diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index c519ffe2..1fb6420d 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -7,8 +7,20 @@ "ppa": true, "version": "system" }, - "ghcr.io/devcontainers-contrib/features/pipenv:2": { + "ghcr.io/devcontainers-extra/features/pipenv:2": { "version": "latest" + }, + "ghcr.io/va-h/devcontainers-features/uv:1": { + "shellautocompletion": true, + "version": "latest" + }, + "ghcr.io/devcontainers/features/nvidia-cuda:1": { + "installCudnn": true, + "installCudnnDev": true, + "installNvtx": true, + "installToolkit": true, + "cudaVersion": "12.5", + "cudnnVersion": "9.3.0.75" } }, diff --git a/.devcontainer/install.sh b/.devcontainer/install.sh index c5232499..8b8120b0 100755 --- a/.devcontainer/install.sh +++ b/.devcontainer/install.sh @@ -5,50 +5,47 @@ export DEBIAN_FRONTEND=noninteractive sudo apt update sudo apt install -y libopenblas-dev libyaml-dev ffmpeg wget ca-certificates -# Install CUDA and cuDNN if not already installed -if ! command -v nvcc &> /dev/null; then +# # Install CUDA and cuDNN if not already installed +# if ! command -v nvcc &> /dev/null; then - CUDA_VERSION="12.3" - CUDNN_VERSION="8.9.7.29-1+cuda12.2" # Not sure why no 12.3 +# CUDA_VERSION="12.3" +# CUDNN_VERSION="8.9.7.29-1+cuda12.2" # Not sure why no 12.3 - NVIDIA_REPO_URL="https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64" - KEYRING_PACKAGE="cuda-keyring_1.1-1_all.deb" - KEYRING_PACKAGE_URL="$NVIDIA_REPO_URL/$KEYRING_PACKAGE" - KEYRING_PACKAGE_PATH="$(mktemp -d)" - KEYRING_PACKAGE_FILE="$KEYRING_PACKAGE_PATH/$KEYRING_PACKAGE" - wget -O "$KEYRING_PACKAGE_FILE" "$KEYRING_PACKAGE_URL" - sudo apt install -yq "$KEYRING_PACKAGE_FILE" - sudo apt update -yq +# NVIDIA_REPO_URL="https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64" +# KEYRING_PACKAGE="cuda-keyring_1.1-1_all.deb" +# KEYRING_PACKAGE_URL="$NVIDIA_REPO_URL/$KEYRING_PACKAGE" +# KEYRING_PACKAGE_PATH="$(mktemp -d)" +# KEYRING_PACKAGE_FILE="$KEYRING_PACKAGE_PATH/$KEYRING_PACKAGE" +# wget -O "$KEYRING_PACKAGE_FILE" "$KEYRING_PACKAGE_URL" +# sudo apt install -yq "$KEYRING_PACKAGE_FILE" +# sudo apt update -yq - # Install CUDA libraries - cuda_pkg="cuda-libraries-${CUDA_VERSION/./-}" - sudo apt install -yq "$cuda_pkg" +# # Install CUDA libraries +# cuda_pkg="cuda-libraries-${CUDA_VERSION/./-}" +# sudo apt install -yq "$cuda_pkg" - # Install cuDNN - cudnn_pkg="libcudnn8=${CUDNN_VERSION}" - sudo apt install -yq "$cudnn_pkg_version" +# # Install cuDNN +# cudnn_pkg="libcudnn8=${CUDNN_VERSION}" +# sudo apt install -yq "$cudnn_pkg_version" - # Install cuDNN dev - cudnn_dev_pkg="libcudnn8-dev=${CUDNN_VERSION}" - sudo apt install -yq "$cudnn_dev_pkg" +# # Install cuDNN dev +# cudnn_dev_pkg="libcudnn8-dev=${CUDNN_VERSION}" +# sudo apt install -yq "$cudnn_dev_pkg" - # Install NVTX - nvtx_pkg="cuda-nvtx-${CUDA_VERSION/./-}" - sudo apt install -yq "$nvtx_pkg" +# # Install NVTX +# nvtx_pkg="cuda-nvtx-${CUDA_VERSION/./-}" +# sudo apt install -yq "$nvtx_pkg" - # Install CUDA Toolkit - toolkit_pkg="cuda-toolkit-${CUDA_VERSION/./-}" - sudo apt install -yq "$toolkit_pkg" +# # Install CUDA Toolkit +# toolkit_pkg="cuda-toolkit-${CUDA_VERSION/./-}" +# sudo apt install -yq "$toolkit_pkg" - export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} - export LD_LIBRARY_PATH=/usr/local/cuda-${CUDA_VERSION}/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} +# export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} +# export LD_LIBRARY_PATH=/usr/local/cuda-${CUDA_VERSION}/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} - # Clean up - sudo rm -rf /var/lib/apt/lists/* -fi - -# Install poetry -pipx install poetry --pip-args '--no-cache-dir --force-reinstall' +# # Clean up +# sudo rm -rf /var/lib/apt/lists/* +# fi # Install project dependencies -poetry install +uv sync diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index ff85525a..d06c43ca 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -2,19 +2,27 @@ name: HeartKit CI on: [push] +env: + PYTHON_VERSION: '3.11' + jobs: build: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 - - name: Install poetry - run: pipx install poetry - - uses: actions/setup-python@v4 + - name: Checkout 🛎️ + uses: actions/checkout@v4 + - name: Setup Python 🐍 + uses: actions/setup-python@v4 + with: + python-version: ${{ env.PYTHON_VERSION }} + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - python-version: '3.11' - cache: 'poetry' - - run: poetry install - - run: poetry run task lint - - run: poetry run task test + enable-cache: true + - name: Install dependencies 🔧 + run: | + uv sync + uv run ruff check + uv run pytest tests/ diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml index e2864bf8..7c779925 100644 --- a/.github/workflows/docs.yaml +++ b/.github/workflows/docs.yaml @@ -1,8 +1,9 @@ name: Deploy to GitHub Pages on: - push: - branches: [$default-branch] + release: + types: + - created workflow_dispatch: permissions: @@ -26,24 +27,21 @@ jobs: steps: - name: Checkout 🛎️ - uses: actions/checkout@v3 + uses: actions/checkout@v4 - name: Setup Pages id: pages uses: actions/configure-pages@v4 - - name: Install Poetry - run: pipx install poetry - - name: Install Python - uses: actions/setup-python@v4 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - python-version: '3.11' - cache: 'poetry' + enable-cache: true - name: Install and Build 🔧 env: CI: "" PUBLIC_URL: "${{ steps.pages.outputs.base_url }}/" run: | - poetry install --no-root --only docs - poetry run mkdocs build + uv sync --only-group docs + uv run mkdocs build - name: Upload artifact uses: actions/upload-pages-artifact@v3 with: diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 3510b76f..51460b86 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -1,13 +1,9 @@ name: Release to PyPi -# on: -# release: -# types: -# - created - on: - push: - branches: [$default-branch] + release: + types: + - created workflow_dispatch: jobs: @@ -16,16 +12,13 @@ jobs: steps: - name: Checkout 🛎️ uses: actions/checkout@v4 - - name: Install Poetry - run: pipx install poetry - - name: Install Python - uses: actions/setup-python@v5 + - name: Install uv + uses: astral-sh/setup-uv@v5 with: - python-version: '3.11' - cache: 'poetry' + enable-cache: true - name: Install and Build 🔧 run: | - poetry build + uv build - name: Store the distribution packages uses: actions/upload-artifact@v4 with: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 67c634fe..96a809c2 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -20,7 +20,3 @@ repos: args: [ --config=pyproject.toml ] - id: ruff-format args: [ --config=pyproject.toml ] - - repo: https://github.com/python-poetry/poetry - rev: 1.8.3 - hooks: - - id: poetry-check diff --git a/README.md b/README.md index d0dc8102..f932144d 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ HeartKit is an AI Development Kit (ADK) that enables developers to easily train ## Requirements * [Python ^3.11+](https://www.python.org) -* [Poetry ^1.6.1+](https://python-poetry.org/docs/#installation) +* [uv ^1.6.1+](https://docs.astral.sh/uv/getting-started/installation/) The following are also required to compile/flash the binary for the EVB demo: @@ -47,7 +47,7 @@ Alternatively, you can install the package from source by cloning the repository ```bash git clone https://github.com/AmbiqAI/heartkit.git cd heartkit -poetry install +uv sync ``` --- diff --git a/docs/guides/byot.ipynb b/docs/guides/byot.ipynb index 6384f478..2ef2a16d 100644 --- a/docs/guides/byot.ipynb +++ b/docs/guides/byot.ipynb @@ -62,7 +62,8 @@ ], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n", "import random\n", "from typing import Generator\n", "from collections.abc import Iterable\n", @@ -86,7 +87,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "os.environ['HK_DATASET_PATH'] = os.getenv('HK_DATASET_PATH', './datasets')\n", + "os.environ[\"HK_DATASET_PATH\"] = os.getenv(\"HK_DATASET_PATH\", \"./datasets\")\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -126,7 +127,7 @@ " with self.ds.patient_data(patient_id) as h5:\n", " ecg = h5[\"data\"][:]\n", " # Beat locations. Convert 100Hz to ds.sampling_rate\n", - " blabels = h5[\"blabels\"][:, 0]*(self.ds.sampling_rate/100.0)\n", + " blabels = h5[\"blabels\"][:, 0] * (self.ds.sampling_rate / 100.0)\n", " # END WITH\n", "\n", " for _ in range(samples_per_patient):\n", @@ -146,11 +147,11 @@ " # Resample if needed\n", " if self.ds.sampling_rate != self.sampling_rate:\n", " x = pk.signal.resample_signal(x, self.ds.sampling_rate, self.sampling_rate, axis=0)\n", - " x = x[:self.frame_size] # Ensure frame size\n", + " x = x[: self.frame_size] # Ensure frame size\n", "\n", " x = np.nan_to_num(x).astype(np.float32)\n", " x = x.reshape(-1, 1)\n", - " y = bpm / 60.0 # Make beats per second\n", + " y = bpm / 60.0 # Make beats per second\n", " yield x, y\n", " # END FOR\n", "\n", @@ -166,7 +167,7 @@ " for x, y in self.patient_data_generator(pt_id, samples_per_patient):\n", " yield x, y\n", " # END FOR\n", - " # END FOR\n" + " # END FOR" ] }, { @@ -184,9 +185,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = hk.DatasetFactory.get(\"ptbxl\")(\n", - " path=Path(os.environ['HK_DATASET_PATH']) / \"ptbxl\"\n", - ")\n", + "ds = hk.DatasetFactory.get(\"ptbxl\")(path=Path(os.environ[\"HK_DATASET_PATH\"]) / \"ptbxl\")\n", "dl = PtbxlDataloader(\n", " ds=ds,\n", " frame_size=4000,\n", @@ -222,7 +221,7 @@ ], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n", - "ax.plot(x, label=f\"HR: {60*y:0.0f} BPM\")\n", + "ax.plot(x, label=f\"HR: {60 * y:0.0f} BPM\")\n", "ax.set_title(\"ECG Frame\")\n", "ax.legend()\n", "fig.show()" @@ -303,10 +302,7 @@ " drop_remainder=True,\n", " num_parallel_calls=tf.data.AUTOTUNE,\n", " )\n", - " augmenter = hk.datasets.create_augmentation_pipeline(\n", - " augmentations,\n", - " sampling_rate=sampling_rate\n", - " )\n", + " augmenter = hk.datasets.create_augmentation_pipeline(augmentations, sampling_rate=sampling_rate)\n", "\n", " ds = (\n", " ds.map(\n", @@ -327,6 +323,7 @@ " )\n", " return ds.prefetch(tf.data.AUTOTUNE)\n", "\n", + "\n", "def load_train_datasets(\n", " datasets: list[hk.HKDataset],\n", " dataloaderFactory: nse.utils.ItemFactory[hk.HKDataloader],\n", @@ -367,7 +364,7 @@ " val_steps_per_epoch = val_steps_per_epoch or 50\n", " val_ds = val_ds.take(val_steps_per_epoch).cache()\n", "\n", - " return train_ds, val_ds\n" + " return train_ds, val_ds" ] }, { @@ -383,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -404,16 +401,12 @@ " with open(params.job_dir / \"train_config.json\", \"w\", encoding=\"utf-8\") as fp:\n", " fp.write(params.model_dump_json(indent=2))\n", "\n", - " params.num_classes = 1 # Regression\n", + " params.num_classes = 1 # Regression\n", " feat_shape = (params.frame_size, 1)\n", "\n", " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n", "\n", - " train_ds, val_ds = load_train_datasets(\n", - " datasets=datasets,\n", - " dataloaderFactory=DataloaderFactory,\n", - " params=params\n", - " )\n", + " train_ds, val_ds = load_train_datasets(datasets=datasets, dataloaderFactory=DataloaderFactory, params=params)\n", "\n", " y_true = np.concatenate([y for _, y in val_ds.as_numpy_iterator()])\n", " y_true = np.argmax(y_true, axis=-1).flatten()\n", @@ -459,7 +452,7 @@ " params.model_file = params.job_dir / \"model.keras\"\n", "\n", " model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n", - " logger.debug(f\"Model requires {flops/1e6:0.2f} MFLOPS\")\n", + " logger.debug(f\"Model requires {flops / 1e6:0.2f} MFLOPS\")\n", "\n", " model_callbacks = [\n", " keras.callbacks.EarlyStopping(\n", @@ -500,7 +493,7 @@ "\n", " # Summarize results\n", " rst = model.evaluate(val_ds, return_dict=True)\n", - " logger.info(f\"[VAL SET] \" + \", \".join(f\"{k.upper()}={v:.4f}\" for k, v in rst.items()))\n" + " logger.info(\"[VAL SET] \" + \", \".join(f\"{k.upper()}={v:.4f}\" for k, v in rst.items()))" ] }, { @@ -509,7 +502,7 @@ "metadata": {}, "outputs": [], "source": [ - "def evaluate(params:hk.HKTaskParams):\n", + "def evaluate(params: hk.HKTaskParams):\n", " \"\"\"Evaluate model\n", "\n", " Args:\n", @@ -524,11 +517,7 @@ "\n", " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n", "\n", - " _, test_ds = load_train_datasets(\n", - " datasets=datasets,\n", - " dataloaderFactory=DataloaderFactory,\n", - " params=params\n", - " )\n", + " _, test_ds = load_train_datasets(datasets=datasets, dataloaderFactory=DataloaderFactory, params=params)\n", " test_x = np.concatenate([x for x, _ in test_ds.as_numpy_iterator()])\n", " test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()])\n", "\n", @@ -542,15 +531,15 @@ " y_pred = model.predict(test_x)\n", "\n", " fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n", - " ax.scatter(y_pred*60, test_y*60)\n", + " ax.scatter(y_pred * 60, test_y * 60)\n", " ax.set_title(\"Predicted vs True BPM\")\n", " ax.set_xlabel(\"Predicted BPM\")\n", " ax.set_ylabel(\"True BPM\")\n", - " ax.annotate(f\"R2={rst['rsq']:.2f}\", xy=(0.05, 0.95), xycoords='axes fraction')\n", + " ax.annotate(f\"R2={rst['rsq']:.2f}\", xy=(0.05, 0.95), xycoords=\"axes fraction\")\n", "\n", " fig.tight_layout()\n", " fig.show()\n", - " fig.savefig(params.job_dir / \"bpm_plot.png\")\n" + " fig.savefig(params.job_dir / \"bpm_plot.png\")" ] }, { @@ -581,7 +570,7 @@ "\n", " @staticmethod\n", " def demo(params: hk.HKTaskParams) -> None:\n", - " raise NotImplementedError(\"Demo not implemented\")\n" + " raise NotImplementedError(\"Demo not implemented\")" ] }, { @@ -669,10 +658,7 @@ " preprocesses=[\n", " hk.NamedParams(\n", " name=\"layer_norm\",\n", - " params=dict(\n", - " epsilon=0.01,\n", - " name=\"znorm\"\n", - " ),\n", + " params=dict(epsilon=0.01, name=\"znorm\"),\n", " ),\n", " ],\n", " augmentations=[],\n", @@ -683,14 +669,14 @@ " input_kernel_size=[1, 9],\n", " input_strides=[1, 2],\n", " blocks=[\n", - " {\"filters\": 16, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", - " {\"filters\": 24, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", - " {\"filters\": 32, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", - " {\"filters\": 40, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2}\n", + " {\"filters\": 16, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", + " {\"filters\": 24, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", + " {\"filters\": 32, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", + " {\"filters\": 40, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", " ],\n", " output_filters=0,\n", " include_top=True,\n", - " use_logits=True\n", + " use_logits=True,\n", " ),\n", " ),\n", ")" @@ -1075,7 +1061,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.5" + "version": "3.11.12" } }, "nbformat": 4, diff --git a/docs/guides/ecg-foundation-model.ipynb b/docs/guides/ecg-foundation-model.ipynb index 3e973457..355f2320 100644 --- a/docs/guides/ecg-foundation-model.ipynb +++ b/docs/guides/ecg-foundation-model.ipynb @@ -63,7 +63,8 @@ ], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 3\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\" # 3\n", "import contextlib\n", "from pathlib import Path\n", "import tempfile\n", @@ -92,26 +93,26 @@ "outputs": [], "source": [ "# File paths\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "job_dir = Path(tempfile.gettempdir()) / \"hk-foundation\"\n", "model_file = job_dir / \"model.keras\"\n", "val_file = job_dir / \"val.pkl\"\n", "\n", "# Data settings\n", - "sampling_rate = 100 # 100 Hz\n", - "input_size = 1000 # 10 seconds\n", - "frame_size = 800 # 8 seconds\n", + "sampling_rate = 100 # 100 Hz\n", + "input_size = 1000 # 10 seconds\n", + "frame_size = 800 # 8 seconds\n", "\n", "# Training settings\n", - "batch_size = 1024 # Batch size for training\n", - "buffer_size = 2000 # How many samples are shuffled each epoch\n", - "epochs = 150 # Increase this to 100+\n", - "steps_per_epoch = 25 # # Steps per epoch (must set since ds has unknown size)\n", + "batch_size = 1024 # Batch size for training\n", + "buffer_size = 2000 # How many samples are shuffled each epoch\n", + "epochs = 150 # Increase this to 100+\n", + "steps_per_epoch = 25 # # Steps per epoch (must set since ds has unknown size)\n", "samples_per_patient = 1 # Number of samples per patient\n", - "val_metric = \"loss\" # Metric to monitor for early stopping\n", - "val_mode = \"min\" # Mode for early stopping\n", - "val_size = 10000 # Number of samples used for validation\n", - "learning_rate = 1e-3 # Learning rate for Adam optimizer\n", + "val_metric = \"loss\" # Metric to monitor for early stopping\n", + "val_mode = \"min\" # Mode for early stopping\n", + "val_size = 10000 # Number of samples used for validation\n", + "learning_rate = 1e-3 # Learning rate for Adam optimizer\n", "epsilon = 0.001\n", "\n", "# Model settings\n", @@ -119,9 +120,9 @@ "temperature = 0.1\n", "\n", "# Other settings\n", - "seed = 42 # Seed for reproducibility\n", - "verbose = 1 # Verbosity level\n", - "plot_theme = hk.utils.dark_theme\n" + "seed = 42 # Seed for reproducibility\n", + "verbose = 1 # Verbosity level\n", + "plot_theme = hk.utils.dark_theme" ] }, { @@ -168,18 +169,8 @@ "outputs": [], "source": [ "datasets = [\n", - " hk.NamedParams(\n", - " name=\"lsad\",\n", - " params=dict(\n", - " path=datasets_dir / \"lsad\"\n", - " )\n", - " ),\n", - " hk.NamedParams(\n", - " name=\"ptbxl\",\n", - " params=dict(\n", - " path=datasets_dir / \"ptbxl\"\n", - " )\n", - " ),\n", + " hk.NamedParams(name=\"lsad\", params=dict(path=datasets_dir / \"lsad\")),\n", + " hk.NamedParams(name=\"ptbxl\", params=dict(path=datasets_dir / \"ptbxl\")),\n", "]" ] }, @@ -197,9 +188,7 @@ "outputs": [], "source": [ "for dataset in datasets:\n", - " ds = hk.DatasetFactory.get(dataset.name)(\n", - " **dataset.params\n", - " )\n", + " ds = hk.DatasetFactory.get(dataset.name)(**dataset.params)\n", " ds.download(force=False)" ] }, @@ -256,7 +245,6 @@ "train_datasets = []\n", "val_datasets = []\n", "for ds in dsets:\n", - "\n", " # Create dataloader specific to dataset\n", " dataloader = hk.tasks.foundation.FoundationTaskFactory.get(ds.name)(\n", " ds=ds,\n", @@ -269,16 +257,12 @@ "\n", " # Create train dataset\n", " train_ds = dataloader.create_dataloader(\n", - " patient_ids=train_patients,\n", - " samples_per_patient=samples_per_patient,\n", - " shuffle=True\n", + " patient_ids=train_patients, samples_per_patient=samples_per_patient, shuffle=True\n", " )\n", "\n", " # Create validation dataset\n", " val_ds = dataloader.create_dataloader(\n", - " patient_ids=val_patients,\n", - " samples_per_patient=samples_per_patient,\n", - " shuffle=False\n", + " patient_ids=val_patients, samples_per_patient=samples_per_patient, shuffle=False\n", " )\n", " train_datasets.append(train_ds)\n", " val_datasets.append(val_ds)\n", @@ -286,7 +270,7 @@ "\n", "# Combine datasets\n", "train_ds = tf.data.Dataset.sample_from_datasets(train_datasets, weights=dset_weights)\n", - "val_ds = tf.data.Dataset.sample_from_datasets(val_datasets, weights=dset_weights)\n" + "val_ds = tf.data.Dataset.sample_from_datasets(val_datasets, weights=dset_weights)" ] }, { @@ -326,7 +310,7 @@ "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", "fig.tight_layout()\n", - "fig.show()\n" + "fig.show()" ] }, { @@ -345,56 +329,32 @@ "outputs": [], "source": [ "nstdb = hk.datasets.nstdb.NstdbNoise(target_rate=sampling_rate)\n", - "noises = np.hstack((nstdb.get_noise(noise_type=\"bw\"), nstdb.get_noise(noise_type=\"ma\"), nstdb.get_noise(noise_type=\"em\")))\n", + "noises = np.hstack(\n", + " (nstdb.get_noise(noise_type=\"bw\"), nstdb.get_noise(noise_type=\"ma\"), nstdb.get_noise(noise_type=\"em\"))\n", + ")\n", "noises = noises.astype(np.float32)\n", "\n", - "preprocessor = nse.layers.preprocessing.LayerNormalization1D(\n", - " epsilon=epsilon,\n", - " name=\"LayerNormalization\"\n", - ")\n", + "preprocessor = nse.layers.preprocessing.LayerNormalization1D(epsilon=epsilon, name=\"LayerNormalization\")\n", "\n", "augmenter = nse.layers.preprocessing.AugmentationPipeline(\n", " layers=[\n", " nse.layers.preprocessing.RandomNoiseDistortion1D(\n", - " sample_rate=sampling_rate,\n", - " amplitude=(0, 1.0),\n", - " frequency=(0.5, 1.5),\n", - " name=\"BaselineWander\"\n", + " sample_rate=sampling_rate, amplitude=(0, 1.0), frequency=(0.5, 1.5), name=\"BaselineWander\"\n", " ),\n", " nse.layers.preprocessing.RandomSineWave(\n", - " sample_rate=sampling_rate,\n", - " amplitude=(0, 0.05),\n", - " frequency=(45, 50),\n", - " name=\"PowerlineNoise\"\n", + " sample_rate=sampling_rate, amplitude=(0, 0.05), frequency=(45, 50), name=\"PowerlineNoise\"\n", " ),\n", " nse.layers.preprocessing.AmplitudeWarp(\n", - " sample_rate=sampling_rate,\n", - " amplitude=(0.9, 1.1),\n", - " frequency=(0.5, 1.5),\n", - " name=\"AmplitudeWarp\"\n", - " ),\n", - " nse.layers.preprocessing.RandomGaussianNoise1D(\n", - " factor=(0.05, 0.2),\n", - " name=\"GaussianNoise\"\n", + " sample_rate=sampling_rate, amplitude=(0.9, 1.1), frequency=(0.5, 1.5), name=\"AmplitudeWarp\"\n", " ),\n", + " nse.layers.preprocessing.RandomGaussianNoise1D(factor=(0.05, 0.2), name=\"GaussianNoise\"),\n", " nse.layers.preprocessing.RandomBackgroundNoises1D(\n", - " noises=noises,\n", - " amplitude=(0.05, 0.2),\n", - " num_noises=2,\n", - " name=\"RandomBackgroundNoises\"\n", + " noises=noises, amplitude=(0.05, 0.2), num_noises=2, name=\"RandomBackgroundNoises\"\n", " ),\n", " nse.layers.preprocessing.RandomCutout1D(\n", - " factor=(0.01, 0.05),\n", - " cutouts=2,\n", - " fill_mode=\"constant\",\n", - " fill_value=0.0,\n", - " name=\"RandomCutout\"\n", + " factor=(0.01, 0.05), cutouts=2, fill_mode=\"constant\", fill_value=0.0, name=\"RandomCutout\"\n", " ),\n", - " nse.layers.preprocessing.RandomCrop1D(\n", - " duration=frame_size,\n", - " name=\"RandomCrop\",\n", - " auto_vectorize=True\n", - " )\n", + " nse.layers.preprocessing.RandomCrop1D(duration=frame_size, name=\"RandomCrop\", auto_vectorize=True),\n", " ],\n", ")" ] @@ -458,41 +418,46 @@ "metadata": {}, "outputs": [], "source": [ - "train_ds = train_ds.shuffle(\n", - " buffer_size=buffer_size,\n", - " reshuffle_each_iteration=True,\n", - ").batch(\n", - " batch_size=batch_size,\n", - " drop_remainder=True,\n", - " num_parallel_calls=tf.data.AUTOTUNE,\n", - ").map(\n", - " lambda x1, x2: {\n", - " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", - " },\n", - " num_parallel_calls=tf.data.AUTOTUNE\n", - ").prefetch(\n", - " tf.data.AUTOTUNE\n", + "train_ds = (\n", + " train_ds.shuffle(\n", + " buffer_size=buffer_size,\n", + " reshuffle_each_iteration=True,\n", + " )\n", + " .batch(\n", + " batch_size=batch_size,\n", + " drop_remainder=True,\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .map(\n", + " lambda x1, x2: {\n", + " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", + " },\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .prefetch(tf.data.AUTOTUNE)\n", ")\n", "\n", - "val_ds = val_ds.batch(\n", - " batch_size=batch_size,\n", - " drop_remainder=True,\n", - " num_parallel_calls=tf.data.AUTOTUNE,\n", - ").map(\n", - " lambda x1, x2: {\n", - " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", - " },\n", - " num_parallel_calls=tf.data.AUTOTUNE\n", - ").prefetch(\n", - " tf.data.AUTOTUNE\n", + "val_ds = (\n", + " val_ds.batch(\n", + " batch_size=batch_size,\n", + " drop_remainder=True,\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .map(\n", + " lambda x1, x2: {\n", + " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", + " },\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .prefetch(tf.data.AUTOTUNE)\n", ")\n", "\n", "# Cache the validation dataset\n", - "val_ds = val_ds.take(val_size//batch_size).cache()" + "val_ds = val_ds.take(val_size // batch_size).cache()" ] }, { @@ -512,7 +477,7 @@ "source": [ "inputs = keras.Input(shape=(frame_size, 1), name=\"input\")\n", "\n", - "encoder_params=dict(\n", + "encoder_params = dict(\n", " input_filters=24,\n", " input_kernel_size=(1, 9),\n", " input_strides=(1, 2),\n", @@ -527,11 +492,7 @@ " include_top=True,\n", ")\n", "\n", - "encoder = nse.models.efficientnet.efficientnetv2_from_object(\n", - " x=inputs,\n", - " params=encoder_params,\n", - " num_classes=None\n", - ")\n" + "encoder = nse.models.efficientnet.efficientnetv2_from_object(x=inputs, params=encoder_params, num_classes=None)" ] }, { @@ -662,9 +623,9 @@ } ], "source": [ - "encoder.summary(print_fn=logger.info, layer_range=('input', encoder.layers[10].name))\n", + "encoder.summary(print_fn=logger.info, layer_range=(\"input\", encoder.layers[10].name))\n", "flops = nse.metrics.flops.get_flops(encoder, batch_size=1, fpath=os.devnull)\n", - "logger.info(f\"Computation: {flops/1e6:0.2f} MFLOPs\")\n", + "logger.info(f\"Computation: {flops / 1e6:0.2f} MFLOPs\")\n", "encoder_output = encoder(inputs)" ] }, @@ -732,7 +693,7 @@ "projector = keras.Model(inputs=projector_input, outputs=projector_output, name=\"projector\")\n", "flops = nse.metrics.flops.get_flops(projector, batch_size=1, fpath=os.devnull)\n", "projector.summary(print_fn=logger.info)\n", - "logger.debug(f\"Projector requires {flops/1e6:0.2f} MFLOPS\")" + "logger.debug(f\"Projector requires {flops / 1e6:0.2f} MFLOPS\")" ] }, { @@ -776,6 +737,7 @@ " decay_steps=steps_per_epoch * epochs,\n", " )\n", "\n", + "\n", "optimizer = keras.optimizers.Adam(get_scheduler())\n", "loss = nse.losses.simclr.SimCLRLoss(temperature=temperature)\n", "\n", @@ -790,14 +752,10 @@ " patience=max(int(0.25 * epochs), 1),\n", " mode=val_mode,\n", " restore_best_weights=True,\n", - " verbose=verbose - 1\n", + " verbose=verbose - 1,\n", " ),\n", " keras.callbacks.ModelCheckpoint(\n", - " filepath=str(model_file),\n", - " monitor=f\"val_{val_metric}\",\n", - " save_best_only=True,\n", - " mode=val_mode,\n", - " verbose=verbose - 1\n", + " filepath=str(model_file), monitor=f\"val_{val_metric}\", save_best_only=True, mode=val_mode, verbose=verbose - 1\n", " ),\n", " keras.callbacks.CSVLogger(job_dir / \"history.csv\"),\n", "]\n", @@ -813,7 +771,7 @@ " encoder_optimizer=optimizer,\n", " encoder_loss=loss,\n", " encoder_metrics=metrics,\n", - ")\n" + ")" ] }, { @@ -1312,15 +1270,10 @@ "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", "\n", "# Redirect stdout and stderr to devnull since TFLite converter is very verbose\n", - "with open(os.devnull, 'w') as devnull:\n", + "with open(os.devnull, \"w\") as devnull:\n", " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", " tflite_content = converter.convert(\n", - " test_x=test_x1,\n", - " quantization=\"FP32\",\n", - " io_type=\"float32\",\n", - " mode=\"KERAS\",\n", - " strict=False,\n", - " verbose=verbose\n", + " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", " )" ] }, @@ -1337,14 +1290,12 @@ "metadata": {}, "outputs": [], "source": [ - "converter.export(\n", - " tflite_path=job_dir / \"model.tflite\"\n", - ")\n", + "converter.export(tflite_path=job_dir / \"model.tflite\")\n", "\n", "converter.export_header(\n", " header_path=job_dir / \"model.h\",\n", " name=\"model\",\n", - ")\n" + ")" ] }, { @@ -1527,13 +1478,8 @@ "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", "\n", "tflite_content = converter.convert(\n", - " test_x=test_x1,\n", - " quantization=\"FP32\",\n", - " io_type=\"float32\",\n", - " mode=\"KERAS\",\n", - " strict=False,\n", - " verbose=verbose\n", - ")\n" + " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", + ")" ] }, { diff --git a/docs/guides/train-arrhythmia-model.ipynb b/docs/guides/train-arrhythmia-model.ipynb index 4af5d849..f7b04305 100644 --- a/docs/guides/train-arrhythmia-model.ipynb +++ b/docs/guides/train-arrhythmia-model.ipynb @@ -74,20 +74,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", "import IPython\n", "import contextlib\n", "from pathlib import Path\n", "import tempfile\n", "import keras\n", "import heartkit as hk\n", - "import neuralspot_edge as nse\n", - "import plotly.io as pio" + "import neuralspot_edge as nse" ] }, { @@ -97,7 +97,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -120,12 +120,14 @@ "metadata": {}, "outputs": [], "source": [ - "datasets = [hk.NamedParams(\n", - " name=\"lsad\",\n", - " params=dict(\n", - " path=datasets_dir / \"lsad\",\n", + "datasets = [\n", + " hk.NamedParams(\n", + " name=\"lsad\",\n", + " params=dict(\n", + " path=datasets_dir / \"lsad\",\n", + " ),\n", " )\n", - ")]" + "]" ] }, { @@ -156,15 +158,10 @@ " hk.tasks.HKRhythm.afib: 2,\n", " hk.tasks.HKRhythm.aflut: 2,\n", " hk.tasks.HKRhythm.stach: 3,\n", - " hk.tasks.HKRhythm.svt: 3\n", + " hk.tasks.HKRhythm.svt: 3,\n", "}\n", "\n", - "class_names=[\n", - " \"SR\",\n", - " \"SB\",\n", - " \"AFIB\",\n", - " \"GSVT\"\n", - "]" + "class_names = [\"SR\", \"SB\", \"AFIB\", \"GSVT\"]" ] }, { @@ -185,9 +182,7 @@ "metadata": {}, "outputs": [], "source": [ - "preprocesses = [\n", - " hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))\n", - "]" + "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))]" ] }, { @@ -217,13 +212,13 @@ " dict(filters=48, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", " dict(filters=64, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", " dict(filters=80, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", - " dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4)\n", + " dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", " ],\n", " output_filters=0,\n", " include_top=True,\n", - " use_logits=True\n", - " )\n", - ")\n" + " use_logits=True,\n", + " ),\n", + ")" ] }, { @@ -290,7 +285,7 @@ " display_report=False,\n", " # Extra arguments\n", " verbose=1,\n", - " seed=42\n", + " seed=42,\n", ")" ] }, @@ -309,7 +304,7 @@ "metadata": {}, "outputs": [], "source": [ - "task = hk.TaskFactory.get('rhythm')" + "task = hk.TaskFactory.get(\"rhythm\")" ] }, { @@ -494,11 +489,9 @@ ], "source": [ "model = nse.models.efficientnet.efficientnetv2_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"),\n", - " params=architecture.params,\n", - " num_classes=len(class_names)\n", + " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture.params, num_classes=len(class_names)\n", ")\n", - "model.summary(layer_range=('inputs', model.layers[10].name))" + "model.summary(layer_range=(\"inputs\", model.layers[10].name))" ] }, { @@ -851,9 +844,9 @@ ], "source": [ "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, 'w') as devnull:\n", + "with open(os.devnull, \"w\") as devnull:\n", " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params=params)\n" + " task.export(params=params)" ] }, { @@ -916,7 +909,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.11.12" } }, "nbformat": 4, diff --git a/docs/guides/train-ecg-denoiser.ipynb b/docs/guides/train-ecg-denoiser.ipynb index b450b12d..51590975 100644 --- a/docs/guides/train-ecg-denoiser.ipynb +++ b/docs/guides/train-ecg-denoiser.ipynb @@ -75,7 +75,8 @@ "outputs": [], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", "import contextlib\n", "from pathlib import Path\n", "import tempfile\n", @@ -93,7 +94,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -124,49 +125,22 @@ "metadata": {}, "outputs": [], "source": [ - "preprocesses = [hk.NamedParams(\n", - " name=\"layer_norm\",\n", - " params=dict(\n", - " epsilon=0.01\n", - " )\n", - ")]\n", + "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01))]\n", "\n", - "augmentations = [hk.NamedParams(\n", - " name=\"random_noise_distortion\",\n", - " params=dict(\n", - " amplitude=[0.1, 1.5],\n", - " frequency=[0.5, 1.5],\n", - " name=\"baseline_wander\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_sine_wave\",\n", - " params=dict(\n", - " amplitude=[0, 0.05],\n", - " frequency=[45, 50],\n", - " auto_vectorize=False,\n", - " name=\"powerline_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"amplitude_warp\",\n", - " params=dict(\n", - " amplitude=[0.9, 1.1],\n", - " frequency=[0.5, 1.5],\n", - " name=\"amplitude_warp\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_noise\",\n", - " params=dict(\n", - " factor=[0.1, 0.5],\n", - " name=\"random_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_background_noise\",\n", - " params=dict(\n", - " amplitude=[0.1, 0.5],\n", - " num_noises=2,\n", - " name=\"nstdb\"\n", - " )\n", - ")]\n" + "augmentations = [\n", + " hk.NamedParams(\n", + " name=\"random_noise_distortion\", params=dict(amplitude=[0.1, 1.5], frequency=[0.5, 1.5], name=\"baseline_wander\")\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"random_sine_wave\",\n", + " params=dict(amplitude=[0, 0.05], frequency=[45, 50], auto_vectorize=False, name=\"powerline_noise\"),\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"amplitude_warp\", params=dict(amplitude=[0.9, 1.1], frequency=[0.5, 1.5], name=\"amplitude_warp\")\n", + " ),\n", + " hk.NamedParams(name=\"random_noise\", params=dict(factor=[0.1, 0.5], name=\"random_noise\")),\n", + " hk.NamedParams(name=\"random_background_noise\", params=dict(amplitude=[0.1, 0.5], num_noises=2, name=\"nstdb\")),\n", + "]" ] }, { @@ -185,11 +159,21 @@ "outputs": [], "source": [ "mbconv_blocks = [\n", - " dict(depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=0, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 2), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=40, kernel=(1, 7), dilation=(1, 4), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\")\n", + " dict(\n", + " depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=0, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 2), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=40, kernel=(1, 7), dilation=(1, 4), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", "]\n", "\n", "architecture = dict(\n", @@ -201,9 +185,9 @@ " output_kernel=(1, 7),\n", " include_top=True,\n", " use_logits=True,\n", - " model_name=\"tcn\"\n", - " )\n", - ")\n" + " model_name=\"tcn\",\n", + " ),\n", + ")" ] }, { @@ -236,17 +220,17 @@ " p_multiplier=[0.7, 1.3],\n", " t_multiplier=[0.7, 1.3],\n", " noise_multiplier=[0, 0.01],\n", - " voltage_factor=[800, 1000]\n", - " )\n", - " )\n", + " voltage_factor=[800, 1000],\n", + " ),\n", + " ),\n", " ),\n", " hk.NamedParams(\n", " name=\"ptbxl\",\n", " params=dict(\n", " path=datasets_dir / \"ptbxl\",\n", - " )\n", - " )\n", - "]\n" + " ),\n", + " ),\n", + "]" ] }, { @@ -314,7 +298,7 @@ " display_report=True,\n", " # Extra arguments\n", " verbose=1,\n", - " seed=42\n", + " seed=42,\n", ")" ] }, @@ -380,10 +364,7 @@ } ], "source": [ - "ds = hk.DatasetFactory.get(params.datasets[0].name)(\n", - " cacheable=False,\n", - " **params.datasets[0].params\n", - ")\n", + "ds = hk.DatasetFactory.get(params.datasets[0].name)(cacheable=False, **params.datasets[0].params)\n", "\n", "ds_gen = ds.signal_generator(\n", " patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids()),\n", @@ -400,7 +381,7 @@ "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", "fig.tight_layout()\n", - "fig.show()\n" + "fig.show()" ] }, { @@ -624,11 +605,9 @@ ], "source": [ "model = nse.models.tcn.tcn_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name='inputs'),\n", - " params=architecture[\"params\"],\n", - " num_classes=1\n", + " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture[\"params\"], num_classes=1\n", ")\n", - "model.summary(layer_range=('inputs', model.layers[10].name))" + "model.summary(layer_range=(\"inputs\", model.layers[10].name))" ] }, { @@ -795,7 +774,7 @@ } ], "source": [ - "task.evaluate(params)\n" + "task.evaluate(params)" ] }, { @@ -942,9 +921,9 @@ ], "source": [ "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, 'w') as devnull:\n", + "with open(os.devnull, \"w\") as devnull:\n", " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params)\n" + " task.export(params)" ] }, { @@ -980,7 +959,9 @@ ], "source": [ "ecg = next(ds_gen)\n", - "aug_ecg = augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg, (1, -1, 1)))), training=True).numpy().squeeze()\n", + "aug_ecg = (\n", + " augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg, (1, -1, 1)))), training=True).numpy().squeeze()\n", + ")\n", "clean_ecg = model.predict(np.reshape(aug_ecg, (1, -1, 1)))\n", "snr = nse.metrics.Snr()\n", "snr.update_state(ecg.reshape(1, -1, 1), aug_ecg.reshape(1, -1, 1))\n", @@ -1016,8 +997,8 @@ "ax[1].set_ylabel(\"Noisy\")\n", "ax[2].set_ylabel(\"Denoised\")\n", "\n", - "ax[1].text(0.98, 0.15, f\"{aug_snr:4.02f} dB SNR\", transform=ax[1].transAxes, ha=\"right\", va=\"top\", weight='bold')\n", - "ax[2].text(0.98, 0.15, f\"{clean_snr:4.02f} dB SNR\", transform=ax[2].transAxes, ha=\"right\", va=\"top\", weight='bold')\n", + "ax[1].text(0.98, 0.15, f\"{aug_snr:4.02f} dB SNR\", transform=ax[1].transAxes, ha=\"right\", va=\"top\", weight=\"bold\")\n", + "ax[2].text(0.98, 0.15, f\"{clean_snr:4.02f} dB SNR\", transform=ax[2].transAxes, ha=\"right\", va=\"top\", weight=\"bold\")\n", "# Disable y-axis ticks for all plots\n", "for axes in ax:\n", " axes.yaxis.set_ticks([])\n", diff --git a/docs/guides/train-ecg-segmentation.ipynb b/docs/guides/train-ecg-segmentation.ipynb index e688dba0..4177ae29 100644 --- a/docs/guides/train-ecg-segmentation.ipynb +++ b/docs/guides/train-ecg-segmentation.ipynb @@ -80,12 +80,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", "import IPython\n", "import contextlib\n", "from pathlib import Path\n", @@ -95,8 +96,7 @@ "import physiokit as pk\n", "import numpy as np\n", "import neuralspot_edge as nse\n", - "import matplotlib.pyplot as plt\n", - "import plotly.io as pio\n" + "import matplotlib.pyplot as plt" ] }, { @@ -106,7 +106,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -145,16 +145,16 @@ " p_multiplier=[0.7, 1.3],\n", " t_multiplier=[0.7, 1.3],\n", " noise_multiplier=[0, 0.01],\n", - " voltage_factor=[800, 1000]\n", - " )\n", - " )\n", + " voltage_factor=[800, 1000],\n", + " ),\n", + " ),\n", " ),\n", " hk.NamedParams(\n", " name=\"ludb\",\n", " params=dict(\n", " path=datasets_dir / \"ludb\",\n", - " )\n", - " )\n", + " ),\n", + " ),\n", "]" ] }, @@ -186,15 +186,10 @@ " hk.tasks.HKSegment.qrs: 2,\n", " hk.tasks.HKSegment.twave: 3,\n", " hk.tasks.HKSegment.uwave: 0,\n", - " hk.tasks.HKSegment.noise: 0\n", + " hk.tasks.HKSegment.noise: 0,\n", "}\n", "\n", - "class_names=[\n", - " \"NONE\",\n", - " \"P-WAVE\",\n", - " \"QRS\",\n", - " \"T-WAVE\"\n", - "]" + "class_names = [\"NONE\", \"P-WAVE\", \"QRS\", \"T-WAVE\"]" ] }, { @@ -218,17 +213,56 @@ " input_kernel=(1, 7),\n", " input_norm=\"batch\",\n", " blocks=[\n", - " dict(depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0.1, ex_ratio=1, se_ratio=0, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 2), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 4), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\")\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=16,\n", + " kernel=(1, 7),\n", + " dilation=(1, 1),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=0,\n", + " norm=\"batch\",\n", + " ),\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=24,\n", + " kernel=(1, 7),\n", + " dilation=(1, 2),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=2,\n", + " norm=\"batch\",\n", + " ),\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=32,\n", + " kernel=(1, 7),\n", + " dilation=(1, 4),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=2,\n", + " norm=\"batch\",\n", + " ),\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=48,\n", + " kernel=(1, 7),\n", + " dilation=(1, 8),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=2,\n", + " norm=\"batch\",\n", + " ),\n", " ],\n", " output_kernel=(1, 7),\n", " include_top=True,\n", " use_logits=True,\n", - " model_name=\"tcn\"\n", - " )\n", - "\n", + " model_name=\"tcn\",\n", + " ),\n", ")" ] }, @@ -251,13 +285,7 @@ "metadata": {}, "outputs": [], "source": [ - "preprocesses = [hk.NamedParams(\n", - " name=\"layer_norm\",\n", - " params=dict(\n", - " epsilon=0.01,\n", - " name=\"znorm\"\n", - " )\n", - ")]" + "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))]" ] }, { @@ -281,42 +309,20 @@ "metadata": {}, "outputs": [], "source": [ - "augmentations = [hk.NamedParams(\n", - " name=\"random_noise_distortion\",\n", - " params=dict(\n", - " amplitude=[0, 0.5],\n", - " frequency=[0.5, 1.5],\n", - " name=\"baseline_wander\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_sine_wave\",\n", - " params=dict(\n", - " amplitude=[0, 0.05],\n", - " frequency=[45, 50],\n", - " auto_vectorize=False,\n", - " name=\"powerline_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"amplitude_warp\",\n", - " params=dict(\n", - " amplitude=[0.9, 1.1],\n", - " frequency=[0.5, 1.5],\n", - " name=\"amplitude_warp\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_noise\",\n", - " params=dict(\n", - " factor=[0, 0.025],\n", - " name=\"random_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_background_noise\",\n", - " params=dict(\n", - " amplitude=[0, 0.025],\n", - " num_noises=1,\n", - " name=\"nstdb\"\n", - " )\n", - ")]\n" + "augmentations = [\n", + " hk.NamedParams(\n", + " name=\"random_noise_distortion\", params=dict(amplitude=[0, 0.5], frequency=[0.5, 1.5], name=\"baseline_wander\")\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"random_sine_wave\",\n", + " params=dict(amplitude=[0, 0.05], frequency=[45, 50], auto_vectorize=False, name=\"powerline_noise\"),\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"amplitude_warp\", params=dict(amplitude=[0.9, 1.1], frequency=[0.5, 1.5], name=\"amplitude_warp\")\n", + " ),\n", + " hk.NamedParams(name=\"random_noise\", params=dict(factor=[0, 0.025], name=\"random_noise\")),\n", + " hk.NamedParams(name=\"random_background_noise\", params=dict(amplitude=[0, 0.025], num_noises=1, name=\"nstdb\")),\n", + "]" ] }, { @@ -384,7 +390,7 @@ " display_report=False,\n", " # Extra arguments\n", " verbose=1,\n", - " seed=42\n", + " seed=42,\n", ")" ] }, @@ -456,7 +462,7 @@ " heart_rate=60,\n", " leads=1,\n", " preset=pk.ecg.EcgPreset.SR,\n", - " noise_multiplier=0.0\n", + " noise_multiplier=0.0,\n", ")\n", "ecg = ecg.squeeze()\n", "segs = segs.squeeze()\n", @@ -477,7 +483,7 @@ "plt.title(\"Synthetic ECG w/ Segments\")\n", "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -553,7 +559,7 @@ "plt.title(\"Synthetic ECG w/ Noise\")\n", "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -704,11 +710,9 @@ ], "source": [ "model = nse.models.tcn.tcn_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"),\n", - " params=architecture.params,\n", - " num_classes=len(class_names)\n", + " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture.params, num_classes=len(class_names)\n", ")\n", - "model.summary(layer_range=('inputs', model.layers[10].name))" + "model.summary(layer_range=(\"inputs\", model.layers[10].name))" ] }, { @@ -1059,9 +1063,9 @@ ], "source": [ "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, 'w') as devnull:\n", + "with open(os.devnull, \"w\") as devnull:\n", " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params)\n" + " task.export(params)" ] }, { @@ -1124,7 +1128,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.5" + "version": "3.11.12" } }, "nbformat": 4, diff --git a/docs/index.md b/docs/index.md index 2747f2db..d65300d9 100644 --- a/docs/index.md +++ b/docs/index.md @@ -25,7 +25,7 @@ Please explore the HeartKit Docs, a comprehensive resource designed to help you ## Getting Started -- **Install** `HeartKit` with pip/poetry and getting up and running in minutes.   [:material-clock-fast: Install HeartKit](./quickstart.md/#install-heartkit){ .md-button } +- **Install** `HeartKit` with pip/uv and getting up and running in minutes.   [:material-clock-fast: Install HeartKit](./quickstart.md/#install-heartkit){ .md-button } - **Train** a model with a custom network   [:fontawesome-solid-brain: Train a Model](modes/train.md){ .md-button } - **Tasks** `HeartKit` provides tasks like rhythm, segment, and denoising   [:material-magnify-expand: Explore Tasks](tasks/index.md){ .md-button } - **Datasets** Several built-in datasets can be leveraged   [:material-database-outline: Explore Datasets](./datasets/index.md){ .md-button } @@ -57,7 +57,7 @@ To get started, first install the python package `heartkit` along with its depen Cloning into 'heartkit'... Resolving deltas: 100% (3491/3491), done. $ cd heartkit - $ poetry install + $ uv install ---> 100% ``` diff --git a/docs/quickstart.md b/docs/quickstart.md index ea6103cc..edc3df0e 100644 --- a/docs/quickstart.md +++ b/docs/quickstart.md @@ -3,13 +3,13 @@ ## Install HeartKit -We provide several installation methods including pip, poetry, and Docker. Install __HeartKit__ via pip/poetry for the latest stable release or by cloning the GitHub repo for the most up-to-date. Additionally, a [VSCode Dev Container](https://code.visualstudio.com/docs/devcontainers/containers) is available and defined in [./.devcontainer](https://github.com/AmbiqAI/heartkit/tree/main/.devcontainer) to run in an isolated Docker environment. +We provide several installation methods including pip, uv, and Docker. Install __HeartKit__ via pip/uv for the latest stable release or by cloning the GitHub repo for the most up-to-date. Additionally, a [VSCode Dev Container](https://code.visualstudio.com/docs/devcontainers/containers) is available and defined in [./.devcontainer](https://github.com/AmbiqAI/heartkit/tree/main/.devcontainer) to run in an isolated Docker environment. !!! install === "Git clone" - Clone the repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package. In this mode, Poetry is recommended. + Clone the repository if you are interested in contributing to the development or wish to experiment with the latest source code. After cloning, navigate into the directory and install the package. In this mode, uv is recommended. ```bash # Clone the repository @@ -19,15 +19,15 @@ We provide several installation methods including pip, poetry, and Docker. Insta cd heartkit # Install the package in editable mode for development - poetry install + uv sync ``` - When using editable mode via Poetry, be sure to activate the python environment: `poetry shell`.
- On Windows using Powershell, use `.venv\Scripts\activate.ps1`. + When using editable mode via uv, be sure to activate the python environment: `source .venv/bin/activate`.
+ On Windows using Powershell, use `.venv\Scripts\activate`. === "PyPI install" - Install the HeartKit package using pip or Poetry. + Install the HeartKit package using pip or uv. Visit the Python Package Index (PyPI) for more details on the package: [https://pypi.org/project/heartkit/](https://pypi.org/project/heartkit/) ```bash @@ -35,11 +35,11 @@ We provide several installation methods including pip, poetry, and Docker. Insta pip install heartkit ``` - Or, if you prefer to use Poetry, you can install the package with the following command: + Or, if you prefer to use uv, you can install the package with the following command: ```bash - # Install with poetry - poetry add heartkit + # Install with uv + uv add heartkit ``` Alternatively, you can install the latest development version directly from the GitHub repository. Make sure to have the Git command-line tool installed on your system. The @main command installs the main branch and may be modified to another branch, i.e. @canary. @@ -48,17 +48,17 @@ We provide several installation methods including pip, poetry, and Docker. Insta pip install git+https://github.com/AmbiqAI/heartkit.git@main ``` - Or, using Poetry: + Or, using uv: ```bash - poetry add git+https://github.com/AmbiqAI/heartkit.git@main + uv add git+https://github.com/AmbiqAI/heartkit.git@main ``` ## Requirements * [Python ^3.11+](https://www.python.org) -* [Poetry ^1.6.1+](https://python-poetry.org/docs/#installation) +* [uv ^0.7.10+](https://docs.astral.sh/uv/getting-started/installation/) Check the project's [pyproject.toml](https://github.com/AmbiqAI/heartkit/blob/main/pyproject.toml) file for a list of up-to-date Python dependencies. Note that the installation methods above install all required dependencies. The following are optional dependencies only needed when running `demo` command using Ambiq's evaluation board (`EVB`) backend: diff --git a/heartkit/datasets/ptbxl.py b/heartkit/datasets/ptbxl.py index 50e90793..ca8b1f90 100644 --- a/heartkit/datasets/ptbxl.py +++ b/heartkit/datasets/ptbxl.py @@ -766,7 +766,7 @@ def _convert_dataset_zip_to_hdf5( # Grab ECG data with tempfile.TemporaryDirectory() as tmpdir: - zip_path = os.path.join(zp_root, "records500", f"{1000*(patient//1000):05d}", f"{pt_id}_hr") + zip_path = os.path.join(zp_root, "records500", f"{1000 * (patient // 1000):05d}", f"{pt_id}_hr") rec_fpath = os.path.join(tmpdir, f"{pt_id}_hr") with open(f"{rec_fpath}.hea", "wb") as fp: fp.write(zp.read(f"{zip_path}.hea")) diff --git a/heartkit/tasks/beat/evaluate.py b/heartkit/tasks/beat/evaluate.py index c6a6300d..e0190de0 100644 --- a/heartkit/tasks/beat/evaluate.py +++ b/heartkit/tasks/beat/evaluate.py @@ -43,7 +43,7 @@ def evaluate(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug("Performing inference") y_true = np.argmax(test_y, axis=-1).flatten() diff --git a/heartkit/tasks/beat/export.py b/heartkit/tasks/beat/export.py index 3322d1b3..8bb360e0 100644 --- a/heartkit/tasks/beat/export.py +++ b/heartkit/tasks/beat/export.py @@ -53,7 +53,7 @@ def export(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug(f"Converting model to TFLite (quantization={params.quantization.mode})") converter = nse.converters.tflite.TfLiteKerasConverter(model=model) diff --git a/heartkit/tasks/beat/train.py b/heartkit/tasks/beat/train.py index 983f513e..eb9fec01 100644 --- a/heartkit/tasks/beat/train.py +++ b/heartkit/tasks/beat/train.py @@ -5,7 +5,7 @@ import numpy as np import sklearn.utils import wandb -from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -102,7 +102,7 @@ def train(params: HKTaskParams): model.compile(optimizer=optimizer, loss=loss, metrics=metrics) flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint if nse.utils.env_flag("WANDB"): diff --git a/heartkit/tasks/denoise/demo.py b/heartkit/tasks/denoise/demo.py index 8ec81e08..52199b92 100644 --- a/heartkit/tasks/denoise/demo.py +++ b/heartkit/tasks/denoise/demo.py @@ -83,7 +83,7 @@ def demo(params: HKTaskParams): cos_sim = np.dot(y_act, y_pred) / (np.linalg.norm(y_act) * np.linalg.norm(y_pred)) cos_sim_diff = cos_sim - prev_cos_sim prev_cos_sim = cos_sim - logger.debug(f"Trial {trial+1}: Cosine Similarity: {cos_sim:.2%} (diff: {cos_sim_diff:.2%})") + logger.debug(f"Trial {trial + 1}: Cosine Similarity: {cos_sim:.2%} (diff: {cos_sim_diff:.2%})") if cos_sim_diff < 1e-3: break # END FOR diff --git a/heartkit/tasks/denoise/evaluate.py b/heartkit/tasks/denoise/evaluate.py index 54cc72e2..7cdf1395 100644 --- a/heartkit/tasks/denoise/evaluate.py +++ b/heartkit/tasks/denoise/evaluate.py @@ -37,7 +37,7 @@ def evaluate(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") # Summarize results logger.debug("Performing inference") diff --git a/heartkit/tasks/denoise/export.py b/heartkit/tasks/denoise/export.py index 9e4b7b27..9ec6486b 100644 --- a/heartkit/tasks/denoise/export.py +++ b/heartkit/tasks/denoise/export.py @@ -52,7 +52,7 @@ def export(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug(f"Converting model to TFLite (quantization={params.quantization.format})") converter = nse.converters.tflite.TfLiteKerasConverter(model=model) diff --git a/heartkit/tasks/denoise/train.py b/heartkit/tasks/denoise/train.py index 596a1c7d..a2beaa16 100644 --- a/heartkit/tasks/denoise/train.py +++ b/heartkit/tasks/denoise/train.py @@ -3,7 +3,7 @@ import numpy as np import keras import wandb -from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint import neuralspot_edge as nse from ...defines import HKTaskParams @@ -95,7 +95,7 @@ def train(params: HKTaskParams): model.compile(optimizer=optimizer, loss=loss, metrics=metrics) flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") val_mode = "max" if params.val_metric in ("f1", "cos") else "auto" ModelCheckpoint = keras.callbacks.ModelCheckpoint diff --git a/heartkit/tasks/diagnostic/evaluate.py b/heartkit/tasks/diagnostic/evaluate.py index e721bec9..0c3a6601 100644 --- a/heartkit/tasks/diagnostic/evaluate.py +++ b/heartkit/tasks/diagnostic/evaluate.py @@ -47,7 +47,7 @@ def evaluate(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug("Performing inference") y_true = test_y diff --git a/heartkit/tasks/diagnostic/export.py b/heartkit/tasks/diagnostic/export.py index f695afdd..636ee665 100644 --- a/heartkit/tasks/diagnostic/export.py +++ b/heartkit/tasks/diagnostic/export.py @@ -49,7 +49,7 @@ def export(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug(f"Converting model to TFLite (quantization={params.quantization.mode})") converter = nse.converters.tflite.TfLiteKerasConverter(model=model) diff --git a/heartkit/tasks/diagnostic/train.py b/heartkit/tasks/diagnostic/train.py index 6e3f4564..d2e14cb2 100644 --- a/heartkit/tasks/diagnostic/train.py +++ b/heartkit/tasks/diagnostic/train.py @@ -5,7 +5,7 @@ import pandas as pd import wandb from sklearn.metrics import classification_report -from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint import neuralspot_edge as nse @@ -116,7 +116,7 @@ def train(params: HKTaskParams): model.compile(optimizer=optimizer, loss=loss, metrics=metrics) model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint if nse.utils.env_flag("WANDB"): diff --git a/heartkit/tasks/foundation/evaluate.py b/heartkit/tasks/foundation/evaluate.py index f84d6d76..46349e91 100644 --- a/heartkit/tasks/foundation/evaluate.py +++ b/heartkit/tasks/foundation/evaluate.py @@ -49,7 +49,7 @@ def evaluate(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug("Performing inference") test_y1 = model.predict(test_x1) diff --git a/heartkit/tasks/foundation/export.py b/heartkit/tasks/foundation/export.py index 89e5f0a2..7104191c 100644 --- a/heartkit/tasks/foundation/export.py +++ b/heartkit/tasks/foundation/export.py @@ -46,7 +46,7 @@ def export(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug(f"Converting model to TFLite (quantization={params.quantization.mode})") converter = nse.converters.tflite.TfLiteKerasConverter(model=model) diff --git a/heartkit/tasks/foundation/train.py b/heartkit/tasks/foundation/train.py index 9391f869..ef788f07 100644 --- a/heartkit/tasks/foundation/train.py +++ b/heartkit/tasks/foundation/train.py @@ -3,7 +3,7 @@ import keras import wandb import numpy as np -from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint import neuralspot_edge as nse from ...defines import HKTaskParams @@ -58,7 +58,7 @@ def train(params: HKTaskParams): flops = nse.metrics.flops.get_flops(encoder, batch_size=1, fpath=params.job_dir / "encoder_flops.log") encoder.summary(print_fn=logger.info) - logger.debug(f"Encoder requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Encoder requires {flops / 1e6:0.2f} MFLOPS") # Create projector # encoder_output = encoder(encoder_input) diff --git a/heartkit/tasks/rhythm/evaluate.py b/heartkit/tasks/rhythm/evaluate.py index 66befb98..3bc6d5e2 100644 --- a/heartkit/tasks/rhythm/evaluate.py +++ b/heartkit/tasks/rhythm/evaluate.py @@ -43,7 +43,7 @@ def evaluate(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug("Performing inference") rst = model.evaluate(test_ds, verbose=params.verbose, return_dict=True) diff --git a/heartkit/tasks/rhythm/export.py b/heartkit/tasks/rhythm/export.py index bffa19a2..e22a2496 100644 --- a/heartkit/tasks/rhythm/export.py +++ b/heartkit/tasks/rhythm/export.py @@ -59,7 +59,7 @@ def export(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug(f"Converting model to TFLite (quantization={params.quantization.format})") converter = nse.converters.tflite.TfLiteKerasConverter(model=model) diff --git a/heartkit/tasks/rhythm/train.py b/heartkit/tasks/rhythm/train.py index 35489baf..27ab4b88 100644 --- a/heartkit/tasks/rhythm/train.py +++ b/heartkit/tasks/rhythm/train.py @@ -4,7 +4,7 @@ import numpy as np import sklearn.utils import wandb -from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint import neuralspot_edge as nse from ...defines import HKTaskParams @@ -116,7 +116,7 @@ def train(params: HKTaskParams): model.compile(optimizer=optimizer, loss=loss, metrics=metrics) model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint if nse.utils.env_flag("WANDB"): diff --git a/heartkit/tasks/segmentation/evaluate.py b/heartkit/tasks/segmentation/evaluate.py index 339a1c04..3c751c30 100644 --- a/heartkit/tasks/segmentation/evaluate.py +++ b/heartkit/tasks/segmentation/evaluate.py @@ -43,7 +43,7 @@ def evaluate(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug("Performing inference") rst = model.evaluate(test_ds, verbose=params.verbose, return_dict=True) diff --git a/heartkit/tasks/segmentation/export.py b/heartkit/tasks/segmentation/export.py index 4aa566f2..0f13bf43 100644 --- a/heartkit/tasks/segmentation/export.py +++ b/heartkit/tasks/segmentation/export.py @@ -58,7 +58,7 @@ def export(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug(f"Converting model to TFLite (quantization={params.quantization.format})") converter = nse.converters.tflite.TfLiteKerasConverter(model=model) diff --git a/heartkit/tasks/segmentation/train.py b/heartkit/tasks/segmentation/train.py index ea69759a..07f31552 100644 --- a/heartkit/tasks/segmentation/train.py +++ b/heartkit/tasks/segmentation/train.py @@ -4,7 +4,7 @@ import numpy as np import sklearn.utils import wandb -from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint import neuralspot_edge as nse from ...defines import HKTaskParams @@ -115,7 +115,7 @@ def train(params: HKTaskParams): model.compile(optimizer=optimizer, loss=loss, metrics=metrics) model(inputs) model.summary(print_fn=logger.debug) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint if nse.utils.env_flag("WANDB"): diff --git a/heartkit/tasks/translate/demo.py b/heartkit/tasks/translate/demo.py index 2df78b3f..ad97cb1c 100644 --- a/heartkit/tasks/translate/demo.py +++ b/heartkit/tasks/translate/demo.py @@ -84,7 +84,7 @@ def demo(params: HKTaskParams): cos_sim = np.dot(y, y_pred) / (np.linalg.norm(y) * np.linalg.norm(y_pred)) cos_sim_diff = cos_sim - prev_cos_sim prev_cos_sim = cos_sim - logger.debug(f"Trial {trial+1}: Cosine Similarity: {cos_sim:.2%} (diff: {cos_sim_diff:.2%})") + logger.debug(f"Trial {trial + 1}: Cosine Similarity: {cos_sim:.2%} (diff: {cos_sim_diff:.2%})") if cos_sim_diff < 1e-3: break # END FOR diff --git a/heartkit/tasks/translate/evaluate.py b/heartkit/tasks/translate/evaluate.py index d0dd1551..64c391be 100644 --- a/heartkit/tasks/translate/evaluate.py +++ b/heartkit/tasks/translate/evaluate.py @@ -40,7 +40,7 @@ def evaluate(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug("Performing inference") # y_true = test_y.squeeze() diff --git a/heartkit/tasks/translate/export.py b/heartkit/tasks/translate/export.py index aed98426..16d21019 100644 --- a/heartkit/tasks/translate/export.py +++ b/heartkit/tasks/translate/export.py @@ -44,7 +44,7 @@ def export(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") logger.debug(f"Converting model to TFLite (quantization={params.quantization.mode})") converter = nse.converters.tflite.TfLiteKerasConverter(model=model) diff --git a/heartkit/tasks/translate/train.py b/heartkit/tasks/translate/train.py index adb50474..29073b70 100644 --- a/heartkit/tasks/translate/train.py +++ b/heartkit/tasks/translate/train.py @@ -4,7 +4,7 @@ import neuralspot_edge as nse import numpy as np import wandb -from wandb.keras import WandbMetricsLogger, WandbModelCheckpoint +from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -95,7 +95,7 @@ def train(params: HKTaskParams): flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") model(inputs) model.summary(print_fn=logger.info) - logger.debug(f"Model requires {flops/1e6:0.2f} MFLOPS") + logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint if nse.utils.env_flag("WANDB"): diff --git a/notebooks/byot.ipynb b/notebooks/byot.ipynb index 6384f478..2ef2a16d 100644 --- a/notebooks/byot.ipynb +++ b/notebooks/byot.ipynb @@ -62,7 +62,8 @@ ], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n", "import random\n", "from typing import Generator\n", "from collections.abc import Iterable\n", @@ -86,7 +87,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "os.environ['HK_DATASET_PATH'] = os.getenv('HK_DATASET_PATH', './datasets')\n", + "os.environ[\"HK_DATASET_PATH\"] = os.getenv(\"HK_DATASET_PATH\", \"./datasets\")\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -126,7 +127,7 @@ " with self.ds.patient_data(patient_id) as h5:\n", " ecg = h5[\"data\"][:]\n", " # Beat locations. Convert 100Hz to ds.sampling_rate\n", - " blabels = h5[\"blabels\"][:, 0]*(self.ds.sampling_rate/100.0)\n", + " blabels = h5[\"blabels\"][:, 0] * (self.ds.sampling_rate / 100.0)\n", " # END WITH\n", "\n", " for _ in range(samples_per_patient):\n", @@ -146,11 +147,11 @@ " # Resample if needed\n", " if self.ds.sampling_rate != self.sampling_rate:\n", " x = pk.signal.resample_signal(x, self.ds.sampling_rate, self.sampling_rate, axis=0)\n", - " x = x[:self.frame_size] # Ensure frame size\n", + " x = x[: self.frame_size] # Ensure frame size\n", "\n", " x = np.nan_to_num(x).astype(np.float32)\n", " x = x.reshape(-1, 1)\n", - " y = bpm / 60.0 # Make beats per second\n", + " y = bpm / 60.0 # Make beats per second\n", " yield x, y\n", " # END FOR\n", "\n", @@ -166,7 +167,7 @@ " for x, y in self.patient_data_generator(pt_id, samples_per_patient):\n", " yield x, y\n", " # END FOR\n", - " # END FOR\n" + " # END FOR" ] }, { @@ -184,9 +185,7 @@ "metadata": {}, "outputs": [], "source": [ - "ds = hk.DatasetFactory.get(\"ptbxl\")(\n", - " path=Path(os.environ['HK_DATASET_PATH']) / \"ptbxl\"\n", - ")\n", + "ds = hk.DatasetFactory.get(\"ptbxl\")(path=Path(os.environ[\"HK_DATASET_PATH\"]) / \"ptbxl\")\n", "dl = PtbxlDataloader(\n", " ds=ds,\n", " frame_size=4000,\n", @@ -222,7 +221,7 @@ ], "source": [ "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n", - "ax.plot(x, label=f\"HR: {60*y:0.0f} BPM\")\n", + "ax.plot(x, label=f\"HR: {60 * y:0.0f} BPM\")\n", "ax.set_title(\"ECG Frame\")\n", "ax.legend()\n", "fig.show()" @@ -303,10 +302,7 @@ " drop_remainder=True,\n", " num_parallel_calls=tf.data.AUTOTUNE,\n", " )\n", - " augmenter = hk.datasets.create_augmentation_pipeline(\n", - " augmentations,\n", - " sampling_rate=sampling_rate\n", - " )\n", + " augmenter = hk.datasets.create_augmentation_pipeline(augmentations, sampling_rate=sampling_rate)\n", "\n", " ds = (\n", " ds.map(\n", @@ -327,6 +323,7 @@ " )\n", " return ds.prefetch(tf.data.AUTOTUNE)\n", "\n", + "\n", "def load_train_datasets(\n", " datasets: list[hk.HKDataset],\n", " dataloaderFactory: nse.utils.ItemFactory[hk.HKDataloader],\n", @@ -367,7 +364,7 @@ " val_steps_per_epoch = val_steps_per_epoch or 50\n", " val_ds = val_ds.take(val_steps_per_epoch).cache()\n", "\n", - " return train_ds, val_ds\n" + " return train_ds, val_ds" ] }, { @@ -383,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -404,16 +401,12 @@ " with open(params.job_dir / \"train_config.json\", \"w\", encoding=\"utf-8\") as fp:\n", " fp.write(params.model_dump_json(indent=2))\n", "\n", - " params.num_classes = 1 # Regression\n", + " params.num_classes = 1 # Regression\n", " feat_shape = (params.frame_size, 1)\n", "\n", " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n", "\n", - " train_ds, val_ds = load_train_datasets(\n", - " datasets=datasets,\n", - " dataloaderFactory=DataloaderFactory,\n", - " params=params\n", - " )\n", + " train_ds, val_ds = load_train_datasets(datasets=datasets, dataloaderFactory=DataloaderFactory, params=params)\n", "\n", " y_true = np.concatenate([y for _, y in val_ds.as_numpy_iterator()])\n", " y_true = np.argmax(y_true, axis=-1).flatten()\n", @@ -459,7 +452,7 @@ " params.model_file = params.job_dir / \"model.keras\"\n", "\n", " model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n", - " logger.debug(f\"Model requires {flops/1e6:0.2f} MFLOPS\")\n", + " logger.debug(f\"Model requires {flops / 1e6:0.2f} MFLOPS\")\n", "\n", " model_callbacks = [\n", " keras.callbacks.EarlyStopping(\n", @@ -500,7 +493,7 @@ "\n", " # Summarize results\n", " rst = model.evaluate(val_ds, return_dict=True)\n", - " logger.info(f\"[VAL SET] \" + \", \".join(f\"{k.upper()}={v:.4f}\" for k, v in rst.items()))\n" + " logger.info(\"[VAL SET] \" + \", \".join(f\"{k.upper()}={v:.4f}\" for k, v in rst.items()))" ] }, { @@ -509,7 +502,7 @@ "metadata": {}, "outputs": [], "source": [ - "def evaluate(params:hk.HKTaskParams):\n", + "def evaluate(params: hk.HKTaskParams):\n", " \"\"\"Evaluate model\n", "\n", " Args:\n", @@ -524,11 +517,7 @@ "\n", " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n", "\n", - " _, test_ds = load_train_datasets(\n", - " datasets=datasets,\n", - " dataloaderFactory=DataloaderFactory,\n", - " params=params\n", - " )\n", + " _, test_ds = load_train_datasets(datasets=datasets, dataloaderFactory=DataloaderFactory, params=params)\n", " test_x = np.concatenate([x for x, _ in test_ds.as_numpy_iterator()])\n", " test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()])\n", "\n", @@ -542,15 +531,15 @@ " y_pred = model.predict(test_x)\n", "\n", " fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n", - " ax.scatter(y_pred*60, test_y*60)\n", + " ax.scatter(y_pred * 60, test_y * 60)\n", " ax.set_title(\"Predicted vs True BPM\")\n", " ax.set_xlabel(\"Predicted BPM\")\n", " ax.set_ylabel(\"True BPM\")\n", - " ax.annotate(f\"R2={rst['rsq']:.2f}\", xy=(0.05, 0.95), xycoords='axes fraction')\n", + " ax.annotate(f\"R2={rst['rsq']:.2f}\", xy=(0.05, 0.95), xycoords=\"axes fraction\")\n", "\n", " fig.tight_layout()\n", " fig.show()\n", - " fig.savefig(params.job_dir / \"bpm_plot.png\")\n" + " fig.savefig(params.job_dir / \"bpm_plot.png\")" ] }, { @@ -581,7 +570,7 @@ "\n", " @staticmethod\n", " def demo(params: hk.HKTaskParams) -> None:\n", - " raise NotImplementedError(\"Demo not implemented\")\n" + " raise NotImplementedError(\"Demo not implemented\")" ] }, { @@ -669,10 +658,7 @@ " preprocesses=[\n", " hk.NamedParams(\n", " name=\"layer_norm\",\n", - " params=dict(\n", - " epsilon=0.01,\n", - " name=\"znorm\"\n", - " ),\n", + " params=dict(epsilon=0.01, name=\"znorm\"),\n", " ),\n", " ],\n", " augmentations=[],\n", @@ -683,14 +669,14 @@ " input_kernel_size=[1, 9],\n", " input_strides=[1, 2],\n", " blocks=[\n", - " {\"filters\": 16, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", - " {\"filters\": 24, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", - " {\"filters\": 32, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", - " {\"filters\": 40, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2}\n", + " {\"filters\": 16, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", + " {\"filters\": 24, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", + " {\"filters\": 32, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", + " {\"filters\": 40, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1, \"se_ratio\": 2},\n", " ],\n", " output_filters=0,\n", " include_top=True,\n", - " use_logits=True\n", + " use_logits=True,\n", " ),\n", " ),\n", ")" @@ -1075,7 +1061,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.5" + "version": "3.11.12" } }, "nbformat": 4, diff --git a/notebooks/ecg-foundation-model.ipynb b/notebooks/ecg-foundation-model.ipynb index 3e973457..355f2320 100644 --- a/notebooks/ecg-foundation-model.ipynb +++ b/notebooks/ecg-foundation-model.ipynb @@ -63,7 +63,8 @@ ], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # 3\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\" # 3\n", "import contextlib\n", "from pathlib import Path\n", "import tempfile\n", @@ -92,26 +93,26 @@ "outputs": [], "source": [ "# File paths\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "job_dir = Path(tempfile.gettempdir()) / \"hk-foundation\"\n", "model_file = job_dir / \"model.keras\"\n", "val_file = job_dir / \"val.pkl\"\n", "\n", "# Data settings\n", - "sampling_rate = 100 # 100 Hz\n", - "input_size = 1000 # 10 seconds\n", - "frame_size = 800 # 8 seconds\n", + "sampling_rate = 100 # 100 Hz\n", + "input_size = 1000 # 10 seconds\n", + "frame_size = 800 # 8 seconds\n", "\n", "# Training settings\n", - "batch_size = 1024 # Batch size for training\n", - "buffer_size = 2000 # How many samples are shuffled each epoch\n", - "epochs = 150 # Increase this to 100+\n", - "steps_per_epoch = 25 # # Steps per epoch (must set since ds has unknown size)\n", + "batch_size = 1024 # Batch size for training\n", + "buffer_size = 2000 # How many samples are shuffled each epoch\n", + "epochs = 150 # Increase this to 100+\n", + "steps_per_epoch = 25 # # Steps per epoch (must set since ds has unknown size)\n", "samples_per_patient = 1 # Number of samples per patient\n", - "val_metric = \"loss\" # Metric to monitor for early stopping\n", - "val_mode = \"min\" # Mode for early stopping\n", - "val_size = 10000 # Number of samples used for validation\n", - "learning_rate = 1e-3 # Learning rate for Adam optimizer\n", + "val_metric = \"loss\" # Metric to monitor for early stopping\n", + "val_mode = \"min\" # Mode for early stopping\n", + "val_size = 10000 # Number of samples used for validation\n", + "learning_rate = 1e-3 # Learning rate for Adam optimizer\n", "epsilon = 0.001\n", "\n", "# Model settings\n", @@ -119,9 +120,9 @@ "temperature = 0.1\n", "\n", "# Other settings\n", - "seed = 42 # Seed for reproducibility\n", - "verbose = 1 # Verbosity level\n", - "plot_theme = hk.utils.dark_theme\n" + "seed = 42 # Seed for reproducibility\n", + "verbose = 1 # Verbosity level\n", + "plot_theme = hk.utils.dark_theme" ] }, { @@ -168,18 +169,8 @@ "outputs": [], "source": [ "datasets = [\n", - " hk.NamedParams(\n", - " name=\"lsad\",\n", - " params=dict(\n", - " path=datasets_dir / \"lsad\"\n", - " )\n", - " ),\n", - " hk.NamedParams(\n", - " name=\"ptbxl\",\n", - " params=dict(\n", - " path=datasets_dir / \"ptbxl\"\n", - " )\n", - " ),\n", + " hk.NamedParams(name=\"lsad\", params=dict(path=datasets_dir / \"lsad\")),\n", + " hk.NamedParams(name=\"ptbxl\", params=dict(path=datasets_dir / \"ptbxl\")),\n", "]" ] }, @@ -197,9 +188,7 @@ "outputs": [], "source": [ "for dataset in datasets:\n", - " ds = hk.DatasetFactory.get(dataset.name)(\n", - " **dataset.params\n", - " )\n", + " ds = hk.DatasetFactory.get(dataset.name)(**dataset.params)\n", " ds.download(force=False)" ] }, @@ -256,7 +245,6 @@ "train_datasets = []\n", "val_datasets = []\n", "for ds in dsets:\n", - "\n", " # Create dataloader specific to dataset\n", " dataloader = hk.tasks.foundation.FoundationTaskFactory.get(ds.name)(\n", " ds=ds,\n", @@ -269,16 +257,12 @@ "\n", " # Create train dataset\n", " train_ds = dataloader.create_dataloader(\n", - " patient_ids=train_patients,\n", - " samples_per_patient=samples_per_patient,\n", - " shuffle=True\n", + " patient_ids=train_patients, samples_per_patient=samples_per_patient, shuffle=True\n", " )\n", "\n", " # Create validation dataset\n", " val_ds = dataloader.create_dataloader(\n", - " patient_ids=val_patients,\n", - " samples_per_patient=samples_per_patient,\n", - " shuffle=False\n", + " patient_ids=val_patients, samples_per_patient=samples_per_patient, shuffle=False\n", " )\n", " train_datasets.append(train_ds)\n", " val_datasets.append(val_ds)\n", @@ -286,7 +270,7 @@ "\n", "# Combine datasets\n", "train_ds = tf.data.Dataset.sample_from_datasets(train_datasets, weights=dset_weights)\n", - "val_ds = tf.data.Dataset.sample_from_datasets(val_datasets, weights=dset_weights)\n" + "val_ds = tf.data.Dataset.sample_from_datasets(val_datasets, weights=dset_weights)" ] }, { @@ -326,7 +310,7 @@ "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", "fig.tight_layout()\n", - "fig.show()\n" + "fig.show()" ] }, { @@ -345,56 +329,32 @@ "outputs": [], "source": [ "nstdb = hk.datasets.nstdb.NstdbNoise(target_rate=sampling_rate)\n", - "noises = np.hstack((nstdb.get_noise(noise_type=\"bw\"), nstdb.get_noise(noise_type=\"ma\"), nstdb.get_noise(noise_type=\"em\")))\n", + "noises = np.hstack(\n", + " (nstdb.get_noise(noise_type=\"bw\"), nstdb.get_noise(noise_type=\"ma\"), nstdb.get_noise(noise_type=\"em\"))\n", + ")\n", "noises = noises.astype(np.float32)\n", "\n", - "preprocessor = nse.layers.preprocessing.LayerNormalization1D(\n", - " epsilon=epsilon,\n", - " name=\"LayerNormalization\"\n", - ")\n", + "preprocessor = nse.layers.preprocessing.LayerNormalization1D(epsilon=epsilon, name=\"LayerNormalization\")\n", "\n", "augmenter = nse.layers.preprocessing.AugmentationPipeline(\n", " layers=[\n", " nse.layers.preprocessing.RandomNoiseDistortion1D(\n", - " sample_rate=sampling_rate,\n", - " amplitude=(0, 1.0),\n", - " frequency=(0.5, 1.5),\n", - " name=\"BaselineWander\"\n", + " sample_rate=sampling_rate, amplitude=(0, 1.0), frequency=(0.5, 1.5), name=\"BaselineWander\"\n", " ),\n", " nse.layers.preprocessing.RandomSineWave(\n", - " sample_rate=sampling_rate,\n", - " amplitude=(0, 0.05),\n", - " frequency=(45, 50),\n", - " name=\"PowerlineNoise\"\n", + " sample_rate=sampling_rate, amplitude=(0, 0.05), frequency=(45, 50), name=\"PowerlineNoise\"\n", " ),\n", " nse.layers.preprocessing.AmplitudeWarp(\n", - " sample_rate=sampling_rate,\n", - " amplitude=(0.9, 1.1),\n", - " frequency=(0.5, 1.5),\n", - " name=\"AmplitudeWarp\"\n", - " ),\n", - " nse.layers.preprocessing.RandomGaussianNoise1D(\n", - " factor=(0.05, 0.2),\n", - " name=\"GaussianNoise\"\n", + " sample_rate=sampling_rate, amplitude=(0.9, 1.1), frequency=(0.5, 1.5), name=\"AmplitudeWarp\"\n", " ),\n", + " nse.layers.preprocessing.RandomGaussianNoise1D(factor=(0.05, 0.2), name=\"GaussianNoise\"),\n", " nse.layers.preprocessing.RandomBackgroundNoises1D(\n", - " noises=noises,\n", - " amplitude=(0.05, 0.2),\n", - " num_noises=2,\n", - " name=\"RandomBackgroundNoises\"\n", + " noises=noises, amplitude=(0.05, 0.2), num_noises=2, name=\"RandomBackgroundNoises\"\n", " ),\n", " nse.layers.preprocessing.RandomCutout1D(\n", - " factor=(0.01, 0.05),\n", - " cutouts=2,\n", - " fill_mode=\"constant\",\n", - " fill_value=0.0,\n", - " name=\"RandomCutout\"\n", + " factor=(0.01, 0.05), cutouts=2, fill_mode=\"constant\", fill_value=0.0, name=\"RandomCutout\"\n", " ),\n", - " nse.layers.preprocessing.RandomCrop1D(\n", - " duration=frame_size,\n", - " name=\"RandomCrop\",\n", - " auto_vectorize=True\n", - " )\n", + " nse.layers.preprocessing.RandomCrop1D(duration=frame_size, name=\"RandomCrop\", auto_vectorize=True),\n", " ],\n", ")" ] @@ -458,41 +418,46 @@ "metadata": {}, "outputs": [], "source": [ - "train_ds = train_ds.shuffle(\n", - " buffer_size=buffer_size,\n", - " reshuffle_each_iteration=True,\n", - ").batch(\n", - " batch_size=batch_size,\n", - " drop_remainder=True,\n", - " num_parallel_calls=tf.data.AUTOTUNE,\n", - ").map(\n", - " lambda x1, x2: {\n", - " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", - " },\n", - " num_parallel_calls=tf.data.AUTOTUNE\n", - ").prefetch(\n", - " tf.data.AUTOTUNE\n", + "train_ds = (\n", + " train_ds.shuffle(\n", + " buffer_size=buffer_size,\n", + " reshuffle_each_iteration=True,\n", + " )\n", + " .batch(\n", + " batch_size=batch_size,\n", + " drop_remainder=True,\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .map(\n", + " lambda x1, x2: {\n", + " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", + " },\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .prefetch(tf.data.AUTOTUNE)\n", ")\n", "\n", - "val_ds = val_ds.batch(\n", - " batch_size=batch_size,\n", - " drop_remainder=True,\n", - " num_parallel_calls=tf.data.AUTOTUNE,\n", - ").map(\n", - " lambda x1, x2: {\n", - " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", - " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", - " },\n", - " num_parallel_calls=tf.data.AUTOTUNE\n", - ").prefetch(\n", - " tf.data.AUTOTUNE\n", + "val_ds = (\n", + " val_ds.batch(\n", + " batch_size=batch_size,\n", + " drop_remainder=True,\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .map(\n", + " lambda x1, x2: {\n", + " nse.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", + " },\n", + " num_parallel_calls=tf.data.AUTOTUNE,\n", + " )\n", + " .prefetch(tf.data.AUTOTUNE)\n", ")\n", "\n", "# Cache the validation dataset\n", - "val_ds = val_ds.take(val_size//batch_size).cache()" + "val_ds = val_ds.take(val_size // batch_size).cache()" ] }, { @@ -512,7 +477,7 @@ "source": [ "inputs = keras.Input(shape=(frame_size, 1), name=\"input\")\n", "\n", - "encoder_params=dict(\n", + "encoder_params = dict(\n", " input_filters=24,\n", " input_kernel_size=(1, 9),\n", " input_strides=(1, 2),\n", @@ -527,11 +492,7 @@ " include_top=True,\n", ")\n", "\n", - "encoder = nse.models.efficientnet.efficientnetv2_from_object(\n", - " x=inputs,\n", - " params=encoder_params,\n", - " num_classes=None\n", - ")\n" + "encoder = nse.models.efficientnet.efficientnetv2_from_object(x=inputs, params=encoder_params, num_classes=None)" ] }, { @@ -662,9 +623,9 @@ } ], "source": [ - "encoder.summary(print_fn=logger.info, layer_range=('input', encoder.layers[10].name))\n", + "encoder.summary(print_fn=logger.info, layer_range=(\"input\", encoder.layers[10].name))\n", "flops = nse.metrics.flops.get_flops(encoder, batch_size=1, fpath=os.devnull)\n", - "logger.info(f\"Computation: {flops/1e6:0.2f} MFLOPs\")\n", + "logger.info(f\"Computation: {flops / 1e6:0.2f} MFLOPs\")\n", "encoder_output = encoder(inputs)" ] }, @@ -732,7 +693,7 @@ "projector = keras.Model(inputs=projector_input, outputs=projector_output, name=\"projector\")\n", "flops = nse.metrics.flops.get_flops(projector, batch_size=1, fpath=os.devnull)\n", "projector.summary(print_fn=logger.info)\n", - "logger.debug(f\"Projector requires {flops/1e6:0.2f} MFLOPS\")" + "logger.debug(f\"Projector requires {flops / 1e6:0.2f} MFLOPS\")" ] }, { @@ -776,6 +737,7 @@ " decay_steps=steps_per_epoch * epochs,\n", " )\n", "\n", + "\n", "optimizer = keras.optimizers.Adam(get_scheduler())\n", "loss = nse.losses.simclr.SimCLRLoss(temperature=temperature)\n", "\n", @@ -790,14 +752,10 @@ " patience=max(int(0.25 * epochs), 1),\n", " mode=val_mode,\n", " restore_best_weights=True,\n", - " verbose=verbose - 1\n", + " verbose=verbose - 1,\n", " ),\n", " keras.callbacks.ModelCheckpoint(\n", - " filepath=str(model_file),\n", - " monitor=f\"val_{val_metric}\",\n", - " save_best_only=True,\n", - " mode=val_mode,\n", - " verbose=verbose - 1\n", + " filepath=str(model_file), monitor=f\"val_{val_metric}\", save_best_only=True, mode=val_mode, verbose=verbose - 1\n", " ),\n", " keras.callbacks.CSVLogger(job_dir / \"history.csv\"),\n", "]\n", @@ -813,7 +771,7 @@ " encoder_optimizer=optimizer,\n", " encoder_loss=loss,\n", " encoder_metrics=metrics,\n", - ")\n" + ")" ] }, { @@ -1312,15 +1270,10 @@ "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", "\n", "# Redirect stdout and stderr to devnull since TFLite converter is very verbose\n", - "with open(os.devnull, 'w') as devnull:\n", + "with open(os.devnull, \"w\") as devnull:\n", " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", " tflite_content = converter.convert(\n", - " test_x=test_x1,\n", - " quantization=\"FP32\",\n", - " io_type=\"float32\",\n", - " mode=\"KERAS\",\n", - " strict=False,\n", - " verbose=verbose\n", + " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", " )" ] }, @@ -1337,14 +1290,12 @@ "metadata": {}, "outputs": [], "source": [ - "converter.export(\n", - " tflite_path=job_dir / \"model.tflite\"\n", - ")\n", + "converter.export(tflite_path=job_dir / \"model.tflite\")\n", "\n", "converter.export_header(\n", " header_path=job_dir / \"model.h\",\n", " name=\"model\",\n", - ")\n" + ")" ] }, { @@ -1527,13 +1478,8 @@ "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", "\n", "tflite_content = converter.convert(\n", - " test_x=test_x1,\n", - " quantization=\"FP32\",\n", - " io_type=\"float32\",\n", - " mode=\"KERAS\",\n", - " strict=False,\n", - " verbose=verbose\n", - ")\n" + " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", + ")" ] }, { diff --git a/notebooks/train-arrhythmia-model.ipynb b/notebooks/train-arrhythmia-model.ipynb index 4af5d849..f7b04305 100644 --- a/notebooks/train-arrhythmia-model.ipynb +++ b/notebooks/train-arrhythmia-model.ipynb @@ -74,20 +74,20 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", "import IPython\n", "import contextlib\n", "from pathlib import Path\n", "import tempfile\n", "import keras\n", "import heartkit as hk\n", - "import neuralspot_edge as nse\n", - "import plotly.io as pio" + "import neuralspot_edge as nse" ] }, { @@ -97,7 +97,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -120,12 +120,14 @@ "metadata": {}, "outputs": [], "source": [ - "datasets = [hk.NamedParams(\n", - " name=\"lsad\",\n", - " params=dict(\n", - " path=datasets_dir / \"lsad\",\n", + "datasets = [\n", + " hk.NamedParams(\n", + " name=\"lsad\",\n", + " params=dict(\n", + " path=datasets_dir / \"lsad\",\n", + " ),\n", " )\n", - ")]" + "]" ] }, { @@ -156,15 +158,10 @@ " hk.tasks.HKRhythm.afib: 2,\n", " hk.tasks.HKRhythm.aflut: 2,\n", " hk.tasks.HKRhythm.stach: 3,\n", - " hk.tasks.HKRhythm.svt: 3\n", + " hk.tasks.HKRhythm.svt: 3,\n", "}\n", "\n", - "class_names=[\n", - " \"SR\",\n", - " \"SB\",\n", - " \"AFIB\",\n", - " \"GSVT\"\n", - "]" + "class_names = [\"SR\", \"SB\", \"AFIB\", \"GSVT\"]" ] }, { @@ -185,9 +182,7 @@ "metadata": {}, "outputs": [], "source": [ - "preprocesses = [\n", - " hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))\n", - "]" + "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))]" ] }, { @@ -217,13 +212,13 @@ " dict(filters=48, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", " dict(filters=64, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", " dict(filters=80, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", - " dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4)\n", + " dict(filters=96, depth=1, kernel_size=(1, 9), strides=(1, 2), ex_ratio=1, se_ratio=4),\n", " ],\n", " output_filters=0,\n", " include_top=True,\n", - " use_logits=True\n", - " )\n", - ")\n" + " use_logits=True,\n", + " ),\n", + ")" ] }, { @@ -290,7 +285,7 @@ " display_report=False,\n", " # Extra arguments\n", " verbose=1,\n", - " seed=42\n", + " seed=42,\n", ")" ] }, @@ -309,7 +304,7 @@ "metadata": {}, "outputs": [], "source": [ - "task = hk.TaskFactory.get('rhythm')" + "task = hk.TaskFactory.get(\"rhythm\")" ] }, { @@ -494,11 +489,9 @@ ], "source": [ "model = nse.models.efficientnet.efficientnetv2_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"),\n", - " params=architecture.params,\n", - " num_classes=len(class_names)\n", + " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture.params, num_classes=len(class_names)\n", ")\n", - "model.summary(layer_range=('inputs', model.layers[10].name))" + "model.summary(layer_range=(\"inputs\", model.layers[10].name))" ] }, { @@ -851,9 +844,9 @@ ], "source": [ "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, 'w') as devnull:\n", + "with open(os.devnull, \"w\") as devnull:\n", " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params=params)\n" + " task.export(params=params)" ] }, { @@ -916,7 +909,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.11.12" } }, "nbformat": 4, diff --git a/notebooks/train-ecg-denoiser.ipynb b/notebooks/train-ecg-denoiser.ipynb index b450b12d..51590975 100644 --- a/notebooks/train-ecg-denoiser.ipynb +++ b/notebooks/train-ecg-denoiser.ipynb @@ -75,7 +75,8 @@ "outputs": [], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", "import contextlib\n", "from pathlib import Path\n", "import tempfile\n", @@ -93,7 +94,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -124,49 +125,22 @@ "metadata": {}, "outputs": [], "source": [ - "preprocesses = [hk.NamedParams(\n", - " name=\"layer_norm\",\n", - " params=dict(\n", - " epsilon=0.01\n", - " )\n", - ")]\n", + "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01))]\n", "\n", - "augmentations = [hk.NamedParams(\n", - " name=\"random_noise_distortion\",\n", - " params=dict(\n", - " amplitude=[0.1, 1.5],\n", - " frequency=[0.5, 1.5],\n", - " name=\"baseline_wander\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_sine_wave\",\n", - " params=dict(\n", - " amplitude=[0, 0.05],\n", - " frequency=[45, 50],\n", - " auto_vectorize=False,\n", - " name=\"powerline_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"amplitude_warp\",\n", - " params=dict(\n", - " amplitude=[0.9, 1.1],\n", - " frequency=[0.5, 1.5],\n", - " name=\"amplitude_warp\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_noise\",\n", - " params=dict(\n", - " factor=[0.1, 0.5],\n", - " name=\"random_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_background_noise\",\n", - " params=dict(\n", - " amplitude=[0.1, 0.5],\n", - " num_noises=2,\n", - " name=\"nstdb\"\n", - " )\n", - ")]\n" + "augmentations = [\n", + " hk.NamedParams(\n", + " name=\"random_noise_distortion\", params=dict(amplitude=[0.1, 1.5], frequency=[0.5, 1.5], name=\"baseline_wander\")\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"random_sine_wave\",\n", + " params=dict(amplitude=[0, 0.05], frequency=[45, 50], auto_vectorize=False, name=\"powerline_noise\"),\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"amplitude_warp\", params=dict(amplitude=[0.9, 1.1], frequency=[0.5, 1.5], name=\"amplitude_warp\")\n", + " ),\n", + " hk.NamedParams(name=\"random_noise\", params=dict(factor=[0.1, 0.5], name=\"random_noise\")),\n", + " hk.NamedParams(name=\"random_background_noise\", params=dict(amplitude=[0.1, 0.5], num_noises=2, name=\"nstdb\")),\n", + "]" ] }, { @@ -185,11 +159,21 @@ "outputs": [], "source": [ "mbconv_blocks = [\n", - " dict(depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=0, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 2), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=40, kernel=(1, 7), dilation=(1, 4), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\")\n", + " dict(\n", + " depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=0, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 1), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 2), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=40, kernel=(1, 7), dilation=(1, 4), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", + " dict(\n", + " depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0, ex_ratio=1, se_ratio=2, norm=\"batch\"\n", + " ),\n", "]\n", "\n", "architecture = dict(\n", @@ -201,9 +185,9 @@ " output_kernel=(1, 7),\n", " include_top=True,\n", " use_logits=True,\n", - " model_name=\"tcn\"\n", - " )\n", - ")\n" + " model_name=\"tcn\",\n", + " ),\n", + ")" ] }, { @@ -236,17 +220,17 @@ " p_multiplier=[0.7, 1.3],\n", " t_multiplier=[0.7, 1.3],\n", " noise_multiplier=[0, 0.01],\n", - " voltage_factor=[800, 1000]\n", - " )\n", - " )\n", + " voltage_factor=[800, 1000],\n", + " ),\n", + " ),\n", " ),\n", " hk.NamedParams(\n", " name=\"ptbxl\",\n", " params=dict(\n", " path=datasets_dir / \"ptbxl\",\n", - " )\n", - " )\n", - "]\n" + " ),\n", + " ),\n", + "]" ] }, { @@ -314,7 +298,7 @@ " display_report=True,\n", " # Extra arguments\n", " verbose=1,\n", - " seed=42\n", + " seed=42,\n", ")" ] }, @@ -380,10 +364,7 @@ } ], "source": [ - "ds = hk.DatasetFactory.get(params.datasets[0].name)(\n", - " cacheable=False,\n", - " **params.datasets[0].params\n", - ")\n", + "ds = hk.DatasetFactory.get(params.datasets[0].name)(cacheable=False, **params.datasets[0].params)\n", "\n", "ds_gen = ds.signal_generator(\n", " patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids()),\n", @@ -400,7 +381,7 @@ "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", "fig.tight_layout()\n", - "fig.show()\n" + "fig.show()" ] }, { @@ -624,11 +605,9 @@ ], "source": [ "model = nse.models.tcn.tcn_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name='inputs'),\n", - " params=architecture[\"params\"],\n", - " num_classes=1\n", + " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture[\"params\"], num_classes=1\n", ")\n", - "model.summary(layer_range=('inputs', model.layers[10].name))" + "model.summary(layer_range=(\"inputs\", model.layers[10].name))" ] }, { @@ -795,7 +774,7 @@ } ], "source": [ - "task.evaluate(params)\n" + "task.evaluate(params)" ] }, { @@ -942,9 +921,9 @@ ], "source": [ "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - "with open(os.devnull, 'w') as devnull:\n", + "with open(os.devnull, \"w\") as devnull:\n", " with contextlib.redirect_stdout(devnull), contextlib.redirect_stderr(devnull):\n", - " task.export(params)\n" + " task.export(params)" ] }, { @@ -980,7 +959,9 @@ ], "source": [ "ecg = next(ds_gen)\n", - "aug_ecg = augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg, (1, -1, 1)))), training=True).numpy().squeeze()\n", + "aug_ecg = (\n", + " augmenter(preprocessor(keras.ops.convert_to_tensor(np.reshape(ecg, (1, -1, 1)))), training=True).numpy().squeeze()\n", + ")\n", "clean_ecg = model.predict(np.reshape(aug_ecg, (1, -1, 1)))\n", "snr = nse.metrics.Snr()\n", "snr.update_state(ecg.reshape(1, -1, 1), aug_ecg.reshape(1, -1, 1))\n", @@ -1016,8 +997,8 @@ "ax[1].set_ylabel(\"Noisy\")\n", "ax[2].set_ylabel(\"Denoised\")\n", "\n", - "ax[1].text(0.98, 0.15, f\"{aug_snr:4.02f} dB SNR\", transform=ax[1].transAxes, ha=\"right\", va=\"top\", weight='bold')\n", - "ax[2].text(0.98, 0.15, f\"{clean_snr:4.02f} dB SNR\", transform=ax[2].transAxes, ha=\"right\", va=\"top\", weight='bold')\n", + "ax[1].text(0.98, 0.15, f\"{aug_snr:4.02f} dB SNR\", transform=ax[1].transAxes, ha=\"right\", va=\"top\", weight=\"bold\")\n", + "ax[2].text(0.98, 0.15, f\"{clean_snr:4.02f} dB SNR\", transform=ax[2].transAxes, ha=\"right\", va=\"top\", weight=\"bold\")\n", "# Disable y-axis ticks for all plots\n", "for axes in ax:\n", " axes.yaxis.set_ticks([])\n", diff --git a/notebooks/train-ecg-segmentation.ipynb b/notebooks/train-ecg-segmentation.ipynb index e688dba0..4177ae29 100644 --- a/notebooks/train-ecg-segmentation.ipynb +++ b/notebooks/train-ecg-segmentation.ipynb @@ -80,12 +80,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", - "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n", + "\n", + "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\"\n", "import IPython\n", "import contextlib\n", "from pathlib import Path\n", @@ -95,8 +96,7 @@ "import physiokit as pk\n", "import numpy as np\n", "import neuralspot_edge as nse\n", - "import matplotlib.pyplot as plt\n", - "import plotly.io as pio\n" + "import matplotlib.pyplot as plt" ] }, { @@ -106,7 +106,7 @@ "outputs": [], "source": [ "# Be sure to set the dataset path to the correct location\n", - "datasets_dir = Path(os.getenv('HK_DATASET_PATH', './datasets'))\n", + "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", "nse.utils.silence_tensorflow()\n", @@ -145,16 +145,16 @@ " p_multiplier=[0.7, 1.3],\n", " t_multiplier=[0.7, 1.3],\n", " noise_multiplier=[0, 0.01],\n", - " voltage_factor=[800, 1000]\n", - " )\n", - " )\n", + " voltage_factor=[800, 1000],\n", + " ),\n", + " ),\n", " ),\n", " hk.NamedParams(\n", " name=\"ludb\",\n", " params=dict(\n", " path=datasets_dir / \"ludb\",\n", - " )\n", - " )\n", + " ),\n", + " ),\n", "]" ] }, @@ -186,15 +186,10 @@ " hk.tasks.HKSegment.qrs: 2,\n", " hk.tasks.HKSegment.twave: 3,\n", " hk.tasks.HKSegment.uwave: 0,\n", - " hk.tasks.HKSegment.noise: 0\n", + " hk.tasks.HKSegment.noise: 0,\n", "}\n", "\n", - "class_names=[\n", - " \"NONE\",\n", - " \"P-WAVE\",\n", - " \"QRS\",\n", - " \"T-WAVE\"\n", - "]" + "class_names = [\"NONE\", \"P-WAVE\", \"QRS\", \"T-WAVE\"]" ] }, { @@ -218,17 +213,56 @@ " input_kernel=(1, 7),\n", " input_norm=\"batch\",\n", " blocks=[\n", - " dict(depth=1, branch=1, filters=16, kernel=(1, 7), dilation=(1, 1), dropout=0.1, ex_ratio=1, se_ratio=0, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=24, kernel=(1, 7), dilation=(1, 2), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=32, kernel=(1, 7), dilation=(1, 4), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\"),\n", - " dict(depth=1, branch=1, filters=48, kernel=(1, 7), dilation=(1, 8), dropout=0.1, ex_ratio=1, se_ratio=2, norm=\"batch\")\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=16,\n", + " kernel=(1, 7),\n", + " dilation=(1, 1),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=0,\n", + " norm=\"batch\",\n", + " ),\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=24,\n", + " kernel=(1, 7),\n", + " dilation=(1, 2),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=2,\n", + " norm=\"batch\",\n", + " ),\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=32,\n", + " kernel=(1, 7),\n", + " dilation=(1, 4),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=2,\n", + " norm=\"batch\",\n", + " ),\n", + " dict(\n", + " depth=1,\n", + " branch=1,\n", + " filters=48,\n", + " kernel=(1, 7),\n", + " dilation=(1, 8),\n", + " dropout=0.1,\n", + " ex_ratio=1,\n", + " se_ratio=2,\n", + " norm=\"batch\",\n", + " ),\n", " ],\n", " output_kernel=(1, 7),\n", " include_top=True,\n", " use_logits=True,\n", - " model_name=\"tcn\"\n", - " )\n", - "\n", + " model_name=\"tcn\",\n", + " ),\n", ")" ] }, @@ -251,13 +285,7 @@ "metadata": {}, "outputs": [], "source": [ - "preprocesses = [hk.NamedParams(\n", - " name=\"layer_norm\",\n", - " params=dict(\n", - " epsilon=0.01,\n", - " name=\"znorm\"\n", - " )\n", - ")]" + "preprocesses = [hk.NamedParams(name=\"layer_norm\", params=dict(epsilon=0.01, name=\"znorm\"))]" ] }, { @@ -281,42 +309,20 @@ "metadata": {}, "outputs": [], "source": [ - "augmentations = [hk.NamedParams(\n", - " name=\"random_noise_distortion\",\n", - " params=dict(\n", - " amplitude=[0, 0.5],\n", - " frequency=[0.5, 1.5],\n", - " name=\"baseline_wander\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_sine_wave\",\n", - " params=dict(\n", - " amplitude=[0, 0.05],\n", - " frequency=[45, 50],\n", - " auto_vectorize=False,\n", - " name=\"powerline_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"amplitude_warp\",\n", - " params=dict(\n", - " amplitude=[0.9, 1.1],\n", - " frequency=[0.5, 1.5],\n", - " name=\"amplitude_warp\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_noise\",\n", - " params=dict(\n", - " factor=[0, 0.025],\n", - " name=\"random_noise\"\n", - " )\n", - "), hk.NamedParams(\n", - " name=\"random_background_noise\",\n", - " params=dict(\n", - " amplitude=[0, 0.025],\n", - " num_noises=1,\n", - " name=\"nstdb\"\n", - " )\n", - ")]\n" + "augmentations = [\n", + " hk.NamedParams(\n", + " name=\"random_noise_distortion\", params=dict(amplitude=[0, 0.5], frequency=[0.5, 1.5], name=\"baseline_wander\")\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"random_sine_wave\",\n", + " params=dict(amplitude=[0, 0.05], frequency=[45, 50], auto_vectorize=False, name=\"powerline_noise\"),\n", + " ),\n", + " hk.NamedParams(\n", + " name=\"amplitude_warp\", params=dict(amplitude=[0.9, 1.1], frequency=[0.5, 1.5], name=\"amplitude_warp\")\n", + " ),\n", + " hk.NamedParams(name=\"random_noise\", params=dict(factor=[0, 0.025], name=\"random_noise\")),\n", + " hk.NamedParams(name=\"random_background_noise\", params=dict(amplitude=[0, 0.025], num_noises=1, name=\"nstdb\")),\n", + "]" ] }, { @@ -384,7 +390,7 @@ " display_report=False,\n", " # Extra arguments\n", " verbose=1,\n", - " seed=42\n", + " seed=42,\n", ")" ] }, @@ -456,7 +462,7 @@ " heart_rate=60,\n", " leads=1,\n", " preset=pk.ecg.EcgPreset.SR,\n", - " noise_multiplier=0.0\n", + " noise_multiplier=0.0,\n", ")\n", "ecg = ecg.squeeze()\n", "segs = segs.squeeze()\n", @@ -477,7 +483,7 @@ "plt.title(\"Synthetic ECG w/ Segments\")\n", "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -553,7 +559,7 @@ "plt.title(\"Synthetic ECG w/ Noise\")\n", "ax.set_xlabel(\"Time (s)\")\n", "ax.set_ylabel(\"Amplitude\")\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -704,11 +710,9 @@ ], "source": [ "model = nse.models.tcn.tcn_from_object(\n", - " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"),\n", - " params=architecture.params,\n", - " num_classes=len(class_names)\n", + " x=keras.Input(shape=(params.frame_size, 1), name=\"inputs\"), params=architecture.params, num_classes=len(class_names)\n", ")\n", - "model.summary(layer_range=('inputs', model.layers[10].name))" + "model.summary(layer_range=(\"inputs\", model.layers[10].name))" ] }, { @@ -1059,9 +1063,9 @@ ], "source": [ "# TF dumps a lot of info to stdout, so we redirect it to /dev/null\n", - 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