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bioimageio.core_usage notebook and requirements txt file
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‎example/bioimageio.core_usage.ipynb

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"source": [
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"# Bioimage Model Zoo Core Example notebook\n",
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"\n",
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"This notebook shows how to interact with the `bioimageio.core` programmatically to explore, load, use, and export content from the [BioImage Model Zoo](https://bioimage.io).\""
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"This notebook shows how to interact with the `bioimageio.core` programmatically to explore, load, use, and export content from the [BioImage Model Zoo](https://bioimage.io).\n",
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"\n",
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"For a local use of this notebook the minimum package requirements can be found in `bioimageio.core_usage_requirements.txt` located in the same folder as the notebook."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"If the notebook is being run on Google Colab, install necessary dependencies"
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"### 0.1. If running on Google Colab, install necessary dependencies"
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]
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},
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{
@@ -32,14 +34,16 @@
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"import os\n",
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"\n",
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"if os.getenv(\"COLAB_RELEASE_TAG\"):\n",
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" %pip install bioimageio.core torch onnxruntime"
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" %pip install bioimageio.core==0.6.7 torch==2.3.1 onnxruntime==1.18.0"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Enable pretty validation errors"
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"### 0.2.Enable pretty_validation_errors\n",
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"\n",
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"This function displays validation errors in a human readable format."
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"# enable pretty validation errors in ipynb\n",
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"from bioimageio.spec.pretty_validation_errors import (\n",
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" enable_pretty_validation_errors_in_ipynb,\n",
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")\n",
@@ -59,7 +62,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Load general dependencies"
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"### 0.3. Load general dependencies"
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]
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},
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{
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"source": [
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"# Load general dependencies\n",
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"from imageio.v2 import imread\n",
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"from pprint import pprint\n",
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"from bioimageio.spec.utils import download\n",
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"from pprint import pprint\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"# function to display input and prediction output images\n",
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"# Function to display input and prediction output images\n",
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"def show_images(sample_tensor, prediction_tensor):\n",
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" input_array = sample_tensor.members['input0'].data\n",
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" \n",
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" # Check for the number of channels to enable display\n",
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" input_array = np.squeeze(input_array)\n",
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" if len(input_array.shape)>2:\n",
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" input_array = input_array[0]\n",
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"\n",
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" output_array = prediction_tensor.members['output0'].data\n",
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" \n",
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" # Check for the number of channels to enable display\n",
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" output_array = np.squeeze(output_array)\n",
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" if len(output_array.shape)>2:\n",
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" output_array = output_array[0]\n",
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"\n",
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" plt.figure()\n",
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" ax1 =plt.subplot(1,2,1)\n",
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" ax1 = plt.subplot(1,2,1)\n",
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" ax1.set_title(\"Input\")\n",
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" ax1.axis('off')\n",
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" plt.imshow(input_array)\n",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"bioimage.io resources may be identified via their bioimage.io ID, e.g. \"affable-shark\" or the [DOI](https://doi.org/) of their [Zenodo](https://zenodo.org/) backup.\n",
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"`bioimage.io` resources may be identified via their bioimage.io __ID__, e.g. \"affable-shark\" or the [__DOI__](https://doi.org/) of their [__Zenodo__](https://zenodo.org/) backup.\n",
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"\n",
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"Both of these options may be version specific (\"affable-shark\" or a version specific [Zenodo](https://zenodo.org/) backup [DOI](https://doi.org/)).\n",
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"Both of these options may be version specific (\"affable-shark/1\" or a version specific [__Zenodo__](https://zenodo.org/) backup [__DOI__](https://doi.org/)).\n",
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"\n",
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"Alternativly any RDF source may be loaded by providing a local path or URL."
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"Alternatively, any rdf.yaml source, single file or in a .zip, may be loaded by providing its __local path__ or __URL__."
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"BMZ_MODEL_ID = \"\" #\"affable-shark\"\n",
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"BMZ_MODEL_ID = \"\"#\"affable-shark\"\n",
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"BMZ_MODEL_DOI = \"\" #\"10.5281/zenodo.6287342\"\n",
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"BMZ_MODEL_URL = \"https://uk1s3.embassy.ebi.ac.uk/public-datasets/bioimage.io/affable-shark/draft/files/rdf.yaml\""
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]
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"cell_type": "markdown",
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"metadata": {},
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"load_description is a function of the `bioimageio.spec` package, but as it is a sub-package of `bioimageio.core` it can be called as `bioimageio.core.load_description`.\n",
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"`load_description` is a function of the `bioimageio.spec` package, but as it is a sub-package of `bioimageio.core` it can also be called from it by `bioimageio.core.load_description`.\n",
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"\n",
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"To learn more about the functionalities of the `bioimageio.spec` package, see the [bioimageio.spec package example notebook](https://github.com/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb), also available as a [Google Colab](https://colab.research.google.com/github/bioimage-io/spec-bioimage-io/blob/main/example/load_model_and_create_your_own.ipynb) notebook."
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"computing SHA256 of 512ac1bb6de19fba42ae180732aa2f74-sample_input_0.tif (result: a24b3c708b6ca6825494eb7c5a4d221335fb3eef5eb9d03f4108907cdaad2bf9): 100%|██████████| 1/1 [00:00<00:00, 868.03it/s] \n",
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{
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"metadata": {},
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"### 1.3 Inspect the model metadata"
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"### 1.3 Inspect the model metadata\n",
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"\n",
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"Model metadata includes author names, affiliations, license, and documentation."
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"This test should be run before using the model to ensure that it works properly.\n",
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"`bioimageio.core.test_model` returns a dictionary with 'status'='passed'/'failed' and other detailed information.\n",
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"----\n",
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"\n",
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"A model description is validated with our format specification. \n",
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"To inspect the corresponding validation summary access the `validation_summary` attribute.\n",
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"`bioimageio.core.test_model` returns a validation dictionary with 'status'='passed'/'failed' and other detailed information that can be inspected by calling `.display()` on it.\n",
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"\n",
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"The validation summary will indicate:\n",
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"- the version of the `bioimageio.spec` library used to run the validation\n",
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"- the versions of the `bioimageio.spec` and `bioimageio.core` libraries used to run the validation\n",
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"- the status of several validation steps\n",
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" - ✔️: Success\n",
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" - 🔍: information about the validation context\n",
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" - ⚠: Warning\n",
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" - ❌: Error\n"
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" - ❌: Error"
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"\u001b[32m2024-06-12 19:00:31.827\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mbioimageio.core._resource_tests\u001b[0m:\u001b[36m_test_model_inference\u001b[0m:\u001b[36m122\u001b[0m - \u001b[1mstarting 'Reproduce test outputs from test inputs'\u001b[0m\n",
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"\u001b[32m2024-06-12 19:00:33.804\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mbioimageio.core._resource_tests\u001b[0m:\u001b[36m_test_model_inference_parametrized\u001b[0m:\u001b[36m192\u001b[0m - \u001b[1mTesting inference with 4 different input tensor sizes\u001b[0m\n"
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"\u001b[32m2024-06-13 12:20:28.532\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mbioimageio.core._resource_tests\u001b[0m:\u001b[36m_test_model_inference\u001b[0m:\u001b[36m122\u001b[0m - \u001b[1mstarting 'Reproduce test outputs from test inputs'\u001b[0m\n",
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"\u001b[32m2024-06-13 12:20:30.337\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mbioimageio.core._resource_tests\u001b[0m:\u001b[36m_test_model_inference_parametrized\u001b[0m:\u001b[36m192\u001b[0m - \u001b[1mTesting inference with 4 different input tensor sizes\u001b[0m\n"
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{
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"metadata": {},
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"source": [
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"`bioimageio.core` implements functionality to run a prediction with models described in the `bioimage.io` format.\n",
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"`bioimageio.core` implements the functionality to run a prediction with models described in the `bioimage.io` format.\n",
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"\n",
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"This includes functions to run predictions on `numpy.ndarray`s/`xarray.DataArrays` as input and convenience functions to run predictions for images stored on disc.\n",
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"This includes functions to run predictions on `numpy.ndarray`/`xarray.DataArray` as input and convenience functions to run predictions for images stored on disc.\n",
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"\n",
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"### 3.1. Load the test image and convert into a tensor"
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]
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{
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"data": {
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"text/plain": [
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"Sample(members={'raw': <bioimageio.core.tensor.Tensor object at 0x15729a010>}, stat=None, id='sample-from-numpy')"
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"Sample(members={'raw': <bioimageio.core.tensor.Tensor object at 0x147024410>}, stat=None, id='sample-from-numpy')"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"source": [
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"`bioimageio.core` provides a helper function `create_sample_for_model` to automatically create the `Sample` for the given model."
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"`bioimageio.core` provides the helper function `create_sample_for_model` to automatically create the `Sample` for the given model."
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]
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{
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{
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"Sample(members={'input0': <bioimageio.core.tensor.Tensor object at 0x157214ad0>}, stat={}, id='my_demo_sample')"
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"Sample(members={'input0': <bioimageio.core.tensor.Tensor object at 0x16c0e7990>}, stat={}, id='my_demo_sample')"
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]
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"Sample(members={'input0': <bioimageio.core.tensor.Tensor object at 0x157244e10>}, stat={}, id='test-input')"
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"Sample(members={'input0': <bioimageio.core.tensor.Tensor object at 0x16de5b8d0>}, stat={}, id='test-input')"
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},
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"### 3.3. Recover input and output tensors as numpy arrays"
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This example code shows how to recover the image information from the input and output tensors as numpy arrays."
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]
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},
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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"text/plain": [
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"<matplotlib.image.AxesImage at 0x16b069dd0>"
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"<matplotlib.image.AxesImage at 0x1763ce410>"
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"execution_count": 17,
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"np_input_list = []\n",
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"np_output_list = []\n",
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"\n",
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"# iterate over the number of tensors inside the input sample\n",
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"for ipt in range(len(sample.members.keys())):\n",
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" input_array = sample.members[f\"input{ipt}\"].data\n",
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"\n",
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" np_input_list.append(input_array)\n",
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"\n",
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"\n",
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"# iterate over the number of tensors inside the output prediction\n",
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"for out in range(len(prediction.members.keys())):\n",
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" output_array = prediction.members[f\"output{ipt}\"].data\n",
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"\n",

‎example/bioimageio.core_usage_requirements.txt

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python == 3.11
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bioimageio.core == 0.6.7
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ipykernel == 6.29.4
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matplotlib == 3.9.0
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torch == 2.3.1
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onnxruntime == 1.18.0

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