +- [ ] **Step 3: Write `.github/workflows/pr-title.yml`** + +```yaml +name: PR title + +on: + pull_request: + types: [opened, edited, synchronize, reopened] + +permissions: + pull-requests: read + +jobs: + conventional-title: + runs-on: ubuntu-latest + steps: + - uses: amannn/action-semantic-pull-request@v6 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} +``` + +- [ ] **Step 4: Validate the YAML locally** + +Run: `poetry run python -c "import yaml,glob; [yaml.safe_load(open(f)) for f in glob.glob('.github/workflows/*.yml')]; print('ok')"` +Expected: `ok`. (The workflows themselves can only be exercised on GitHub after the branch is pushed; the `docs` job will pass once Task 13 is done.) + +- [ ] **Step 5: Commit** + +```bash +git add .github +git commit -m "ci: add lint, test matrix, docs build and PR-title workflows + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 13: MkDocs documentation site + +**Files:** +- Create: `mkdocs.yml`, `docs/index.md`, `docs/getting-started.md`, `docs/data.md`, `docs/training.md`, `docs/configuration.md`, `docs/results.md`, `docs/architecture.md`, `docs/wbc-detection.md`, `docs/contributing.md`, `docs/changelog.md`, `docs/reference/api.md`, `docs/assets/detection/{blur,hsv,color-filtering,mask,final}.png` (moved) +- Create: `.github/workflows/docs.yml` + +**Interfaces:** +- Produces: a site that builds with `poetry run mkdocs build --strict` (after generating `docs/reference/cli.md`), and a Pages deploy workflow. + +- [ ] **Step 1: Move the detection screenshots** + +```bash +mkdir -p docs/assets/detection docs/reference +git mv "WBC-Detection/blur.png" docs/assets/detection/blur.png +git mv "WBC-Detection/hsv.png" docs/assets/detection/hsv.png +git mv "WBC-Detection/color filtering.png" docs/assets/detection/color-filtering.png +git mv "WBC-Detection/mask.png" docs/assets/detection/mask.png +git mv "WBC-Detection/final.png" docs/assets/detection/final.png +``` + +- [ ] **Step 2: Write `mkdocs.yml`** + +```yaml +site_name: WBC Classification +site_description: Classify white blood cell images with PyTorch. +site_url: https://includeamin.github.io/WBC-Classification/ +repo_url: https://github.com/includeamin/WBC-Classification +repo_name: includeamin/WBC-Classification + +theme: + name: material + features: + - navigation.sections + - content.code.copy + palette: + - media: "(prefers-color-scheme: light)" + scheme: default + toggle: {icon: material/weather-night, name: Switch to dark mode} + - media: "(prefers-color-scheme: dark)" + scheme: slate + toggle: {icon: material/weather-sunny, name: Switch to light mode} + +nav: + - Home: index.md + - Getting started: getting-started.md + - Guides: + - Data: data.md + - Training: training.md + - Configuration: configuration.md + - WBC detection walkthrough: wbc-detection.md + - Results: results.md + - Reference: + - CLI: reference/cli.md + - Python API: reference/api.md + - Architecture: architecture.md + - Contributing: contributing.md + - Changelog: changelog.md + +plugins: + - search + - mkdocstrings: + handlers: + python: + paths: [src] + +markdown_extensions: + - admonition + - toc: + permalink: true + - pymdownx.highlight + - pymdownx.superfences + - pymdownx.snippets: + base_path: ["."] +``` + +If `pymdownx` is not importable (it ships with mkdocs-material), run `poetry add --group docs pymdown-extensions`. + +- [ ] **Step 3: Write the pages** + +`docs/index.md`: + +```markdown +# WBC Classification + +Classify white blood cell images into four types — **eosinophil, lymphocyte, monocyte, neutrophil** — with PyTorch. + +- Fine-tune a pretrained backbone (ResNet, EfficientNet) or train the small `baseline` CNN (IncludeNet). +- Config-driven, reproducible training: every run saves its config, metrics, plots and checkpoints. +- An optional OpenCV step crops the cell before classification. +- One command-line tool: `wbc train | evaluate | predict | segment | download-data`. + +Start with [Getting started](getting-started.md). +``` + +`docs/getting-started.md`: + +````markdown +# Getting started + +## Install + +Requires Python 3.12+ and [Poetry](https://python-poetry.org/). + +```bash +git clone https://github.com/includeamin/WBC-Classification.git +cd WBC-Classification +poetry install --extras kaggle +``` + +## Get the data + +```bash +poetry run wbc download-data --dest data +``` + +See [Data](data.md) for the expected layout and Kaggle credentials. + +## Train, evaluate, predict + +```bash +poetry run wbc train -c configs/resnet18.yaml +poetry run wbc evaluate runs/resnet18-/best.pt --output metrics.json +poetry run wbc predict path/to/cell.jpeg -c runs/resnet18-/best.pt +``` + +Use `poetry run wbc --help` or the [CLI reference](reference/cli.md) for all options. +Training on a CPU is slow for the pretrained backbones; see [Training](training.md) for GPU and Colab. +```` + +`docs/data.md`: + +```markdown +# Data + +The project uses the public Kaggle +[Blood Cell Images](https://www.kaggle.com/datasets/paultimothymooney/blood-cells) dataset +(320×240 JPEG, four classes). It is not stored in git. + +```bash +poetry run wbc download-data --dest data # add --force to overwrite +``` + +Expected layout: + + data/ + ├── TRAIN/{EOSINOPHIL,LYMPHOCYTE,MONOCYTE,NEUTROPHIL}/*.jpeg + └── TEST/{EOSINOPHIL,LYMPHOCYTE,MONOCYTE,NEUTROPHIL}/*.jpeg + +- Class names come from the folder names; non-image files are ignored. +- A stratified, seeded validation split is carved from `TRAIN`. `TEST` is only used by `wbc evaluate`. +- If Kaggle asks for authentication, create an API token and set `KAGGLE_USERNAME` / `KAGGLE_KEY` + (or use `~/.kaggle/kaggle.json`). You can also download the dataset manually and copy `TRAIN/` and `TEST/` into `data/`. +``` + +`docs/training.md`: + +````markdown +# Training + +```bash +poetry run wbc train -c configs/resnet18.yaml # pretrained ResNet-18 +poetry run wbc train -c configs/baseline.yaml # small baseline CNN +poetry run wbc train -c configs/resnet18.yaml --epochs 5 --device cpu +poetry run wbc -v train -c configs/resnet18.yaml # log every epoch +``` + +Each run writes `runs/-/`: + +| File | Content | +|---|---| +| `config.yaml` | the exact configuration used | +| `metrics.csv` | per-epoch loss / accuracy / learning rate | +| `curves.png` | training curves | +| `best.pt` / `last.pt` | checkpoints (weights + class names + config) | + +Training uses AdamW, a cosine learning-rate schedule, mixed precision on CUDA and early stopping on validation accuracy. +Set `model.freeze_backbone_epochs` to train only the new classification head for the first N epochs. + +## GPU + +`poetry install` installs the default PyTorch build. For a specific CUDA build follow the +[PyTorch install guide](https://pytorch.org/get-started/locally/) and install that wheel into the Poetry environment. +On Google Colab use `notebooks/train_colab.ipynb`. +Training on CPU is fine for the `baseline` model and for smoke tests, but slow for pretrained backbones. +```` + +`docs/configuration.md`: + +````markdown +# Configuration + +Experiments are described by a YAML file (see `configs/`). Unknown keys are rejected so typos fail fast. +Any value can be overridden on the command line (`wbc train -c cfg.yaml --epochs 5 --lr 0.0003`). + +```yaml +preprocessing: + crop: false # crop to the detected cell first (HSV segmentation) +data: + root: data # folder with TRAIN/ and TEST/ + image_size: 224 # square input size + batch_size: 32 + val_fraction: 0.2 # stratified split from TRAIN, 0 < x < 0.5 + num_workers: 0 +model: + name: resnet18 # baseline | resnet18 | resnet50 | efficientnet_b0 + pretrained: true # ImageNet weights (backbones only) + freeze_backbone_epochs: 0 +train: + name: run # run folder prefix + epochs: 30 + lr: 0.001 + weight_decay: 0.0001 + patience: 7 # early-stopping patience (epochs) + amp: true # mixed precision (CUDA only) + seed: 42 + device: auto # auto | cpu | cuda | mps + output_dir: runs +``` +```` + +`docs/results.md`: + +```markdown +# Results + +!!! note + No pretrained checkpoints or benchmark numbers are published yet. Train a model with + `wbc train` and `wbc evaluate`, then fill in the table below. + +| Model | Image size | Crop | Epochs | TEST accuracy | Macro F1 | Notes | +|---|---|---|---|---|---|---| +| baseline | 64 | no | – | – | – | – | +| resnet18 | 128 | no | – | – | – | – | +| resnet18 | 128 | yes | – | – | – | crop vs no-crop comparison | +| efficientnet_b0 | 160 | no | – | – | – | – | +``` + +`docs/architecture.md`: + +```markdown +# Architecture + +``` +wbc_classification/ +├── config.py pydantic config + YAML loading + overrides +├── data/ labels, datasets/loaders, transforms, Kaggle download +├── preprocessing/ optional HSV cell segmentation and crop +├── models/ baseline (IncludeNet), torchvision backbones, registry +├── engine/ train, evaluate, predict, metrics, checkpoints, runtime +└── cli.py `wbc` Typer application +``` + +Design notes: + +- A checkpoint is one `.pt` file holding weights, class names and the full config, so `predict` and `evaluate` need nothing else. +- Errors at the boundaries (config, data, checkpoint) are `WBCError` subclasses; the CLI prints them without a traceback. +- Adding a model means adding an entry in `models/registry.py`. +- The cell crop is optional; when no cell is found the full image is used and a warning is logged. +``` + +`docs/wbc-detection.md`: + +```markdown +# WBC detection walkthrough + +The optional crop step locates the white blood cell with classical computer vision: + +1. Blur the RGB image (Gaussian, 7×7) — ![blur](assets/detection/blur.png) +2. Convert to HSV — ![hsv](assets/detection/hsv.png) +3. Keep pixels inside the cell colour range (H 80–255, S 60–255, V 140–255) — ![color filtering](assets/detection/color-filtering.png) +4. Take the largest contour as the cell mask — ![mask](assets/detection/mask.png) +5. Crop around the minimum enclosing circle — ![result](assets/detection/final.png) + +Try it on your own image: + +```bash +poetry run wbc segment path/to/cell.jpeg --output-dir segmentation +``` + +or use the code directly: `wbc_classification.preprocessing.segmentation.segment_cell`. +``` + +`docs/contributing.md`: contents `--8<-- "CONTRIBUTING.md"` (created in Task 14; build the docs after Task 14 or create a stub now). +`docs/changelog.md`: contents `--8<-- "CHANGELOG.md"`. + +`docs/reference/api.md`: + +```markdown +# Python API + +::: wbc_classification.config +::: wbc_classification.data.datasets +::: wbc_classification.preprocessing.segmentation +::: wbc_classification.models.registry +::: wbc_classification.engine.train +::: wbc_classification.engine.evaluate +::: wbc_classification.engine.predict +``` + +- [ ] **Step 4: Write `.github/workflows/docs.yml`** + +```yaml +name: Docs + +on: + push: + branches: [main] + +permissions: + contents: read + pages: write + id-token: write + +concurrency: + group: pages + cancel-in-progress: false + +jobs: + deploy: + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - uses: actions/checkout@v5 + - run: pipx install poetry + - uses: actions/setup-python@v6 + with: + python-version: "3.14" + cache: poetry + - run: poetry install --with docs + - run: poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md + - run: poetry run mkdocs build --strict + - uses: actions/upload-pages-artifact@v4 + with: + path: site + - id: deployment + uses: actions/deploy-pages@v4 +``` + +- [ ] **Step 5: Stub CONTRIBUTING so the build can run, then build** + +Create a one-line `CONTRIBUTING.md` (`# Contributing`) if Task 14 hasn't run yet. + +Run: + +```bash +poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md +poetry run mkdocs build --strict +``` +Expected: build succeeds with no warnings. Fix any broken link / mkdocstrings error it reports (strict mode turns warnings into failures). + +- [ ] **Step 6: Commit** + +```bash +git add mkdocs.yml docs .github CONTRIBUTING.md pyproject.toml poetry.lock +git commit -m "docs: add MkDocs Material site and Pages deploy workflow + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 14: README, CONTRIBUTING, notebooks + +**Files:** +- Modify: `README.md` (full rewrite), `CONTRIBUTING.md` (replace the stub) +- Create: `notebooks/train_colab.ipynb`, `notebooks/wbc_detection.ipynb` + +**Interfaces:** +- Produces: a short README that points to the docs; contributor guide describing Poetry workflow and Conventional Commits; two runnable notebooks. + +- [ ] **Step 1: Rewrite `README.md`** + +````markdown +# WBC Classification + +[![CI](https://github.com/includeamin/WBC-Classification/actions/workflows/ci.yml/badge.svg)](https://github.com/includeamin/WBC-Classification/actions/workflows/ci.yml) +[![Release](https://img.shields.io/github/v/release/includeamin/WBC-Classification)](https://github.com/includeamin/WBC-Classification/releases) +[![Python](https://img.shields.io/badge/python-3.12%2B-blue)](pyproject.toml) +[![License](https://img.shields.io/github/license/includeamin/WBC-Classification)](LICENSE) +[![Docs](https://img.shields.io/badge/docs-mkdocs-informational)](https://includeamin.github.io/WBC-Classification/) + +Classify white blood cell images — **eosinophil, lymphocyte, monocyte, neutrophil** — with PyTorch. +Fine-tune a pretrained ResNet / EfficientNet or train the small `baseline` CNN, all from one CLI. + +## Quick start + +```bash +git clone https://github.com/includeamin/WBC-Classification.git +cd WBC-Classification +poetry install --extras kaggle + +poetry run wbc download-data --dest data +poetry run wbc train -c configs/resnet18.yaml +poetry run wbc evaluate runs//best.pt --output metrics.json +poetry run wbc predict path/to/cell.jpeg -c runs//best.pt +``` + +Requires Python 3.12+ and [Poetry](https://python-poetry.org/). Training pretrained models is best done on a GPU +(see the [training guide](https://includeamin.github.io/WBC-Classification/training/) and `notebooks/train_colab.ipynb`). + +## Documentation + +Full docs: — data setup, configuration reference, +CLI and API reference, architecture, results. + +## Dataset + +[Blood Cell Images](https://www.kaggle.com/datasets/paultimothymooney/blood-cells) on Kaggle (not stored in this repository). + +## Contributing + +See [CONTRIBUTING.md](CONTRIBUTING.md). Releases and the changelog are automated from +[Conventional Commits](https://www.conventionalcommits.org/); see [CHANGELOG.md](CHANGELOG.md). + +## License + +BSD 3-Clause — see [LICENSE](LICENSE). +```` + +- [ ] **Step 2: Write `CONTRIBUTING.md`** + +````markdown +# Contributing + +Thanks for helping! This project uses [Poetry](https://python-poetry.org/) and Python 3.12+. + +## Setup + +```bash +poetry install --with dev,docs --extras kaggle +``` + +## Checks (all run in CI) + +```bash +poetry run ruff check . && poetry run ruff format --check . +poetry run mypy src +poetry run pytest +poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md +poetry run mkdocs build --strict +``` + +Tests use the small fixture dataset in `tests/fixtures/`; they never download data or pretrained weights. +Mark long-running or network tests with `@pytest.mark.slow` (they are skipped by default). + +## Commit messages and PR titles + +Releases are automated from [Conventional Commits](https://www.conventionalcommits.org/). PRs are +squash-merged, so the **PR title** must follow the format: + +| Prefix | Effect | +|---|---| +| `feat:` | minor release | +| `fix:`, `perf:` | patch release | +| `feat!:` or a `BREAKING CHANGE:` footer | major release (minor while version is 0.x) | +| `docs:`, `chore:`, `ci:`, `test:`, `refactor:` | no release | + +Do not edit `CHANGELOG.md` or the version in `pyproject.toml` by hand; the release workflow does it on merge to `main`. + +## Adding a model + +Add an entry to `models/registry.py`, a config in `configs/`, and a shape test in `tests/test_models.py`. +```` + +- [ ] **Step 3: Generate the notebooks** + +Run this script (do not commit the script itself): + +```bash +poetry run python - <<'EOF' +import json +from pathlib import Path + +def nb(cells): + return { + "cells": [ + {"cell_type": kind, "metadata": {}, "source": src.splitlines(keepends=True), + **({"outputs": [], "execution_count": None} if kind == "code" else {})} + for kind, src in cells + ], + "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}}, + "nbformat": 4, "nbformat_minor": 5, + } + +colab = nb([ + ("markdown", "# Train WBC Classification on a GPU (Colab / Kaggle)\n\nRuntime → Change runtime type → GPU."), + ("code", "!git clone https://github.com/includeamin/WBC-Classification.git\n%cd WBC-Classification\n!pip install -q -e '.[kaggle]'"), + ("code", "!wbc download-data --dest data"), + ("code", "!wbc -v train -c configs/resnet18.yaml"), + ("code", "!wbc evaluate $(ls -d runs/*/ | tail -1)best.pt --output metrics.json\n!cat metrics.json | head -40"), +]) +detection = nb([ + ("markdown", "# WBC detection walkthrough\n\nHow the optional cell crop works."), + ("code", "import numpy as np\nfrom PIL import Image\nfrom wbc_classification.preprocessing.segmentation import crop_cell, draw_overlay, segment_cell\n\nrgb = np.asarray(Image.open('../tests/fixtures/cell.jpeg').convert('RGB'))\nseg = segment_cell(rgb)\nImage.fromarray(draw_overlay(rgb, seg))"), + ("code", "Image.fromarray(crop_cell(rgb))"), +]) +Path("notebooks").mkdir(exist_ok=True) +Path("notebooks/train_colab.ipynb").write_text(json.dumps(colab, indent=1)) +Path("notebooks/wbc_detection.ipynb").write_text(json.dumps(detection, indent=1)) +EOF +poetry run python -c "import json; [json.load(open(f)) for f in ('notebooks/train_colab.ipynb','notebooks/wbc_detection.ipynb')]; print('ok')" +``` +Expected: `ok`. The Colab notebook's `pip install -e '.[kaggle]'` relies on the project's PEP 621 `kaggle` extra; verify `pip install -e .` works in a fresh venv (`python -m venv /tmp/v && /tmp/v/bin/pip install -e .`) — if the Poetry build backend or the Python 3.12+ requirement blocks it on Colab, note that in `docs/training.md`. + +- [ ] **Step 4: Rebuild docs and run checks** + +Run: `poetry run mkdocs build --strict` — expected: success (the `contributing.md` snippet now includes the real file). + +- [ ] **Step 5: Commit** + +```bash +git add README.md CONTRIBUTING.md notebooks +git commit -m "docs: rewrite README, add contributing guide and notebooks + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 15: Release automation + +**Files:** +- Modify: `pyproject.toml` (add `[tool.semantic_release]` config) +- Create: `.github/workflows/release.yml` + +**Interfaces:** +- Produces: on every push to `main`, a workflow that runs python-semantic-release: next version from Conventional Commits, updates `pyproject.toml` (`project.version`) and `CHANGELOG.md`, tags `vX.Y.Z`, creates a GitHub Release with the built wheel/sdist; optional PyPI publish gated by repo variable `PUBLISH_PYPI == 'true'`. + +- [ ] **Step 1: Check the configuration keys against the installed version** + +Run: `poetry run semantic-release --version && poetry run semantic-release generate-config -f toml | head -60` +Use that output as the source of truth for key names; adjust the block below if any key differs in the installed major version. + +- [ ] **Step 2: Append to `pyproject.toml`** + +```toml +[tool.semantic_release] +version_toml = ["pyproject.toml:project.version"] +commit_parser = "conventional" +allow_zero_version = true +major_on_zero = false +tag_format = "v{version}" +build_command = "pip install -q poetry && poetry build" + +[tool.semantic_release.branches.main] +match = "main" + +[tool.semantic_release.changelog] +mode = "update" + +[tool.semantic_release.commit_parser_options] +allowed_tags = ["feat", "fix", "perf", "docs", "chore", "ci", "test", "refactor", "build", "style"] +minor_tags = ["feat"] +patch_tags = ["fix", "perf"] + +[tool.semantic_release.publish] +dist_glob_patterns = ["dist/*"] +upload_to_vcs_release = true +``` + +- [ ] **Step 3: Dry-run locally (no tags, no push)** + +Run: `poetry run semantic-release --noop version --print` +Expected: prints the next version (`0.1.0`, because there are `feat:` commits and the current version is `0.0.0`). Branch-name errors ("not on a release branch") are expected here because the branch is `refactor/modernization`, not `main`; in that case confirm the config parses by running `poetry run semantic-release --noop -vv version --print 2>&1 | head -30` and checking that it reaches the branch check without a config error. + +- [ ] **Step 4: Write `.github/workflows/release.yml`** + +```yaml +name: Release + +on: + push: + branches: [main] + +permissions: + contents: write + id-token: write # needed only for PyPI trusted publishing + +concurrency: + group: release + cancel-in-progress: false + +jobs: + release: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v5 + with: + fetch-depth: 0 + ref: ${{ github.ref_name }} + + - name: Semantic release + id: release + uses: python-semantic-release/python-semantic-release@v10 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + + - name: Attach distributions to the GitHub release + if: steps.release.outputs.released == 'true' + uses: python-semantic-release/publish-action@v10 + with: + github_token: ${{ secrets.GITHUB_TOKEN }} + tag: ${{ steps.release.outputs.tag }} + + - name: Publish to PyPI (enable with repo variable PUBLISH_PYPI=true) + if: steps.release.outputs.released == 'true' && vars.PUBLISH_PYPI == 'true' + uses: pypa/gh-action-pypi-publish@release/v1 +``` + +Notes to record in `CONTRIBUTING.md` under a new "Releases" heading: the workflow pushes the version-bump commit and tag to `main`, so branch protection must allow `github-actions[bot]` to push (or use a PAT/GitHub App token); PyPI publishing needs the project registered on PyPI with a trusted publisher configured, then set the repository variable `PUBLISH_PYPI` to `true`. + +- [ ] **Step 5: Validate YAML and commit** + +Run: `poetry run python -c "import yaml; yaml.safe_load(open('.github/workflows/release.yml')); print('ok')"` +Expected: `ok` + +```bash +git add pyproject.toml .github CONTRIBUTING.md +git commit -m "ci: automate releases and changelog with python-semantic-release + +Co-Authored-By: Claude Sonnet 5.5 " +``` + +--- + +### Task 16: Remove legacy code and final verification + +**Files:** +- Delete: `CNN/` (remaining: `README.md`, `datasets/DatasetLoader.py`, `nn/`, `preprocessing/`), `learning.py`, `load_test_model.py`, `requirements.txt`, `_config.yml`, `WBC-Detection/` (remaining: `README.md`, `WBC-Detection.py`), `TrainedModel/`, `image.png` + +- [ ] **Step 1: Remove the legacy files** + +```bash +git rm -r -q CNN learning.py load_test_model.py requirements.txt _config.yml WBC-Detection TrainedModel image.png +git status --short | head -20 +``` +Expected: only deletions; `ls` shows no `CNN/`, `WBC-Detection/`, `TrainedModel/`. + +- [ ] **Step 2: Verify nothing references the removed files** + +Run: `grep -rnE "learning\.py|load_test_model|requirements\.txt|CNN/|TrainedModel|WBC-Detection/|IncludeNet.py" --include='*.md' --include='*.py' --include='*.yml' --include='*.yaml' --include='*.toml' . | grep -v '^./docs/superpowers/' | grep -v '^./.git/'` +Expected: no output (the spec/plan documents are excluded on purpose). Fix any hit. + +- [ ] **Step 3: Full verification** + +Run: + +```bash +poetry run ruff check . && poetry run ruff format --check . && poetry run mypy src && poetry run pytest -q +poetry run typer wbc_classification.cli utils docs --name wbc --output docs/reference/cli.md && poetry run mkdocs build --strict +poetry build && ls dist +``` +Expected: every command succeeds; `dist/` holds a wheel and an sdist named `wbc_classification-0.0.0-*`. Then `git status --short` must be clean apart from ignored files (`dist/`, `site/`, `docs/reference/cli.md` are ignored — add `dist/` to `.gitignore` if it shows up). + +- [ ] **Step 4: Smoke-test the built wheel in a clean environment** + +```bash +python3.14 -m venv /tmp/wbc-wheel-test && /tmp/wbc-wheel-test/bin/pip install -q dist/*.whl && /tmp/wbc-wheel-test/bin/wbc --version && /tmp/wbc-wheel-test/bin/wbc --help | head -20 +rm -rf /tmp/wbc-wheel-test +``` +Expected: version prints and the five commands are listed. + +- [ ] **Step 5: Commit** + +```bash +git add -A +git commit -m "chore: remove legacy Keras code and obsolete files + +BREAKING CHANGE: the Keras scripts, requirements.txt and the HDF5 model are removed; use the new `wbc` CLI. + +Co-Authored-By: Claude Sonnet 5.5 " +``` +(Note: the `BREAKING CHANGE:` footer on a 0.x project bumps the minor version only, because `major_on_zero = false`. The commit message should keep the footer separate from the `Co-Authored-By` trailer by a blank line, as shown.) + +- [ ] **Step 6: Owner steps (do not perform; report them to the project owner)** + +1. Rename the default branch on GitHub: Settings → Branches → rename `master` to `main` (then `git branch -m master main && git fetch && git branch -u origin/main main` locally). +2. Open a PR from `refactor/modernization` into `main` with a Conventional Commit title (e.g. `feat: modernize project with PyTorch, Poetry, docs and releases`) and squash-merge it; this triggers the first release (`0.1.0`). +3. Enable GitHub Pages (Settings → Pages → Source: GitHub Actions). +4. Allow `github-actions[bot]` to push to `main` (branch protection), or switch the release workflow to a PAT / GitHub App token. +5. Optional: register the project on PyPI with a trusted publisher and set the repo variable `PUBLISH_PYPI=true`. +6. Run full GPU training (`configs/resnet18.yaml`, `configs/efficientnet_b0.yaml`, `configs/baseline.yaml`, plus a `--crop` variant) and fill in `docs/results.md`. +7. Decide separately whether to purge the old dataset images from git history (destructive; needs a force-push). diff --git a/docs/superpowers/specs/2026-10-04-wbc-modernization-design.md b/docs/superpowers/specs/2026-10-04-wbc-modernization-design.md new file mode 100644 index 0000000..f4096e3 --- /dev/null +++ b/docs/superpowers/specs/2026-10-04-wbc-modernization-design.md @@ -0,0 +1,138 @@ +# WBC-Classification Modernization — Design + +Date: 2026-10-04 +Status: Draft for review + +## 1. Goal + +Turn WBC-Classification (a Keras CNN that classifies four white-blood-cell types) into a credible open-source project: installable, reproducible, documented, tested, and released automatically. This covers repository structure, the training/inference pipeline, and the model itself. + +**Success criteria** + +- A stranger can clone the repo, run `poetry install`, and use `wbc train | evaluate | predict | segment | download-data`. +- Training is config-driven and reproducible (seeded, config saved with every run and checkpoint). +- Evaluation is honest: the TEST set is used only for final evaluation. +- Tests, lint, type-check and docs build run in CI; merges to `main` produce a versioned release with changelog. +- A pretrained-backbone model is the main model; a cleaned-up IncludeNet remains as a lightweight baseline. README/docs report metrics only once real numbers exist. + +**Non-goals (may come later):** Grad-CAM, ONNX export, multi-GPU, experiment-tracking integrations, rewriting git history, backward compatibility with the old Keras scripts or the `.hdf5` model. + +## 2. Decisions (agreed with the project owner) + +| Topic | Decision | +|---|---| +| Framework | PyTorch (Keras code is dropped) | +| Dependency/project management | Poetry, `src/` layout, committed `poetry.lock` | +| Python | Develop on 3.14; support `>=3.12,<3.15`; CI matrix 3.12/3.13/3.14 | +| Dependencies | Latest releases at implementation time; caret ranges in `pyproject.toml`, exact pins in the lock | +| Dataset | Removed from the working tree; download script + `data/README.md`; a few fixture images kept for tests; git history not rewritten | +| Model | Pretrained torchvision backbone (main) + modernized IncludeNet baseline (lightweight) | +| Cell cropping | Optional (`preprocessing.crop`), off by default; rewritten, tested module | +| Training compute | Code built and tested on CPU here; full training run by the owner on GPU/Colab | +| Training stack | Plain PyTorch loop, YAML configs validated with pydantic, Typer CLI (not Lightning/Hydra) | +| Docs | MkDocs + Material + mkdocstrings, deployed to GitHub Pages | +| Releases | Conventional Commits + python-semantic-release on merge to `main`; PyPI publish wired but off by default | +| Default branch | Rename `master` → `main` (GitHub-side step done by the owner) | + +## 3. Repository layout + +``` +wbc-classification/ +├── src/wbc_classification/ +│ ├── config.py # pydantic config models + YAML loading + CLI overrides +│ ├── data/ # datasets.py, transforms.py, labels.py +│ ├── preprocessing/ # segmentation.py (optional HSV cell crop) +│ ├── models/ # baseline.py (IncludeNet), backbone.py, registry.py +│ ├── engine/ # train.py, evaluate.py, predict.py, metrics.py +│ └── cli.py # `wbc` Typer app +├── configs/ # baseline.yaml, resnet18.yaml, efficientnet_b0.yaml +├── data/README.md # layout + download instructions (data itself is git-ignored) +├── scripts/ # thin helpers only if not covered by the CLI +├── tests/ # unit, CLI, CPU smoke test; fixtures in tests/fixtures/ +├── notebooks/ # Colab training notebook, WBC-Detection walkthrough +├── docs/ # MkDocs site sources +├── .github/workflows/ # ci.yml, release.yml, docs.yml, pr-title.yml +├── pyproject.toml, poetry.lock, mkdocs.yml +└── README.md, CONTRIBUTING.md, CHANGELOG.md, LICENSE +``` + +**Removed:** Keras code (`CNN/`, `learning.py`, `load_test_model.py`), `requirements.txt`, `_config.yml`, stub READMEs, PayPal/hit-counter badges, `TrainedModel/*.hdf5`, committed dataset images. `WBC-Detection/` content moves into `docs/` and a notebook; its sample image becomes a test fixture. + +**Packaging:** `[tool.poetry]` with `packages = [{include = "wbc_classification", from = "src"}]`, console script `wbc = "wbc_classification.cli:app"`. Dependency groups: + +- main: torch, torchvision, numpy, opencv-python-headless, pillow, scikit-learn, matplotlib, typer, pydantic, pyyaml +- dev: pytest, ruff, mypy +- docs: mkdocs, mkdocs-material, mkdocstrings +- release: python-semantic-release + +Plain PyPI torch is declared; the training guide documents installing a CUDA build for GPU users. OpenCV 5 compatibility is verified during implementation (fall back to 4.x only if needed; the new code uses the two-value `findContours` return). + +## 4. Models + +- `build_model(name, num_classes, pretrained)` registry in `models/registry.py`; names: `baseline`, `resnet18`, `resnet50`, `efficientnet_b0`. Adding a model = adding a registry entry. +- `baseline`: IncludeNet in PyTorch — Conv-BatchNorm-ReLU blocks, global average pooling, dropout; independent of input size (the old hard-coded 50×50 flatten is gone). +- Backbones: torchvision pretrained weights, classifier head replaced; optional `freeze_backbone` for the first N epochs, then unfreeze. + +## 5. Data and preprocessing + +- Dataset reads `TRAIN//` and `TEST//` (Kaggle layout) under `data/`. Class names derive from folder names; `labels.py` is the single source of truth. +- Validation: seeded, stratified split from TRAIN. TEST is used only by `evaluate`. +- Train transforms: resize, flips/rotations, color jitter (stain variation), ImageNet normalization. Eval transforms: resize + normalize. Image size is per-config (50 for baseline; 128–224 for backbones). +- Optional crop (`preprocessing.crop: true`): HSV threshold on a correctly converted (BGR→HSV) image, largest contour, bounding crop with clamped bounds. No cell found → `SegmentationError`; the training pipeline catches it, falls back to the full image, and logs a warning. +- Crop vs. no-crop is compared in the docs results so the default is evidence-based. + +## 6. Training, evaluation, inference + +- **Training loop** (`engine/train.py`): AdamW, cosine LR schedule, mixed precision on CUDA, early stopping on validation accuracy. Seed controls Python/NumPy/torch. +- **Run artifacts:** `runs/-/` containing config copy, `metrics.csv`, training-curve PNG, confusion matrix, best and last checkpoints. +- **Checkpoint:** a single `.pt` holding weights, model name, class names, and full config; `predict` rebuilds the model from the file alone. Loading validates stored config/class names against the model. +- **Evaluation:** per-class precision/recall/F1 and confusion matrix on TEST, as console output and JSON. +- **Inference:** `wbc predict --checkpoint X` prints class and probabilities; optional annotated-image output. No `cv2.imshow`. +- **Config:** one YAML per experiment (model, image size, epochs, batch size, LR, augmentation, crop flag, seed); every field overridable from the CLI (`wbc train -c configs/resnet18.yaml --epochs 5`). Invalid configs fail fast with clear messages. + +## 7. CLI + +`wbc train`, `wbc evaluate`, `wbc predict`, `wbc segment`, `wbc download-data`. Nothing runs at import time. + +## 8. Documentation + +MkDocs Material site: getting started, data setup, training guide (incl. GPU/Colab), configuration reference, model zoo/results, CLI reference (generated from Typer), API reference (mkdocstrings), architecture, WBC-Detection walkthrough, contributing, changelog (included from `CHANGELOG.md`). `mkdocs build --strict` runs in CI; deployed to GitHub Pages on merge to `main`. + +The README is short: self-updating badges (CI, release, Python versions, license, docs), quick start, link to docs. Results table lives in the docs. + +## 9. CI and releases + +- `ci.yml` (PRs and pushes): ruff, mypy, pytest (3.12/3.13/3.14), docs build, CPU smoke test. +- `pr-title.yml`: enforces Conventional Commit PR titles; squash-merge so the title becomes the commit. +- `release.yml` (merge to `main`): python-semantic-release bumps the version in `pyproject.toml`, regenerates `CHANGELOG.md`, tags `vX.Y.Z`, creates the GitHub Release, builds with `poetry build`, attaches artifacts. `docs:`/`chore:`/`ci:` commits do not release. PyPI publishing is configured but disabled until the owner registers the project and adds a token. +- `docs.yml` (merge to `main`): deploys the site. +- First release: `0.1.0`. GitHub Actions use current major versions, verified at write time. + +## 10. Testing + +- Unit: transforms, label mapping, config validation (bad configs fail clearly), model output shapes for every registry entry, segmentation on fixtures (including the no-cell case), metrics. +- CLI: Typer `CliRunner` for each command. +- Smoke (CPU, under a minute): 1-epoch train of `baseline` on ~20 fixture images → evaluate → save → reload → predict. +- Marked `slow` (not in CI): full training, pretrained-weight downloads. + +## 11. Error handling + +Validate at boundaries only (config load, dataset discovery, checkpoint load) with actionable messages (e.g. "data dir missing — run `wbc download-data`"). No silent `print` + `return None`. Internal code trusts validated inputs. + +## 12. Migration order + +1. Housekeeping: branch rename to `main` (owner), remove dataset images from the tree, add `data/README.md` + download command, keep fixtures. +2. Scaffold: Poetry `pyproject.toml`, `src/` layout, ruff/mypy/pytest config, CI skeleton. +3. Core: config, labels, data, transforms, then models and registry. +4. Engine and CLI with tests alongside. +5. Docs: MkDocs site, README, CONTRIBUTING, Colab notebook. +6. Release automation workflows. +7. Cleanup: remove old Keras code and leftover files. + +## 13. Risks and open items + +- OpenCV 5.0 is a new major version; may need a fallback to 4.x. +- Python 3.14 + torch wheels exist today (cp314); if a transitive dependency lacks 3.14 support the dev version drops to 3.13. +- No pretrained checkpoint or metrics are published until the owner runs GPU training; docs ship with a results template. +- Kaggle download needs the owner's Kaggle credentials; the command documents this and fails with a clear message otherwise. +- Removing images from the tree does not shrink existing history (clone size stays large) — a separate, explicit decision. diff --git a/docs/training.md b/docs/training.md new file mode 100644 index 0000000..a0f8aaf --- /dev/null +++ b/docs/training.md @@ -0,0 +1,35 @@ +# Training + +```bash +poetry run wbc train -c configs/resnet18.yaml # pretrained ResNet-18 +poetry run wbc train -c configs/baseline.yaml # small baseline CNN +poetry run wbc train -c configs/resnet18.yaml --epochs 5 --device cpu +poetry run wbc train -c configs/resnet18.yaml --set data.num_workers=4 --set train.seed=1 +poetry run wbc -v train -c configs/resnet18.yaml # log every epoch +``` + +Each run writes `runs/-/`: + +| File | Content | +|---|---| +| `config.yaml` | the exact configuration used | +| `metrics.csv` | per-epoch loss / accuracy / learning rate | +| `curves.png` | training curves | +| `confusion_matrix.png` | validation confusion matrix of the best epoch | +| `best.pt` / `last.pt` | checkpoints (weights + class names + config) | + +Use `--set section.field=value` (repeatable) to override any configuration field, such as +`train.seed`, `train.patience` or `data.num_workers`; see [Configuration](configuration.md). + +Training uses AdamW, a cosine learning-rate schedule, mixed precision on CUDA and early stopping on validation accuracy. +Set `model.freeze_backbone_epochs` to train only the new classification head for the first N epochs. +During the frozen epochs only the classification head's weights are trained; BatchNorm layers in the frozen +backbone still update their running statistics (the model stays in train mode), and early-stopping patience also +counts the frozen epochs. + +## GPU + +`poetry install` installs the default PyTorch build. For a specific CUDA build follow the +[PyTorch install guide](https://pytorch.org/get-started/locally/) and install that wheel into the Poetry environment. +On Google Colab use `notebooks/train_colab.ipynb`. +Training on CPU is fine for the `baseline` model and for smoke tests, but slow for pretrained backbones. diff --git a/docs/wbc-detection.md b/docs/wbc-detection.md new file mode 100644 index 0000000..8f9b959 --- /dev/null +++ b/docs/wbc-detection.md @@ -0,0 +1,17 @@ +# WBC detection walkthrough + +The optional crop step locates the white blood cell with classical computer vision: + +1. Blur the RGB image (Gaussian, 7×7) — ![blur](assets/detection/blur.png) +2. Convert to HSV — ![hsv](assets/detection/hsv.png) +3. Keep pixels inside the cell colour range (`HSV_LOWER = (80, 60, 140)`, `HSV_UPPER = (255, 255, 255)` in `segmentation.py`; OpenCV's 8-bit hue only goes up to 179, so the effective hue range is 80–179) — ![color filtering](assets/detection/color-filtering.png) +4. Take the largest contour as the cell mask — ![mask](assets/detection/mask.png) +5. Crop around the minimum enclosing circle — ![result](assets/detection/final.png) + +Try it on your own image: + +```bash +poetry run wbc segment path/to/cell.jpeg --output-dir segmentation +``` + +or use the code directly: `wbc_classification.preprocessing.segmentation.segment_cell`. diff --git a/image.png b/image.png deleted file mode 100644 index 6e2de4c..0000000 Binary files a/image.png and /dev/null differ diff --git a/learning.py b/learning.py deleted file mode 100644 index dea158e..0000000 --- a/learning.py +++ /dev/null @@ -1,96 +0,0 @@ -from sklearn.preprocessing import LabelBinarizer -from sklearn.model_selection import train_test_split -from sklearn.metrics import classification_report -from CNN.preprocessing import ImageToArray -from CNN.preprocessing.PreProcessor import PreProcessor -from CNN.datasets.DatasetLoader import DatasetLoader -from CNN.nn.conv import IncludeNet -from keras.optimizers import SGD -import matplotlib.pyplot as plt -import numpy as np -import argparse -from imutils import paths - -ap = argparse.ArgumentParser() -ap.add_argument("-d", "--dataset", required=True, help="insert dataset") -ap.add_argument("-m", "--model", required=True, help="path to output model") -args = vars(ap.parse_args()) -size = 50 -ep = 100 -dpt = 3 - - -class Learning: - @staticmethod - def learning(): - test_x, test_y, train_x, train_y = Learning.loading_dataset() - - model = Learning.compile() - - h = Learning.train(model, test_x, test_y, train_x, train_y) - - Learning.save_model(model) - - Learning.evaluating(h, model, test_x, test_y) - - @staticmethod - def loading_dataset(): - print("[INFO] loading Images") - image_paths = list(paths.list_images(args["dataset"])) - sp = PreProcessor(size, size) - iap = ImageToArray.ImageToArrayPreprocessor() - sdl = DatasetLoader(preprocessors=[sp, iap]) - (data, labels) = sdl.load(image_paths, verbose=500) - print(data) - data = data.astype("float") / 255.0 - (train_x, test_x, train_y, test_y) = train_test_split(data, labels, test_size=0.25, random_state=42) - train_y = LabelBinarizer().fit_transform(train_y) - test_y = LabelBinarizer().fit_transform(test_y) - return test_x, test_y, train_x, train_y - - @staticmethod - def compile(): - print("[INFO] compiling model...") - opt = SGD(lr=0.025) - model = IncludeNet.IncludeNet.build(width=size, height=size, depth=dpt, classes=4) - model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"]) - return model - - @staticmethod - def train(model, test_x, test_y, train_x, train_y): - print("[INFO] training network...") - h = model.fit(train_x, train_y, validation_data=(test_x, test_y), batch_size=size, epochs=ep, verbose=1) - return h - - @staticmethod - def save_model(model): - print("Saving network") - model.save(args["model"]) - print("Network have been saved") - - @staticmethod - def evaluating(h, model, test_x, test_y): - print("[INFO] evaluating network...") - predictions = model.predict(test_x, batch_size=size) - print(classification_report(test_y.argmax(axis=1), - predictions.argmax(axis=1), - target_names=["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"])) - Learning.show_plot(h) - - @staticmethod - def show_plot(h): - plt.style.use("ggplot") - plt.figure() - plt.plot(np.arange(0, ep), h.history["loss"], label="train_loss") - plt.plot(np.arange(0, ep), h.history["val_loss"], label="val_loss") - plt.plot(np.arange(0, ep), h.history["acc"], label="acc") - plt.plot(np.arange(0, ep), h.history["val_acc"], label="val_acc") - plt.title("AMINJAMAL") - plt.xlabel("Epoch #") - plt.ylabel("Loss/ACC") - plt.legend() - plt.show() - - -if __name__ == '__main__': - Learning.learning() diff --git a/load_test_model.py b/load_test_model.py deleted file mode 100644 index 5f96304..0000000 --- a/load_test_model.py +++ /dev/null @@ -1,104 +0,0 @@ -from keras.models import load_model -from CNN.preprocessing import ImageToArray -from CNN.preprocessing import PreProcessor -from CNN.datasets.DatasetLoader import DatasetLoader -import matplotlib.pyplot as plt -import numpy as np -import argparse -from imutils import paths -import cv2 - - -def show(image): - # Figure size in inches - plt.figure(figsize=(15, 15)) - - # Show image, with nearest neighbour interpolation - plt.imshow(image, interpolation='nearest') - plt.show() - - -def ready_to_use_images(im): - image_blur = cv2.GaussianBlur(im, (7, 7), 0) - image_blur_hsv = cv2.cvtColor(image_blur, cv2.COLOR_RGB2HSV) - min_red = np.array([80, 60, 140]) - max_red = np.array([255, 255, 255]) - image_red1 = cv2.inRange(image_blur_hsv, min_red, max_red) - big_contour, mask = find_biggest_contour(image_red1) - overlay_mask(mask, im) - moments = cv2.moments(mask) - centre_of_mass = ( - int(moments['m10'] / moments['m00']), - int(moments['m01'] / moments['m00']) - ) - image_with_com = im.copy() - # cv2.rectangle(image_with_com,10,(0,255,0),-1,cv2.LINE_AA) - cv2.circle(image_with_com, centre_of_mass, 10, (0, 255, 0), -1, cv2.LINE_AA) - # show(image_with_com) - image_with_ellipse = im.copy() - ellipse = cv2.fitEllipse(big_contour) - # print(centre_of_mass) - dst = cv2.bitwise_and(im, im, mask=mask) - - return (dst) - - -def find_biggest_contour(image): - image = image.copy() - s, contours, hierarchy = cv2.findContours(image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) - biggest_contour = max(contours, key=cv2.contourArea) - mask = np.zeros(image.shape, np.uint8) - cv2.drawContours(mask, [biggest_contour], -1, 255, -1) - return biggest_contour, mask - - -def overlay_mask(mask, image): - rgb_mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB) - img = cv2.addWeighted(rgb_mask, 0.5, image, 0.5, 0) - # show(img) - - -ap = argparse.ArgumentParser() -ap.add_argument("-d", "--dataset", required=True, help="halloo insert dataset") -ap.add_argument("-m", "--model", required=True, help="path to output model") -args = vars(ap.parse_args()) -size = 50 -classLabels = ["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"] - - -def test(): - data, image_paths = sampling_images() - print("[INFO] loading pre-trained network ...") - model = load_model(args["model"]) - print("[INFO] predicting ...") - predicting(data, image_paths, model) - - -def predicting(data, image_paths, model): - preds = model.predict(data, batch_size=size).argmax(axis=1) - print(preds) - for (i, imagePath) in enumerate(image_paths): - # load the example image, draw the prediction, and display it - # to our screen - image = cv2.imread(imagePath) - # image=ReadyToUseImage(image) - cv2.putText(image, "Label: {}".format(classLabels[preds[i]]), - (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2) - cv2.imshow("Image", image) - cv2.waitKey(0) - - -def sampling_images(): - print("[INFO] sampling images ...") - image_paths = np.array(list(paths.list_images(args["dataset"]))) - idxs = np.random.randint(0, len(image_paths), size=(10,)) - image_paths = image_paths[idxs] - sp = PreProcessor.PreProcessor(size, size) - iap = ImageToArray.ImageToArrayPreprocessor() - sdl = DatasetLoader(preprocessors=[sp, iap]) - (data, labels) = sdl.load(image_paths) - data = data.astype("float") / 255.0 - return data, image_paths - - -test() diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..72398c5 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,59 @@ +site_name: WBC Classification +site_description: Classify white blood cell images with PyTorch. +site_url: https://includeamin.github.io/WBC-Classification/ +repo_url: https://github.com/includeamin/WBC-Classification +repo_name: includeamin/WBC-Classification + +exclude_docs: | + superpowers/ + +theme: + name: material + features: + - navigation.sections + - content.code.copy + palette: + - media: "(prefers-color-scheme: light)" + scheme: default + toggle: {icon: material/weather-night, name: Switch to dark mode} + - media: "(prefers-color-scheme: dark)" + scheme: slate + toggle: {icon: material/weather-sunny, name: Switch to light mode} + +nav: + - Home: index.md + - Getting started: getting-started.md + - Guides: + - Data: data.md + - Pipeline (detect and classify): pipeline.md + - Training: training.md + - Configuration: configuration.md + - WBC detection walkthrough: wbc-detection.md + - Results: results.md + - Reference: + - Architecture: architecture.md + - Models: models.md + - CLI: reference/cli.md + - Python API: reference/api.md + - Contributing: contributing.md + - Changelog: changelog.md + +plugins: + - search + - mkdocstrings: + handlers: + python: + paths: [src] + +markdown_extensions: + - admonition + - toc: + permalink: true + - pymdownx.highlight + - pymdownx.superfences: + custom_fences: + - name: mermaid + class: mermaid + format: !!python/name:pymdownx.superfences.fence_code_format + - pymdownx.snippets: + base_path: ["."] diff --git a/notebooks/train_colab.ipynb b/notebooks/train_colab.ipynb new file mode 100644 index 0000000..7617a00 --- /dev/null +++ b/notebooks/train_colab.ipynb @@ -0,0 +1,61 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Train WBC Classification on a GPU (Colab / Kaggle)\n", + "\n", + "Runtime \u2192 Change runtime type \u2192 GPU." + ] + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "!git clone https://github.com/includeamin/WBC-Classification.git\n", + "%cd WBC-Classification\n", + "!pip install -q -e '.[kaggle]'" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "!wbc download-data --dest data" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "!wbc -v train -c configs/resnet18.yaml" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "code", + "metadata": {}, + "source": [ + "!wbc evaluate $(ls -d runs/*/ | tail -1)best.pt --output $(ls -d runs/*/ | tail -1)test_metrics.json\n", + "!cat $(ls -d runs/*/ | tail -1)test_metrics.json | head -40" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/notebooks/wbc_detection.ipynb b/notebooks/wbc_detection.ipynb new file mode 100644 index 0000000..30ac3c9 --- /dev/null +++ b/notebooks/wbc_detection.ipynb @@ -0,0 +1,50 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fb27b941602401d91542211134fc71a", + "metadata": {}, + "source": [ + "# WBC detection walkthrough\n", + "\n", + "How the optional cell crop works." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acae54e37e7d407bbb7b55eff062a284", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from PIL import Image\n", + "\n", + "from wbc_classification.preprocessing.segmentation import crop_cell, draw_overlay, segment_cell\n", + "\n", + "rgb = np.asarray(Image.open(\"../tests/fixtures/cell.jpeg\").convert(\"RGB\"))\n", + "seg = segment_cell(rgb)\n", + "Image.fromarray(draw_overlay(rgb, seg))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a63283cbaf04dbcab1f6479b197f3a8", + "metadata": {}, + "outputs": [], + "source": [ + "Image.fromarray(crop_cell(rgb))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..6866cd1 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3500 @@ +# This file is automatically @generated by Poetry 2.4.1 and should not be changed by hand. + +[[package]] +name = "annotated-doc" +version = "0.0.5" +description = "Document parameters, class attributes, return types, and variables inline, with Annotated." +optional = false +python-versions = ">=3.9" +groups = ["main"] 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= "Amin Jamal" }] +keywords = ["white blood cells", "image classification", "pytorch", "medical imaging"] +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: BSD License", + "Intended Audience :: Science/Research", + "Topic :: Scientific/Engineering :: Image Recognition", +] +dependencies = ["torch (>=2.14.1,<3.0.0)", "torchvision (>=0.29.1,<0.30.0)", "numpy (>=2.5.3,<3.0.0)", "opencv-python-headless (>=5.0.0.93,<6.0.0.0)", "pillow (>=12.3.0,<13.0.0)", "scikit-learn (>=1.9.1,<2.0.0)", "matplotlib (>=3.11.2,<4.0.0)", "typer (>=0.27.2,<0.28.0)", "pydantic (>=2.13.5,<3.0.0)", "pyyaml (>=6.0.3,<7.0.0)"] + +[project.urls] +Homepage = "https://github.com/includeamin/WBC-Classification" +Documentation = "https://includeamin.github.io/WBC-Classification/" +Changelog = "https://github.com/includeamin/WBC-Classification/blob/main/CHANGELOG.md" + +[project.scripts] +wbc = "wbc_classification.cli:app" + +[project.optional-dependencies] +kaggle = ["kagglehub>=1.0.2"] + +[tool.poetry] +packages = [{ include = "wbc_classification", from = "src" }] + +[build-system] +requires = ["poetry-core>=2.0.0,<3.0.0"] +build-backend = "poetry.core.masonry.api" + +[tool.ruff] +line-length = 100 +src = ["src", "tests"] +extend-exclude = ["docs/superpowers"] + +[tool.ruff.lint] +select = ["E", "F", "I", "UP", "B", "SIM"] + +[tool.mypy] +python_version = "3.12" +ignore_missing_imports = true +check_untyped_defs = true +warn_unused_ignores = true + +[tool.pytest.ini_options] +testpaths = ["tests"] +addopts = "-m 'not slow'" +markers = ["slow: long-running tests (full training, network); not run in CI"] + +[dependency-groups] +dev = [ + "pytest (>=9.1.1,<10.0.0)", + "ruff (>=0.16.10,<0.17.0)", + "mypy (>=2.4.0,<3.0.0)", + "types-pyyaml (>=6.0.12.20260906,<7.0.0.0)" +] +docs = [ + "mkdocs (>=1.6.1,<2.0.0)", + "mkdocs-material (>=9.7.7,<10.0.0)", + "mkdocstrings[python] (>=1.0.6,<2.0.0)" +] +release = [ + "python-semantic-release (>=10.7.0,<11.0.0)" 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a/src/wbc_classification/__init__.py b/src/wbc_classification/__init__.py new file mode 100644 index 0000000..8759047 --- /dev/null +++ b/src/wbc_classification/__init__.py @@ -0,0 +1,7 @@ +"""Classification of white blood cell images with PyTorch.""" + +from importlib.metadata import version + +__version__ = version("wbc-classification") + +__all__ = ["__version__"] diff --git a/src/wbc_classification/__main__.py b/src/wbc_classification/__main__.py new file mode 100644 index 0000000..98dbbd0 --- /dev/null +++ b/src/wbc_classification/__main__.py @@ -0,0 +1,3 @@ +from wbc_classification.cli import app + +app() diff --git a/src/wbc_classification/cli.py b/src/wbc_classification/cli.py new file mode 100644 index 0000000..8e2a13b --- /dev/null +++ b/src/wbc_classification/cli.py @@ -0,0 +1,201 @@ +"""`wbc` command-line interface.""" + +import functools +import logging +from collections.abc import Callable +from pathlib import Path +from typing import Annotated, Any + +import numpy as np +import typer +import yaml +from PIL import Image + +from wbc_classification import __version__ +from wbc_classification.config import load_config +from wbc_classification.data.datasets import load_image +from wbc_classification.data.download import download_dataset +from wbc_classification.engine.evaluate import evaluate_checkpoint +from wbc_classification.engine.metrics import format_report +from wbc_classification.engine.predict import annotate_predictions, predict_images +from wbc_classification.engine.train import train as run_training +from wbc_classification.errors import ConfigError, WBCError +from wbc_classification.preprocessing.segmentation import crop_cell, draw_overlay, segment_cell + +app = typer.Typer( + help="Classify white blood cell images (eosinophil, lymphocyte, monocyte, neutrophil).", + no_args_is_help=True, + add_completion=False, +) + + +def _version_callback(value: bool) -> None: + if value: + typer.echo(__version__) + raise typer.Exit() + + +@app.callback() +def main( + version: Annotated[ + bool, + typer.Option("--version", callback=_version_callback, is_eager=True, help="Show version."), + ] = False, + verbose: Annotated[ + bool, typer.Option("--verbose", "-v", help="Log progress per epoch.") + ] = False, +) -> None: + logging.basicConfig(level=logging.INFO if verbose else logging.WARNING, format="%(message)s") + + +def _handle_errors(func: Callable[..., Any]) -> Callable[..., Any]: + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + try: + return func(*args, **kwargs) + except WBCError as exc: + typer.secho(f"Error: {exc}", fg=typer.colors.RED, err=True) + raise typer.Exit(code=1) from exc + + return wrapper + + +def _parse_set_options(items: list[str]) -> dict[str, Any]: + """Turn ``section.field=value`` strings into typed config overrides.""" + parsed: dict[str, Any] = {} + for item in items: + key, sep, raw = item.partition("=") + key = key.strip() + if not sep or "." not in key or key.startswith(".") or key.endswith("."): + raise ConfigError(f"--set expects section.field=value, got {item!r}") + try: + value = yaml.safe_load(raw) + except yaml.YAMLError as exc: + raise ConfigError(f"--set {item!r}: cannot parse value: {exc}") from exc + if value is None: + raise ConfigError(f"--set {item!r} has an empty value; expected section.field=value") + parsed[key] = value + return parsed + + +@app.command() +@_handle_errors +def train( + config: Annotated[Path | None, typer.Option("--config", "-c", help="YAML config file.")] = None, + model: Annotated[ + str | None, typer.Option(help="baseline, resnet18, resnet50, efficientnet_b0.") + ] = None, + epochs: Annotated[int | None, typer.Option(help="Number of epochs.")] = None, + batch_size: Annotated[int | None, typer.Option(help="Batch size.")] = None, + image_size: Annotated[int | None, typer.Option(help="Square input size in pixels.")] = None, + lr: Annotated[float | None, typer.Option(help="Learning rate.")] = None, + data_root: Annotated[ + Path | None, typer.Option(help="Folder containing TRAIN/ and TEST/.") + ] = None, + output_dir: Annotated[Path | None, typer.Option(help="Where run folders are written.")] = None, + device: Annotated[str | None, typer.Option(help="auto, cpu, cuda, mps.")] = None, + name: Annotated[str | None, typer.Option(help="Run name prefix.")] = None, + pretrained: Annotated[ + bool | None, + typer.Option( + "--pretrained/--no-pretrained", + help="Use ImageNet-pretrained weights (backbones only).", + ), + ] = None, + crop: Annotated[ + bool | None, typer.Option("--crop/--no-crop", help="Crop to the cell first.") + ] = None, + set_options: Annotated[ + list[str] | None, + typer.Option( + "--set", + "-s", + help="Override any config field, e.g. --set train.seed=1 (repeatable).", + ), + ] = None, +) -> None: + """Train a model and write a run folder with checkpoints, metrics and plots.""" + cfg = load_config( + config, + { + "model.name": model, + "train.epochs": epochs, + "data.batch_size": batch_size, + "data.image_size": image_size, + "train.lr": lr, + "data.root": data_root, + "train.output_dir": output_dir, + "train.device": device, + "train.name": name, + "model.pretrained": pretrained, + "preprocessing.crop": crop, + **_parse_set_options(set_options or []), + }, + ) + result = run_training(cfg) + typer.echo(f"Best validation accuracy: {result.best_val_accuracy:.4f}") + typer.echo(f"Run folder: {result.run_dir}") + typer.echo(f"Best checkpoint: {result.best_checkpoint}") + + +@app.command() +@_handle_errors +def evaluate( + checkpoint: Annotated[Path, typer.Argument(help="Checkpoint (.pt) written by `wbc train`.")], + data_root: Annotated[Path | None, typer.Option(help="Folder containing TEST/.")] = None, + device: Annotated[str, typer.Option(help="auto, cpu, cuda, mps.")] = "auto", + output: Annotated[ + Path | None, typer.Option(help="Write metrics JSON (+ confusion PNG) here.") + ] = None, +) -> None: + """Evaluate a checkpoint on the TEST split.""" + metrics = evaluate_checkpoint(checkpoint, data_root, device, output) + typer.echo(format_report(metrics)) + + +@app.command() +@_handle_errors +def predict( + inputs: Annotated[list[Path], typer.Argument(help="Image files and/or folders of images.")], + checkpoint: Annotated[Path, typer.Option("--checkpoint", "-c", help="Checkpoint (.pt).")], + device: Annotated[str, typer.Option(help="auto, cpu, cuda, mps.")] = "auto", + annotate_dir: Annotated[ + Path | None, typer.Option(help="Save labelled copies of the images here.") + ] = None, +) -> None: + """Predict the cell type of one or more images.""" + predictions = predict_images(checkpoint, inputs, device) + for prediction in predictions: + confidence = prediction.probabilities[prediction.label] + typer.echo(f"{prediction.path}: {prediction.label} ({confidence:.1%})") + if annotate_dir is not None: + annotate_predictions(predictions, annotate_dir) + + +@app.command() +@_handle_errors +def segment( + image: Annotated[Path, typer.Argument(help="Image to segment.")], + output_dir: Annotated[Path, typer.Option(help="Where to write crop/overlay/mask PNGs.")] = Path( + "segmentation" + ), +) -> None: + """Locate the cell with HSV segmentation and save the crop, overlay and mask.""" + rgb = np.asarray(load_image(image)) + segmentation = segment_cell(rgb) + output_dir.mkdir(parents=True, exist_ok=True) + Image.fromarray(crop_cell(rgb)).save(output_dir / f"{image.stem}_crop.png") + Image.fromarray(draw_overlay(rgb, segmentation)).save(output_dir / f"{image.stem}_overlay.png") + Image.fromarray(segmentation.mask).save(output_dir / f"{image.stem}_mask.png") + typer.echo(f"Saved crop, overlay and mask to {output_dir}") + + +@app.command("download-data") +@_handle_errors +def download_data( + dest: Annotated[Path, typer.Option(help="Destination folder.")] = Path("data"), + force: Annotated[bool, typer.Option(help="Overwrite existing TRAIN/TEST folders.")] = False, +) -> None: + """Download the Kaggle blood-cells dataset into DEST (needs the `kaggle` extra).""" + path = download_dataset(dest, force) + typer.echo(f"Dataset installed in {path}") diff --git a/src/wbc_classification/config.py b/src/wbc_classification/config.py new file mode 100644 index 0000000..296405a --- /dev/null +++ b/src/wbc_classification/config.py @@ -0,0 +1,91 @@ +"""Experiment configuration: pydantic models, YAML loading and CLI overrides.""" + +from pathlib import Path +from typing import Any + +import yaml +from pydantic import BaseModel, ConfigDict, Field, ValidationError + +from wbc_classification.errors import ConfigError + + +class _Section(BaseModel): + model_config = ConfigDict(extra="forbid") + + +class PreprocessingConfig(_Section): + crop: bool = False + + +class DataConfig(_Section): + root: Path = Path("data") + image_size: int = Field(default=224, ge=16) + batch_size: int = Field(default=32, ge=1) + val_fraction: float = Field(default=0.2, gt=0, lt=0.5) + num_workers: int = Field(default=0, ge=0) + + +class ModelConfig(_Section): + name: str = "resnet18" + pretrained: bool = True + freeze_backbone_epochs: int = Field(default=0, ge=0) + + +class TrainConfig(_Section): + name: str = "run" + epochs: int = Field(default=30, ge=1) + lr: float = Field(default=1e-3, gt=0) + weight_decay: float = Field(default=1e-4, ge=0) + patience: int = Field(default=7, ge=1) + amp: bool = True + seed: int = 42 + device: str = "auto" + output_dir: Path = Path("runs") + + +class Config(_Section): + preprocessing: PreprocessingConfig = PreprocessingConfig() + data: DataConfig = DataConfig() + model: ModelConfig = ModelConfig() + train: TrainConfig = TrainConfig() + + +def load_config(path: Path | None = None, overrides: dict[str, Any] | None = None) -> Config: + """Load a YAML config (optional) and apply ``{"section.field": value}`` overrides.""" + raw: dict[str, Any] = {} + if path is not None: + if not path.is_file(): + raise ConfigError(f"Config file not found: {path}") + try: + loaded = yaml.safe_load(path.read_text()) + except yaml.YAMLError as exc: + raise ConfigError(f"Invalid YAML in {path}: {exc}") from exc + if loaded is None: + loaded = {} + if not isinstance(loaded, dict): + raise ConfigError(f"Config {path} must be a mapping of sections") + raw = loaded + + for key, value in (overrides or {}).items(): + if value is None: + continue + section, _, field = key.partition(".") + if not field: + raise ConfigError(f"Override key must look like 'section.field', got {key!r}") + target = raw.setdefault(section, {}) + if not isinstance(target, dict): + raise ConfigError(f"Config section {section!r} must be a mapping") + target[field] = value + + try: + return Config.model_validate(raw) + except ValidationError as exc: + details = "; ".join( + f"{'.'.join(str(p) for p in err['loc'])}: {err['msg']}" for err in exc.errors() + ) + raise ConfigError(f"Invalid configuration: {details}") from exc + + +def dump_config(config: Config) -> str: + """Serialise a config to YAML (paths become strings).""" + return yaml.safe_dump(config.model_dump(mode="json"), sort_keys=False) diff --git a/src/wbc_classification/data/__init__.py b/src/wbc_classification/data/__init__.py new file mode 100644 index 0000000..19bb6da --- /dev/null +++ b/src/wbc_classification/data/__init__.py @@ -0,0 +1 @@ +"""Datasets, transforms and data download.""" diff --git a/src/wbc_classification/data/datasets.py b/src/wbc_classification/data/datasets.py new file mode 100644 index 0000000..9be6e1c --- /dev/null +++ b/src/wbc_classification/data/datasets.py @@ -0,0 +1,147 @@ +"""Dataset listing, splitting, loading and DataLoader construction.""" + +import logging +import math +from collections import Counter +from collections.abc import Callable, Sequence +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from PIL import Image +from sklearn.model_selection import train_test_split +from torch.utils.data import DataLoader, Dataset + +from wbc_classification.config import Config +from wbc_classification.data.labels import discover_classes, is_image_file, require_split_dir +from wbc_classification.data.transforms import build_transforms +from wbc_classification.errors import DataError, SegmentationError +from wbc_classification.preprocessing.segmentation import crop_cell + +logger = logging.getLogger(__name__) + +Sample = tuple[Path, int] + + +def list_samples(split_dir: Path, class_names: Sequence[str]) -> list[Sample]: + samples: list[Sample] = [] + for index, name in enumerate(class_names): + class_dir = split_dir / name + if not class_dir.is_dir(): + raise DataError(f"Missing class folder {class_dir}") + files = sorted(p for p in class_dir.iterdir() if is_image_file(p)) + if not files: + raise DataError(f"Class folder {class_dir} contains no images") + samples.extend((p, index) for p in files) + return samples + + +def stratified_split( + samples: list[Sample], val_fraction: float, seed: int +) -> tuple[list[Sample], list[Sample]]: + labels = [label for _, label in samples] + counts = Counter(labels) + n_classes = len(counts) + n_val = math.ceil(val_fraction * len(samples)) + n_train = len(samples) - n_val + if min(counts.values()) < 2 or n_val < n_classes or n_train < n_classes: + raise DataError( + f"Not enough images to make a stratified validation split " + f"({len(samples)} images, {n_classes} classes, val_fraction={val_fraction}); " + "add more images or raise data.val_fraction." + ) + train, val = train_test_split( + samples, test_size=val_fraction, stratify=labels, random_state=seed + ) + return train, val + + +def load_image(path: Path, crop: bool = False) -> Image.Image: + """Load an image as RGB, optionally cropped to the cell (full image if none found).""" + try: + with Image.open(path) as handle: + image = handle.convert("RGB") + except OSError as exc: + raise DataError(f"Cannot read image {path}: {exc}") from exc + if crop: + try: + image = Image.fromarray(crop_cell(np.asarray(image))) + except SegmentationError as exc: + logger.warning("No cell found in %s (%s); using the full image", path, exc) + return image + + +class WBCDataset(Dataset[tuple[torch.Tensor, int]]): + def __init__( + self, + samples: list[Sample], + transform: Callable[[Image.Image], torch.Tensor], + crop: bool = False, + ) -> None: + self.samples = samples + self.transform = transform + self.crop = crop + + def __len__(self) -> int: + return len(self.samples) + + def __getitem__(self, index: int) -> tuple[torch.Tensor, int]: + path, label = self.samples[index] + return self.transform(load_image(path, self.crop)), label + + +@dataclass(frozen=True) +class Loaders: + train: DataLoader + val: DataLoader + class_names: list[str] + + +def build_train_val_loaders(cfg: Config) -> Loaders: + train_dir = cfg.data.root / "TRAIN" + class_names = discover_classes(train_dir) + train_samples, val_samples = stratified_split( + list_samples(train_dir, class_names), cfg.data.val_fraction, cfg.train.seed + ) + crop = cfg.preprocessing.crop + train_ds = WBCDataset(train_samples, build_transforms(cfg.data.image_size, True), crop) + val_ds = WBCDataset(val_samples, build_transforms(cfg.data.image_size, False), crop) + batch_size = cfg.data.batch_size + num_workers = cfg.data.num_workers + pin_memory = torch.cuda.is_available() + generator = torch.Generator().manual_seed(cfg.train.seed) + return Loaders( + train=DataLoader( + train_ds, + batch_size=batch_size, + shuffle=True, + generator=generator, + num_workers=num_workers, + pin_memory=pin_memory, + ), + val=DataLoader( + val_ds, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=pin_memory, + ), + class_names=class_names, + ) + + +def build_test_loader(cfg: Config, class_names: Sequence[str]) -> DataLoader: + test_dir = cfg.data.root / "TEST" + require_split_dir(test_dir) + samples = list_samples(test_dir, class_names) + dataset = WBCDataset( + samples, build_transforms(cfg.data.image_size, False), cfg.preprocessing.crop + ) + return DataLoader( + dataset, + batch_size=cfg.data.batch_size, + shuffle=False, + num_workers=cfg.data.num_workers, + pin_memory=torch.cuda.is_available(), + ) diff --git a/src/wbc_classification/data/download.py b/src/wbc_classification/data/download.py new file mode 100644 index 0000000..00568b5 --- /dev/null +++ b/src/wbc_classification/data/download.py @@ -0,0 +1,46 @@ +"""Download and install the Kaggle blood-cells dataset.""" + +import shutil +from pathlib import Path + +from wbc_classification.errors import DataError + +DATASET_HANDLE = "paultimothymooney/blood-cells" +SPLITS = ("TRAIN", "TEST") + + +def find_dataset_root(base: Path) -> Path: + """Find the folder that contains both TRAIN/ and TEST/ below ``base``.""" + for train_dir in sorted(base.rglob("TRAIN")): + if train_dir.is_dir() and (train_dir.parent / "TEST").is_dir(): + return train_dir.parent + raise DataError(f"Could not find TRAIN/ and TEST/ folders under {base}") + + +def install_dataset(source: Path, dest: Path, force: bool = False) -> Path: + root = find_dataset_root(source) + for split in SPLITS: + target = dest / split + if target.exists(): + if not force: + raise DataError(f"{target} already exists; pass --force to overwrite") + shutil.rmtree(target) + shutil.copytree(root / split, target) + return dest + + +def download_dataset(dest: Path, force: bool = False) -> Path: + try: + import kagglehub + except ImportError as exc: + raise DataError( + "kagglehub is not installed. Run `poetry install --extras kaggle` " + "(or `pip install 'wbc-classification[kaggle]'`)." + ) from exc + try: + source = Path(kagglehub.dataset_download(DATASET_HANDLE)) + except Exception as exc: # network / auth errors from kagglehub vary widely + raise DataError( + f"Download failed: {exc}. If Kaggle requires authentication see data/README.md." + ) from exc + return install_dataset(source, dest, force) diff --git a/src/wbc_classification/data/labels.py b/src/wbc_classification/data/labels.py new file mode 100644 index 0000000..93a7d20 --- /dev/null +++ b/src/wbc_classification/data/labels.py @@ -0,0 +1,31 @@ +"""Class discovery from the dataset folder layout.""" + +from pathlib import Path + +from wbc_classification.errors import DataError + +IMAGE_EXTENSIONS = frozenset({".jpg", ".jpeg", ".png"}) + + +def is_image_file(path: Path) -> bool: + return path.is_file() and path.suffix.lower() in IMAGE_EXTENSIONS + + +def require_split_dir(path: Path) -> None: + if not path.is_dir(): + raise DataError( + f"Dataset directory not found: {path}. " + "Run `wbc download-data` or point data.root at your dataset." + ) + + +def discover_classes(split_dir: Path) -> list[str]: + """Class names are the sorted sub-folder names of a split directory.""" + require_split_dir(split_dir) + class_dirs = sorted(p for p in split_dir.iterdir() if p.is_dir() and not p.name.startswith(".")) + if not class_dirs: + raise DataError(f"No class folders found in {split_dir}") + for class_dir in class_dirs: + if not any(is_image_file(f) for f in class_dir.iterdir()): + raise DataError(f"Class folder {class_dir} contains no images") + return [p.name for p in class_dirs] diff --git a/src/wbc_classification/data/transforms.py b/src/wbc_classification/data/transforms.py new file mode 100644 index 0000000..e2df8ec --- /dev/null +++ b/src/wbc_classification/data/transforms.py @@ -0,0 +1,24 @@ +"""Train / eval image transforms.""" + +import torch +from torchvision.transforms import v2 + +IMAGENET_MEAN = (0.485, 0.456, 0.406) +IMAGENET_STD = (0.229, 0.224, 0.225) + + +def build_transforms(image_size: int, train: bool) -> v2.Compose: + steps: list[v2.Transform] = [v2.Resize((image_size, image_size))] + if train: + steps += [ + v2.RandomHorizontalFlip(), + v2.RandomVerticalFlip(), + v2.RandomRotation(180), + v2.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.02), + ] + steps += [ + v2.ToImage(), + v2.ToDtype(torch.float32, scale=True), + v2.Normalize(IMAGENET_MEAN, IMAGENET_STD), + ] + return v2.Compose(steps) diff --git a/src/wbc_classification/engine/__init__.py b/src/wbc_classification/engine/__init__.py new file mode 100644 index 0000000..ddaef1b --- /dev/null +++ b/src/wbc_classification/engine/__init__.py @@ -0,0 +1 @@ +"""Training, evaluation and inference.""" diff --git a/src/wbc_classification/engine/checkpoint.py b/src/wbc_classification/engine/checkpoint.py new file mode 100644 index 0000000..ca3ca93 --- /dev/null +++ b/src/wbc_classification/engine/checkpoint.py @@ -0,0 +1,58 @@ +"""Single-file checkpoints: weights + class names + config.""" + +from collections.abc import Sequence +from pathlib import Path + +import torch +from torch import nn + +from wbc_classification.config import Config +from wbc_classification.errors import CheckpointError, ConfigError +from wbc_classification.models.registry import build_model + +_REQUIRED_KEYS = {"state_dict", "class_names", "config"} + + +def save_checkpoint( + path: Path, + model: nn.Module, + class_names: Sequence[str], + config: Config, + epoch: int, + val_accuracy: float, +) -> None: + torch.save( + { + "state_dict": {k: v.detach().cpu() for k, v in model.state_dict().items()}, + "class_names": list(class_names), + "config": config.model_dump(mode="json"), + "epoch": epoch, + "val_accuracy": val_accuracy, + }, + path, + ) + + +def load_checkpoint(path: Path) -> tuple[nn.Module, list[str], Config]: + if not path.is_file(): + raise CheckpointError(f"Checkpoint not found: {path}") + try: + payload = torch.load(path, map_location="cpu", weights_only=True) + except Exception as exc: # torch raises varying types for corrupt files across versions + raise CheckpointError(f"Cannot read checkpoint {path}: {exc}") from exc + if not isinstance(payload, dict) or not payload.keys() >= _REQUIRED_KEYS: + raise CheckpointError(f"{path} is not a wbc-classification checkpoint") + try: + config = Config.model_validate(payload["config"]) + class_names = list(payload["class_names"]) + model = build_model(config.model.name, num_classes=len(class_names), pretrained=False) + model.load_state_dict(payload["state_dict"]) + except ConfigError as exc: + raise CheckpointError(f"{path} stores an unusable model config: {exc}") from exc + except ValueError as exc: # pydantic.ValidationError subclasses ValueError + raise CheckpointError(f"{path} stores an invalid config: {exc}") from exc + except RuntimeError as exc: + raise CheckpointError( + f"Weights in {path} do not match its stored class names / model ({exc})" + ) from exc + return model, class_names, config diff --git a/src/wbc_classification/engine/evaluate.py b/src/wbc_classification/engine/evaluate.py new file mode 100644 index 0000000..6d64caf --- /dev/null +++ b/src/wbc_classification/engine/evaluate.py @@ -0,0 +1,55 @@ +"""Evaluate a checkpoint on the TEST split.""" + +import json +from pathlib import Path +from typing import Any + +import torch +from torch import nn +from torch.utils.data import DataLoader + +from wbc_classification.data.datasets import build_test_loader +from wbc_classification.engine.checkpoint import load_checkpoint +from wbc_classification.engine.metrics import compute_metrics, save_confusion_matrix +from wbc_classification.engine.runtime import resolve_device + + +def collect_predictions( + model: nn.Module, loader: DataLoader, device: torch.device +) -> tuple[list[int], list[int]]: + """Run ``model`` over ``loader`` in eval mode; return ``(y_true, y_pred)`` label lists.""" + model.eval() + y_true: list[int] = [] + y_pred: list[int] = [] + with torch.no_grad(): + for images, labels in loader: + logits = model(images.to(device)) + y_pred.extend(logits.argmax(dim=1).cpu().tolist()) + y_true.extend(labels.tolist()) + return y_true, y_pred + + +def evaluate_checkpoint( + checkpoint: Path, + data_root: Path | None = None, + device: str = "auto", + output: Path | None = None, +) -> dict[str, Any]: + model, class_names, cfg = load_checkpoint(checkpoint) + if data_root is not None: + cfg.data.root = data_root + torch_device = resolve_device(device) + model.to(torch_device).eval() + loader = build_test_loader(cfg, class_names) + + y_true, y_pred = collect_predictions(model, loader, torch_device) + metrics = compute_metrics(y_true, y_pred, class_names) + if output is not None: + output.parent.mkdir(parents=True, exist_ok=True) + output.write_text(json.dumps(metrics, indent=2)) + save_confusion_matrix( + metrics["confusion_matrix"], + class_names, + output.with_name(f"{output.stem}_confusion.png"), + ) + return metrics diff --git a/src/wbc_classification/engine/metrics.py b/src/wbc_classification/engine/metrics.py new file mode 100644 index 0000000..098443c --- /dev/null +++ b/src/wbc_classification/engine/metrics.py @@ -0,0 +1,75 @@ +"""Classification metrics and plots.""" + +import json +from collections.abc import Sequence +from pathlib import Path +from typing import Any + +from matplotlib.figure import Figure +from sklearn.metrics import accuracy_score, classification_report, confusion_matrix + + +def compute_metrics( + y_true: Sequence[int], y_pred: Sequence[int], class_names: Sequence[str] +) -> dict[str, Any]: + labels = list(range(len(class_names))) + report = classification_report( + y_true, + y_pred, + labels=labels, + target_names=list(class_names), + output_dict=True, + zero_division=0, + ) + matrix = confusion_matrix(y_true, y_pred, labels=labels) + result = { + "accuracy": float(accuracy_score(y_true, y_pred)), + "report": report, + "confusion_matrix": matrix.tolist(), + "class_names": list(class_names), + } + return json.loads(json.dumps(result, default=float)) # plain, JSON-safe types + + +def format_report(metrics: dict[str, Any]) -> str: + lines = [f"{'class':<14}{'precision':>10}{'recall':>10}{'f1':>10}{'support':>10}"] + for name in metrics["class_names"]: + row = metrics["report"][name] + lines.append( + f"{name:<14}{row['precision']:>10.3f}{row['recall']:>10.3f}" + f"{row['f1-score']:>10.3f}{int(row['support']):>10}" + ) + lines.append(f"{'accuracy':<14}{metrics['accuracy']:>40.3f}") + return "\n".join(lines) + + +def save_confusion_matrix( + matrix: Sequence[Sequence[int]], class_names: Sequence[str], path: Path +) -> None: + fig = Figure(figsize=(6, 5)) + ax = fig.subplots() + image = ax.imshow(matrix, cmap="Blues") + ax.set_xticks(range(len(class_names)), labels=class_names, rotation=45, ha="right") + ax.set_yticks(range(len(class_names)), labels=class_names) + ax.set_xlabel("Predicted") + ax.set_ylabel("True") + for i, row in enumerate(matrix): + for j, value in enumerate(row): + ax.text(j, i, str(value), ha="center", va="center") + fig.colorbar(image, ax=ax) + fig.savefig(path, dpi=150, bbox_inches="tight") + + +def save_curves(history: Sequence[dict[str, float]], path: Path) -> None: + epochs = [row["epoch"] for row in history] + fig = Figure(figsize=(10, 4)) + loss_ax, acc_ax = fig.subplots(1, 2) + loss_ax.plot(epochs, [r["train_loss"] for r in history], label="train") + loss_ax.plot(epochs, [r["val_loss"] for r in history], label="val") + loss_ax.set(title="Loss", xlabel="Epoch") + acc_ax.plot(epochs, [r["train_acc"] for r in history], label="train") + acc_ax.plot(epochs, [r["val_acc"] for r in history], label="val") + acc_ax.set(title="Accuracy", xlabel="Epoch") + loss_ax.legend() + acc_ax.legend() + fig.savefig(path, dpi=150, bbox_inches="tight") diff --git a/src/wbc_classification/engine/predict.py b/src/wbc_classification/engine/predict.py new file mode 100644 index 0000000..7aeede7 --- /dev/null +++ b/src/wbc_classification/engine/predict.py @@ -0,0 +1,84 @@ +"""Predict classes for images or folders of images.""" + +from collections.abc import Sequence +from dataclasses import dataclass +from pathlib import Path + +import torch +from PIL import ImageDraw + +from wbc_classification.data.datasets import load_image +from wbc_classification.data.labels import is_image_file +from wbc_classification.data.transforms import build_transforms +from wbc_classification.engine.checkpoint import load_checkpoint +from wbc_classification.engine.runtime import resolve_device +from wbc_classification.errors import DataError + + +@dataclass(frozen=True) +class Prediction: + path: Path + label: str + probabilities: dict[str, float] + + +def collect_images(inputs: Sequence[Path]) -> list[Path]: + images: list[Path] = [] + for item in inputs: + if item.is_dir(): + images.extend(sorted(p for p in item.rglob("*") if is_image_file(p))) + elif item.is_file(): + images.append(item) + else: + raise DataError(f"Path not found: {item}") + if not images: + raise DataError("No images found in the given inputs") + return images + + +def predict_images( + checkpoint: Path, inputs: Sequence[Path], device: str = "auto" +) -> list[Prediction]: + model, class_names, cfg = load_checkpoint(checkpoint) + torch_device = resolve_device(device) + model.to(torch_device).eval() + transform = build_transforms(cfg.data.image_size, train=False) + + predictions: list[Prediction] = [] + with torch.no_grad(): + for path in collect_images(inputs): + image = load_image(path, crop=cfg.preprocessing.crop) + batch = transform(image).unsqueeze(0).to(torch_device) + probs = torch.softmax(model(batch), dim=1)[0].cpu().tolist() + best = max(range(len(class_names)), key=probs.__getitem__) + predictions.append( + Prediction(path, class_names[best], dict(zip(class_names, probs, strict=True))) + ) + return predictions + + +def annotate_image(prediction: Prediction, output_dir: Path, name: str | None = None) -> Path: + """Write a copy of the image with the predicted label drawn on it. + + The file is called ``name`` if given, otherwise ``_pred.png``. + """ + image = load_image(prediction.path) + confidence = prediction.probabilities[prediction.label] + ImageDraw.Draw(image).text((8, 8), f"{prediction.label} {confidence:.0%}", fill=(0, 255, 0)) + output_dir.mkdir(parents=True, exist_ok=True) + out = output_dir / (name or f"{prediction.path.stem}_pred.png") + image.save(out) + return out + + +def annotate_predictions(predictions: Sequence[Prediction], output_dir: Path) -> list[Path]: + """Annotate a batch, giving predictions that share a stem unique file names.""" + seen: dict[str, int] = {} + written: list[Path] = [] + for prediction in predictions: + stem = prediction.path.stem + seen[stem] = seen.get(stem, 0) + 1 + count = seen[stem] + name = f"{stem}_pred.png" if count == 1 else f"{stem}_{count}_pred.png" + written.append(annotate_image(prediction, output_dir, name=name)) + return written diff --git a/src/wbc_classification/engine/runtime.py b/src/wbc_classification/engine/runtime.py new file mode 100644 index 0000000..d500d7f --- /dev/null +++ b/src/wbc_classification/engine/runtime.py @@ -0,0 +1,38 @@ +"""Seeding and device selection.""" + +import random + +import numpy as np +import torch + +from wbc_classification.errors import ConfigError + + +def set_seed(seed: int) -> None: + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + + +def resolve_device(name: str) -> torch.device: + if name == "auto": + if torch.cuda.is_available(): + return torch.device("cuda") + if torch.backends.mps.is_available(): + return torch.device("mps") + return torch.device("cpu") + try: + device = torch.device(name) + except RuntimeError as exc: + raise ConfigError(f"Unknown device {name!r} (use auto, cpu, cuda, cuda:0, mps)") from exc + if device.type == "cuda": + if not torch.cuda.is_available(): + raise ConfigError(f"CUDA was requested ({name!r}) but no CUDA device is available") + if device.index is not None and device.index >= torch.cuda.device_count(): + raise ConfigError( + f"CUDA device {device.index} was requested but only " + f"{torch.cuda.device_count()} CUDA device(s) are available" + ) + elif device.type == "mps" and not torch.backends.mps.is_available(): + raise ConfigError(f"MPS was requested ({name!r}) but no MPS device is available") + return device diff --git a/src/wbc_classification/engine/train.py b/src/wbc_classification/engine/train.py new file mode 100644 index 0000000..65d9c58 --- /dev/null +++ b/src/wbc_classification/engine/train.py @@ -0,0 +1,142 @@ +"""Training loop.""" + +import csv +import logging +from dataclasses import dataclass +from datetime import datetime +from pathlib import Path + +import torch +from torch import nn +from torch.optim import AdamW +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader + +from wbc_classification.config import Config, dump_config +from wbc_classification.data.datasets import build_train_val_loaders +from wbc_classification.engine.checkpoint import load_checkpoint, save_checkpoint +from wbc_classification.engine.evaluate import collect_predictions +from wbc_classification.engine.metrics import compute_metrics, save_confusion_matrix, save_curves +from wbc_classification.engine.runtime import resolve_device, set_seed +from wbc_classification.errors import ConfigError +from wbc_classification.models.backbone import set_backbone_frozen +from wbc_classification.models.registry import build_model, supports_freezing + +logger = logging.getLogger(__name__) + +HISTORY_FIELDS = ["epoch", "train_loss", "train_acc", "val_loss", "val_acc", "lr"] + + +@dataclass(frozen=True) +class TrainResult: + run_dir: Path + best_checkpoint: Path + last_checkpoint: Path + best_val_accuracy: float + history: list[dict[str, float]] + + +def _run_epoch( + model: nn.Module, + loader: DataLoader, + device: torch.device, + criterion: nn.Module, + optimizer: torch.optim.Optimizer | None = None, + scaler: torch.amp.GradScaler | None = None, + amp: bool = False, +) -> tuple[float, float]: + training = optimizer is not None + model.train(training) + total_loss, correct, count = 0.0, 0, 0 + with torch.set_grad_enabled(training): + for images, labels in loader: + images, labels = images.to(device), labels.to(device) + with torch.autocast(device_type=device.type, enabled=amp): + logits = model(images) + loss = criterion(logits, labels) + if optimizer is not None and scaler is not None: + optimizer.zero_grad(set_to_none=True) + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + total_loss += loss.item() * labels.size(0) + correct += (logits.argmax(dim=1) == labels).sum().item() + count += labels.size(0) + return total_loss / count, correct / count + + +def train(cfg: Config) -> TrainResult: + if cfg.model.freeze_backbone_epochs and not supports_freezing(cfg.model.name): + raise ConfigError( + f"model.freeze_backbone_epochs is not supported for {cfg.model.name!r} " + "(it has no pretrained backbone)" + ) + set_seed(cfg.train.seed) + device = resolve_device(cfg.train.device) + loaders = build_train_val_loaders(cfg) + model = build_model(cfg.model.name, len(loaders.class_names), cfg.model.pretrained).to(device) + + run_dir = cfg.train.output_dir / f"{cfg.train.name}-{datetime.now():%Y%m%d-%H%M%S}" + run_dir.mkdir(parents=True, exist_ok=True) + (run_dir / "config.yaml").write_text(dump_config(cfg)) + + criterion = nn.CrossEntropyLoss() + optimizer = AdamW(model.parameters(), lr=cfg.train.lr, weight_decay=cfg.train.weight_decay) + scheduler = CosineAnnealingLR(optimizer, T_max=cfg.train.epochs) + amp = cfg.train.amp and device.type == "cuda" + scaler = torch.amp.GradScaler("cuda", enabled=amp) + + best_path, last_path = run_dir / "best.pt", run_dir / "last.pt" + best_acc, bad_epochs = -1.0, 0 + history: list[dict[str, float]] = [] + + for epoch in range(1, cfg.train.epochs + 1): + if cfg.model.freeze_backbone_epochs: + set_backbone_frozen(model, epoch <= cfg.model.freeze_backbone_epochs) + train_loss, train_acc = _run_epoch( + model, loaders.train, device, criterion, optimizer, scaler, amp + ) + val_loss, val_acc = _run_epoch(model, loaders.val, device, criterion, amp=amp) + history.append( + { + "epoch": epoch, + "train_loss": train_loss, + "train_acc": train_acc, + "val_loss": val_loss, + "val_acc": val_acc, + "lr": optimizer.param_groups[0]["lr"], + } + ) + scheduler.step() + logger.info( + "epoch %d/%d train_loss=%.4f train_acc=%.3f val_loss=%.4f val_acc=%.3f", + epoch, + cfg.train.epochs, + train_loss, + train_acc, + val_loss, + val_acc, + ) + save_checkpoint(last_path, model, loaders.class_names, cfg, epoch, val_acc) + if val_acc > best_acc: + best_acc, bad_epochs = val_acc, 0 + save_checkpoint(best_path, model, loaders.class_names, cfg, epoch, val_acc) + else: + bad_epochs += 1 + if bad_epochs >= cfg.train.patience: + logger.info("early stopping at epoch %d", epoch) + break + + with (run_dir / "metrics.csv").open("w", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=HISTORY_FIELDS) + writer.writeheader() + writer.writerows(history) + save_curves(history, run_dir / "curves.png") + + best_model, _, _ = load_checkpoint(best_path) + y_true, y_pred = collect_predictions(best_model.to(device), loaders.val, device) + metrics = compute_metrics(y_true, y_pred, loaders.class_names) + save_confusion_matrix( + metrics["confusion_matrix"], loaders.class_names, run_dir / "confusion_matrix.png" + ) + return TrainResult(run_dir, best_path, last_path, best_acc, history) diff --git a/src/wbc_classification/errors.py b/src/wbc_classification/errors.py new file mode 100644 index 0000000..a7c485c --- /dev/null +++ b/src/wbc_classification/errors.py @@ -0,0 +1,21 @@ +"""Exception types raised for user-facing problems.""" + + +class WBCError(Exception): + """Base class for errors the CLI reports without a traceback.""" + + +class ConfigError(WBCError): + """Invalid or unreadable configuration.""" + + +class DataError(WBCError): + """Missing, empty or unreadable dataset / image.""" + + +class SegmentationError(WBCError): + """No cell could be located in an image.""" + + +class CheckpointError(WBCError): + """Checkpoint is missing, corrupt or inconsistent.""" diff --git a/src/wbc_classification/models/__init__.py b/src/wbc_classification/models/__init__.py new file mode 100644 index 0000000..fc3004f --- /dev/null +++ b/src/wbc_classification/models/__init__.py @@ -0,0 +1 @@ +"""Model definitions and registry.""" diff --git a/src/wbc_classification/models/backbone.py b/src/wbc_classification/models/backbone.py new file mode 100644 index 0000000..c47c6dd --- /dev/null +++ b/src/wbc_classification/models/backbone.py @@ -0,0 +1,36 @@ +"""Pretrained torchvision backbones with a replaced classification head.""" + +from typing import Any, cast + +from torch import nn +from torchvision import models as tv_models + +from wbc_classification.errors import ConfigError + +SUPPORTED_BACKBONES = ("resnet18", "resnet50", "efficientnet_b0") + + +def build_backbone(name: str, num_classes: int, pretrained: bool) -> nn.Module: + try: + model = cast(Any, tv_models.get_model(name, weights="DEFAULT" if pretrained else None)) + except Exception as exc: + if not pretrained: + raise + raise ConfigError( + f"Could not download pretrained weights for {name!r}: {exc}. " + "Check your network connection or set model.pretrained: false." + ) from exc + if name.startswith("resnet"): + model.fc = nn.Linear(model.fc.in_features, num_classes) + else: + model.classifier[-1] = nn.Linear(model.classifier[-1].in_features, num_classes) + return cast(nn.Module, model) + + +def set_backbone_frozen(model: nn.Module, frozen: bool) -> None: + """Freeze everything except the classification head (or unfreeze everything).""" + m = cast(Any, model) + head = m.fc if hasattr(m, "fc") else m.classifier + head_ids = {id(p) for p in head.parameters()} + for param in model.parameters(): + param.requires_grad = (not frozen) or id(param) in head_ids diff --git a/src/wbc_classification/models/baseline.py b/src/wbc_classification/models/baseline.py new file mode 100644 index 0000000..49bb2d8 --- /dev/null +++ b/src/wbc_classification/models/baseline.py @@ -0,0 +1,35 @@ +"""IncludeNet: the project's original small CNN, rewritten in PyTorch.""" + +import torch +from torch import nn + + +def _block(in_channels: int, out_channels: int, pool: bool) -> nn.Sequential: + layers: list[nn.Module] = [ + nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ] + if pool: + layers.append(nn.MaxPool2d(2)) + return nn.Sequential(*layers) + + +class IncludeNet(nn.Module): + """Four conv blocks, global average pooling and a linear head; input-size independent.""" + + def __init__(self, num_classes: int, width: int = 32, dropout: float = 0.5) -> None: + super().__init__() + self.features = nn.Sequential( + _block(3, width, pool=True), + _block(width, 2 * width, pool=True), + _block(2 * width, 4 * width, pool=True), + _block(4 * width, 4 * width, pool=False), + ) + self.pool = nn.AdaptiveAvgPool2d(1) + self.dropout = nn.Dropout(dropout) + self.classifier = nn.Linear(4 * width, num_classes) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.pool(self.features(x)).flatten(1) + return self.classifier(self.dropout(x)) diff --git a/src/wbc_classification/models/registry.py b/src/wbc_classification/models/registry.py new file mode 100644 index 0000000..c4eefb3 --- /dev/null +++ b/src/wbc_classification/models/registry.py @@ -0,0 +1,21 @@ +"""Name -> model factory.""" + +from torch import nn + +from wbc_classification.errors import ConfigError +from wbc_classification.models.backbone import SUPPORTED_BACKBONES, build_backbone +from wbc_classification.models.baseline import IncludeNet + +MODEL_NAMES = ("baseline", *SUPPORTED_BACKBONES) + + +def build_model(name: str, num_classes: int, pretrained: bool = False) -> nn.Module: + if name == "baseline": + return IncludeNet(num_classes) # no pretrained weights exist for the baseline + if name in SUPPORTED_BACKBONES: + return build_backbone(name, num_classes, pretrained) + raise ConfigError(f"Unknown model {name!r}. Available: {', '.join(MODEL_NAMES)}") + + +def supports_freezing(name: str) -> bool: + return name in SUPPORTED_BACKBONES diff --git a/src/wbc_classification/preprocessing/__init__.py b/src/wbc_classification/preprocessing/__init__.py new file mode 100644 index 0000000..6006591 --- /dev/null +++ b/src/wbc_classification/preprocessing/__init__.py @@ -0,0 +1 @@ +"""Image preprocessing.""" diff --git a/src/wbc_classification/preprocessing/segmentation.py b/src/wbc_classification/preprocessing/segmentation.py new file mode 100644 index 0000000..2f1a8d5 --- /dev/null +++ b/src/wbc_classification/preprocessing/segmentation.py @@ -0,0 +1,59 @@ +"""Classical HSV-threshold segmentation used for the optional cell crop.""" + +from dataclasses import dataclass + +import cv2 +import numpy as np + +from wbc_classification.errors import SegmentationError + +HSV_LOWER = np.array([80, 60, 140], dtype=np.uint8) +HSV_UPPER = np.array([255, 255, 255], dtype=np.uint8) +MIN_AREA_FRACTION = 0.01 + + +@dataclass(frozen=True) +class CellSegmentation: + mask: np.ndarray + contour: np.ndarray + center: tuple[int, int] + radius: int + + +def segment_cell(rgb: np.ndarray) -> CellSegmentation: + """Locate the largest cell-coloured region in an RGB image.""" + if rgb.ndim != 3 or rgb.shape[2] != 3: + raise SegmentationError(f"expected an (H, W, 3) RGB image, got shape {rgb.shape}") + blurred = cv2.GaussianBlur(rgb, (7, 7), 0) + hsv = cv2.cvtColor(blurred, cv2.COLOR_RGB2HSV) + threshold = cv2.inRange(hsv, HSV_LOWER, HSV_UPPER) + contours, _ = cv2.findContours(threshold, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) + if not contours: + raise SegmentationError("no cell-coloured region found") + biggest = max(contours, key=cv2.contourArea) + if cv2.contourArea(biggest) < MIN_AREA_FRACTION * rgb.shape[0] * rgb.shape[1]: + raise SegmentationError("detected region is too small to be a cell") + mask = np.zeros(threshold.shape, dtype=np.uint8) + cv2.drawContours(mask, [biggest], -1, 255, -1) + (cx, cy), radius = cv2.minEnclosingCircle(biggest) + return CellSegmentation(mask, biggest, (int(cx), int(cy)), int(radius)) + + +def crop_cell(rgb: np.ndarray) -> np.ndarray: + """Crop the image to the cell's enclosing square, clamped to the image bounds.""" + segmentation = segment_cell(rgb) + height, width = rgb.shape[:2] + cx, cy = segmentation.center + radius = max(segmentation.radius, 1) + return rgb[ + max(cy - radius, 0) : min(cy + radius, height), + max(cx - radius, 0) : min(cx + radius, width), + ] + + +def draw_overlay(rgb: np.ndarray, segmentation: CellSegmentation) -> np.ndarray: + """Return a copy of the image with the cell contour and enclosing circle drawn.""" + overlay = rgb.copy() + cv2.drawContours(overlay, [segmentation.contour], -1, (255, 0, 0), 2) + cv2.circle(overlay, segmentation.center, segmentation.radius, (0, 255, 0), 2) + return overlay diff --git a/src/wbc_classification/py.typed b/src/wbc_classification/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..60978fa --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,49 @@ +from pathlib import Path + +import pytest +from PIL import Image + +from wbc_classification.config import Config, DataConfig, ModelConfig, TrainConfig + +FIXTURES = Path(__file__).parent / "fixtures" + + +@pytest.fixture(scope="session") +def data_root() -> Path: + return FIXTURES / "data" + + +@pytest.fixture(scope="session") +def cell_image() -> Path: + return FIXTURES / "cell.jpeg" + + +def _write_image(path: Path, mode: str = "RGB", size: tuple[int, int] = (64, 48)) -> Path: + path.parent.mkdir(parents=True, exist_ok=True) + Image.new(mode, size).save(path) + return path + + +@pytest.fixture +def write_image(): + return _write_image + + +def make_tiny_config(output_dir: Path, data_root: Path) -> Config: + return Config( + data=DataConfig(root=data_root, image_size=32, batch_size=4, val_fraction=0.25), + model=ModelConfig(name="baseline", pretrained=False), + train=TrainConfig(name="test", epochs=1, amp=False, device="cpu", output_dir=output_dir), + ) + + +@pytest.fixture +def tiny_config(tmp_path: Path, data_root: Path) -> Config: + return make_tiny_config(tmp_path / "runs", data_root) + + +@pytest.fixture(scope="session") +def trained(tmp_path_factory: pytest.TempPathFactory, data_root: Path): + from wbc_classification.engine.train import train + + return train(make_tiny_config(tmp_path_factory.mktemp("runs"), data_root)) diff --git a/CNN/datasets/TRAIN/NEUTROPHIL/_1_4083.jpeg b/tests/fixtures/cell.jpeg similarity index 100% rename from CNN/datasets/TRAIN/NEUTROPHIL/_1_4083.jpeg rename to tests/fixtures/cell.jpeg diff --git a/CNN/datasets/TEST/EOSINOPHIL/_0_1616.jpeg b/tests/fixtures/data/TEST/EOSINOPHIL/_0_1616.jpeg similarity index 100% rename from CNN/datasets/TEST/EOSINOPHIL/_0_1616.jpeg rename to 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test_help_lists_commands(): + result = runner.invoke(app, ["--help"]) + assert result.exit_code == 0 + for command in ("train", "evaluate", "predict", "segment", "download-data"): + assert command in result.output + + +def test_train_command(data_root, tmp_path): + result = runner.invoke( + app, + [ + "train", + "--data-root", + str(data_root), + "--output-dir", + str(tmp_path), + "--model", + "baseline", + "--no-pretrained", + "--epochs", + "1", + "--batch-size", + "4", + "--image-size", + "32", + "--device", + "cpu", + "--name", + "cli", + ], + ) + assert result.exit_code == 0, result.output + assert "best validation accuracy" in result.output.lower() + assert list(tmp_path.glob("cli-*/best.pt")) + + +def test_evaluate_command(trained, data_root, tmp_path): + out = tmp_path / "m.json" + result = runner.invoke( + app, + [ + "evaluate", + str(trained.best_checkpoint), + "--data-root", + str(data_root), + "--device", + "cpu", + "--output", + str(out), + ], + ) + assert result.exit_code == 0, result.output + assert "accuracy" in result.output and out.is_file() + + +def test_predict_command(trained, cell_image, tmp_path): + result = runner.invoke( + app, + [ + "predict", + str(cell_image), + "-c", + str(trained.best_checkpoint), + "--device", + "cpu", + "--annotate-dir", + str(tmp_path), + ], + ) + assert result.exit_code == 0, result.output + assert "cell.jpeg" in result.output + assert (tmp_path / "cell_pred.png").is_file() + + +def test_segment_command(cell_image, tmp_path): + result = runner.invoke(app, ["segment", str(cell_image), "--output-dir", str(tmp_path)]) + assert result.exit_code == 0, result.output + for suffix in ("crop", "overlay", "mask"): + assert (tmp_path / f"cell_{suffix}.png").is_file() + + +def test_segment_no_cell_is_a_clean_error(tmp_path, write_image): + black = write_image(tmp_path / "black.png") + result = runner.invoke(app, ["segment", str(black), "--output-dir", str(tmp_path)]) + assert result.exit_code == 1 + assert "Error:" in result.output and "Traceback" not in result.output + + +def test_missing_checkpoint_is_a_clean_error(tmp_path): + result = runner.invoke(app, ["evaluate", str(tmp_path / "nope.pt")]) + assert result.exit_code == 1 and "Checkpoint not found" in result.output + + +def test_bad_config_is_a_clean_error(tmp_path): + cfg = tmp_path / "c.yaml" + cfg.write_text("train:\n epohcs: 2\n") + result = runner.invoke(app, ["train", "-c", str(cfg)]) + assert result.exit_code == 1 and "epohcs" in result.output + + +def test_predict_same_stem_inputs_get_distinct_annotations(trained, tmp_path, write_image): + first = write_image(tmp_path / "a" / "cell.png") + second = write_image(tmp_path / "b" / "cell.png") + out = tmp_path / "out" + result = runner.invoke( + app, + [ + "predict", + str(first), + str(second), + "-c", + str(trained.best_checkpoint), + "--device", + "cpu", + "--annotate-dir", + str(out), + ], + ) + assert result.exit_code == 0, result.output + assert len(list(out.glob("*.png"))) == 2 + + +_TINY = [ + "--model", "baseline", "--no-pretrained", "--epochs", "1", + "--batch-size", "4", "--image-size", "32", "--device", "cpu", "--name", "cli", +] # fmt: skip + + +def test_set_overrides_any_field(data_root, tmp_path): + result = runner.invoke( + app, + ["train", "--data-root", str(data_root), "--output-dir", str(tmp_path), *_TINY, + "--set", "train.seed=7", "--set", "data.num_workers=0", "--set", "train.patience=3"], + ) # fmt: skip + assert result.exit_code == 0, result.output + saved = next(tmp_path.glob("cli-*/config.yaml")).read_text() + assert "seed: 7" in saved and "patience: 3" in saved + + +def test_set_wins_over_named_flag(data_root, tmp_path): + result = runner.invoke( + app, + ["train", "--data-root", str(data_root), "--output-dir", str(tmp_path), *_TINY, + "--set", "train.name=winner"], + ) # fmt: skip + assert result.exit_code == 0, result.output + assert list(tmp_path.glob("winner-*/best.pt")) + + +@pytest.mark.parametrize("item", ["train.seed", "seed=3", "train.seed=", "train.seed=null"]) +def test_set_malformed_is_a_clean_error(item): + result = runner.invoke(app, ["train", "--set", item]) + assert result.exit_code == 1 + assert "Error:" in result.output and "Traceback" not in result.output + + +def test_set_unknown_field_names_it(): + result = runner.invoke(app, ["train", "--set", "train.epohcs=3"]) + assert result.exit_code == 1 and "epohcs" in result.output diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 0000000..daa5ed5 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,66 @@ +from pathlib import Path + +import pytest + +from wbc_classification.config import Config, dump_config, load_config +from wbc_classification.errors import ConfigError + + +def test_defaults(): + cfg = Config() + assert cfg.model.name == "resnet18" + assert cfg.preprocessing.crop is False + assert cfg.data.root == Path("data") + + +def test_load_yaml_with_overrides(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("model:\n name: baseline\ntrain:\n epochs: 3\n") + cfg = load_config(path, {"train.epochs": 5, "data.image_size": None, "data.batch_size": 8}) + assert cfg.model.name == "baseline" + assert cfg.train.epochs == 5 + assert cfg.data.image_size == 224 # None override ignored + assert cfg.data.batch_size == 8 + + +def test_missing_file(tmp_path): + with pytest.raises(ConfigError, match="not found"): + load_config(tmp_path / "nope.yaml") + + +def test_typo_in_section_is_rejected(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("trian:\n epochs: 3\n") + with pytest.raises(ConfigError, match="trian"): + load_config(path) + + +def test_typo_in_field_names_the_key(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("train:\n epohcs: 3\n") + with pytest.raises(ConfigError, match="epohcs"): + load_config(path) + + +def test_out_of_range_value(): + with pytest.raises(ConfigError, match="val_fraction"): + load_config(None, {"data.val_fraction": 0.9}) + + +def test_bad_override_key(): + with pytest.raises(ConfigError, match="section.field"): + load_config(None, {"epochs": 3}) + + +def test_invalid_yaml(tmp_path): + path = tmp_path / "c.yaml" + path.write_text("train: [unclosed") + with pytest.raises(ConfigError, match="Invalid YAML"): + load_config(path) + + +def test_dump_roundtrip(tmp_path): + cfg = load_config(None, {"train.epochs": 7, "data.root": "somewhere"}) + path = tmp_path / "out.yaml" + path.write_text(dump_config(cfg)) + assert load_config(path) == cfg diff --git a/tests/test_configs_and_smoke.py b/tests/test_configs_and_smoke.py new file mode 100644 index 0000000..42bf765 --- /dev/null +++ b/tests/test_configs_and_smoke.py @@ -0,0 +1,56 @@ +from pathlib import Path + +import pytest +from typer.testing import CliRunner + +from wbc_classification.cli import app +from wbc_classification.config import load_config +from wbc_classification.models.registry import MODEL_NAMES + +CONFIGS = sorted((Path(__file__).parent.parent / "configs").glob("*.yaml")) + + +def test_configs_exist(): + assert len(CONFIGS) >= 3 + + +@pytest.mark.parametrize("path", CONFIGS, ids=lambda p: p.stem) +def test_shipped_configs_are_valid(path): + cfg = load_config(path) + assert cfg.model.name in MODEL_NAMES + + +def test_end_to_end_smoke(data_root, tmp_path): + """train -> evaluate -> predict through the CLI, reloading the saved checkpoint.""" + runner = CliRunner() + train = runner.invoke( + app, + [ + "train", + "-c", + str(CONFIGS[0].parent / "baseline.yaml"), + "--data-root", + str(data_root), + "--output-dir", + str(tmp_path), + "--epochs", + "1", + "--batch-size", + "4", + "--image-size", + "32", + "--device", + "cpu", + ], + ) + assert train.exit_code == 0, train.output + (checkpoint,) = tmp_path.glob("baseline-*/best.pt") + evaluate = runner.invoke( + app, ["evaluate", str(checkpoint), "--data-root", str(data_root), "--device", "cpu"] + ) + assert evaluate.exit_code == 0, evaluate.output + predict = runner.invoke( + app, ["predict", str(data_root / "TEST"), "-c", str(checkpoint), "--device", "cpu"] + ) + assert predict.exit_code == 0, predict.output + assert predict.output.count("(") == 8 # one confidence per TEST image diff --git a/tests/test_data.py b/tests/test_data.py new file mode 100644 index 0000000..33bf285 --- /dev/null +++ b/tests/test_data.py @@ -0,0 +1,147 @@ +import logging + +import pytest +import torch +from PIL import Image + +from wbc_classification.config import Config, DataConfig +from wbc_classification.data.datasets import ( + WBCDataset, + build_test_loader, + build_train_val_loaders, + list_samples, + load_image, + stratified_split, +) +from wbc_classification.data.download import find_dataset_root, install_dataset +from wbc_classification.data.labels import discover_classes +from wbc_classification.data.transforms import build_transforms +from wbc_classification.errors import DataError + +CLASSES = ["EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"] + + +def _cfg(data_root): + return Config(data=DataConfig(root=data_root, image_size=32, batch_size=4, val_fraction=0.25)) + + +def test_discover_classes_sorted(data_root): + assert discover_classes(data_root / "TRAIN") == CLASSES + + +def test_missing_dir_hints_download(tmp_path): + with pytest.raises(DataError, match="wbc download-data"): + discover_classes(tmp_path / "nope") + + +def test_empty_class_folder_raises(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + (tmp_path / "B").mkdir() + with pytest.raises(DataError, match="B"): + discover_classes(tmp_path) + + +def test_hidden_dirs_ignored(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + (tmp_path / ".ipynb_checkpoints").mkdir() + assert discover_classes(tmp_path) == ["A"] + + +def test_list_samples_ignores_non_images(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + (tmp_path / "A" / ".DS_Store").write_bytes(b"x") + (tmp_path / "A" / "notes.txt").write_text("hi") + assert list_samples(tmp_path, ["A"]) == [(tmp_path / "A" / "1.png", 0)] + + +def test_list_samples_missing_class(tmp_path, write_image): + write_image(tmp_path / "A" / "1.png") + with pytest.raises(DataError, match="B"): + list_samples(tmp_path, ["A", "B"]) + + +def test_stratified_split_keeps_all_classes(data_root): + samples = list_samples(data_root / "TRAIN", CLASSES) + train, val = stratified_split(samples, 0.25, seed=0) + assert len(train) + len(val) == len(samples) == 24 + assert {label for _, label in val} == {0, 1, 2, 3} + + +def test_stratified_split_is_seeded(data_root): + samples = list_samples(data_root / "TRAIN", CLASSES) + assert stratified_split(samples, 0.25, 1) == stratified_split(samples, 0.25, 1) + + +def test_stratified_split_too_small_raises(tmp_path, write_image): + for cls in "AB": + write_image(tmp_path / cls / "1.png") # one image per class + with pytest.raises(DataError, match="validation split"): + stratified_split(list_samples(tmp_path, ["A", "B"]), 0.25, 0) + + +@pytest.mark.parametrize("mode", ["L", "RGBA", "RGB"]) +def test_load_image_always_rgb(tmp_path, write_image, mode): + path = write_image(tmp_path / "x.png", mode=mode) + assert load_image(path).mode == "RGB" + + +def test_load_image_corrupt_names_file(tmp_path): + bad = tmp_path / "broken.jpeg" + bad.write_bytes(b"not an image") + with pytest.raises(DataError, match="broken.jpeg"): + load_image(bad) + + +def test_crop_falls_back_to_full_image(tmp_path, caplog): + path = tmp_path / "black.png" + Image.new("RGB", (64, 48)).save(path) # black: no cell + with caplog.at_level(logging.WARNING): + image = load_image(path, crop=True) + assert image.size == (64, 48) + assert "No cell found" in caplog.text + + +def test_transforms_shape_and_dtype(data_root): + sample = list_samples(data_root / "TRAIN", CLASSES)[0][0] + for train in (True, False): + out = build_transforms(32, train)(load_image(sample)) + assert out.shape == (3, 32, 32) and out.dtype == torch.float32 + + +def test_dataset_item(data_root): + samples = list_samples(data_root / "TRAIN", CLASSES) + tensor, label = WBCDataset(samples, build_transforms(32, False))[0] + assert tensor.shape == (3, 32, 32) and isinstance(label, int) + + +def test_build_loaders(data_root): + loaders = build_train_val_loaders(_cfg(data_root)) + images, labels = next(iter(loaders.train)) + assert images.shape == (4, 3, 32, 32) and labels.shape == (4,) + assert loaders.class_names == CLASSES + test_loader = build_test_loader(_cfg(data_root), loaders.class_names) + assert len(test_loader.dataset) == 8 + + +def test_build_test_loader_missing_test_dir(tmp_path): + with pytest.raises(DataError, match="wbc download-data"): + build_test_loader(_cfg(tmp_path), CLASSES) + + +def test_install_dataset_from_nested_kaggle_layout(tmp_path, write_image): + source = tmp_path / "cache" / "dataset2-master" / "images" + for split in ("TRAIN", "TEST", "TEST_SIMPLE"): + write_image(source / split / "A" / "1.png") + assert find_dataset_root(tmp_path / "cache") == source + dest = tmp_path / "data" + install_dataset(tmp_path / "cache", dest) + assert (dest / "TRAIN" / "A" / "1.png").is_file() and (dest / "TEST").is_dir() + assert not (dest / "TEST_SIMPLE").exists() + with pytest.raises(DataError, match="--force"): + install_dataset(tmp_path / "cache", dest) + install_dataset(tmp_path / "cache", dest, force=True) + + +def test_find_dataset_root_missing(tmp_path): + with pytest.raises(DataError, match="TRAIN"): + find_dataset_root(tmp_path) diff --git a/tests/test_engine_basics.py b/tests/test_engine_basics.py new file mode 100644 index 0000000..d5ca728 --- /dev/null +++ b/tests/test_engine_basics.py @@ -0,0 +1,166 @@ +import json + +import pytest +import torch + +from wbc_classification.config import Config, ModelConfig +from wbc_classification.engine.checkpoint import load_checkpoint, save_checkpoint +from wbc_classification.engine.metrics import ( + compute_metrics, + format_report, + save_confusion_matrix, + save_curves, +) +from wbc_classification.engine.runtime import resolve_device, set_seed +from wbc_classification.errors import CheckpointError, ConfigError +from wbc_classification.models.registry import build_model + +NAMES = ["A", "B", "C", "D"] + + +def test_resolve_device(): + assert resolve_device("cpu").type == "cpu" + assert resolve_device("auto").type in {"cpu", "cuda", "mps"} + with pytest.raises(ConfigError, match="bogus"): + resolve_device("bogus") + + +def test_resolve_device_rejects_unavailable_cuda(monkeypatch): + monkeypatch.setattr(torch.cuda, "is_available", lambda: False) + with pytest.raises(ConfigError, match="CUDA"): + resolve_device("cuda") + + +def test_resolve_device_rejects_bad_cuda_index(monkeypatch): + monkeypatch.setattr(torch.cuda, "is_available", lambda: True) + monkeypatch.setattr(torch.cuda, "device_count", lambda: 1) + with pytest.raises(ConfigError, match="1"): + resolve_device("cuda:3") + + +def test_resolve_device_rejects_unavailable_mps(monkeypatch): + monkeypatch.setattr(torch.backends.mps, "is_available", lambda: False) + with pytest.raises(ConfigError, match="mps"): + resolve_device("mps") + + +def test_set_seed_makes_torch_deterministic(): + set_seed(3) + first = torch.rand(3) + set_seed(3) + assert torch.equal(first, torch.rand(3)) + + +def test_compute_metrics_perfect(): + m = compute_metrics([0, 1, 2, 3], [0, 1, 2, 3], NAMES) + assert m["accuracy"] == 1.0 + assert m["confusion_matrix"] == [[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]] + json.dumps(m) # must be JSON-serialisable + assert "A" in format_report(m) + + +def test_compute_metrics_handles_missing_class_in_predictions(): + m = compute_metrics([0, 0, 1, 1], [0, 0, 0, 0], NAMES) + assert m["accuracy"] == 0.5 and len(m["confusion_matrix"]) == 4 + json.dumps(m) + + +def test_plots_are_written(tmp_path): + save_confusion_matrix([[1, 0], [0, 1]], ["A", "B"], tmp_path / "cm.png") + history = [ + { + "epoch": 1, + "train_loss": 1.0, + "train_acc": 0.5, + "val_loss": 1.1, + "val_acc": 0.4, + "lr": 0.1, + }, + { + "epoch": 2, + "train_loss": 0.8, + "train_acc": 0.6, + "val_loss": 0.9, + "val_acc": 0.5, + "lr": 0.05, + }, + ] + save_curves(history, tmp_path / "curves.png") + assert (tmp_path / "cm.png").stat().st_size > 0 and (tmp_path / "curves.png").stat().st_size > 0 + + +def _baseline(): + return build_model("baseline", num_classes=4) + + +def test_checkpoint_roundtrip(tmp_path): + model = _baseline().eval() + cfg = Config(model=ModelConfig(name="baseline", pretrained=False)) + path = tmp_path / "m.pt" + save_checkpoint(path, model, NAMES, cfg, epoch=3, val_accuracy=0.9) + loaded, names, loaded_cfg = load_checkpoint(path) + assert names == NAMES and loaded_cfg == cfg + x = torch.rand(1, 3, 32, 32) + assert torch.allclose(model(x), loaded.eval()(x)) + + +def test_checkpoint_missing(tmp_path): + with pytest.raises(CheckpointError, match="not found"): + load_checkpoint(tmp_path / "nope.pt") + + +def test_checkpoint_corrupt_file(tmp_path): + bad = tmp_path / "bad.pt" + bad.write_bytes(b"junk") + with pytest.raises(CheckpointError, match="bad.pt"): + load_checkpoint(bad) + + +def test_checkpoint_not_ours(tmp_path): + path = tmp_path / "other.pt" + torch.save({"hello": 1}, path) + with pytest.raises(CheckpointError, match="not a wbc-classification checkpoint"): + load_checkpoint(path) + + +def test_checkpoint_class_count_mismatch(tmp_path): + cfg = Config(model=ModelConfig(name="baseline", pretrained=False)) + path = tmp_path / "m.pt" + save_checkpoint( + path, _baseline(), ["A", "B", "C"], cfg, epoch=1, val_accuracy=0.1 + ) # 4 outputs vs 3 names + with pytest.raises(CheckpointError, match="do not match"): + load_checkpoint(path) + + +def _real_checkpoint(tmp_path): + cfg = Config(model=ModelConfig(name="baseline", pretrained=False)) + path = tmp_path / "real.pt" + save_checkpoint(path, _baseline(), NAMES, cfg, epoch=1, val_accuracy=0.1) + return path.read_bytes() + + +def test_checkpoint_plain_text_file(tmp_path): + bad = tmp_path / "text.pt" + bad.write_text("hello world") + with pytest.raises(CheckpointError, match="text.pt"): + load_checkpoint(bad) + + +def test_checkpoint_truncated_file(tmp_path): + data = _real_checkpoint(tmp_path) + bad = tmp_path / "truncated.pt" + bad.write_bytes(data[: len(data) // 2]) + with pytest.raises(CheckpointError, match="truncated.pt"): + load_checkpoint(bad) + + +def test_checkpoint_bit_flipped_file(tmp_path): + data = bytearray(_real_checkpoint(tmp_path)) + start = 100 # inside the zip's pickle record (tensor payload bytes would load silently) + for i in range(start, start + 64): + data[i] ^= 0xFF + bad = tmp_path / "flipped.pt" + bad.write_bytes(bytes(data)) + with pytest.raises(CheckpointError, match="flipped.pt"): + load_checkpoint(bad) diff --git a/tests/test_evaluate_predict.py b/tests/test_evaluate_predict.py new file mode 100644 index 0000000..4ba9528 --- /dev/null +++ b/tests/test_evaluate_predict.py @@ -0,0 +1,100 @@ +import json + +import pytest + +from wbc_classification.engine.evaluate import evaluate_checkpoint +from wbc_classification.engine.predict import ( + annotate_image, + annotate_predictions, + collect_images, + predict_images, +) +from wbc_classification.errors import CheckpointError, DataError + + +def test_evaluate_reports_per_class_and_writes_outputs(trained, data_root, tmp_path): + out = tmp_path / "metrics.json" + metrics = evaluate_checkpoint( + trained.best_checkpoint, data_root=data_root, device="cpu", output=out + ) + assert 0.0 <= metrics["accuracy"] <= 1.0 + assert set(metrics["class_names"]) == {"EOSINOPHIL", "LYMPHOCYTE", "MONOCYTE", "NEUTROPHIL"} + assert sum(sum(row) for row in metrics["confusion_matrix"]) == 8 # the 8 TEST fixtures + assert json.loads(out.read_text())["accuracy"] == metrics["accuracy"] + assert (tmp_path / "metrics_confusion.png").is_file() + + +def test_evaluate_missing_checkpoint(tmp_path): + with pytest.raises(CheckpointError): + evaluate_checkpoint(tmp_path / "nope.pt") + + +def test_evaluate_missing_data_dir(trained, tmp_path): + with pytest.raises(DataError, match="wbc download-data"): + evaluate_checkpoint(trained.best_checkpoint, data_root=tmp_path, device="cpu") + + +def test_predict_directory(trained, data_root): + preds = predict_images(trained.best_checkpoint, [data_root / "TEST"], device="cpu") + assert len(preds) == 8 + for pred in preds: + assert pred.label in pred.probabilities + assert sum(pred.probabilities.values()) == pytest.approx(1.0, abs=1e-4) + + +def test_predict_single_file(trained, cell_image): + (pred,) = predict_images(trained.best_checkpoint, [cell_image], device="cpu") + assert pred.path == cell_image + + +def test_predict_empty_dir_raises(trained, tmp_path): + with pytest.raises(DataError, match="No images"): + predict_images(trained.best_checkpoint, [tmp_path], device="cpu") + + +def test_predict_missing_path_raises(trained, tmp_path): + with pytest.raises(DataError, match="not found"): + predict_images(trained.best_checkpoint, [tmp_path / "ghost.jpg"], device="cpu") + + +def test_predict_corrupt_image_raises(trained, tmp_path): + bad = tmp_path / "bad.jpeg" + bad.write_bytes(b"nope") + with pytest.raises(DataError, match="bad.jpeg"): + predict_images(trained.best_checkpoint, [bad], device="cpu") + + +def test_collect_images_ignores_non_images(tmp_path, write_image): + write_image(tmp_path / "a.png") + (tmp_path / "notes.txt").write_text("x") + assert collect_images([tmp_path]) == [tmp_path / "a.png"] + + +def test_annotate_writes_file(trained, cell_image, tmp_path): + (pred,) = predict_images(trained.best_checkpoint, [cell_image], device="cpu") + assert annotate_image(pred, tmp_path).is_file() + + +def test_annotate_predictions_same_stem_in_subdirs(trained, write_image, tmp_path): + write_image(tmp_path / "a" / "x.png") + write_image(tmp_path / "b" / "x.png") + preds = predict_images(trained.best_checkpoint, [tmp_path / "a", tmp_path / "b"], device="cpu") + out = tmp_path / "out" + paths = annotate_predictions(preds, out) + assert [p.name for p in paths] == ["x_pred.png", "x_2_pred.png"] + assert all(p.is_file() for p in paths) + + +def test_annotate_predictions_same_stem_different_suffix(trained, write_image, tmp_path): + write_image(tmp_path / "x.png") + write_image(tmp_path / "x.jpg") + preds = predict_images(trained.best_checkpoint, [tmp_path], device="cpu") + paths = annotate_predictions(preds, tmp_path / "out") + assert len({p.name for p in paths}) == 2 + assert all(p.is_file() for p in paths) + + +def test_annotate_predictions_unique_stem(trained, cell_image, tmp_path): + preds = predict_images(trained.best_checkpoint, [cell_image], device="cpu") + (path,) = annotate_predictions(preds, tmp_path) + assert path.name == f"{cell_image.stem}_pred.png" diff --git a/tests/test_models.py b/tests/test_models.py new file mode 100644 index 0000000..23db564 --- /dev/null +++ b/tests/test_models.py @@ -0,0 +1,48 @@ +import pytest +import torch + +from wbc_classification.errors import ConfigError +from wbc_classification.models.backbone import set_backbone_frozen +from wbc_classification.models.registry import MODEL_NAMES, build_model, supports_freezing + + +@pytest.mark.parametrize("name", MODEL_NAMES) +def test_forward_shape(name): + model = build_model(name, num_classes=4).eval() + with torch.no_grad(): + assert model(torch.zeros(2, 3, 64, 64)).shape == (2, 4) + + +def test_baseline_accepts_tiny_and_odd_sizes(): + model = build_model("baseline", num_classes=4).eval() + with torch.no_grad(): + assert model(torch.zeros(1, 3, 16, 16)).shape == (1, 4) + assert model(torch.zeros(1, 3, 50, 70)).shape == (1, 4) + + +def test_unknown_model_lists_choices(): + with pytest.raises(ConfigError, match="resnet18"): + build_model("vgg", num_classes=4) + + +@pytest.mark.parametrize("name", ["resnet18", "efficientnet_b0"]) +def test_freeze_leaves_only_head_trainable(name): + model = build_model(name, num_classes=4) + set_backbone_frozen(model, True) + trainable = [n for n, p in model.named_parameters() if p.requires_grad] + assert trainable and all(n.startswith(("fc", "classifier")) for n in trainable) + set_backbone_frozen(model, False) + assert all(p.requires_grad for p in model.parameters()) + + +def test_supports_freezing(): + assert supports_freezing("resnet18") and not supports_freezing("baseline") + + +def test_pretrained_download_failure_is_a_config_error(monkeypatch): + def offline(*args, **kwargs): + raise OSError("offline") + + monkeypatch.setattr("wbc_classification.models.backbone.tv_models.get_model", offline) + with pytest.raises(ConfigError, match="resnet18"): + build_model("resnet18", num_classes=4, pretrained=True) diff --git a/tests/test_package.py b/tests/test_package.py new file mode 100644 index 0000000..03b0b48 --- /dev/null +++ b/tests/test_package.py @@ -0,0 +1,6 @@ +import wbc_classification + + +def test_version_is_a_string(): + assert isinstance(wbc_classification.__version__, str) + assert wbc_classification.__version__.count(".") == 2 diff --git a/tests/test_segmentation.py b/tests/test_segmentation.py new file mode 100644 index 0000000..49721d2 --- /dev/null +++ b/tests/test_segmentation.py @@ -0,0 +1,54 @@ +import cv2 +import numpy as np +import pytest +from PIL import Image + +from wbc_classification.errors import SegmentationError +from wbc_classification.preprocessing.segmentation import crop_cell, draw_overlay, segment_cell + + +def _synthetic_cell() -> np.ndarray: + image = np.full((200, 300, 3), 255, dtype=np.uint8) + cv2.circle(image, (150, 100), 40, (120, 60, 160), -1) # purple disc on white + return image + + +def test_segments_synthetic_cell(): + seg = segment_cell(_synthetic_cell()) + assert abs(seg.center[0] - 150) <= 3 and abs(seg.center[1] - 100) <= 3 + assert abs(seg.radius - 40) <= 4 + assert seg.mask.shape == (200, 300) + + +def test_crop_is_around_the_cell(): + crop = crop_cell(_synthetic_cell()) + assert 70 <= crop.shape[0] <= 90 and 70 <= crop.shape[1] <= 90 + + +def test_crop_is_clamped_at_image_border(): + image = np.full((100, 100, 3), 255, dtype=np.uint8) + cv2.circle(image, (5, 5), 30, (120, 60, 160), -1) # cell hanging off the corner + crop = crop_cell(image) + assert crop.size > 0 and crop.shape[0] <= 100 and crop.shape[1] <= 100 + + +def test_no_cell_raises(): + with pytest.raises(SegmentationError): + segment_cell(np.zeros((240, 320, 3), dtype=np.uint8)) + + +def test_bad_shape_raises(): + with pytest.raises(SegmentationError): + segment_cell(np.zeros((10, 10), dtype=np.uint8)) + + +def test_real_image_fixture(cell_image): + rgb = np.asarray(Image.open(cell_image).convert("RGB")) + crop = crop_cell(rgb) + assert crop.ndim == 3 and crop.size > 0 + assert crop.shape[0] <= rgb.shape[0] and crop.shape[1] <= rgb.shape[1] + + +def test_overlay_keeps_shape(): + image = _synthetic_cell() + assert draw_overlay(image, segment_cell(image)).shape == image.shape diff --git a/tests/test_train.py b/tests/test_train.py new file mode 100644 index 0000000..ad06fbd --- /dev/null +++ b/tests/test_train.py @@ -0,0 +1,49 @@ +import csv + +import pytest + +from wbc_classification.engine.checkpoint import load_checkpoint +from wbc_classification.engine.train import train +from wbc_classification.errors import ConfigError + + +def test_train_writes_all_artifacts(trained): + run = trained.run_dir + for name in ( + "config.yaml", + "metrics.csv", + "curves.png", + "confusion_matrix.png", + "best.pt", + "last.pt", + ): + assert (run / name).is_file(), name + assert len(trained.history) == 1 + assert 0.0 <= trained.best_val_accuracy <= 1.0 + with (run / "metrics.csv").open() as handle: + rows = list(csv.DictReader(handle)) + assert rows[0]["epoch"] == "1" and "val_acc" in rows[0] + + +def test_trained_checkpoint_loads(trained): + model, class_names, cfg = load_checkpoint(trained.best_checkpoint) + assert len(class_names) == 4 and cfg.model.name == "baseline" + + +def test_freeze_on_baseline_is_rejected(tiny_config): + tiny_config.model.freeze_backbone_epochs = 1 + with pytest.raises(ConfigError, match="baseline"): + train(tiny_config) + + +def test_early_stopping(tiny_config): + tiny_config.train.epochs = 6 + tiny_config.train.patience = 1 + result = train(tiny_config) + assert 1 <= len(result.history) <= 6 + + +def test_same_seed_same_first_epoch(tiny_config, tmp_path): + first = train(tiny_config).history[0]["train_loss"] + tiny_config.train.output_dir = tmp_path / "again" + assert train(tiny_config).history[0]["train_loss"] == pytest.approx(first, rel=1e-4)