diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 1fb6420d..b1d4698c 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -46,6 +46,7 @@ "customizations": { "vscode": { "extensions": [ + "openai.chatgpt", "GitHub.copilot", "GitHub.copilot-chat", "ms-toolsai.jupyter", diff --git a/.github/workflows/release-please-lock.yaml b/.github/workflows/release-please-lock.yaml new file mode 100644 index 00000000..11bc4fe9 --- /dev/null +++ b/.github/workflows/release-please-lock.yaml @@ -0,0 +1,39 @@ +name: Sync uv.lock for release PRs + +on: + pull_request: + types: + - opened + - synchronize + - ready_for_review + - reopened + +jobs: + update-uv-lock: + if: startsWith(github.head_ref, 'release-please--') + runs-on: ubuntu-latest + permissions: + contents: write + steps: + - name: Checkout release PR branch + uses: actions/checkout@v4 + with: + ref: ${{ github.head_ref }} + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + enable-cache: true + - name: Regenerate uv.lock + run: uv lock + - name: Commit updated lockfile + if: ${{ !cancelled() }} + run: | + if git diff --quiet -- uv.lock; then + echo "uv.lock already up to date." + exit 0 + fi + git config user.name "github-actions[bot]" + git config user.email "41898282+github-actions[bot]@users.noreply.github.com" + git add uv.lock + git commit -m "chore: sync uv.lock after version bump" + git push diff --git a/.github/workflows/release-please.yaml b/.github/workflows/release-please.yaml new file mode 100644 index 00000000..505d376f --- /dev/null +++ b/.github/workflows/release-please.yaml @@ -0,0 +1,20 @@ +name: Release Please + +on: + push: + branches: + - main + workflow_dispatch: + +jobs: + release-please: + runs-on: ubuntu-latest + permissions: + contents: write + pull-requests: write + issues: write + steps: + - uses: googleapis/release-please-action@v4 + with: + config-file: release-please-config.json + manifest-file: .release-please-manifest.json diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 51460b86..cd2b1fcf 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -1,9 +1,11 @@ -name: Release to PyPi +name: Release to PyPI on: release: - types: - - created + types: [published] + push: + tags: + - "v*" workflow_dispatch: jobs: diff --git a/.release-please-manifest.json b/.release-please-manifest.json new file mode 100644 index 00000000..0d1bebe1 --- /dev/null +++ b/.release-please-manifest.json @@ -0,0 +1,3 @@ +{ + ".": "1.6.0" +} diff --git a/README.md b/README.md index f932144d..91bd3809 100644 --- a/README.md +++ b/README.md @@ -100,15 +100,15 @@ __HeartKit__ exposes several open-source datasets for training each of the Heart __HeartKit__ provides a __model factory__ that allows you to easily create and train customized models. The model factory includes a number of modern networks well suited for efficient, real-time edge applications. Each model architecture exposes a number of high-level parameters that can be used to customize the network for a given application. These parameters can be set as part of the configuration accessible via the CLI and Python package. -- **[TCN](https://ambiqai.github.io/neuralspot-edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) -- **[U-Net](https://ambiqai.github.io/neuralspot-edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) -- **[U-NeXt](https://ambiqai.github.io/neuralspot-edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) -- **[EfficientNetV2](https://ambiqai.github.io/neuralspot-edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) -- **[MobileOne](https://ambiqai.github.io/neuralspot-edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) -- **[ResNet](https://ambiqai.github.io/neuralspot-edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) -- **[Conformer](https://ambiqai.github.io/neuralspot-edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) -- **[MetaFormer](https://ambiqai.github.io/neuralspot-edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) -- **[TSMixer](https://ambiqai.github.io/neuralspot-edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) +- **[TCN](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) +- **[U-Net](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) +- **[U-NeXt](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) +- **[EfficientNetV2](https://ambiqai.github.io/helia-edge/api/helia_edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) +- **[MobileOne](https://ambiqai.github.io/helia-edge/api/helia_edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) +- **[ResNet](https://ambiqai.github.io/helia-edge/api/helia_edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) +- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) +- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) +- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) - **[Bring-Your-Own-Model (BYOM)](https://ambiqai.github.io/heartkit/models/byom)**: Register new SoTA model architectures w/ custom configurations --- diff --git a/docs/assets/heartkit-banner.png b/docs/assets/heartkit-banner.png index 9f576f21..4e8247ee 100644 Binary files a/docs/assets/heartkit-banner.png and b/docs/assets/heartkit-banner.png differ diff --git a/docs/css/custom.css b/docs/css/custom.css index e8f701d9..2b3c6a78 100644 --- a/docs/css/custom.css +++ b/docs/css/custom.css @@ -38,7 +38,7 @@ a.internal-link::after { } .md-grid { - max-width: 1440px; + max-width: 1840px; } /* Give space to lower icons so Gitter chat doesn't get on top of them */ diff --git a/docs/datasets/icentia11k.md b/docs/datasets/icentia11k.md index a8f85a72..f31df5c9 100644 --- a/docs/datasets/icentia11k.md +++ b/docs/datasets/icentia11k.md @@ -12,7 +12,7 @@ More info available on [PhysioNet website](https://physionet.org/content/icentia ```py linenums="1" from pathlib import Path - import neuralspot_edge as nse + import helia_edge as helia import heartkit as hk ds = hk.DatasetFactory.get('icentia11k')( @@ -24,7 +24,7 @@ More info available on [PhysioNet website](https://physionet.org/content/icentia # Create signal generator data_gen = self.ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), + patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), frame_size=256, samples_per_patient=5, target_rate=100, diff --git a/docs/datasets/lsad.md b/docs/datasets/lsad.md index 44b70c71..5aeb4495 100644 --- a/docs/datasets/lsad.md +++ b/docs/datasets/lsad.md @@ -12,7 +12,7 @@ Please visit [Physionet](https://physionet.org/content/ecg-arrhythmia/1.0.0/) fo ```py linenums="1" from pathlib import Path - import neuralspot_edge as nse + import helia_edge as helia import heartkit as hk ds = hk.DatasetFactory.get('lsad')( @@ -24,7 +24,7 @@ Please visit [Physionet](https://physionet.org/content/ecg-arrhythmia/1.0.0/) fo # Create signal generator data_gen = self.ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), + patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), frame_size=256, samples_per_patient=5, target_rate=100, diff --git a/docs/datasets/ludb.md b/docs/datasets/ludb.md index e6885450..927cd8d2 100644 --- a/docs/datasets/ludb.md +++ b/docs/datasets/ludb.md @@ -12,7 +12,7 @@ Please visit [Physionet](https://physionet.org/content/ludb/1.0.1/) for more det ```py linenums="1" from pathlib import Path - import neuralspot_edge as nse + import helia_edge as helia import heartkit as hk ds = hk.DatasetFactory.get('ludb')( @@ -24,7 +24,7 @@ Please visit [Physionet](https://physionet.org/content/ludb/1.0.1/) for more det # Create signal generator data_gen = self.ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), + patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), frame_size=256, samples_per_patient=5, target_rate=100, diff --git a/docs/datasets/ptbxl.md b/docs/datasets/ptbxl.md index 22fea239..2188eb51 100644 --- a/docs/datasets/ptbxl.md +++ b/docs/datasets/ptbxl.md @@ -12,7 +12,7 @@ Please visit [Physionet](https://physionet.org/content/ptb-xl/1.0.3/) for more d ```py linenums="1" from pathlib import Path - import neuralspot_edge as nse + import helia_edge as helia import heartkit as hk ds = hk.DatasetFactory.get('ptbxl')( @@ -24,7 +24,7 @@ Please visit [Physionet](https://physionet.org/content/ptb-xl/1.0.3/) for more d # Create signal generator data_gen = self.ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), + patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), frame_size=256, samples_per_patient=5, target_rate=100, diff --git a/docs/datasets/qtdb.md b/docs/datasets/qtdb.md index 4e79d806..050c9152 100644 --- a/docs/datasets/qtdb.md +++ b/docs/datasets/qtdb.md @@ -10,7 +10,7 @@ Please visit [Physionet](https://doi.org/10.13026/C24K53) for more details. ```py linenums="1" from pathlib import Path - import neuralspot_edge as nse + import helia_edge as helia import heartkit as hk ds = hk.DatasetFactory.get('qtdb')( @@ -22,7 +22,7 @@ Please visit [Physionet](https://doi.org/10.13026/C24K53) for more details. # Create signal generator data_gen = self.ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), + patient_generator=helia.utils.uniform_id_generator(ds.patient_ids, repeat=True, shuffle=True), frame_size=256, samples_per_patient=5, target_rate=100, diff --git a/docs/guides/byot.ipynb b/docs/guides/byot.ipynb index 2ef2a16d..9ec05f58 100644 --- a/docs/guides/byot.ipynb +++ b/docs/guides/byot.ipynb @@ -76,7 +76,7 @@ "import tensorflow as tf\n", "import numpy as np\n", "import numpy.typing as npt\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt" ] }, @@ -90,7 +90,7 @@ "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", + "helia.utils.silence_tensorflow()\n", "_ = hk.utils.setup_plotting(plot_theme)" ] }, @@ -163,7 +163,7 @@ " ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]:\n", " if isinstance(samples_per_patient, Iterable):\n", " samples_per_patient = samples_per_patient[0]\n", - " for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n", + " for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n", " for x, y in self.patient_data_generator(pt_id, samples_per_patient):\n", " yield x, y\n", " # END FOR\n", @@ -242,7 +242,7 @@ "metadata": {}, "outputs": [], "source": [ - "DataloaderFactory = nse.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n", + "DataloaderFactory = helia.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n", "DataloaderFactory.register(\"ptbxl\", PtbxlDataloader)" ] }, @@ -326,14 +326,14 @@ "\n", "def load_train_datasets(\n", " datasets: list[hk.HKDataset],\n", - " dataloaderFactory: nse.utils.ItemFactory[hk.HKDataloader],\n", + " dataloaderFactory: helia.utils.ItemFactory[hk.HKDataloader],\n", " params: hk.HKTaskParams,\n", ") -> tuple[tf.data.Dataset, tf.data.Dataset]:\n", " \"\"\"Loads training and validation datasets.\n", "\n", " Args:\n", " datasets(list[hk.HKDataset]): List of datasets to load.\n", - " dataloaderFactory(nse.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n", + " dataloaderFactory(helia.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n", " params(hk.HKTaskParams): Task parameters.\n", "\n", " Returns:\n", @@ -392,10 +392,10 @@ " \"\"\"\n", " os.makedirs(params.job_dir, exist_ok=True)\n", "\n", - " logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n", + " logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n", " logger.debug(f\"Creating working directory in {params.job_dir}\")\n", "\n", - " params.seed = nse.utils.set_random_seed(params.seed)\n", + " params.seed = helia.utils.set_random_seed(params.seed)\n", " logger.debug(f\"Random seed {params.seed}\")\n", "\n", " with open(params.job_dir / \"train_config.json\", \"w\", encoding=\"utf-8\") as fp:\n", @@ -416,7 +416,7 @@ " # Load existing model\n", " if params.resume and params.model_file:\n", " logger.debug(f\"Loading model from file {params.model_file}\")\n", - " model = nse.models.load_model(params.model_file)\n", + " model = helia.models.load_model(params.model_file)\n", " params.model_file = None\n", " else:\n", " logger.debug(\"Creating model from scratch\")\n", @@ -429,7 +429,7 @@ " )\n", " # END IF\n", "\n", - " flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n", + " flops = helia.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n", "\n", " t_mul = 1\n", " first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1)\n", @@ -482,7 +482,7 @@ " )\n", " logger.debug(f\"Model saved to {params.model_file}\")\n", "\n", - " nse.plotting.plot_history_metrics(\n", + " helia.plotting.plot_history_metrics(\n", " history.history,\n", " metrics=[\"loss\", metrics[0].name],\n", " save_path=params.job_dir / \"history.png\",\n", @@ -509,10 +509,10 @@ " params (HKTaskParams): Evaluation parameters\n", " \"\"\"\n", " os.makedirs(params.job_dir, exist_ok=True)\n", - " logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n", + " logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n", " logger.debug(f\"Creating working directory in {params.job_dir}\")\n", "\n", - " params.seed = nse.utils.set_random_seed(params.seed)\n", + " params.seed = helia.utils.set_random_seed(params.seed)\n", " logger.debug(f\"Random seed {params.seed}\")\n", "\n", " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n", @@ -522,7 +522,7 @@ " test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()])\n", "\n", " logger.debug(\"Loading model\")\n", - " model = nse.models.load_model(params.model_file)\n", + " model = helia.models.load_model(params.model_file)\n", "\n", " logger.debug(\"Performing inference\")\n", " rst = model.evaluate(test_ds, verbose=params.verbose, return_dict=True)\n", diff --git a/docs/guides/ecg-foundation-model.ipynb b/docs/guides/ecg-foundation-model.ipynb index 355f2320..ca5dde91 100644 --- a/docs/guides/ecg-foundation-model.ipynb +++ b/docs/guides/ecg-foundation-model.ipynb @@ -72,7 +72,7 @@ "import heartkit as hk\n", "import tensorflow as tf\n", "import numpy as np\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt\n", "from sklearn.manifold import TSNE" ] @@ -145,9 +145,9 @@ } ], "source": [ - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__, level=verbose)\n", + "logger = helia.utils.setup_logger(__name__, level=verbose)\n", "\n", "os.makedirs(job_dir, exist_ok=True)\n", "logger.info(f\"Job directory: {job_dir}\")" @@ -334,27 +334,27 @@ ")\n", "noises = noises.astype(np.float32)\n", "\n", - "preprocessor = nse.layers.preprocessing.LayerNormalization1D(epsilon=epsilon, name=\"LayerNormalization\")\n", + "preprocessor = helia.layers.preprocessing.LayerNormalization1D(epsilon=epsilon, name=\"LayerNormalization\")\n", "\n", - "augmenter = nse.layers.preprocessing.AugmentationPipeline(\n", + "augmenter = helia.layers.preprocessing.AugmentationPipeline(\n", " layers=[\n", - " nse.layers.preprocessing.RandomNoiseDistortion1D(\n", + " helia.layers.preprocessing.RandomNoiseDistortion1D(\n", " sample_rate=sampling_rate, amplitude=(0, 1.0), frequency=(0.5, 1.5), name=\"BaselineWander\"\n", " ),\n", - " nse.layers.preprocessing.RandomSineWave(\n", + " helia.layers.preprocessing.RandomSineWave(\n", " sample_rate=sampling_rate, amplitude=(0, 0.05), frequency=(45, 50), name=\"PowerlineNoise\"\n", " ),\n", - " nse.layers.preprocessing.AmplitudeWarp(\n", + " helia.layers.preprocessing.AmplitudeWarp(\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", + " helia.layers.preprocessing.RandomGaussianNoise1D(factor=(0.05, 0.2), name=\"GaussianNoise\"),\n", + " helia.layers.preprocessing.RandomBackgroundNoises1D(\n", " noises=noises, amplitude=(0.05, 0.2), num_noises=2, name=\"RandomBackgroundNoises\"\n", " ),\n", - " nse.layers.preprocessing.RandomCutout1D(\n", + " helia.layers.preprocessing.RandomCutout1D(\n", " factor=(0.01, 0.05), cutouts=2, fill_mode=\"constant\", fill_value=0.0, name=\"RandomCutout\"\n", " ),\n", - " nse.layers.preprocessing.RandomCrop1D(duration=frame_size, name=\"RandomCrop\", auto_vectorize=True),\n", + " helia.layers.preprocessing.RandomCrop1D(duration=frame_size, name=\"RandomCrop\", auto_vectorize=True),\n", " ],\n", ")" ] @@ -430,9 +430,9 @@ " )\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", + " helia.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", " },\n", " num_parallel_calls=tf.data.AUTOTUNE,\n", " )\n", @@ -447,9 +447,9 @@ " )\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", + " helia.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", " },\n", " num_parallel_calls=tf.data.AUTOTUNE,\n", " )\n", @@ -492,7 +492,7 @@ " include_top=True,\n", ")\n", "\n", - "encoder = nse.models.efficientnet.efficientnetv2_from_object(x=inputs, params=encoder_params, num_classes=None)" + "encoder = helia.models.efficientnet.efficientnetv2_from_object(x=inputs, params=encoder_params, num_classes=None)" ] }, { @@ -624,7 +624,7 @@ ], "source": [ "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", + "flops = helia.metrics.flops.get_flops(encoder, batch_size=1, fpath=os.devnull)\n", "logger.info(f\"Computation: {flops / 1e6:0.2f} MFLOPs\")\n", "encoder_output = encoder(inputs)" ] @@ -691,7 +691,7 @@ "projector_output = keras.layers.Dense(projection_width, activation=\"relu6\")(projector_input)\n", "projector_output = keras.layers.Dense(projection_width)(projector_output)\n", "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", + "flops = helia.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\")" ] @@ -709,7 +709,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = nse.trainers.SimCLRTrainer(\n", + "model = helia.trainers.SimCLRTrainer(\n", " encoder=encoder,\n", " augmenter=None, # We augment in the data pipeline\n", " projector=projector,\n", @@ -739,7 +739,7 @@ "\n", "\n", "optimizer = keras.optimizers.Adam(get_scheduler())\n", - "loss = nse.losses.simclr.SimCLRLoss(temperature=temperature)\n", + "loss = helia.losses.simclr.SimCLRLoss(temperature=temperature)\n", "\n", "metrics = [\n", " keras.metrics.MeanSquaredError(name=\"mse\"),\n", @@ -759,7 +759,7 @@ " ),\n", " keras.callbacks.CSVLogger(job_dir / \"history.csv\"),\n", "]\n", - "if nse.utils.env_flag(\"TENSORBOARD\"):\n", + "if helia.utils.env_flag(\"TENSORBOARD\"):\n", " model_callbacks.append(\n", " keras.callbacks.TensorBoard(\n", " log_dir=job_dir,\n", @@ -1160,7 +1160,7 @@ } ], "source": [ - "fig, _ = nse.plotting.plot_history_metrics(\n", + "fig, _ = helia.plotting.plot_history_metrics(\n", " history.history,\n", " metrics=[\"loss\", \"cos\"],\n", " title=\"Training History\",\n", @@ -1190,8 +1190,8 @@ "# Convert validation dataset to numpy arrays\n", "test_x1, test_x2 = [], []\n", "for inputs in val_ds.as_numpy_iterator():\n", - " test_x1.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_0])\n", - " test_x2.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_1])\n", + " test_x1.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_0])\n", + " test_x2.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_1])\n", "test_x1 = np.concatenate(test_x1)\n", "test_x2 = np.concatenate(test_x2)" ] @@ -1235,7 +1235,7 @@ } ], "source": [ - "rst = nse.metrics.compute_metrics(metrics, test_y1, test_y2)\n", + "rst = helia.metrics.compute_metrics(metrics, test_y1, test_y2)\n", "logger.info(\"[VAL SET] \" + \", \".join([f\"{k.upper()}={v:.4f}\" for k, v in rst.items()]))" ] }, @@ -1267,7 +1267,7 @@ } ], "source": [ - "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", + "converter = helia.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", @@ -1321,7 +1321,7 @@ } ], "source": [ - "tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", + "tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", "tflite.compile()" ] }, @@ -1475,7 +1475,7 @@ } ], "source": [ - "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", + "converter = helia.converters.tflite.TfLiteKerasConverter(model=encoder)\n", "\n", "tflite_content = converter.convert(\n", " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", @@ -1488,7 +1488,7 @@ "metadata": {}, "outputs": [], "source": [ - "tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", + "tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", "tflite.compile()" ] }, @@ -1554,8 +1554,8 @@ } ], "source": [ - "tf_rst = nse.metrics.compute_metrics(metrics, y1_pred_tf, y2_pred_tf)\n", - "tfl_rst = nse.metrics.compute_metrics(metrics, y1_pred_tfl, y2_pred_tfl)\n", + "tf_rst = helia.metrics.compute_metrics(metrics, y1_pred_tf, y2_pred_tf)\n", + "tfl_rst = helia.metrics.compute_metrics(metrics, y1_pred_tfl, y2_pred_tfl)\n", "logger.info(\"[TF METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tf_rst.items()]))\n", "logger.info(\"[TFL METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tfl_rst.items()]))" ] diff --git a/docs/guides/train-arrhythmia-model.ipynb b/docs/guides/train-arrhythmia-model.ipynb index f7b04305..f3cc56ce 100644 --- a/docs/guides/train-arrhythmia-model.ipynb +++ b/docs/guides/train-arrhythmia-model.ipynb @@ -87,7 +87,7 @@ "import tempfile\n", "import keras\n", "import heartkit as hk\n", - "import neuralspot_edge as nse" + "import helia_edge as helia" ] }, { @@ -100,9 +100,9 @@ "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__)" + "logger = helia.utils.setup_logger(__name__)" ] }, { @@ -488,7 +488,7 @@ } ], "source": [ - "model = nse.models.efficientnet.efficientnetv2_from_object(\n", + "model = helia.models.efficientnet.efficientnetv2_from_object(\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))" diff --git a/docs/guides/train-ecg-denoiser.ipynb b/docs/guides/train-ecg-denoiser.ipynb index 51590975..d53e5495 100644 --- a/docs/guides/train-ecg-denoiser.ipynb +++ b/docs/guides/train-ecg-denoiser.ipynb @@ -83,7 +83,7 @@ "import keras\n", "import heartkit as hk\n", "import numpy as np\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt" ] }, @@ -97,9 +97,9 @@ "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__)" + "logger = helia.utils.setup_logger(__name__)" ] }, { @@ -110,7 +110,7 @@ "\n", "Since our goal is to denoise ECG signals, we need to create an augmentation pipeline to generate noisy samples. \n", "\n", - "We will leverage `neuralspot-edge` preprocessing layers to create the following augmentations:\n", + "We will leverage `helia-edge` preprocessing layers to create the following augmentations:\n", "\n", "* Baseline wander: Simulate baseline wander by adding a low frequency sine signal\n", "* Powerline noise: Simulate powerline noise by adding a 50 Hz sinusoidal signal \n", @@ -367,7 +367,7 @@ "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", + " patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids()),\n", " frame_size=params.frame_size,\n", " samples_per_patient=params.samples_per_patient,\n", " target_rate=params.sampling_rate,\n", @@ -604,7 +604,7 @@ } ], "source": [ - "model = nse.models.tcn.tcn_from_object(\n", + "model = helia.models.tcn.tcn_from_object(\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))" @@ -941,7 +941,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = nse.models.load_model(params.model_file)" + "model = helia.models.load_model(params.model_file)" ] }, { @@ -963,7 +963,7 @@ " 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 = helia.metrics.Snr()\n", "snr.update_state(ecg.reshape(1, -1, 1), aug_ecg.reshape(1, -1, 1))\n", "aug_snr = snr.result().numpy()\n", "snr.reset_state()\n", diff --git a/docs/guides/train-ecg-segmentation.ipynb b/docs/guides/train-ecg-segmentation.ipynb index 4177ae29..3a396cdc 100644 --- a/docs/guides/train-ecg-segmentation.ipynb +++ b/docs/guides/train-ecg-segmentation.ipynb @@ -95,7 +95,7 @@ "import heartkit as hk\n", "import physiokit as pk\n", "import numpy as np\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt" ] }, @@ -109,9 +109,9 @@ "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__)" + "logger = helia.utils.setup_logger(__name__)" ] }, { @@ -709,7 +709,7 @@ } ], "source": [ - "model = nse.models.tcn.tcn_from_object(\n", + "model = helia.models.tcn.tcn_from_object(\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))" diff --git a/docs/index.md b/docs/index.md index d65300d9..0e78b887 100644 --- a/docs/index.md +++ b/docs/index.md @@ -113,17 +113,17 @@ The ADK includes several built-in [datasets](./datasets/index.md) for training _ ## [Models](./models/index.md) -__HeartKit__ provides a variety of model architectures geared towards efficient, real-time edge applications. These models are provided by Ambiq's [neuralspot-edge](https://ambiqai.github.io/neuralspot-edge/) and expose a set of parameters that can be used to fully customize the network for a given application. In addition, HeartKit includes a model factory, [ModelFactory](./models/index.md#model-factory), to register current models as well as allow new custom architectures to be added. Check out the [Models Guide](./models/index.md) to learn more about the available network architectures and model factory. - -- **[TCN](https://ambiqai.github.io/neuralspot-edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) -- **[U-Net](https://ambiqai.github.io/neuralspot-edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) -- **[U-NeXt](https://ambiqai.github.io/neuralspot-edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) -- **[EfficientNetV2](https://ambiqai.github.io/neuralspot-edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) -- **[MobileOne](https://ambiqai.github.io/neuralspot-edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) -- **[ResNet](https://ambiqai.github.io/neuralspot-edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) -- **[Conformer](https://ambiqai.github.io/neuralspot-edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) -- **[MetaFormer](https://ambiqai.github.io/neuralspot-edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) -- **[TSMixer](https://ambiqai.github.io/neuralspot-edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) +__HeartKit__ provides a variety of model architectures geared towards efficient, real-time edge applications. These models are provided by Ambiq's [helia-edge](https://ambiqai.github.io/helia-edge/) and expose a set of parameters that can be used to fully customize the network for a given application. In addition, HeartKit includes a model factory, [ModelFactory](./models/index.md#model-factory), to register current models as well as allow new custom architectures to be added. Check out the [Models Guide](./models/index.md) to learn more about the available network architectures and model factory. + +- **[TCN](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) +- **[U-Net](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) +- **[U-NeXt](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) +- **[EfficientNetV2](https://ambiqai.github.io/helia-edge/api/helia_edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) +- **[MobileOne](https://ambiqai.github.io/helia-edge/api/helia_edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) +- **[ResNet](https://ambiqai.github.io/helia-edge/api/helia_edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) +- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) +- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) +- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) - **[Bring-Your-Own-Model (BYOM)](./models/byom.md)**: Register new SoTA model architectures w/ custom configurations --- diff --git a/docs/models/index.md b/docs/models/index.md index edabbfa6..2cce74a0 100644 --- a/docs/models/index.md +++ b/docs/models/index.md @@ -1,20 +1,20 @@ # :material-graph-outline: Models -HeartKit provides a number of model architectures that can be used for training __heart-monitoring tasks__. While a number of off-the-shelf models exist, they are often not efficient nor optimized for real-time, edge applications. To address this, HeartKit provides a model factory that allows you to easily create and train customized models via [neuralspot-edge](https://ambiqai.github.io/neuralspot-edge/). `neuralspot-edge` includes a growing number of state-of-the-art models that can be easily configured and trained using high-level parameters. The models are designed to be efficient and well-suited for real-time, edge applications. Most of the models are based on state-of-the-art architectures that have been modified to allow for more fine-grain customization. In addition, the models support 1D variants to allow for training on time-series data. Please check [neuralspot-edge](https://ambiqai.github.io/neuralspot-edge/) for list of available models and their configurations. +HeartKit provides a number of model architectures that can be used for training __heart-monitoring tasks__. While a number of off-the-shelf models exist, they are often not efficient nor optimized for real-time, edge applications. To address this, HeartKit provides a model factory that allows you to easily create and train customized models via [helia-edge](https://ambiqai.github.io/helia-edge/). `helia-edge` includes a growing number of state-of-the-art models that can be easily configured and trained using high-level parameters. The models are designed to be efficient and well-suited for real-time, edge applications. Most of the models are based on state-of-the-art architectures that have been modified to allow for more fine-grain customization. In addition, the models support 1D variants to allow for training on time-series data. Please check [helia-edge](https://ambiqai.github.io/helia-edge/) for list of available models and their configurations. --- ## Available Models -- **[TCN](https://ambiqai.github.io/neuralspot-edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) -- **[U-Net](https://ambiqai.github.io/neuralspot-edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) -- **[U-NeXt](https://ambiqai.github.io/neuralspot-edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) -- **[EfficientNetV2](https://ambiqai.github.io/neuralspot-edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) -- **[MobileOne](https://ambiqai.github.io/neuralspot-edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) -- **[ResNet](https://ambiqai.github.io/neuralspot-edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) -- **[Conformer](https://ambiqai.github.io/neuralspot-edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) -- **[MetaFormer](https://ambiqai.github.io/neuralspot-edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) -- **[TSMixer](https://ambiqai.github.io/neuralspot-edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) +- **[TCN](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) +- **[U-Net](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) +- **[U-NeXt](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) +- **[EfficientNetV2](https://ambiqai.github.io/helia-edge/api/helia_edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) +- **[MobileOne](https://ambiqai.github.io/helia-edge/api/helia_edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) +- **[ResNet](https://ambiqai.github.io/helia-edge/api/helia_edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) +- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) +- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) +- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) * **[Bring-Your-Own-Model](./byom.md)**: Add a custom model architecture to HeartKit. --- diff --git a/docs/usage/python.md b/docs/usage/python.md index 7344155f..4cb7dc02 100644 --- a/docs/usage/python.md +++ b/docs/usage/python.md @@ -28,7 +28,7 @@ ds = hk.DatasetFactory.get('ecg-synthetic')(num_pts=100) ### [Models](../models/index.md) -Lastly, HeartKit leverages [neuralspot-edge's](https://ambiqai.github.io/neuralspot-edge/) customizable model architectures. To enable creating custom network topologies from configuration files, HeartKit provides a `ModelFactory` that allows you to create models by specifying the model key and the model parameters. Each item in the factory is a callable that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`. +Lastly, HeartKit leverages [helia-edge's](https://ambiqai.github.io/helia-edge/) customizable model architectures. To enable creating custom network topologies from configuration files, HeartKit provides a `ModelFactory` that allows you to create models by specifying the model key and the model parameters. Each item in the factory is a callable that takes a `keras.Input`, model parameters, and number of classes as arguments and returns a `keras.Model`. ``` import keras diff --git a/heartkit/__init__.py b/heartkit/__init__.py index 9b4a5b8d..99c4ca98 100644 --- a/heartkit/__init__.py +++ b/heartkit/__init__.py @@ -22,10 +22,10 @@ from .models import ModelFactory from .tasks import HKBeat, HKRhythm, HKSegment, HKTask, TaskFactory from .backends import BackendFactory -import neuralspot_edge as nse +import helia_edge as helia __version__ = version(__name__) if "TF_CPP_MIN_LOG_LEVEL" not in os.environ: os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" -nse.utils.setup_logger(__name__) +helia.utils.setup_logger(__name__) diff --git a/heartkit/backends/__init__.py b/heartkit/backends/__init__.py index 10a7a4f3..0d1cc900 100644 --- a/heartkit/backends/__init__.py +++ b/heartkit/backends/__init__.py @@ -10,7 +10,7 @@ """ -import neuralspot_edge as nse +import helia_edge as helia from . import backend, utils, evb, pc @@ -18,7 +18,7 @@ from .evb import EvbBackend from .pc import PcBackend -BackendFactory = nse.utils.create_factory("HKDemoBackend", HKInferenceBackend) +BackendFactory = helia.utils.create_factory("HKDemoBackend", HKInferenceBackend) BackendFactory.register("pc", PcBackend) BackendFactory.register("evb", EvbBackend) diff --git a/heartkit/backends/backend.py b/heartkit/backends/backend.py index 4b7ec9f8..1e418699 100644 --- a/heartkit/backends/backend.py +++ b/heartkit/backends/backend.py @@ -1,11 +1,11 @@ import abc -import neuralspot_edge as nse +import helia_edge as helia import numpy.typing as npt from ..defines import HKTaskParams -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class HKInferenceBackend(abc.ABC): diff --git a/heartkit/backends/evb.py b/heartkit/backends/evb.py index 97041e34..161dbf11 100644 --- a/heartkit/backends/evb.py +++ b/heartkit/backends/evb.py @@ -1,7 +1,7 @@ import time from enum import IntEnum -import neuralspot_edge as nse +import helia_edge as helia import numpy as np import numpy.typing as npt @@ -135,7 +135,7 @@ def send_model(self): with open(self.params.model_file, "rb") as fp: model_content = fp.read() - self._interpreter = nse.converters.tflite.TfLiteKerasInterpreter(model_content=model_content) + self._interpreter = helia.converters.tflite.TfLiteKerasInterpreter(model_content=model_content) self._interpreter.compile() self._send_binary("MODEL", RpcCommands.SEND_MODEL, model_content) diff --git a/heartkit/backends/pc.py b/heartkit/backends/pc.py index 3ed019f6..50be21c4 100644 --- a/heartkit/backends/pc.py +++ b/heartkit/backends/pc.py @@ -1,6 +1,6 @@ import numpy as np import numpy.typing as npt -import neuralspot_edge as nse +import helia_edge as helia from ..defines import HKTaskParams from .backend import HKInferenceBackend @@ -28,11 +28,11 @@ def _is_tf_model(self) -> bool: def open(self): """This method will simply load the keras or TFLite model""" if self._is_tf_model(): - self._model = nse.models.load_model(self.params.model_file) + self._model = helia.models.load_model(self.params.model_file) else: with open(self.params.model_file, "rb") as fp: model_content = fp.read() - self._model = nse.interpreters.tflite.TfLiteKerasInterpreter(model_content=model_content) + self._model = helia.interpreters.tflite.TfLiteKerasInterpreter(model_content=model_content) def close(self): """This method will unload the model""" diff --git a/heartkit/cli.py b/heartkit/cli.py index 3f74d2d4..539a627c 100644 --- a/heartkit/cli.py +++ b/heartkit/cli.py @@ -19,14 +19,14 @@ from argdantic import ArgField, ArgParser from pydantic import BaseModel -import neuralspot_edge as nse +import helia_edge as helia from .defines import HKMode, HKTaskParams from .tasks import TaskFactory B = TypeVar("B", bound=BaseModel) -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) parser = ArgParser() diff --git a/heartkit/datasets/augmentation.py b/heartkit/datasets/augmentation.py index c13c00b9..37889e01 100644 --- a/heartkit/datasets/augmentation.py +++ b/heartkit/datasets/augmentation.py @@ -1,6 +1,6 @@ import keras import numpy as np -import neuralspot_edge as nse +import helia_edge as helia from ..defines import NamedParams from .nstdb import NstdbNoise @@ -30,11 +30,11 @@ def create_augmentation_layer(augmentation: NamedParams, sampling_rate: int) -> """ match augmentation.name: case "amplitude_warp": - return nse.layers.preprocessing.AmplitudeWarp(sample_rate=sampling_rate, **augmentation.params) + return helia.layers.preprocessing.AmplitudeWarp(sample_rate=sampling_rate, **augmentation.params) case "augmentation_pipeline": return create_augmentation_pipeline(augmentation.params) case "random_augmentation": - return nse.layers.preprocessing.RandomAugmentation1DPipeline( + return helia.layers.preprocessing.RandomAugmentation1DPipeline( layers=[ create_augmentation_layer(augmentation, sampling_rate=sampling_rate) for augmentation in [NamedParams(**p) for p in augmentation.params["layers"]] @@ -49,23 +49,23 @@ def create_augmentation_layer(augmentation: NamedParams, sampling_rate: int) -> (nstdb.get_noise(noise_type="bw"), nstdb.get_noise(noise_type="ma"), nstdb.get_noise(noise_type="em")) ) noises = noises.astype(np.float32) - return nse.layers.preprocessing.RandomBackgroundNoises1D(noises=noises, **augmentation.params) + return helia.layers.preprocessing.RandomBackgroundNoises1D(noises=noises, **augmentation.params) case "random_sine_wave": - return nse.layers.preprocessing.RandomSineWave(**augmentation.params, sample_rate=sampling_rate) + return helia.layers.preprocessing.RandomSineWave(**augmentation.params, sample_rate=sampling_rate) case "random_cutout": - return nse.layers.preprocessing.RandomCutout1D(**augmentation.params) + return helia.layers.preprocessing.RandomCutout1D(**augmentation.params) case "random_noise": - return nse.layers.preprocessing.RandomGaussianNoise1D(**augmentation.params) + return helia.layers.preprocessing.RandomGaussianNoise1D(**augmentation.params) case "random_noise_distortion": - return nse.layers.preprocessing.RandomNoiseDistortion1D(sample_rate=sampling_rate, **augmentation.params) + return helia.layers.preprocessing.RandomNoiseDistortion1D(sample_rate=sampling_rate, **augmentation.params) case "resizing": - return nse.layers.preprocessing.Resizing1D(**augmentation.params) + return helia.layers.preprocessing.Resizing1D(**augmentation.params) case "sine_wave": - return nse.layers.preprocessing.AddSineWave(**augmentation.params) + return helia.layers.preprocessing.AddSineWave(**augmentation.params) case "filter": - return nse.layers.preprocessing.CascadedBiquadFilter(sample_rate=sampling_rate, **augmentation.params) + return helia.layers.preprocessing.CascadedBiquadFilter(sample_rate=sampling_rate, **augmentation.params) case "layer_norm": - return nse.layers.preprocessing.LayerNormalization1D(**augmentation.params) + return helia.layers.preprocessing.LayerNormalization1D(**augmentation.params) case _: raise ValueError(f"Unknown augmentation '{augmentation.name}'") # END MATCH @@ -73,7 +73,7 @@ def create_augmentation_layer(augmentation: NamedParams, sampling_rate: int) -> def create_augmentation_pipeline( augmentations: list[NamedParams], sampling_rate: int -) -> nse.layers.preprocessing.AugmentationPipeline: +) -> helia.layers.preprocessing.AugmentationPipeline: """Create an augmentation pipeline from a list of augmentation configurations. This is useful when running from a configuration file to hydrate the pipeline. @@ -83,7 +83,7 @@ def create_augmentation_pipeline( sampling_rate (int): Sampling rate of the data Returns: - nse.layers.preprocessing.AugmentationPipeline: Augmentation pipeline + helia.layers.preprocessing.AugmentationPipeline: Augmentation pipeline Example: @@ -100,7 +100,7 @@ def create_augmentation_pipeline( """ if not augmentations: return keras.layers.Lambda(lambda x: x) - aug = nse.layers.preprocessing.AugmentationPipeline( + aug = helia.layers.preprocessing.AugmentationPipeline( layers=[create_augmentation_layer(augmentation, sampling_rate=sampling_rate) for augmentation in augmentations] ) return aug diff --git a/heartkit/datasets/dataloader.py b/heartkit/datasets/dataloader.py index 21e03f39..652c48d1 100644 --- a/heartkit/datasets/dataloader.py +++ b/heartkit/datasets/dataloader.py @@ -6,7 +6,7 @@ import numpy as np import numpy.typing as npt import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset @@ -75,7 +75,7 @@ def data_generator( samples_per_patient: int | list[int], shuffle: bool = False, ) -> Generator[npt.NDArray, None, None]: - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): # Implement data generator yield data # END FOR @@ -202,7 +202,7 @@ def data_generator( Generator[tuple[npt.NDArray, ...], None, None]: Data generator """ - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for data in self.patient_data_generator(pt_id, samples_per_patient): yield data # END FOR @@ -229,7 +229,7 @@ def create_dataloader( ) # Compute output signature from generator - sig = nse.utils.get_output_signature_from_gen(data_gen) + sig = helia.utils.get_output_signature_from_gen(data_gen) ds = tf.data.Dataset.from_generator( data_gen, diff --git a/heartkit/datasets/ecg_synthetic.py b/heartkit/datasets/ecg_synthetic.py index e73b9954..b385584b 100644 --- a/heartkit/datasets/ecg_synthetic.py +++ b/heartkit/datasets/ecg_synthetic.py @@ -9,7 +9,7 @@ import numpy.typing as npt import physiokit as pk from pydantic import BaseModel, Field -import neuralspot_edge as nse +import helia_edge as helia from tqdm.contrib.concurrent import process_map @@ -17,7 +17,7 @@ from .defines import PatientGenerator, PatientData from .nstdb import NstdbNoise -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class EcgSyntheticParams(BaseModel, extra="allow"): diff --git a/heartkit/datasets/factory.py b/heartkit/datasets/factory.py index 146d116c..b8775dc8 100644 --- a/heartkit/datasets/factory.py +++ b/heartkit/datasets/factory.py @@ -3,8 +3,8 @@ value (HKDataset): Dataset class """ -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset -DatasetFactory = nse.utils.create_factory(factory="HKDatasetFactory", type=HKDataset) +DatasetFactory = helia.utils.create_factory(factory="HKDatasetFactory", type=HKDataset) diff --git a/heartkit/datasets/icentia11k.py b/heartkit/datasets/icentia11k.py index dd1c2d30..9d50589a 100644 --- a/heartkit/datasets/icentia11k.py +++ b/heartkit/datasets/icentia11k.py @@ -14,12 +14,12 @@ import sklearn.model_selection import sklearn.preprocessing from tqdm.contrib.concurrent import process_map -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset from .defines import PatientGenerator -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class IcentiaRhythm(IntEnum): @@ -193,7 +193,7 @@ def download(self, num_workers: int | None = None, force: bool = False): num_workers (int | None, optional): # parallel workers. Defaults to None. force (bool, optional): Force redownload. Defaults to False. """ - nse.utils.download_s3_objects( + helia.utils.download_s3_objects( bucket="ambiq-ai-datasets", prefix=self.path.stem, dst=self.path.parent, @@ -342,7 +342,7 @@ def download_raw_dataset(self, num_workers: int | None = None, force: bool = Fal f"Zip file already exists. Please delete or set `force` flag to redownload. PATH={ds_zip_path}" ) else: - nse.utils.download_file(ds_url, ds_zip_path, progress=True) + helia.utils.download_file(ds_url, ds_zip_path, progress=True) # 2. Extract and convert patient ECG data to H5 files logger.debug("Generating icentia11k patient data") diff --git a/heartkit/datasets/icentia_mini.py b/heartkit/datasets/icentia_mini.py index c250f8fb..2d6bc4c1 100644 --- a/heartkit/datasets/icentia_mini.py +++ b/heartkit/datasets/icentia_mini.py @@ -14,12 +14,12 @@ import sklearn.preprocessing from tqdm.contrib.concurrent import process_map -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset from .defines import PatientGenerator, PatientData -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class IcentiaMiniRhythm(IntEnum): @@ -202,7 +202,7 @@ def download(self, num_workers: int | None = None, force: bool = False): os.makedirs(self.path, exist_ok=True) zip_path = self.path / f"{self.name}.zip" - did_download = nse.utils.download_s3_file( + did_download = helia.utils.download_s3_file( key=f"{self.name}/{self.name}.zip", dst=zip_path, bucket="ambiq-ai-datasets", diff --git a/heartkit/datasets/lsad.py b/heartkit/datasets/lsad.py index e91e83e8..05398cb1 100644 --- a/heartkit/datasets/lsad.py +++ b/heartkit/datasets/lsad.py @@ -14,12 +14,12 @@ import sklearn.model_selection from tqdm import tqdm from tqdm.contrib.concurrent import process_map -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset from .defines import PatientGenerator, PatientData -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class LsadScpCode(IntEnum): @@ -562,7 +562,7 @@ def download(self, num_workers: int | None = None, force: bool = False): os.makedirs(self.path, exist_ok=True) zip_path = self.path / f"{self.name}.zip" - did_download = nse.utils.download_s3_file( + did_download = helia.utils.download_s3_file( key=f"{self.name}/{self.name}.zip", dst=zip_path, bucket="ambiq-ai-datasets", @@ -591,7 +591,7 @@ def download_raw_dataset(self, num_workers: int | None = None, force: bool = Fal f"Zip file already exists. Please delete or set `force` flag to redownload. PATH={ds_zip_path}" ) else: - nse.utils.download_file(ds_url, ds_zip_path, progress=True) + helia.utils.download_file(ds_url, ds_zip_path, progress=True) # 2. Extract and convert patient ECG data to H5 files logger.debug("Processing LSAD patient data") diff --git a/heartkit/datasets/ludb.py b/heartkit/datasets/ludb.py index 4818ebb1..2c46e96a 100644 --- a/heartkit/datasets/ludb.py +++ b/heartkit/datasets/ludb.py @@ -13,12 +13,12 @@ import numpy.typing as npt import physiokit as pk from tqdm.contrib.concurrent import process_map -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset from .defines import PatientGenerator, PatientData -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) LudbSymbolMap = { "o": 0, # Other @@ -221,7 +221,7 @@ def download(self, num_workers: int | None = None, force: bool = False): os.makedirs(self.path, exist_ok=True) zip_path = self.path / f"{self.name}.zip" - did_download = nse.utils.download_s3_file( + did_download = helia.utils.download_s3_file( key=f"{self.name}/{self.name}.zip", dst=zip_path, bucket="ambiq-ai-datasets", @@ -250,7 +250,7 @@ def download_raw_dataset(self, num_workers: int | None = None, force: bool = Fal f"Zip file already exists. Please delete or set `force` flag to redownload. PATH={ds_zip_path}" ) else: - nse.utils.download_file(ds_url, ds_zip_path, progress=True) + helia.utils.download_file(ds_url, ds_zip_path, progress=True) # 2. Extract and convert patient ECG data to H5 files logger.debug("Generating LUDB patient data") diff --git a/heartkit/datasets/nstdb.py b/heartkit/datasets/nstdb.py index 89490db8..2d2f8add 100644 --- a/heartkit/datasets/nstdb.py +++ b/heartkit/datasets/nstdb.py @@ -5,9 +5,9 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class NstdbNoise: diff --git a/heartkit/datasets/ppg_synthetic.py b/heartkit/datasets/ppg_synthetic.py index 0627c47b..b2299f5d 100644 --- a/heartkit/datasets/ppg_synthetic.py +++ b/heartkit/datasets/ppg_synthetic.py @@ -8,14 +8,14 @@ import numpy.typing as npt import physiokit as pk from pydantic import BaseModel, Field -import neuralspot_edge as nse +import helia_edge as helia from tqdm.contrib.concurrent import process_map from .dataset import HKDataset from .defines import PatientGenerator, PatientData from .nstdb import NstdbNoise -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class PpgSyntheticParams(BaseModel, extra="allow"): diff --git a/heartkit/datasets/ptbxl.py b/heartkit/datasets/ptbxl.py index ca8b1f90..e24d1e4b 100644 --- a/heartkit/datasets/ptbxl.py +++ b/heartkit/datasets/ptbxl.py @@ -14,12 +14,12 @@ import sklearn.model_selection from tqdm import tqdm from tqdm.contrib.concurrent import process_map -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset from .defines import PatientGenerator, PatientData -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) class PtbxlScpCode(IntEnum): @@ -657,7 +657,7 @@ def download(self, num_workers: int | None = None, force: bool = False): os.makedirs(self.path, exist_ok=True) zip_path = self.path / f"{self.name}.zip" - did_download = nse.utils.download_s3_file( + did_download = helia.utils.download_s3_file( key=f"{self.name}/{self.name}.zip", dst=zip_path, bucket="ambiq-ai-datasets", @@ -686,7 +686,7 @@ def download_raw_dataset(self, num_workers: int | None = None, force: bool = Fal f"Zip file already exists. Please delete or set `force` flag to redownload. PATH={ds_zip_path}" ) else: - nse.utils.download_file(ds_url, ds_zip_path, progress=True) + helia.utils.download_file(ds_url, ds_zip_path, progress=True) # 2. Extract and convert patient ECG data to H5 files logger.debug("Processing PTB-XL patient data") diff --git a/heartkit/datasets/qtdb.py b/heartkit/datasets/qtdb.py index 6a75f887..41287d89 100644 --- a/heartkit/datasets/qtdb.py +++ b/heartkit/datasets/qtdb.py @@ -11,12 +11,12 @@ import numpy.typing as npt import physiokit as pk from tqdm.contrib.concurrent import process_map -import neuralspot_edge as nse +import helia_edge as helia from .dataset import HKDataset from .defines import PatientGenerator, PatientData -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) QtdbSymbolMap = { "o": 0, # Other @@ -298,7 +298,7 @@ def download(self, num_workers: int | None = None, force: bool = False): os.makedirs(self.path, exist_ok=True) zip_path = self.path / f"{self.name}.zip" - did_download = nse.utils.download_s3_file( + did_download = helia.utils.download_s3_file( key=f"{self.name}/{self.name}.zip", dst=zip_path, bucket="ambiq-ai-datasets", @@ -324,7 +324,7 @@ def download_raw_dataset(self, num_workers: int | None = None, force: bool = Fal f"Zip file already exists. Please delete or set `force` flag to redownload. PATH={ds_zip_path}" ) else: - nse.utils.download_file(ds_url, ds_zip_path, progress=True) + helia.utils.download_file(ds_url, ds_zip_path, progress=True) # 2. Extract and convert patient ECG data to H5 files logger.debug("Generating QT patient data") diff --git a/heartkit/defines.py b/heartkit/defines.py index ce0db77c..2b17705f 100644 --- a/heartkit/defines.py +++ b/heartkit/defines.py @@ -5,7 +5,7 @@ from typing import Any, Literal from pydantic import BaseModel, ConfigDict, Field -from neuralspot_edge.converters.tflite import QuantizationType, ConversionType +from helia_edge.converters.tflite import QuantizationType, ConversionType class QuantizationParams(BaseModel, extra="allow"): diff --git a/heartkit/models/__init__.py b/heartkit/models/__init__.py index 9562fa27..cdf03c9b 100644 --- a/heartkit/models/__init__.py +++ b/heartkit/models/__init__.py @@ -3,25 +3,25 @@ HeartKit provides a number of model architectures that can be used for training __heart-monitoring tasks__. While a number of off-the-shelf models exist, they are often not efficient nor optimized for real-time, edge applications. -To address this, HeartKit provides a model factory that allows you to easily create and train customized models via [neuralspot-edge](https://ambiqai.github.io/neuralspot-edge/). -`neuralspot-edge` includes a growing number of state-of-the-art models that can be easily configured and trained using high-level parameters. +To address this, HeartKit provides a model factory that allows you to easily create and train customized models via [helia-edge](https://ambiqai.github.io/helia-edge/). +`helia-edge` includes a growing number of state-of-the-art models that can be easily configured and trained using high-level parameters. The models are designed to be efficient and well-suited for real-time, edge applications. Most of the models are based on state-of-the-art architectures that have been modified to allow for more fine-grain customization. In addition, the models support 1D variants to allow for training on time-series data. -Please check [neuralspot-edge](https://ambiqai.github.io/neuralspot-edge/) for list of available models and their configurations. +Please check [helia-edge](https://ambiqai.github.io/helia-edge/) for list of available models and their configurations. ## Available Models -- **[TCN](https://ambiqai.github.io/neuralspot-edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) -- **[U-Net](https://ambiqai.github.io/neuralspot-edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) -- **[U-NeXt](https://ambiqai.github.io/neuralspot-edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) -- **[EfficientNetV2](https://ambiqai.github.io/neuralspot-edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) -- **[MobileOne](https://ambiqai.github.io/neuralspot-edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) -- **[ResNet](https://ambiqai.github.io/neuralspot-edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) -- **[Conformer](https://ambiqai.github.io/neuralspot-edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) -- **[MetaFormer](https://ambiqai.github.io/neuralspot-edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) -- **[TSMixer](https://ambiqai.github.io/neuralspot-edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) +- **[TCN](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tcn)**: A CNN leveraging dilated convolutions (key=`tcn`) +- **[U-Net](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unet)**: A CNN with encoder-decoder architecture for segmentation tasks (key=`unet`) +- **[U-NeXt](https://ambiqai.github.io/helia-edge/api/helia_edge/models/unext)**: A U-Net variant leveraging MBConv blocks (key=`unext`) +- **[EfficientNetV2](https://ambiqai.github.io/helia-edge/api/helia_edge/models/efficientnet)**: A CNN leveraging MBConv blocks (key=`efficientnet`) +- **[MobileOne](https://ambiqai.github.io/helia-edge/api/helia_edge/models/mobileone)**: A CNN aimed at sub-1ms inference (key=`mobileone`) +- **[ResNet](https://ambiqai.github.io/helia-edge/api/helia_edge/models/resnet)**: A popular CNN often used for vision tasks (key=`resnet`) +- **[Conformer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/conformer)**: A transformer composed of both convolutional and self-attention blocks (key=`conformer`) +- **[MetaFormer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/metaformer)**: A transformer composed of both spatial mixing and channel mixing blocks (key=`metaformer`) +- **[TSMixer](https://ambiqai.github.io/helia-edge/api/helia_edge/models/tsmixer)**: An All-MLP Architecture for Time Series Classification (key=`tsmixer`) ## Model Factory @@ -46,7 +46,7 @@ ```py linenums="1" import keras - from neuralspot_edge.models import TcnModel, TcnParams, TcnBlockParams + from helia_edge.models import TcnModel, TcnParams, TcnBlockParams inputs = keras.Input(shape=(800, 1)) num_classes = 5 @@ -76,7 +76,7 @@ from typing import Protocol import keras -import neuralspot_edge as nse +import helia_edge as helia class ModelFactoryItem(Protocol): @@ -94,12 +94,12 @@ def __call__(self, inputs: keras.KerasTensor, params: dict, num_classes: int) -> """ -ModelFactory = nse.utils.ItemFactory[ModelFactoryItem].shared("HKModelFactory") +ModelFactory = helia.utils.ItemFactory[ModelFactoryItem].shared("HKModelFactory") -ModelFactory.register("unet", nse.models.UNetModel.model_from_params) -ModelFactory.register("unext", nse.models.UNextModel.model_from_params) -ModelFactory.register("resnet", nse.models.ResNetModel.model_from_params) -ModelFactory.register("efficientnetv2", nse.models.EfficientNetV2Model.model_from_params) -ModelFactory.register("mobileone", nse.models.MobileNetV1Model.model_from_params) -ModelFactory.register("tcn", nse.models.TcnModel.model_from_params) -ModelFactory.register("composer", nse.models.ComposerModel.model_from_params) +ModelFactory.register("unet", helia.models.UNetModel.model_from_params) +ModelFactory.register("unext", helia.models.UNextModel.model_from_params) +ModelFactory.register("resnet", helia.models.ResNetModel.model_from_params) +ModelFactory.register("efficientnetv2", helia.models.EfficientNetV2Model.model_from_params) +ModelFactory.register("mobileone", helia.models.MobileNetV1Model.model_from_params) +ModelFactory.register("tcn", helia.models.TcnModel.model_from_params) +ModelFactory.register("composer", helia.models.ComposerModel.model_from_params) diff --git a/heartkit/tasks/__init__.py b/heartkit/tasks/__init__.py index 212c3fef..c023da2f 100644 --- a/heartkit/tasks/__init__.py +++ b/heartkit/tasks/__init__.py @@ -41,7 +41,7 @@ """ -import neuralspot_edge as nse +import helia_edge as helia from . import beat, denoise, diagnostic, foundation, rhythm, segmentation, utils @@ -54,7 +54,7 @@ from .task import HKTask from .translate import HKTranslate, TranslateTask -TaskFactory = nse.utils.create_factory(factory="HKTaskFactory", type=HKTask) +TaskFactory = helia.utils.create_factory(factory="HKTaskFactory", type=HKTask) TaskFactory.register("rhythm", RhythmTask) TaskFactory.register("beat", BeatTask) diff --git a/heartkit/tasks/beat/dataloaders/__init__.py b/heartkit/tasks/beat/dataloaders/__init__.py index 0c782b30..e2a6f8a2 100644 --- a/heartkit/tasks/beat/dataloaders/__init__.py +++ b/heartkit/tasks/beat/dataloaders/__init__.py @@ -8,13 +8,13 @@ """ -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader from .icentia11k import Icentia11kDataloader from .icentia_mini import IcentiaMiniDataloader -BeatTaskFactory = nse.utils.create_factory(factory="HKBeatTaskFactory", type=HKDataloader) +BeatTaskFactory = helia.utils.create_factory(factory="HKBeatTaskFactory", type=HKDataloader) BeatTaskFactory.register("icentia11k", Icentia11kDataloader) BeatTaskFactory.register("icentia_mini", IcentiaMiniDataloader) diff --git a/heartkit/tasks/beat/dataloaders/icentia11k.py b/heartkit/tasks/beat/dataloaders/icentia11k.py index a349ca19..55cb671f 100644 --- a/heartkit/tasks/beat/dataloaders/icentia11k.py +++ b/heartkit/tasks/beat/dataloaders/icentia11k.py @@ -6,7 +6,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, IcentiaDataset, IcentiaBeat from ..defines import HKBeat @@ -168,7 +168,7 @@ def data_generator( samples_per_tgt = num_per_tgt * [num_classes] pt_ids = copy.deepcopy(patient_ids) - for pt_id in nse.utils.uniform_id_generator(pt_ids, repeat=True, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(pt_ids, repeat=True, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_tgt): yield x, y # END FOR diff --git a/heartkit/tasks/beat/datasets.py b/heartkit/tasks/beat/datasets.py index b8bcb491..4b38f3e2 100644 --- a/heartkit/tasks/beat/datasets.py +++ b/heartkit/tasks/beat/datasets.py @@ -1,12 +1,12 @@ import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import HKDataset, create_augmentation_pipeline from ...defines import HKTaskParams, NamedParams from ..utils import load_train_dataloader_split, load_test_dataloader_split from .dataloaders import BeatTaskFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def create_data_pipeline( diff --git a/heartkit/tasks/beat/demo.py b/heartkit/tasks/beat/demo.py index dfdf8a09..1705cccf 100644 --- a/heartkit/tasks/beat/demo.py +++ b/heartkit/tasks/beat/demo.py @@ -7,13 +7,13 @@ import plotly.graph_objects as go from plotly.subplots import make_subplots from tqdm import tqdm -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import IcentiaDataset, PtbxlDataset, DatasetFactory, create_augmentation_pipeline from ...defines import HKTaskParams from ...backends import BackendFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def get_ptbxl_patient_data( @@ -139,7 +139,7 @@ def demo(params: HKTaskParams): else: # Need to manually locate peaks, compute ds_gen = ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), + patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), frame_size=params.demo_size, samples_per_patient=5, target_rate=params.sampling_rate, diff --git a/heartkit/tasks/beat/evaluate.py b/heartkit/tasks/beat/evaluate.py index e0190de0..0497108e 100644 --- a/heartkit/tasks/beat/evaluate.py +++ b/heartkit/tasks/beat/evaluate.py @@ -4,7 +4,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -18,10 +18,10 @@ def evaluate(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") class_names = params.class_names or [f"Class {i}" for i in range(params.num_classes)] @@ -39,8 +39,8 @@ def evaluate(params: HKTaskParams): test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()]) logger.debug("Loading model") - model = nse.models.load_model(params.model_file) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + model = helia.models.load_model(params.model_file) + flops = helia.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") @@ -57,13 +57,13 @@ def evaluate(params: HKTaskParams): if params.num_classes == 2: roc_path = params.job_dir / "roc_auc_test.png" - nse.plotting.roc_auc_plot(y_true, y_prob[:, 1], labels=class_names, save_path=roc_path) + helia.plotting.roc_auc_plot(y_true, y_prob[:, 1], labels=class_names, save_path=roc_path) # END IF # If threshold given, only count predictions above threshold if params.threshold: prev_numel = len(y_true) - indices = nse.metrics.threshold.get_predicted_threshold_indices(y_prob, y_pred, params.threshold) + indices = helia.metrics.threshold.get_predicted_threshold_indices(y_prob, y_pred, params.threshold) test_x, test_y = test_x[indices], test_y[indices] y_true, y_pred = y_true[indices], y_pred[indices] rst = model.evaluate(test_x, test_y, verbose=params.verbose, return_dict=True) @@ -72,8 +72,8 @@ def evaluate(params: HKTaskParams): # END IF cm_path = params.job_dir / "confusion_matrix_test.png" - nse.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") - nse.plotting.px_plot_confusion_matrix( + helia.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") + helia.plotting.px_plot_confusion_matrix( y_true, y_pred, labels=class_names, diff --git a/heartkit/tasks/beat/export.py b/heartkit/tasks/beat/export.py index 8bb360e0..24da3ea9 100644 --- a/heartkit/tasks/beat/export.py +++ b/heartkit/tasks/beat/export.py @@ -4,7 +4,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -18,7 +18,7 @@ def export(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") logger.debug(f"Creating working directory in {params.job_dir}") tfl_model_path = params.job_dir / "model.tflite" @@ -40,23 +40,23 @@ def export(params: HKTaskParams): # Load model logger.debug("Loading trained model") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) # Add softmax layer if required if not params.use_logits and not isinstance(model.layers[-1], keras.layers.Softmax): - model = nse.models.append_layers(model, layers=[keras.layers.Softmax()], copy_weights=True) + model = helia.models.append_layers(model, layers=[keras.layers.Softmax()], copy_weights=True) # END IF # Fix batch size to 1 inputs = keras.Input(shape=feat_shape, batch_size=1, name="input", dtype="float32") model(inputs) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.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"Converting model to TFLite (quantization={params.quantization.mode})") - converter = nse.converters.tflite.TfLiteKerasConverter(model=model) + converter = helia.converters.tflite.TfLiteKerasConverter(model=model) tflite_content = converter.convert( test_x=test_x, @@ -79,7 +79,7 @@ def export(params: HKTaskParams): converter.export_header(tflm_model_path, name=params.tflm_var_name) converter.cleanup() - tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) + tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) tflite.compile() # Verify TFLite results match TF results on example data @@ -97,8 +97,8 @@ def export(params: HKTaskParams): y_pred_tf = model.predict(test_x) y_pred_tfl = tflite.predict(x=test_x) - tf_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tf) - tfl_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tfl) + tf_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tf) + tfl_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tfl) logger.info("[TF METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tf_rst.items()])) logger.info("[TFL METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tfl_rst.items()])) diff --git a/heartkit/tasks/beat/train.py b/heartkit/tasks/beat/train.py index eb9fec01..e0d183bb 100644 --- a/heartkit/tasks/beat/train.py +++ b/heartkit/tasks/beat/train.py @@ -1,7 +1,7 @@ import os import keras -import neuralspot_edge as nse +import helia_edge as helia import numpy as np import sklearn.utils import wandb @@ -21,16 +21,16 @@ def train(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") with open(params.job_dir / "configuration.json", "w", encoding="utf-8") as fp: fp.write(params.model_dump_json(indent=2)) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): wandb.init(project=params.project, entity="ambiq", dir=params.job_dir) wandb.config.update(params.model_dump()) # END IF @@ -100,12 +100,12 @@ 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") + flops = helia.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") ModelCheckpoint = keras.callbacks.ModelCheckpoint - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): ModelCheckpoint = WandbModelCheckpoint model_callbacks = [ keras.callbacks.EarlyStopping( @@ -124,19 +124,19 @@ def train(params: HKTaskParams): ), keras.callbacks.CSVLogger(params.job_dir / "history.csv"), ] - if nse.utils.env_flag("TENSORBOARD"): + if helia.utils.env_flag("TENSORBOARD"): model_callbacks.append( keras.callbacks.TensorBoard( log_dir=params.job_dir, write_steps_per_second=True, ) ) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): model_callbacks.append(WandbMetricsLogger()) # Use minimal progress bar if params.verbose <= 1: model_callbacks.append( - nse.callbacks.TQDMProgressBar( + helia.callbacks.TQDMProgressBar( show_epoch_progress=False, ) ) @@ -156,7 +156,7 @@ def train(params: HKTaskParams): setup_plotting() if history: - nse.plotting.plot_history_metrics( + helia.plotting.plot_history_metrics( history.history, metrics=["loss", "acc"], save_path=params.job_dir / "history.png", @@ -170,8 +170,8 @@ def train(params: HKTaskParams): y_pred = np.argmax(model.predict(val_ds, verbose=params.verbose), axis=-1) cm_path = params.job_dir / "confusion_matrix.png" - nse.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") - nse.plotting.px_plot_confusion_matrix( + helia.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") + helia.plotting.px_plot_confusion_matrix( y_true, y_pred, labels=class_names, diff --git a/heartkit/tasks/denoise/dataloaders.py b/heartkit/tasks/denoise/dataloaders.py index 9f149bca..3566f431 100644 --- a/heartkit/tasks/denoise/dataloaders.py +++ b/heartkit/tasks/denoise/dataloaders.py @@ -2,7 +2,7 @@ import numpy as np import numpy.typing as npt -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import HKDataloader @@ -23,7 +23,7 @@ def data_generator( Leveraging the signal_generator method from the dataset class to generate data. """ gen = self.ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), + patient_generator=helia.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), frame_size=self.frame_size, samples_per_patient=samples_per_patient, target_rate=self.sampling_rate, @@ -34,7 +34,7 @@ def data_generator( yield x -DenoiseTaskFactory = nse.utils.create_factory(factory="HKDenoiseTaskFactory", type=HKDataloader) +DenoiseTaskFactory = helia.utils.create_factory(factory="HKDenoiseTaskFactory", type=HKDataloader) DenoiseTaskFactory.register("ecg-synthetic", DenoiseDataloader) DenoiseTaskFactory.register("ppg-synthetic", DenoiseDataloader) DenoiseTaskFactory.register("icentia11k", DenoiseDataloader) diff --git a/heartkit/tasks/denoise/datasets.py b/heartkit/tasks/denoise/datasets.py index dd1cd664..a933fe20 100644 --- a/heartkit/tasks/denoise/datasets.py +++ b/heartkit/tasks/denoise/datasets.py @@ -1,12 +1,12 @@ import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import HKDataset, create_augmentation_pipeline from ...defines import HKTaskParams, NamedParams from ..utils import load_train_dataloader_split, load_test_dataloader_split from .dataloaders import DenoiseTaskFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def create_data_pipeline( diff --git a/heartkit/tasks/denoise/demo.py b/heartkit/tasks/denoise/demo.py index 52199b92..f8ca10ab 100644 --- a/heartkit/tasks/denoise/demo.py +++ b/heartkit/tasks/denoise/demo.py @@ -5,7 +5,7 @@ from plotly.subplots import make_subplots import matplotlib.pyplot as plt from tqdm import tqdm -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...backends import BackendFactory @@ -19,7 +19,7 @@ def demo(params: HKTaskParams): Args: params (HKTaskParams): Task parameters """ - logger = nse.utils.setup_logger(__name__, level=params.verbose) + logger = helia.utils.setup_logger(__name__, level=params.verbose) plot_theme = setup_plotting() @@ -33,7 +33,7 @@ def demo(params: HKTaskParams): ds = random.choice(datasets) ds_gen = ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), + patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), frame_size=params.demo_size, samples_per_patient=5, target_rate=params.sampling_rate, diff --git a/heartkit/tasks/denoise/evaluate.py b/heartkit/tasks/denoise/evaluate.py index 7cdf1395..433df742 100644 --- a/heartkit/tasks/denoise/evaluate.py +++ b/heartkit/tasks/denoise/evaluate.py @@ -3,7 +3,7 @@ import keras import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -17,10 +17,10 @@ def evaluate(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") datasets = [DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets] @@ -33,8 +33,8 @@ def evaluate(params: HKTaskParams): test_ds = load_test_dataset(datasets=datasets, params=params) logger.debug("Loading model") - model = nse.models.load_model(params.model_file) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + model = helia.models.load_model(params.model_file) + flops = helia.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") diff --git a/heartkit/tasks/denoise/export.py b/heartkit/tasks/denoise/export.py index 9ec6486b..d085a427 100644 --- a/heartkit/tasks/denoise/export.py +++ b/heartkit/tasks/denoise/export.py @@ -4,7 +4,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -18,7 +18,7 @@ def export(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") logger.debug(f"Creating working directory in {params.job_dir}") tfl_model_path = params.job_dir / "model.tflite" @@ -40,22 +40,22 @@ def export(params: HKTaskParams): # Load model and set fixed batch size of 1 logger.debug("Loading trained model") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) # Add softmax layer if required if getattr(params, "flatten", False): - model = nse.models.append_layers(model, layers=[keras.layers.Flatten()], copy_weights=True) + model = helia.models.append_layers(model, layers=[keras.layers.Flatten()], copy_weights=True) # END IF inputs = keras.Input(shape=feat_shape, batch_size=1, name="input", dtype="float32") model(inputs) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.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"Converting model to TFLite (quantization={params.quantization.format})") - converter = nse.converters.tflite.TfLiteKerasConverter(model=model) + converter = helia.converters.tflite.TfLiteKerasConverter(model=model) tflite_content = converter.convert( test_x=test_x, @@ -78,7 +78,7 @@ def export(params: HKTaskParams): converter.export_header(tflm_model_path, name=params.tflm_var_name) converter.cleanup() - tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) + tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) tflite.compile() # Verify TFLite results match TF results on example data @@ -101,8 +101,8 @@ def export(params: HKTaskParams): y_pred_tfl = tflite.predict(x=test_x) print(y_true.shape, y_pred_tf.shape, y_pred_tfl.shape) - tf_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tf) - tfl_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tfl) + tf_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tf) + tfl_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tfl) logger.info("[TF METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tf_rst.items()])) logger.info("[TFL METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tfl_rst.items()])) diff --git a/heartkit/tasks/denoise/train.py b/heartkit/tasks/denoise/train.py index a2beaa16..c20ef884 100644 --- a/heartkit/tasks/denoise/train.py +++ b/heartkit/tasks/denoise/train.py @@ -5,7 +5,7 @@ import wandb from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory from .datasets import load_train_datasets @@ -19,16 +19,16 @@ def train(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") with open(params.job_dir / "configuration.json", "w", encoding="utf-8") as fp: fp.write(params.model_dump_json(indent=2)) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): wandb.init(project=params.project, entity="ambiq", dir=params.job_dir) wandb.config.update(params.model_dump()) # END IF @@ -54,7 +54,7 @@ def train(params: HKTaskParams): # Load existing model if params.resume and params.model_file: logger.debug(f"Loading model from file {params.model_file}") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) params.model_file = None else: logger.debug("Creating model from scratch") @@ -89,17 +89,17 @@ def train(params: HKTaskParams): keras.metrics.MeanAbsoluteError(name="mae"), keras.metrics.MeanSquaredError(name="mse"), keras.metrics.CosineSimilarity(name="cos"), - nse.metrics.Snr(name="snr"), + helia.metrics.Snr(name="snr"), ] 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") + flops = helia.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") val_mode = "max" if params.val_metric in ("f1", "cos") else "auto" ModelCheckpoint = keras.callbacks.ModelCheckpoint - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): ModelCheckpoint = WandbModelCheckpoint model_callbacks = [ keras.callbacks.EarlyStopping( @@ -119,19 +119,19 @@ def train(params: HKTaskParams): ), keras.callbacks.CSVLogger(params.job_dir / "history.csv"), ] - if nse.utils.env_flag("TENSORBOARD"): + if helia.utils.env_flag("TENSORBOARD"): model_callbacks.append( keras.callbacks.TensorBoard( log_dir=params.job_dir, write_steps_per_second=True, ) ) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): model_callbacks.append(WandbMetricsLogger()) # Use minimal progress bar if params.verbose <= 1: model_callbacks.append( - nse.callbacks.TQDMProgressBar( + helia.callbacks.TQDMProgressBar( show_epoch_progress=False, ) ) diff --git a/heartkit/tasks/diagnostic/dataloaders/__init__.py b/heartkit/tasks/diagnostic/dataloaders/__init__.py index 7f8196bb..44c51dcf 100644 --- a/heartkit/tasks/diagnostic/dataloaders/__init__.py +++ b/heartkit/tasks/diagnostic/dataloaders/__init__.py @@ -1,10 +1,10 @@ -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader from .ptbxl import PtbxlDataloader from .lsad import LsadDataloader -DiagnosticDataloaderFactory = nse.utils.create_factory(factory="HKDiagnosticDataloaderFactory", type=HKDataloader) +DiagnosticDataloaderFactory = helia.utils.create_factory(factory="HKDiagnosticDataloaderFactory", type=HKDataloader) DiagnosticDataloaderFactory.register("ptbxl", PtbxlDataloader) DiagnosticDataloaderFactory.register("lsad", LsadDataloader) diff --git a/heartkit/tasks/diagnostic/dataloaders/lsad.py b/heartkit/tasks/diagnostic/dataloaders/lsad.py index b258d1f5..59fc678f 100644 --- a/heartkit/tasks/diagnostic/dataloaders/lsad.py +++ b/heartkit/tasks/diagnostic/dataloaders/lsad.py @@ -1,7 +1,7 @@ from typing import Generator import numpy.typing as npt -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import LsadDataset, LsadScpCode, HKDataloader from ..defines import HKDiagnostic @@ -63,7 +63,7 @@ def data_generator( shuffle: bool = False, ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]: return self.ds.signal_label_generator( - patient_generator=nse.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), + patient_generator=helia.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), frame_size=self.frame_size, samples_per_patient=samples_per_patient, target_rate=self.sampling_rate, diff --git a/heartkit/tasks/diagnostic/dataloaders/ptbxl.py b/heartkit/tasks/diagnostic/dataloaders/ptbxl.py index 34519196..092e0f0e 100644 --- a/heartkit/tasks/diagnostic/dataloaders/ptbxl.py +++ b/heartkit/tasks/diagnostic/dataloaders/ptbxl.py @@ -1,7 +1,7 @@ from typing import Generator import numpy.typing as npt -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import PtbxlDataset, PtbxlScpCode, HKDataloader from ..defines import HKDiagnostic @@ -74,7 +74,7 @@ def data_generator( shuffle: bool = False, ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]: return self.ds.signal_label_generator( - patient_generator=nse.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), + patient_generator=helia.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), frame_size=self.frame_size, samples_per_patient=samples_per_patient, target_rate=self.sampling_rate, diff --git a/heartkit/tasks/diagnostic/datasets.py b/heartkit/tasks/diagnostic/datasets.py index 15ffd77c..7d16c8cc 100644 --- a/heartkit/tasks/diagnostic/datasets.py +++ b/heartkit/tasks/diagnostic/datasets.py @@ -1,5 +1,5 @@ import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import HKDataset, create_augmentation_pipeline from ..utils import load_train_dataloader_split, load_test_dataloader_split @@ -7,7 +7,7 @@ from .dataloaders import DiagnosticDataloaderFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def create_data_pipeline( diff --git a/heartkit/tasks/diagnostic/demo.py b/heartkit/tasks/diagnostic/demo.py index 01a378ed..e20f965a 100644 --- a/heartkit/tasks/diagnostic/demo.py +++ b/heartkit/tasks/diagnostic/demo.py @@ -5,14 +5,14 @@ import plotly.graph_objects as go from plotly.subplots import make_subplots from tqdm import tqdm -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...backends import BackendFactory from ...datasets import DatasetFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def demo(params: HKTaskParams): @@ -48,7 +48,7 @@ def demo(params: HKTaskParams): ds = random.choice(datasets) ds_gen = ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), + patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), frame_size=params.demo_size, samples_per_patient=5, target_rate=params.sampling_rate, diff --git a/heartkit/tasks/diagnostic/evaluate.py b/heartkit/tasks/diagnostic/evaluate.py index 0c3a6601..f7764db3 100644 --- a/heartkit/tasks/diagnostic/evaluate.py +++ b/heartkit/tasks/diagnostic/evaluate.py @@ -6,7 +6,7 @@ import numpy as np import pandas as pd from sklearn.metrics import classification_report -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -20,12 +20,12 @@ def evaluate(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") logger.debug(f"Creating working directory in {params.job_dir}") params.threshold = params.threshold or 0.5 - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") class_names = params.class_names or [f"Class {i}" for i in range(params.num_classes)] @@ -43,8 +43,8 @@ def evaluate(params: HKTaskParams): test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()]) logger.debug("Loading model") - model = nse.models.load_model(params.model_file) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + model = helia.models.load_model(params.model_file) + flops = helia.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") @@ -59,7 +59,7 @@ def evaluate(params: HKTaskParams): y_true = np.argmax(y_true, axis=-1) cm_path = params.job_dir / "confusion_matrix_test.png" - nse.plotting.confusion_matrix_plot( + helia.plotting.confusion_matrix_plot( y_true=y_true, y_pred=y_pred, labels=class_names, diff --git a/heartkit/tasks/diagnostic/export.py b/heartkit/tasks/diagnostic/export.py index 636ee665..1d92ee44 100644 --- a/heartkit/tasks/diagnostic/export.py +++ b/heartkit/tasks/diagnostic/export.py @@ -4,7 +4,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -18,7 +18,7 @@ def export(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") logger.debug(f"Creating working directory in {params.job_dir}") params.threshold = params.threshold or 0.5 @@ -42,17 +42,17 @@ def export(params: HKTaskParams): # Load model and set fixed batch size of 1 logger.debug("Loading trained model") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) inputs = keras.Input(shape=feat_shape, batch_size=1, name="input", dtype="float32") model(inputs) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.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"Converting model to TFLite (quantization={params.quantization.mode})") - converter = nse.converters.tflite.TfLiteKerasConverter(model=model) + converter = helia.converters.tflite.TfLiteKerasConverter(model=model) tflite_content = converter.convert( test_x=test_x, @@ -75,7 +75,7 @@ def export(params: HKTaskParams): converter.export_header(tflm_model_path, name=params.tflm_var_name) converter.cleanup() - tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) + tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) tflite.compile() # Verify TFLite results match TF results @@ -89,8 +89,8 @@ def export(params: HKTaskParams): y_pred_tf = model.predict(test_x) y_pred_tfl = tflite.predict(x=test_x) - tf_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tf) - tfl_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tfl) + tf_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tf) + tfl_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tfl) logger.info("[TF METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tf_rst.items()])) logger.info("[TFL METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tfl_rst.items()])) diff --git a/heartkit/tasks/diagnostic/train.py b/heartkit/tasks/diagnostic/train.py index d2e14cb2..9aedde40 100644 --- a/heartkit/tasks/diagnostic/train.py +++ b/heartkit/tasks/diagnostic/train.py @@ -7,7 +7,7 @@ from sklearn.metrics import classification_report from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -22,18 +22,18 @@ def train(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") logger.debug(f"Creating working directory in {params.job_dir}") params.threshold = params.threshold or 0.5 - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") with open(params.job_dir / "configuration.json", "w", encoding="utf-8") as fp: fp.write(params.model_dump_json(indent=2)) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): wandb.init(project=params.project, entity="ambiq", dir=params.job_dir) wandb.config.update(params.model_dump()) # END IF @@ -74,7 +74,7 @@ def train(params: HKTaskParams): if params.resume and params.model_file: logger.debug(f"Loading model from file {params.model_file}") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) params.model_file = None else: logger.debug("Creating model from scratch") @@ -87,7 +87,7 @@ def train(params: HKTaskParams): ) # END IF - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") t_mul = 1 first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1) @@ -119,7 +119,7 @@ def train(params: HKTaskParams): logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): ModelCheckpoint = WandbModelCheckpoint model_callbacks = [ keras.callbacks.EarlyStopping( @@ -138,19 +138,19 @@ def train(params: HKTaskParams): ), keras.callbacks.CSVLogger(params.job_dir / "history.csv"), ] - if nse.utils.env_flag("TENSORBOARD"): + if helia.utils.env_flag("TENSORBOARD"): model_callbacks.append( keras.callbacks.TensorBoard( log_dir=params.job_dir, write_steps_per_second=True, ) ) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): model_callbacks.append(WandbMetricsLogger()) # Use minimal progress bar if params.verbose <= 1: model_callbacks.append( - nse.callbacks.TQDMProgressBar( + helia.callbacks.TQDMProgressBar( show_epoch_progress=False, ) ) @@ -177,7 +177,7 @@ def train(params: HKTaskParams): y_true = np.argmax(y_true, axis=-1) cm_path = params.job_dir / "confusion_matrix.png" - nse.plotting.confusion_matrix_plot( + helia.plotting.confusion_matrix_plot( y_true=y_true, y_pred=y_pred, labels=class_names, diff --git a/heartkit/tasks/foundation/dataloaders/__init__.py b/heartkit/tasks/foundation/dataloaders/__init__.py index 0e83ddad..027aa69a 100644 --- a/heartkit/tasks/foundation/dataloaders/__init__.py +++ b/heartkit/tasks/foundation/dataloaders/__init__.py @@ -8,13 +8,13 @@ """ -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader from .lsad import LsadDataloader from .ptbxl import PtbxlDataloader -FoundationTaskFactory = nse.utils.create_factory(factory="FoundationTaskFactory", type=HKDataloader) +FoundationTaskFactory = helia.utils.create_factory(factory="FoundationTaskFactory", type=HKDataloader) FoundationTaskFactory.register("lsad", LsadDataloader) FoundationTaskFactory.register("ptbxl", PtbxlDataloader) diff --git a/heartkit/tasks/foundation/dataloaders/lsad.py b/heartkit/tasks/foundation/dataloaders/lsad.py index 880f426c..d37f426a 100644 --- a/heartkit/tasks/foundation/dataloaders/lsad.py +++ b/heartkit/tasks/foundation/dataloaders/lsad.py @@ -4,7 +4,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, LsadDataset @@ -57,7 +57,7 @@ def data_generator( samples_per_patient: int | list[int], shuffle: bool = False, ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]: - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x1, x2 in self.patient_data_generator(pt_id, samples_per_patient): yield x1, x2 # END FOR diff --git a/heartkit/tasks/foundation/dataloaders/ptbxl.py b/heartkit/tasks/foundation/dataloaders/ptbxl.py index 9a2b3beb..50b12167 100644 --- a/heartkit/tasks/foundation/dataloaders/ptbxl.py +++ b/heartkit/tasks/foundation/dataloaders/ptbxl.py @@ -4,7 +4,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, PtbxlDataset @@ -51,7 +51,7 @@ def data_generator( samples_per_patient: int | list[int], shuffle: bool = False, ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]: - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x1, x2 in self.patient_data_generator(pt_id, samples_per_patient): yield x1, x2 # END FOR diff --git a/heartkit/tasks/foundation/datasets.py b/heartkit/tasks/foundation/datasets.py index f590c1f3..d9217f3b 100644 --- a/heartkit/tasks/foundation/datasets.py +++ b/heartkit/tasks/foundation/datasets.py @@ -1,5 +1,5 @@ import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import HKDataset, create_augmentation_pipeline from ..utils import load_train_dataloader_split, load_test_dataloader_split @@ -7,7 +7,7 @@ from .dataloaders import FoundationTaskFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def create_data_pipeline( @@ -45,9 +45,9 @@ def create_data_pipeline( ) ds = ds.map( lambda x1, x2: { - nse.trainers.SimCLRTrainer.SAMPLES: x1, - nse.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(x1), - nse.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(x2), + helia.trainers.SimCLRTrainer.SAMPLES: x1, + helia.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(x1), + helia.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(x2), }, num_parallel_calls=tf.data.AUTOTUNE, ) diff --git a/heartkit/tasks/foundation/demo.py b/heartkit/tasks/foundation/demo.py index c18bbe3a..1f7373e3 100644 --- a/heartkit/tasks/foundation/demo.py +++ b/heartkit/tasks/foundation/demo.py @@ -7,7 +7,7 @@ from plotly.subplots import make_subplots from sklearn.manifold import TSNE from tqdm import tqdm -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...backends import BackendFactory @@ -22,7 +22,7 @@ def demo(params: HKTaskParams): Args: params (HKTaskParams): Task parameters """ - logger = nse.utils.setup_logger(__name__, level=params.verbose) + logger = helia.utils.setup_logger(__name__, level=params.verbose) plot_theme = setup_plotting() feat_shape = (params.frame_size, 1) diff --git a/heartkit/tasks/foundation/evaluate.py b/heartkit/tasks/foundation/evaluate.py index 46349e91..419b936e 100644 --- a/heartkit/tasks/foundation/evaluate.py +++ b/heartkit/tasks/foundation/evaluate.py @@ -5,7 +5,7 @@ import numpy as np import tensorflow as tf import matplotlib.pyplot as plt -import neuralspot_edge as nse +import helia_edge as helia from sklearn.manifold import TSNE from ...defines import HKTaskParams @@ -21,10 +21,10 @@ def evaluate(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") datasets = [DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets] @@ -39,14 +39,14 @@ def evaluate(params: HKTaskParams): # Grab sets of augmented samples test_x1, test_x2 = [], [] for inputs in test_ds.as_numpy_iterator(): - test_x1.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_0]) - test_x2.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_1]) + test_x1.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_0]) + test_x2.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_1]) test_x1 = np.concatenate(test_x1) test_x2 = np.concatenate(test_x2) logger.debug("Loading model") - model = nse.models.load_model(params.model_file) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + model = helia.models.load_model(params.model_file) + flops = helia.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") @@ -61,7 +61,7 @@ def evaluate(params: HKTaskParams): ] setup_plotting() - rst = nse.metrics.compute_metrics(metrics, test_y1, test_y2) + rst = helia.metrics.compute_metrics(metrics, test_y1, test_y2) rst["flops"] = flops rst["parameters"] = model.count_params() rst = {k: float(v) for k, v in rst.items()} diff --git a/heartkit/tasks/foundation/export.py b/heartkit/tasks/foundation/export.py index 7104191c..1f5a6bcb 100644 --- a/heartkit/tasks/foundation/export.py +++ b/heartkit/tasks/foundation/export.py @@ -4,7 +4,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -18,7 +18,7 @@ def export(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") logger.debug(f"Creating working directory in {params.job_dir}") tfl_model_path = params.job_dir / "model.tflite" @@ -35,21 +35,21 @@ def export(params: HKTaskParams): else: test_ds = load_test_dataset(datasets=datasets, params=params) - test_x = np.concatenate([x[nse.trainers.SimCLRTrainer.SAMPLES] for x in test_ds.as_numpy_iterator()]) + test_x = np.concatenate([x[helia.trainers.SimCLRTrainer.SAMPLES] for x in test_ds.as_numpy_iterator()]) # Load model and set fixed batch size of 1 logger.debug("Loading trained model") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) inputs = keras.Input(shape=feat_shape, batch_size=1, dtype="float32") model(inputs) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.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"Converting model to TFLite (quantization={params.quantization.mode})") - converter = nse.converters.tflite.TfLiteKerasConverter(model=model) + converter = helia.converters.tflite.TfLiteKerasConverter(model=model) tflite_content = converter.convert( test_x=test_x, @@ -72,7 +72,7 @@ def export(params: HKTaskParams): converter.export_header(tflm_model_path, name=params.tflm_var_name) converter.cleanup() - tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) + tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) tflite.compile() logger.debug("Validating model results") @@ -84,7 +84,7 @@ def export(params: HKTaskParams): keras.metrics.MeanSquaredError(name="mse"), ] - tfl_rst = nse.metrics.compute_metrics(metrics, y_pred_tf, y_pred_tfl) + tfl_rst = helia.metrics.compute_metrics(metrics, y_pred_tf, y_pred_tfl) logger.info("[TFL METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tfl_rst.items()])) if params.tflm_file and tflm_model_path != params.tflm_file: diff --git a/heartkit/tasks/foundation/train.py b/heartkit/tasks/foundation/train.py index ef788f07..77dea531 100644 --- a/heartkit/tasks/foundation/train.py +++ b/heartkit/tasks/foundation/train.py @@ -4,7 +4,7 @@ import wandb import numpy as np from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...models import ModelFactory @@ -20,18 +20,18 @@ def train(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") logger.debug(f"Creating working directory in {params.job_dir}") params.temperature = float(getattr(params, "temperature", 0.1)) - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") with open(params.job_dir / "configuration.json", "w", encoding="utf-8") as fp: fp.write(params.model_dump_json(indent=2)) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): wandb.init(project=params.project, entity="ambiq", dir=params.job_dir) wandb.config.update(params.model_dump()) # END IF @@ -56,7 +56,7 @@ def train(params: HKTaskParams): num_classes=None, ) - flops = nse.metrics.flops.get_flops(encoder, batch_size=1, fpath=params.job_dir / "encoder_flops.log") + flops = helia.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") @@ -67,14 +67,14 @@ def train(params: HKTaskParams): # projector_output = keras.layers.Dense(projection_width, activation="relu6")(projector_input) # projector_output = keras.layers.Dense(projection_width)(projector_output) # projector = keras.Model(inputs=projector_input, outputs=projector_output, name="projector") - # flops = nse.metrics.flops.get_flops(projector, batch_size=1, fpath=params.job_dir / "projector_flops.log") + # flops = helia.metrics.flops.get_flops(projector, batch_size=1, fpath=params.job_dir / "projector_flops.log") # projector.summary(print_fn=logger.info) # logger.debug(f"Projector requires {flops/1e6:0.2f} MFLOPS") if params.model_file is None: params.model_file = params.job_dir / "model.keras" - model = nse.trainers.SimCLRTrainer( + model = helia.trainers.SimCLRTrainer( encoder=encoder, projector=None, ) @@ -92,12 +92,12 @@ def get_scheduler(): model.compile( encoder_optimizer=keras.optimizers.Adam(get_scheduler()), - encoder_loss=nse.losses.simclr.SimCLRLoss(temperature=params.temperature), + encoder_loss=helia.losses.simclr.SimCLRLoss(temperature=params.temperature), encoder_metrics=[keras.metrics.MeanSquaredError(name="mse"), keras.metrics.CosineSimilarity(name="cos")], ) ModelCheckpoint = keras.callbacks.ModelCheckpoint - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): ModelCheckpoint = WandbModelCheckpoint model_callbacks = [ @@ -117,19 +117,19 @@ def get_scheduler(): ), keras.callbacks.CSVLogger(params.job_dir / "history.csv"), ] - if nse.utils.env_flag("TENSORBOARD"): + if helia.utils.env_flag("TENSORBOARD"): model_callbacks.append( keras.callbacks.TensorBoard( log_dir=params.job_dir, write_steps_per_second=True, ) ) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): model_callbacks.append(WandbMetricsLogger()) # Use minimal progress bar if params.verbose <= 1: model_callbacks.append( - nse.callbacks.TQDMProgressBar( + helia.callbacks.TQDMProgressBar( show_epoch_progress=False, ) ) @@ -149,7 +149,7 @@ def get_scheduler(): setup_plotting() if history: - nse.plotting.plot_history_metrics( + helia.plotting.plot_history_metrics( history.history, metrics=["loss", "cos"], save_path=params.job_dir / "history.png", diff --git a/heartkit/tasks/rhythm/dataloaders/__init__.py b/heartkit/tasks/rhythm/dataloaders/__init__.py index 09ecea91..df8c5bfb 100644 --- a/heartkit/tasks/rhythm/dataloaders/__init__.py +++ b/heartkit/tasks/rhythm/dataloaders/__init__.py @@ -10,7 +10,7 @@ """ -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader @@ -19,7 +19,7 @@ from .ptbxl import PtbxlDataloader from .lsad import LsadDataloader -RhythmDataloaderFactory = nse.utils.create_factory(factory="HKRhythmDataloaderFactory", type=HKDataloader) +RhythmDataloaderFactory = helia.utils.create_factory(factory="HKRhythmDataloaderFactory", type=HKDataloader) RhythmDataloaderFactory.register("icentia11k", Icentia11kDataloader) RhythmDataloaderFactory.register("icentia_mini", IcentiaMiniDataloader) RhythmDataloaderFactory.register("ptbxl", PtbxlDataloader) diff --git a/heartkit/tasks/rhythm/dataloaders/icentia11k.py b/heartkit/tasks/rhythm/dataloaders/icentia11k.py index d6894bd6..711d4a07 100644 --- a/heartkit/tasks/rhythm/dataloaders/icentia11k.py +++ b/heartkit/tasks/rhythm/dataloaders/icentia11k.py @@ -4,7 +4,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, IcentiaDataset, IcentiaRhythm from ..defines import HKRhythm @@ -145,7 +145,7 @@ def data_generator( samples_per_tgt = num_per_tgt * [num_classes] self._pts_beat_map = {} - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_tgt): yield x, y # END FOR diff --git a/heartkit/tasks/rhythm/dataloaders/icentia_mini.py b/heartkit/tasks/rhythm/dataloaders/icentia_mini.py index 591a3892..693271bf 100644 --- a/heartkit/tasks/rhythm/dataloaders/icentia_mini.py +++ b/heartkit/tasks/rhythm/dataloaders/icentia_mini.py @@ -4,7 +4,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, IcentiaMiniDataset, IcentiaMiniRhythm from ..defines import HKRhythm @@ -110,7 +110,7 @@ def data_generator( num_per_tgt = int(max(1, samples_per_patient / num_classes)) samples_per_tgt = num_per_tgt * [num_classes] - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_tgt): yield x, y # END FOR diff --git a/heartkit/tasks/rhythm/dataloaders/lsad.py b/heartkit/tasks/rhythm/dataloaders/lsad.py index 84b1bbd4..c3b27722 100644 --- a/heartkit/tasks/rhythm/dataloaders/lsad.py +++ b/heartkit/tasks/rhythm/dataloaders/lsad.py @@ -1,7 +1,7 @@ from typing import Generator import numpy.typing as npt -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, LsadDataset, LsadScpCode from ..defines import HKRhythm @@ -45,7 +45,7 @@ def data_generator( shuffle: bool = False, ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]: return self.ds.signal_label_generator( - patient_generator=nse.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), + patient_generator=helia.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), frame_size=self.frame_size, samples_per_patient=samples_per_patient, target_rate=self.sampling_rate, diff --git a/heartkit/tasks/rhythm/dataloaders/ptbxl.py b/heartkit/tasks/rhythm/dataloaders/ptbxl.py index 741d1ca2..6bead15d 100644 --- a/heartkit/tasks/rhythm/dataloaders/ptbxl.py +++ b/heartkit/tasks/rhythm/dataloaders/ptbxl.py @@ -1,7 +1,7 @@ from typing import Generator import numpy.typing as npt -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, PtbxlDataset, PtbxlScpCode from ..defines import HKRhythm @@ -36,7 +36,7 @@ def data_generator( shuffle: bool = False, ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]: return self.ds.signal_label_generator( - patient_generator=nse.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), + patient_generator=helia.utils.uniform_id_generator(patient_ids, repeat=True, shuffle=shuffle), frame_size=self.frame_size, samples_per_patient=samples_per_patient, target_rate=self.sampling_rate, diff --git a/heartkit/tasks/rhythm/datasets.py b/heartkit/tasks/rhythm/datasets.py index 227df8e4..5e16d51f 100644 --- a/heartkit/tasks/rhythm/datasets.py +++ b/heartkit/tasks/rhythm/datasets.py @@ -1,5 +1,5 @@ import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import ( HKDataset, @@ -9,7 +9,7 @@ from ..utils import load_train_dataloader_split, load_test_dataloader_split from .dataloaders import RhythmDataloaderFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def create_data_pipeline( diff --git a/heartkit/tasks/rhythm/demo.py b/heartkit/tasks/rhythm/demo.py index f9ccd67b..eb14aa1b 100644 --- a/heartkit/tasks/rhythm/demo.py +++ b/heartkit/tasks/rhythm/demo.py @@ -7,7 +7,7 @@ import plotly.graph_objects as go from plotly.subplots import make_subplots from tqdm import tqdm -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...backends import BackendFactory @@ -21,7 +21,7 @@ def demo(params: HKTaskParams): Args: params (HKTaskParams): Task parameters """ - logger = nse.utils.setup_logger(__name__, level=params.verbose) + logger = helia.utils.setup_logger(__name__, level=params.verbose) plot_theme = setup_plotting() @@ -40,7 +40,7 @@ def demo(params: HKTaskParams): ds = random.choice(datasets) ds_gen = ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), + patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), frame_size=params.demo_size, samples_per_patient=5, target_rate=params.sampling_rate, diff --git a/heartkit/tasks/rhythm/evaluate.py b/heartkit/tasks/rhythm/evaluate.py index 3bc6d5e2..b82ac936 100644 --- a/heartkit/tasks/rhythm/evaluate.py +++ b/heartkit/tasks/rhythm/evaluate.py @@ -4,7 +4,7 @@ import numpy as np import keras import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -18,10 +18,10 @@ def evaluate(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") class_names = params.class_names or [f"Class {i}" for i in range(params.num_classes)] @@ -39,8 +39,8 @@ def evaluate(params: HKTaskParams): test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()]) logger.debug("Loading model") - model = nse.models.load_model(params.model_file) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + model = helia.models.load_model(params.model_file) + flops = helia.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") @@ -55,7 +55,7 @@ def evaluate(params: HKTaskParams): y_pred = np.argmax(y_prob, axis=-1) if params.threshold: prev_numel = len(y_true) - indices = nse.metrics.threshold.get_predicted_threshold_indices(y_prob, y_pred, params.threshold) + indices = helia.metrics.threshold.get_predicted_threshold_indices(y_prob, y_pred, params.threshold) test_x, test_y = test_x[indices], test_y[indices] y_true, y_pred = y_true[indices], y_pred[indices] rst = model.evaluate(test_x, test_y, verbose=params.verbose, return_dict=True) @@ -64,8 +64,8 @@ def evaluate(params: HKTaskParams): # END IF cm_path = params.job_dir / "confusion_matrix_test.png" - nse.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") - nse.plotting.px_plot_confusion_matrix( + helia.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") + helia.plotting.px_plot_confusion_matrix( y_true, y_pred, labels=class_names, diff --git a/heartkit/tasks/rhythm/export.py b/heartkit/tasks/rhythm/export.py index e22a2496..d94338c7 100644 --- a/heartkit/tasks/rhythm/export.py +++ b/heartkit/tasks/rhythm/export.py @@ -6,7 +6,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -19,7 +19,7 @@ def export(params: HKTaskParams): Args: params (HKTaskParams): Task parameters """ - logger = nse.utils.setup_logger(__name__, level=params.verbose) + logger = helia.utils.setup_logger(__name__, level=params.verbose) os.makedirs(params.job_dir, exist_ok=True) logger.debug(f"Creating working directory in {params.job_dir}") @@ -47,22 +47,22 @@ def export(params: HKTaskParams): # Load model and set fixed batch size of 1 logger.debug("Loading trained model") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) # Add softmax layer if required if not params.use_logits and not isinstance(model.layers[-1], keras.layers.Softmax): - model = nse.models.append_layers(model, layers=[keras.layers.Softmax()], copy_weights=True) + model = helia.models.append_layers(model, layers=[keras.layers.Softmax()], copy_weights=True) # END IF inputs = keras.Input(shape=feat_shape, batch_size=1, name="input", dtype="float32") model(inputs) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.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"Converting model to TFLite (quantization={params.quantization.format})") - converter = nse.converters.tflite.TfLiteKerasConverter(model=model) + converter = helia.converters.tflite.TfLiteKerasConverter(model=model) tflite_content = converter.convert( test_x=test_x, @@ -85,7 +85,7 @@ def export(params: HKTaskParams): converter.export_header(tflm_model_path, name=params.tflm_var_name) converter.cleanup() - tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) + tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) tflite.compile() # Verify TFLite results match TF results @@ -103,8 +103,8 @@ def export(params: HKTaskParams): y_pred_tf = model.predict(test_x) y_pred_tfl = tflite.predict(x=test_x) - tf_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tf) - tfl_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tfl) + tf_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tf) + tfl_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tfl) logger.info("[TF METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tf_rst.items()])) logger.info("[TFL METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tfl_rst.items()])) diff --git a/heartkit/tasks/rhythm/train.py b/heartkit/tasks/rhythm/train.py index 27ab4b88..48382ba2 100644 --- a/heartkit/tasks/rhythm/train.py +++ b/heartkit/tasks/rhythm/train.py @@ -5,7 +5,7 @@ import sklearn.utils import wandb from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -22,16 +22,16 @@ def train(params: HKTaskParams): """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") with open(params.job_dir / "configuration.json", "w", encoding="utf-8") as fp: fp.write(params.model_dump_json(indent=2)) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): wandb.init( project=params.project, entity="ambiq", @@ -75,7 +75,7 @@ def train(params: HKTaskParams): # Load existing model if params.resume and params.model_file: logger.debug(f"Loading model from file {params.model_file}") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) params.model_file = None else: logger.debug("Creating model from scratch") @@ -88,7 +88,7 @@ def train(params: HKTaskParams): ) # END IF - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") t_mul = 1 first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1) @@ -119,7 +119,7 @@ def train(params: HKTaskParams): logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): ModelCheckpoint = WandbModelCheckpoint model_callbacks = [ keras.callbacks.EarlyStopping( @@ -138,19 +138,19 @@ def train(params: HKTaskParams): ), keras.callbacks.CSVLogger(params.job_dir / "history.csv"), ] - if nse.utils.env_flag("TENSORBOARD"): + if helia.utils.env_flag("TENSORBOARD"): model_callbacks.append( keras.callbacks.TensorBoard( log_dir=params.job_dir, write_steps_per_second=True, ) ) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): model_callbacks.append(WandbMetricsLogger()) # Use minimal progress bar if params.verbose <= 1: model_callbacks.append( - nse.callbacks.TQDMProgressBar( + helia.callbacks.TQDMProgressBar( show_epoch_progress=False, ) ) @@ -171,7 +171,7 @@ def train(params: HKTaskParams): setup_plotting(dark_theme) if history: - nse.plotting.plot_history_metrics( + helia.plotting.plot_history_metrics( history.history, metrics=["loss", "acc"], save_path=params.job_dir / "history.png", @@ -185,8 +185,8 @@ def train(params: HKTaskParams): y_pred = np.argmax(model.predict(val_ds), axis=-1) cm_path = params.job_dir / "confusion_matrix.png" - nse.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") - if nse.utils.env_flag("WANDB"): + helia.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") + if helia.utils.env_flag("WANDB"): conf_mat = wandb.plot.confusion_matrix(preds=y_pred, y_true=y_true, class_names=class_names) wandb.log({"conf_mat": conf_mat}) # END IF diff --git a/heartkit/tasks/segmentation/dataloaders/__init__.py b/heartkit/tasks/segmentation/dataloaders/__init__.py index 64dd41c5..765e12b1 100644 --- a/heartkit/tasks/segmentation/dataloaders/__init__.py +++ b/heartkit/tasks/segmentation/dataloaders/__init__.py @@ -10,7 +10,7 @@ """ -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader @@ -20,7 +20,7 @@ from .ecg_synthetic import EcgSyntheticDataloader from .ppg_synthetic import PPgSyntheticDataloader -SegmentationDataloaderFactory = nse.utils.create_factory(factory="HKSegmentationDataloaderFactory", type=HKDataloader) +SegmentationDataloaderFactory = helia.utils.create_factory(factory="HKSegmentationDataloaderFactory", type=HKDataloader) SegmentationDataloaderFactory.register("icentia11k", Icentia11kDataloader) SegmentationDataloaderFactory.register("ludb", LudbDataloader) SegmentationDataloaderFactory.register("ptbxl", PtbxlDataloader) diff --git a/heartkit/tasks/segmentation/dataloaders/ecg_synthetic.py b/heartkit/tasks/segmentation/dataloaders/ecg_synthetic.py index cab39684..35f25b26 100644 --- a/heartkit/tasks/segmentation/dataloaders/ecg_synthetic.py +++ b/heartkit/tasks/segmentation/dataloaders/ecg_synthetic.py @@ -4,7 +4,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import EcgSyntheticDataset, HKDataloader from ..defines import HKSegment @@ -82,7 +82,7 @@ def data_generator( if isinstance(samples_per_patient, Iterable): samples_per_patient = samples_per_patient[0] - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_patient): yield x, y # END FOR diff --git a/heartkit/tasks/segmentation/dataloaders/icentia11k.py b/heartkit/tasks/segmentation/dataloaders/icentia11k.py index 5f652c21..95cdb82b 100644 --- a/heartkit/tasks/segmentation/dataloaders/icentia11k.py +++ b/heartkit/tasks/segmentation/dataloaders/icentia11k.py @@ -3,7 +3,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import IcentiaBeat, IcentiaDataset, HKDataloader from ..defines import HKSegment @@ -98,7 +98,7 @@ def data_generator( if isinstance(samples_per_patient, Iterable): samples_per_patient = samples_per_patient[0] - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_patient): yield x, y # END FOR diff --git a/heartkit/tasks/segmentation/dataloaders/ludb.py b/heartkit/tasks/segmentation/dataloaders/ludb.py index ab7275b4..88ae9117 100644 --- a/heartkit/tasks/segmentation/dataloaders/ludb.py +++ b/heartkit/tasks/segmentation/dataloaders/ludb.py @@ -4,7 +4,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, LudbDataset, LudbSegmentation from ....datasets.ludb import FID_LOC_IDX, SEG_BEG_IDX, SEG_END_IDX, SEG_LBL_IDX, SEG_LEAD_IDX @@ -76,7 +76,7 @@ def data_generator( if isinstance(samples_per_patient, Iterable): samples_per_patient = samples_per_patient[0] - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_patient): yield x, y # END FOR diff --git a/heartkit/tasks/segmentation/dataloaders/ppg_synthetic.py b/heartkit/tasks/segmentation/dataloaders/ppg_synthetic.py index fffe8aab..d3467343 100644 --- a/heartkit/tasks/segmentation/dataloaders/ppg_synthetic.py +++ b/heartkit/tasks/segmentation/dataloaders/ppg_synthetic.py @@ -3,7 +3,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import PpgSyntheticDataset, HKDataloader from ..defines import HKSegment @@ -73,7 +73,7 @@ def data_generator( if isinstance(samples_per_patient, Iterable): samples_per_patient = samples_per_patient[0] - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_patient): yield x, y # END FOR diff --git a/heartkit/tasks/segmentation/dataloaders/ptbxl.py b/heartkit/tasks/segmentation/dataloaders/ptbxl.py index 8f339fde..db4e7b97 100644 --- a/heartkit/tasks/segmentation/dataloaders/ptbxl.py +++ b/heartkit/tasks/segmentation/dataloaders/ptbxl.py @@ -4,7 +4,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader, PtbxlDataset from ..defines import HKSegment @@ -89,7 +89,7 @@ def data_generator( if isinstance(samples_per_patient, Iterable): samples_per_patient = samples_per_patient[0] - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_patient): yield x, y # END FOR diff --git a/heartkit/tasks/segmentation/datasets.py b/heartkit/tasks/segmentation/datasets.py index 5f38957d..e925682f 100644 --- a/heartkit/tasks/segmentation/datasets.py +++ b/heartkit/tasks/segmentation/datasets.py @@ -1,12 +1,12 @@ import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import HKDataset, create_augmentation_pipeline from ...defines import HKTaskParams, NamedParams from ..utils import load_train_dataloader_split, load_test_dataloader_split from .dataloaders import SegmentationDataloaderFactory as DataloaderFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def create_data_pipeline( diff --git a/heartkit/tasks/segmentation/demo.py b/heartkit/tasks/segmentation/demo.py index da678409..de03e645 100644 --- a/heartkit/tasks/segmentation/demo.py +++ b/heartkit/tasks/segmentation/demo.py @@ -7,7 +7,7 @@ import plotly.graph_objects as go from plotly.subplots import make_subplots from tqdm import tqdm -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...backends import BackendFactory @@ -22,7 +22,7 @@ def demo(params: HKTaskParams): Args: params (HKTaskParams): Demo parameters """ - logger = nse.utils.setup_logger(__name__, level=params.verbose) + logger = helia.utils.setup_logger(__name__, level=params.verbose) plot_theme = setup_plotting() signal_type = getattr(params, "signal_type", "ECG").upper() # ECG or PPG @@ -41,7 +41,7 @@ def demo(params: HKTaskParams): ds = random.choice(datasets) ds_gen = ds.signal_generator( - patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), + patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False), frame_size=params.demo_size, samples_per_patient=5, target_rate=params.sampling_rate, diff --git a/heartkit/tasks/segmentation/evaluate.py b/heartkit/tasks/segmentation/evaluate.py index 3c751c30..2f3ee232 100644 --- a/heartkit/tasks/segmentation/evaluate.py +++ b/heartkit/tasks/segmentation/evaluate.py @@ -4,7 +4,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -17,9 +17,9 @@ def evaluate(params: HKTaskParams): Args: params (HKTaskParams): Evaluation parameters """ - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") os.makedirs(params.job_dir, exist_ok=True) @@ -39,8 +39,8 @@ def evaluate(params: HKTaskParams): test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()]) logger.debug("Loading model") - model = nse.models.load_model(params.model_file) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + model = helia.models.load_model(params.model_file) + flops = helia.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") @@ -56,8 +56,8 @@ def evaluate(params: HKTaskParams): y_pred = y_pred.flatten() cm_path = params.job_dir / "confusion_matrix_test.png" - nse.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") - nse.plotting.px_plot_confusion_matrix( + helia.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") + helia.plotting.px_plot_confusion_matrix( y_true, y_pred, labels=class_names, diff --git a/heartkit/tasks/segmentation/export.py b/heartkit/tasks/segmentation/export.py index 0f13bf43..d0527035 100644 --- a/heartkit/tasks/segmentation/export.py +++ b/heartkit/tasks/segmentation/export.py @@ -4,7 +4,7 @@ import keras import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -18,7 +18,7 @@ def export(params: HKTaskParams): params (HKTaskParams): Deployment parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") logger.debug(f"Creating working directory in {params.job_dir}") tfl_model_path = params.job_dir / "model.tflite" @@ -46,22 +46,22 @@ def export(params: HKTaskParams): # Load model and set fixed batch size of 1 logger.debug("Loading trained model") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) # Add softmax layer if required if not params.use_logits and not isinstance(model.layers[-1], keras.layers.Softmax): - model = nse.models.append_layers(model, layers=[keras.layers.Softmax()], copy_weights=True) + model = helia.models.append_layers(model, layers=[keras.layers.Softmax()], copy_weights=True) # END IF inputs = keras.Input(feat_shape, batch_size=1, name="input", dtype="float32") model(inputs) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.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"Converting model to TFLite (quantization={params.quantization.format})") - converter = nse.converters.tflite.TfLiteKerasConverter(model=model) + converter = helia.converters.tflite.TfLiteKerasConverter(model=model) tflite_content = converter.convert( test_x=test_x, @@ -84,14 +84,14 @@ def export(params: HKTaskParams): converter.export_header(tflm_model_path, name=params.tflm_var_name) converter.cleanup() - tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) + tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) tflite.compile() # Verify TFLite results match TF results on example data metrics = [ keras.metrics.CategoricalCrossentropy(name="loss", from_logits=params.use_logits), keras.metrics.CategoricalAccuracy(name="acc"), - nse.metrics.MultiF1Score(name="f1", average="weighted"), + helia.metrics.MultiF1Score(name="f1", average="weighted"), keras.metrics.OneHotIoU( num_classes=params.num_classes, target_class_ids=classes, @@ -107,8 +107,8 @@ def export(params: HKTaskParams): y_pred_tf = model.predict(test_x) y_pred_tfl = tflite.predict(x=test_x) - tf_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tf) - tfl_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tfl) + tf_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tf) + tfl_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tfl) logger.info("[TF METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tf_rst.items()])) logger.info("[TFL METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tfl_rst.items()])) diff --git a/heartkit/tasks/segmentation/train.py b/heartkit/tasks/segmentation/train.py index 07f31552..00097ce3 100644 --- a/heartkit/tasks/segmentation/train.py +++ b/heartkit/tasks/segmentation/train.py @@ -5,7 +5,7 @@ import sklearn.utils import wandb from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -21,16 +21,16 @@ def train(params: HKTaskParams): params (HKTaskParams): Training parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") with open(params.job_dir / "configuration.json", "w", encoding="utf-8") as fp: fp.write(params.model_dump_json(indent=2)) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): wandb.init( project=f"hk-segmentation-{params.num_classes}", entity="ambiq", @@ -76,7 +76,7 @@ def train(params: HKTaskParams): if params.resume and params.model_file: logger.debug(f"Loading model from file {params.model_file}") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) params.model_file = None else: logger.debug("Creating model from scratch") @@ -87,7 +87,7 @@ def train(params: HKTaskParams): ) # END IF - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") t_mul = 1 first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1) @@ -103,7 +103,7 @@ def train(params: HKTaskParams): from_logits=True, alpha=class_weights, ) - metrics = [keras.metrics.CategoricalAccuracy(name="acc"), nse.metrics.MultiF1Score(name="f1", average="weighted")] + metrics = [keras.metrics.CategoricalAccuracy(name="acc"), helia.metrics.MultiF1Score(name="f1", average="weighted")] if params.resume and params.weights_file: logger.debug(f"Hydrating model weights from file {params.weights_file}") @@ -118,7 +118,7 @@ def train(params: HKTaskParams): logger.debug(f"Model requires {flops / 1e6:0.2f} MFLOPS") ModelCheckpoint = keras.callbacks.ModelCheckpoint - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): ModelCheckpoint = WandbModelCheckpoint model_callbacks = [ keras.callbacks.EarlyStopping( @@ -138,19 +138,19 @@ def train(params: HKTaskParams): ), keras.callbacks.CSVLogger(params.job_dir / "history.csv"), ] - if nse.utils.env_flag("TENSORBOARD"): + if helia.utils.env_flag("TENSORBOARD"): model_callbacks.append( keras.callbacks.TensorBoard( log_dir=params.job_dir, write_steps_per_second=True, ) ) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): model_callbacks.append(WandbMetricsLogger()) # Use minimal progress bar if params.verbose <= 1: model_callbacks.append( - nse.callbacks.TQDMProgressBar( + helia.callbacks.TQDMProgressBar( show_epoch_progress=False, ) ) @@ -170,7 +170,7 @@ def train(params: HKTaskParams): setup_plotting(dark_theme) if history: - nse.plotting.plot_history_metrics( + helia.plotting.plot_history_metrics( history.history, metrics=["loss", "acc"], save_path=params.job_dir / "history.png", @@ -185,8 +185,8 @@ def train(params: HKTaskParams): y_pred = np.argmax(y_pred, axis=-1).flatten() cm_path = params.job_dir / "confusion_matrix.png" - nse.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") - if nse.utils.env_flag("WANDB"): + helia.plotting.confusion_matrix_plot(y_true, y_pred, labels=class_names, save_path=cm_path, normalize="true") + if helia.utils.env_flag("WANDB"): conf_mat = wandb.plot.confusion_matrix(preds=y_pred, y_true=y_true, class_names=class_names) wandb.log({"conf_mat": conf_mat}) # END IF diff --git a/heartkit/tasks/task.py b/heartkit/tasks/task.py index f1ff18d7..94959c57 100644 --- a/heartkit/tasks/task.py +++ b/heartkit/tasks/task.py @@ -1,7 +1,7 @@ import abc import os -import neuralspot_edge as nse +import helia_edge as helia from ..datasets import DatasetFactory, HKDataset from ..defines import HKTaskParams @@ -29,7 +29,7 @@ def download(params: HKTaskParams) -> None: """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "download.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "download.log") logger.debug(f"Creating working directory in {params.job_dir}") for ds in params.datasets: diff --git a/heartkit/tasks/translate/dataloaders/__init__.py b/heartkit/tasks/translate/dataloaders/__init__.py index 84748ee4..e2ad68e2 100644 --- a/heartkit/tasks/translate/dataloaders/__init__.py +++ b/heartkit/tasks/translate/dataloaders/__init__.py @@ -7,11 +7,11 @@ """ -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import HKDataloader from .bidmc import BidmcDataloader -TranslateTaskFactory = nse.utils.create_factory(factory="HKTranslateTaskFactory", type=HKDataloader) +TranslateTaskFactory = helia.utils.create_factory(factory="HKTranslateTaskFactory", type=HKDataloader) TranslateTaskFactory.register("bidmc", BidmcDataloader) diff --git a/heartkit/tasks/translate/dataloaders/bidmc.py b/heartkit/tasks/translate/dataloaders/bidmc.py index bebf27fb..15640345 100644 --- a/heartkit/tasks/translate/dataloaders/bidmc.py +++ b/heartkit/tasks/translate/dataloaders/bidmc.py @@ -3,7 +3,7 @@ import numpy as np import numpy.typing as npt import physiokit as pk -import neuralspot_edge as nse +import helia_edge as helia from ....datasets import BidmcDataset, HKDataloader from ..defines import HKTranslate @@ -63,7 +63,7 @@ def data_generator( if isinstance(samples_per_patient, Iterable): samples_per_patient = samples_per_patient[0] - for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle): + for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle): for x, y in self.patient_data_generator(pt_id, samples_per_patient): yield x, y # END FOR diff --git a/heartkit/tasks/translate/datasets.py b/heartkit/tasks/translate/datasets.py index 586553b0..6515df8c 100644 --- a/heartkit/tasks/translate/datasets.py +++ b/heartkit/tasks/translate/datasets.py @@ -1,12 +1,12 @@ import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...datasets import HKDataset, create_augmentation_pipeline from ...defines import HKTaskParams, NamedParams from ..utils import load_train_dataloader_split, load_test_dataloader_split from .dataloaders import TranslateTaskFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def create_data_pipeline( diff --git a/heartkit/tasks/translate/demo.py b/heartkit/tasks/translate/demo.py index ad97cb1c..e92315db 100644 --- a/heartkit/tasks/translate/demo.py +++ b/heartkit/tasks/translate/demo.py @@ -4,13 +4,13 @@ import plotly.graph_objects as go from plotly.subplots import make_subplots from tqdm import tqdm -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...backends import BackendFactory from ...datasets import DatasetFactory -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def demo(params: HKTaskParams): @@ -43,7 +43,7 @@ def demo(params: HKTaskParams): # ds, frame_size=params.demo_size, samples_per_patient=5, target_rate=params.sampling_rate # ) - # ds_gen = ds_gen(patient_generator=nse.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False)) + # ds_gen = ds_gen(patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids(), repeat=False)) # x, y = next(ds_gen) diff --git a/heartkit/tasks/translate/evaluate.py b/heartkit/tasks/translate/evaluate.py index 64c391be..0882b08d 100644 --- a/heartkit/tasks/translate/evaluate.py +++ b/heartkit/tasks/translate/evaluate.py @@ -3,7 +3,7 @@ import keras import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -16,9 +16,9 @@ def evaluate(params: HKTaskParams): Args: params (HKTaskParams): Task parameters """ - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "test.log") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") os.makedirs(params.job_dir, exist_ok=True) @@ -36,8 +36,8 @@ def evaluate(params: HKTaskParams): test_x, test_y = next(test_ds.batch(params.test_size).as_numpy_iterator()) logger.debug("Loading model") - model = nse.models.load_model(params.model_file) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + model = helia.models.load_model(params.model_file) + flops = helia.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") diff --git a/heartkit/tasks/translate/export.py b/heartkit/tasks/translate/export.py index 16d21019..8d3abecd 100644 --- a/heartkit/tasks/translate/export.py +++ b/heartkit/tasks/translate/export.py @@ -3,7 +3,7 @@ import keras import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ...defines import HKTaskParams from ...datasets import DatasetFactory @@ -17,7 +17,7 @@ def export(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "export.log") logger.debug(f"Creating working directory in {params.job_dir}") tfl_model_path = params.job_dir / "model.tflite" @@ -38,16 +38,16 @@ def export(params: HKTaskParams): # Load model and set fixed batch size of 1 logger.debug("Loading trained model") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) inputs = keras.Input(shape=feat_shape, batch_size=1, name="input", dtype="float32") model(inputs) - flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / "model_flops.log") + flops = helia.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"Converting model to TFLite (quantization={params.quantization.mode})") - converter = nse.converters.tflite.TfLiteKerasConverter(model=model) + converter = helia.converters.tflite.TfLiteKerasConverter(model=model) tflite_content = converter.convert( test_x=test_x, @@ -70,7 +70,7 @@ def export(params: HKTaskParams): converter.export_header(tflm_model_path, name=params.tflm_var_name) converter.cleanup() - tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) + tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content) tflite.compile() # Verify TFLite results match TF results on example data @@ -88,8 +88,8 @@ def export(params: HKTaskParams): y_pred_tf = model.predict(test_x) y_pred_tfl = tflite.predict(x=test_x) - tf_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tf) - tfl_rst = nse.metrics.compute_metrics(metrics, y_true, y_pred_tfl) + tf_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tf) + tfl_rst = helia.metrics.compute_metrics(metrics, y_true, y_pred_tfl) logger.info("[TF METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tf_rst.items()])) logger.info("[TFL METRICS] " + " ".join([f"{k.upper()}={v:.4f}" for k, v in tfl_rst.items()])) diff --git a/heartkit/tasks/translate/train.py b/heartkit/tasks/translate/train.py index 29073b70..0686a667 100644 --- a/heartkit/tasks/translate/train.py +++ b/heartkit/tasks/translate/train.py @@ -1,7 +1,7 @@ import os import keras -import neuralspot_edge as nse +import helia_edge as helia import numpy as np import wandb from wandb.integration.keras import WandbMetricsLogger, WandbModelCheckpoint @@ -19,16 +19,16 @@ def train(params: HKTaskParams): params (HKTaskParams): Task parameters """ os.makedirs(params.job_dir, exist_ok=True) - logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") + logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / "train.log") logger.debug(f"Creating working directory in {params.job_dir}") - params.seed = nse.utils.set_random_seed(params.seed) + params.seed = helia.utils.set_random_seed(params.seed) logger.debug(f"Random seed {params.seed}") with open(params.job_dir / "configuration.json", "w", encoding="utf-8") as fp: fp.write(params.model_dump_json(indent=2)) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): wandb.init( project=params.project, entity="ambiq", @@ -55,7 +55,7 @@ def train(params: HKTaskParams): inputs = keras.Input(shape=feat_shape, name="input", dtype="float32") if params.resume and params.model_file: logger.debug(f"Loading model from file {params.model_file}") - model = nse.models.load_model(params.model_file) + model = helia.models.load_model(params.model_file) params.model_file = None else: logger.debug("Creating model from scratch") @@ -92,13 +92,13 @@ def train(params: HKTaskParams): params.model_file = params.job_dir / "model.keras" 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") + flops = helia.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") ModelCheckpoint = keras.callbacks.ModelCheckpoint - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): ModelCheckpoint = WandbModelCheckpoint model_callbacks = [ keras.callbacks.EarlyStopping( @@ -118,14 +118,14 @@ def train(params: HKTaskParams): ), keras.callbacks.CSVLogger(params.job_dir / "history.csv"), ] - if nse.utils.env_flag("TENSORBOARD"): + if helia.utils.env_flag("TENSORBOARD"): model_callbacks.append( keras.callbacks.TensorBoard( log_dir=params.job_dir, write_steps_per_second=True, ) ) - if nse.utils.env_flag("WANDB"): + if helia.utils.env_flag("WANDB"): model_callbacks.append(WandbMetricsLogger()) try: diff --git a/heartkit/tasks/utils.py b/heartkit/tasks/utils.py index 0c0fb62a..cea1d39c 100644 --- a/heartkit/tasks/utils.py +++ b/heartkit/tasks/utils.py @@ -11,18 +11,18 @@ import numpy as np import tensorflow as tf -import neuralspot_edge as nse +import helia_edge as helia from ..datasets import HKDataset, HKDataloader from ..defines import HKTaskParams -logger = nse.utils.setup_logger(__name__) +logger = helia.utils.setup_logger(__name__) def load_train_dataloader_split( datasets: list[HKDataset], params: HKTaskParams, - factory: nse.utils.ItemFactory[HKDataloader], + factory: helia.utils.ItemFactory[HKDataloader], ) -> tuple[tf.data.Dataset, tf.data.Dataset]: """Create dataloader pipeline for training and validation sets @@ -69,7 +69,7 @@ def load_train_dataloader_split( def load_test_dataloader_split( datasets: list[HKDataset], params: HKTaskParams, - factory: nse.utils.ItemFactory[HKDataloader], + factory: helia.utils.ItemFactory[HKDataloader], ) -> tf.data.Dataset: """Create dataloader pipeline for test set diff --git a/notebooks/byot.ipynb b/notebooks/byot.ipynb index 2ef2a16d..9ec05f58 100644 --- a/notebooks/byot.ipynb +++ b/notebooks/byot.ipynb @@ -76,7 +76,7 @@ "import tensorflow as tf\n", "import numpy as np\n", "import numpy.typing as npt\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt" ] }, @@ -90,7 +90,7 @@ "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", + "helia.utils.silence_tensorflow()\n", "_ = hk.utils.setup_plotting(plot_theme)" ] }, @@ -163,7 +163,7 @@ " ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]:\n", " if isinstance(samples_per_patient, Iterable):\n", " samples_per_patient = samples_per_patient[0]\n", - " for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n", + " for pt_id in helia.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n", " for x, y in self.patient_data_generator(pt_id, samples_per_patient):\n", " yield x, y\n", " # END FOR\n", @@ -242,7 +242,7 @@ "metadata": {}, "outputs": [], "source": [ - "DataloaderFactory = nse.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n", + "DataloaderFactory = helia.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n", "DataloaderFactory.register(\"ptbxl\", PtbxlDataloader)" ] }, @@ -326,14 +326,14 @@ "\n", "def load_train_datasets(\n", " datasets: list[hk.HKDataset],\n", - " dataloaderFactory: nse.utils.ItemFactory[hk.HKDataloader],\n", + " dataloaderFactory: helia.utils.ItemFactory[hk.HKDataloader],\n", " params: hk.HKTaskParams,\n", ") -> tuple[tf.data.Dataset, tf.data.Dataset]:\n", " \"\"\"Loads training and validation datasets.\n", "\n", " Args:\n", " datasets(list[hk.HKDataset]): List of datasets to load.\n", - " dataloaderFactory(nse.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n", + " dataloaderFactory(helia.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n", " params(hk.HKTaskParams): Task parameters.\n", "\n", " Returns:\n", @@ -392,10 +392,10 @@ " \"\"\"\n", " os.makedirs(params.job_dir, exist_ok=True)\n", "\n", - " logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n", + " logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n", " logger.debug(f\"Creating working directory in {params.job_dir}\")\n", "\n", - " params.seed = nse.utils.set_random_seed(params.seed)\n", + " params.seed = helia.utils.set_random_seed(params.seed)\n", " logger.debug(f\"Random seed {params.seed}\")\n", "\n", " with open(params.job_dir / \"train_config.json\", \"w\", encoding=\"utf-8\") as fp:\n", @@ -416,7 +416,7 @@ " # Load existing model\n", " if params.resume and params.model_file:\n", " logger.debug(f\"Loading model from file {params.model_file}\")\n", - " model = nse.models.load_model(params.model_file)\n", + " model = helia.models.load_model(params.model_file)\n", " params.model_file = None\n", " else:\n", " logger.debug(\"Creating model from scratch\")\n", @@ -429,7 +429,7 @@ " )\n", " # END IF\n", "\n", - " flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n", + " flops = helia.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n", "\n", " t_mul = 1\n", " first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1)\n", @@ -482,7 +482,7 @@ " )\n", " logger.debug(f\"Model saved to {params.model_file}\")\n", "\n", - " nse.plotting.plot_history_metrics(\n", + " helia.plotting.plot_history_metrics(\n", " history.history,\n", " metrics=[\"loss\", metrics[0].name],\n", " save_path=params.job_dir / \"history.png\",\n", @@ -509,10 +509,10 @@ " params (HKTaskParams): Evaluation parameters\n", " \"\"\"\n", " os.makedirs(params.job_dir, exist_ok=True)\n", - " logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n", + " logger = helia.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n", " logger.debug(f\"Creating working directory in {params.job_dir}\")\n", "\n", - " params.seed = nse.utils.set_random_seed(params.seed)\n", + " params.seed = helia.utils.set_random_seed(params.seed)\n", " logger.debug(f\"Random seed {params.seed}\")\n", "\n", " datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n", @@ -522,7 +522,7 @@ " test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()])\n", "\n", " logger.debug(\"Loading model\")\n", - " model = nse.models.load_model(params.model_file)\n", + " model = helia.models.load_model(params.model_file)\n", "\n", " logger.debug(\"Performing inference\")\n", " rst = model.evaluate(test_ds, verbose=params.verbose, return_dict=True)\n", diff --git a/notebooks/ecg-foundation-model.ipynb b/notebooks/ecg-foundation-model.ipynb index 355f2320..ca5dde91 100644 --- a/notebooks/ecg-foundation-model.ipynb +++ b/notebooks/ecg-foundation-model.ipynb @@ -72,7 +72,7 @@ "import heartkit as hk\n", "import tensorflow as tf\n", "import numpy as np\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt\n", "from sklearn.manifold import TSNE" ] @@ -145,9 +145,9 @@ } ], "source": [ - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__, level=verbose)\n", + "logger = helia.utils.setup_logger(__name__, level=verbose)\n", "\n", "os.makedirs(job_dir, exist_ok=True)\n", "logger.info(f\"Job directory: {job_dir}\")" @@ -334,27 +334,27 @@ ")\n", "noises = noises.astype(np.float32)\n", "\n", - "preprocessor = nse.layers.preprocessing.LayerNormalization1D(epsilon=epsilon, name=\"LayerNormalization\")\n", + "preprocessor = helia.layers.preprocessing.LayerNormalization1D(epsilon=epsilon, name=\"LayerNormalization\")\n", "\n", - "augmenter = nse.layers.preprocessing.AugmentationPipeline(\n", + "augmenter = helia.layers.preprocessing.AugmentationPipeline(\n", " layers=[\n", - " nse.layers.preprocessing.RandomNoiseDistortion1D(\n", + " helia.layers.preprocessing.RandomNoiseDistortion1D(\n", " sample_rate=sampling_rate, amplitude=(0, 1.0), frequency=(0.5, 1.5), name=\"BaselineWander\"\n", " ),\n", - " nse.layers.preprocessing.RandomSineWave(\n", + " helia.layers.preprocessing.RandomSineWave(\n", " sample_rate=sampling_rate, amplitude=(0, 0.05), frequency=(45, 50), name=\"PowerlineNoise\"\n", " ),\n", - " nse.layers.preprocessing.AmplitudeWarp(\n", + " helia.layers.preprocessing.AmplitudeWarp(\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", + " helia.layers.preprocessing.RandomGaussianNoise1D(factor=(0.05, 0.2), name=\"GaussianNoise\"),\n", + " helia.layers.preprocessing.RandomBackgroundNoises1D(\n", " noises=noises, amplitude=(0.05, 0.2), num_noises=2, name=\"RandomBackgroundNoises\"\n", " ),\n", - " nse.layers.preprocessing.RandomCutout1D(\n", + " helia.layers.preprocessing.RandomCutout1D(\n", " factor=(0.01, 0.05), cutouts=2, fill_mode=\"constant\", fill_value=0.0, name=\"RandomCutout\"\n", " ),\n", - " nse.layers.preprocessing.RandomCrop1D(duration=frame_size, name=\"RandomCrop\", auto_vectorize=True),\n", + " helia.layers.preprocessing.RandomCrop1D(duration=frame_size, name=\"RandomCrop\", auto_vectorize=True),\n", " ],\n", ")" ] @@ -430,9 +430,9 @@ " )\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", + " helia.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", " },\n", " num_parallel_calls=tf.data.AUTOTUNE,\n", " )\n", @@ -447,9 +447,9 @@ " )\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", + " helia.trainers.SimCLRTrainer.SAMPLES: x1,\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_0: augmenter(preprocessor(x1), training=True),\n", + " helia.trainers.SimCLRTrainer.AUG_SAMPLES_1: augmenter(preprocessor(x2), training=True),\n", " },\n", " num_parallel_calls=tf.data.AUTOTUNE,\n", " )\n", @@ -492,7 +492,7 @@ " include_top=True,\n", ")\n", "\n", - "encoder = nse.models.efficientnet.efficientnetv2_from_object(x=inputs, params=encoder_params, num_classes=None)" + "encoder = helia.models.efficientnet.efficientnetv2_from_object(x=inputs, params=encoder_params, num_classes=None)" ] }, { @@ -624,7 +624,7 @@ ], "source": [ "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", + "flops = helia.metrics.flops.get_flops(encoder, batch_size=1, fpath=os.devnull)\n", "logger.info(f\"Computation: {flops / 1e6:0.2f} MFLOPs\")\n", "encoder_output = encoder(inputs)" ] @@ -691,7 +691,7 @@ "projector_output = keras.layers.Dense(projection_width, activation=\"relu6\")(projector_input)\n", "projector_output = keras.layers.Dense(projection_width)(projector_output)\n", "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", + "flops = helia.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\")" ] @@ -709,7 +709,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = nse.trainers.SimCLRTrainer(\n", + "model = helia.trainers.SimCLRTrainer(\n", " encoder=encoder,\n", " augmenter=None, # We augment in the data pipeline\n", " projector=projector,\n", @@ -739,7 +739,7 @@ "\n", "\n", "optimizer = keras.optimizers.Adam(get_scheduler())\n", - "loss = nse.losses.simclr.SimCLRLoss(temperature=temperature)\n", + "loss = helia.losses.simclr.SimCLRLoss(temperature=temperature)\n", "\n", "metrics = [\n", " keras.metrics.MeanSquaredError(name=\"mse\"),\n", @@ -759,7 +759,7 @@ " ),\n", " keras.callbacks.CSVLogger(job_dir / \"history.csv\"),\n", "]\n", - "if nse.utils.env_flag(\"TENSORBOARD\"):\n", + "if helia.utils.env_flag(\"TENSORBOARD\"):\n", " model_callbacks.append(\n", " keras.callbacks.TensorBoard(\n", " log_dir=job_dir,\n", @@ -1160,7 +1160,7 @@ } ], "source": [ - "fig, _ = nse.plotting.plot_history_metrics(\n", + "fig, _ = helia.plotting.plot_history_metrics(\n", " history.history,\n", " metrics=[\"loss\", \"cos\"],\n", " title=\"Training History\",\n", @@ -1190,8 +1190,8 @@ "# Convert validation dataset to numpy arrays\n", "test_x1, test_x2 = [], []\n", "for inputs in val_ds.as_numpy_iterator():\n", - " test_x1.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_0])\n", - " test_x2.append(inputs[nse.trainers.SimCLRTrainer.AUG_SAMPLES_1])\n", + " test_x1.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_0])\n", + " test_x2.append(inputs[helia.trainers.SimCLRTrainer.AUG_SAMPLES_1])\n", "test_x1 = np.concatenate(test_x1)\n", "test_x2 = np.concatenate(test_x2)" ] @@ -1235,7 +1235,7 @@ } ], "source": [ - "rst = nse.metrics.compute_metrics(metrics, test_y1, test_y2)\n", + "rst = helia.metrics.compute_metrics(metrics, test_y1, test_y2)\n", "logger.info(\"[VAL SET] \" + \", \".join([f\"{k.upper()}={v:.4f}\" for k, v in rst.items()]))" ] }, @@ -1267,7 +1267,7 @@ } ], "source": [ - "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", + "converter = helia.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", @@ -1321,7 +1321,7 @@ } ], "source": [ - "tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", + "tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", "tflite.compile()" ] }, @@ -1475,7 +1475,7 @@ } ], "source": [ - "converter = nse.converters.tflite.TfLiteKerasConverter(model=encoder)\n", + "converter = helia.converters.tflite.TfLiteKerasConverter(model=encoder)\n", "\n", "tflite_content = converter.convert(\n", " test_x=test_x1, quantization=\"FP32\", io_type=\"float32\", mode=\"KERAS\", strict=False, verbose=verbose\n", @@ -1488,7 +1488,7 @@ "metadata": {}, "outputs": [], "source": [ - "tflite = nse.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", + "tflite = helia.interpreters.tflite.TfLiteKerasInterpreter(tflite_content)\n", "tflite.compile()" ] }, @@ -1554,8 +1554,8 @@ } ], "source": [ - "tf_rst = nse.metrics.compute_metrics(metrics, y1_pred_tf, y2_pred_tf)\n", - "tfl_rst = nse.metrics.compute_metrics(metrics, y1_pred_tfl, y2_pred_tfl)\n", + "tf_rst = helia.metrics.compute_metrics(metrics, y1_pred_tf, y2_pred_tf)\n", + "tfl_rst = helia.metrics.compute_metrics(metrics, y1_pred_tfl, y2_pred_tfl)\n", "logger.info(\"[TF METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tf_rst.items()]))\n", "logger.info(\"[TFL METRICS] \" + \" \".join([f\"{k.upper()}={v:.4f}\" for k, v in tfl_rst.items()]))" ] diff --git a/notebooks/train-arrhythmia-model.ipynb b/notebooks/train-arrhythmia-model.ipynb index f7b04305..f3cc56ce 100644 --- a/notebooks/train-arrhythmia-model.ipynb +++ b/notebooks/train-arrhythmia-model.ipynb @@ -87,7 +87,7 @@ "import tempfile\n", "import keras\n", "import heartkit as hk\n", - "import neuralspot_edge as nse" + "import helia_edge as helia" ] }, { @@ -100,9 +100,9 @@ "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__)" + "logger = helia.utils.setup_logger(__name__)" ] }, { @@ -488,7 +488,7 @@ } ], "source": [ - "model = nse.models.efficientnet.efficientnetv2_from_object(\n", + "model = helia.models.efficientnet.efficientnetv2_from_object(\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))" diff --git a/notebooks/train-ecg-denoiser.ipynb b/notebooks/train-ecg-denoiser.ipynb index 51590975..d53e5495 100644 --- a/notebooks/train-ecg-denoiser.ipynb +++ b/notebooks/train-ecg-denoiser.ipynb @@ -83,7 +83,7 @@ "import keras\n", "import heartkit as hk\n", "import numpy as np\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt" ] }, @@ -97,9 +97,9 @@ "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__)" + "logger = helia.utils.setup_logger(__name__)" ] }, { @@ -110,7 +110,7 @@ "\n", "Since our goal is to denoise ECG signals, we need to create an augmentation pipeline to generate noisy samples. \n", "\n", - "We will leverage `neuralspot-edge` preprocessing layers to create the following augmentations:\n", + "We will leverage `helia-edge` preprocessing layers to create the following augmentations:\n", "\n", "* Baseline wander: Simulate baseline wander by adding a low frequency sine signal\n", "* Powerline noise: Simulate powerline noise by adding a 50 Hz sinusoidal signal \n", @@ -367,7 +367,7 @@ "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", + " patient_generator=helia.utils.uniform_id_generator(ds.get_test_patient_ids()),\n", " frame_size=params.frame_size,\n", " samples_per_patient=params.samples_per_patient,\n", " target_rate=params.sampling_rate,\n", @@ -604,7 +604,7 @@ } ], "source": [ - "model = nse.models.tcn.tcn_from_object(\n", + "model = helia.models.tcn.tcn_from_object(\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))" @@ -941,7 +941,7 @@ "metadata": {}, "outputs": [], "source": [ - "model = nse.models.load_model(params.model_file)" + "model = helia.models.load_model(params.model_file)" ] }, { @@ -963,7 +963,7 @@ " 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 = helia.metrics.Snr()\n", "snr.update_state(ecg.reshape(1, -1, 1), aug_ecg.reshape(1, -1, 1))\n", "aug_snr = snr.result().numpy()\n", "snr.reset_state()\n", diff --git a/notebooks/train-ecg-segmentation.ipynb b/notebooks/train-ecg-segmentation.ipynb index 4177ae29..3a396cdc 100644 --- a/notebooks/train-ecg-segmentation.ipynb +++ b/notebooks/train-ecg-segmentation.ipynb @@ -95,7 +95,7 @@ "import heartkit as hk\n", "import physiokit as pk\n", "import numpy as np\n", - "import neuralspot_edge as nse\n", + "import helia_edge as helia\n", "import matplotlib.pyplot as plt" ] }, @@ -109,9 +109,9 @@ "datasets_dir = Path(os.getenv(\"HK_DATASET_PATH\", \"./datasets\"))\n", "\n", "plot_theme = hk.utils.dark_theme\n", - "nse.utils.silence_tensorflow()\n", + "helia.utils.silence_tensorflow()\n", "hk.utils.setup_plotting(plot_theme)\n", - "logger = nse.utils.setup_logger(__name__)" + "logger = helia.utils.setup_logger(__name__)" ] }, { @@ -709,7 +709,7 @@ } ], "source": [ - "model = nse.models.tcn.tcn_from_object(\n", + "model = helia.models.tcn.tcn_from_object(\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))" diff --git a/poetry.toml b/poetry.toml deleted file mode 100644 index ab1033bd..00000000 --- a/poetry.toml +++ /dev/null @@ -1,2 +0,0 @@ -[virtualenvs] -in-project = true diff --git a/pyproject.toml b/pyproject.toml index c3fb5987..af73630f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,10 +7,10 @@ license = { text = "BSD-3-Clause" } authors = [ { name = "Ambiq AI", email = "support.aitg@ambiq.com" }, ] -requires-python = ">=3.11,<3.12" +requires-python = ">=3.12,<3.14" dependencies = [ - "tensorflow>=2.17.0,<3.0", + "tensorflow>=2.20.0,<3.0", "tensorflow-metal>=1.1.0,<2.0; sys_platform == 'darwin'", "numpy>=2.1.0,<3.0", "scikit-learn>=1.4.0,<2.0", @@ -27,10 +27,11 @@ dependencies = [ "scipy>=1.12.0,<2.0", "boto3>=1.34.36,<2.0", "orjson>=3.9.13,<4.0", - "physiokit>=0.9.0,<1.0", + "physiokit>=0.10.1,<1.0", "requests>=2.31.0,<3.0", "argdantic[all]>=1.0.0,<2.0", - "neuralspot-edge~=0.2.2", + "helia-edge>=0.4.1", + "pysz>=1.0.2", ] [project.scripts] diff --git a/release-please-config.json b/release-please-config.json new file mode 100644 index 00000000..133ead53 --- /dev/null +++ b/release-please-config.json @@ -0,0 +1,12 @@ +{ + "$schema": "https://raw.githubusercontent.com/googleapis/release-please/main/schemas/config.json", + "packages": { + ".": { + "release-type": "python", + "prerelease": true, + "package-name": "heartkit", + "changelog-path": "CHANGELOG.md", + "include-component-in-tag": false + } + } +} diff --git a/uv.lock b/uv.lock index e4251fb4..f1d4d178 100644 --- a/uv.lock +++ b/uv.lock @@ -1,9 +1,11 @@ version = 1 -revision = 2 -requires-python = "==3.11.*" +revision = 3 +requires-python = ">=3.12, <3.14" resolution-markers = [ - "sys_platform == 'linux'", - "sys_platform != 'linux'", + "python_full_version >= '3.13' and sys_platform == 'linux'", + "python_full_version < '3.13' and sys_platform == 'linux'", + "python_full_version >= '3.13' and sys_platform != 'linux'", + "python_full_version < '3.13' and sys_platform != 'linux'", ] [[package]] @@ -39,23 +41,40 @@ dependencies = [ ] sdist = { url = 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