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"source": [
"# Inference API: Tutorial using ONNX\n",
"\n",
"[](https://github.com/Dana-Farber-AIOS/pathml/blob/master/examples/)\n",
"\n",
"## Introduction\n",
"\n",
"This notebook is a tutorial on how to use the future ONNX `inference` feature in PathML. The use case for this API is to create an ONNX model in HaloAI or similar software, export it, and run it at scale using PathML. \n",
"\n",
"Some notes:\n",
"\n",
"- The ONNX inference pipeline uses the existing PathML Pipeline and Transforms infrastructure.\n",
" - ONNX labels are saved to a `pathml.core.slide_data.SlideData` object as `tiles`.\n",
" - Users can iterate over the tiles as they would when using this feature for preprocessing. \n",
"\n",
"- Preprocessing images before inference\n",
" - Users will need to create their own bespoke `pathml.preprocessing.transforms.transform` method to preprocess images before inference if necessary.\n",
" - A guide on how to create preprocessing pipelines is [here](https://pathml.readthedocs.io/en/latest/creating_pipelines.html). \n",
" - A guide on how to run preprocessing pipelines is [here](https://pathml.readthedocs.io/en/latest/running_pipelines.html). \n",
"\n",
"- ONNX Model Initializers \n",
" - ONNX models often have neural network initializers stored in the input graph. This means that the user is expected to specify initializer values when running inference. To solve this issue, we have a function that removes the network initializers from the input graph. This functions is adopted from the `onnxruntime` [github](https://github.com/microsoft/onnxruntime/blob/main/tools/python/remove_initializer_from_input.py). \n",
" - We also have a function that checks if the initializers have been removed from the input graph before running inference. Both of these functions are described more below. \n",
"\n",
"- When using a model stored remotely on HuggingFace, the model is *downloaded locally* before being used. The user will need to delete the model after running `Pipeline` with a method that comes with the model class. An example of how to do this is below. \n",
"\n",
"## Quick Sample Code\n",
"- Below is an example of how users would use the ONNX inference feature in PathML with a locally stored model.\n",
"```python\n",
"# load packages\n",
"from pathml.core import SlideData\n",
"\n",
"from pathml.preprocessing import Pipeline\n",
"import pathml.preprocessing.transforms as Transforms\n",
"\n",
"from pathml.inference import Inference, remove_initializer_from_input\n",
"\n",
"# Define slide path\n",
"slide_path = 'PATH TO SLIDE'\n",
"\n",
"# Set path to model \n",
"model_path = 'PATH TO ONNX MODEL'\n",
"# Define path to export fixed model\n",
"new_path = 'PATH TO SAVE NEW ONNX MODEL'\n",
"\n",
"# Fix the ONNX model by removing initializers. Save new model to `new_path`. \n",
"remove_initializer_from_input(model_path, new_path) \n",
"\n",
"inference = Inference(model_path = new_path, input_name = 'data', num_classes = 8, model_type = 'segmentation')\n",
"\n",
"# Create a transformation list\n",
"transformation_list = [\n",
" inference\n",
"] \n",
"\n",
"# Initialize pathml.core.slide_data.SlideData object\n",
"wsi = SlideData(slide_path, stain = 'Fluor')\n",
"\n",
"# Set up PathML pipeline\n",
"pipeline = Pipeline(transformation_list)\n",
"\n",
"# Run Inference\n",
"wsi.run(pipeline, tile_size = 1280, level = 0)\n",
"```\n",
"\n",
"- Below is an example of how users would use the ONNX inference feature in PathML with a model stored in the public HuggingFace repository.\n",
"```python\n",
"# load packages\n",
"from pathml.core import SlideData\n",
"\n",
"from pathml.preprocessing import Pipeline\n",
"import pathml.preprocessing.transforms as Transforms\n",
"\n",
"from pathml.inference import RemoteTestHoverNet\n",
"\n",
"# Define slide path\n",
"slide_path = 'PATH TO SLIDE'\n",
"\n",
"inference = RemoteTestHoverNet()\n",
"\n",
"# Create a transformation list\n",
"transformation_list = [\n",
" inference\n",
"] \n",
"\n",
"# Initialize pathml.core.slide_data.SlideData object\n",
"wsi = SlideData(slide_path)\n",
"\n",
"# Set up PathML pipeline\n",
"pipeline = Pipeline(transformation_list)\n",
"\n",
"# Run Inference\n",
"wsi.run(pipeline, tile_size = 256)\n",
"\n",
"# DELETE ONNX MODEL DOWNLOADED FROM HUGGINGFACE\n",
"inference.remove() \n",
"```"
]
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"source": [
"## Load Packages\n",
"\n",
"**NOTE**\n",
"- Please put in your environment name in the following line if you are using a jupyter notebook. If not, you may remove this line. \n",
" `os.environ[\"JAVA_HOME\"] = \"/opt/conda/envs/YOUR ENVIRONMENET NAME\"` "
]
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{
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"source": [
"import os\n",
"\n",
"os.environ[\"JAVA_HOME\"] = (\n",
" \"/opt/conda/envs/YOUR ENVIRONMENET NAME\" # TO DO: CHANGE THIS TO YOUR ENVIRONMENT NAME\n",
")\n",
"import numpy as np\n",
"import onnx\n",
"import onnxruntime\n",
"import requests\n",
"import torch\n",
"\n",
"from pathml.core import SlideData, Tile\n",
"from dask.distributed import Client\n",
"from pathml.preprocessing import Pipeline\n",
"import pathml.preprocessing.transforms as Transforms\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"\n",
"from PIL import Image"
]
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"## ONNX Inference Class and ONNX Model Fixer\n",
"\n",
"- Here is the raw code for the functions that handle the initializers in the ONNX model and the classes that run the inference.\n",
"\n",
"### Functions to remove initializers and check that initializers have been removed.\n",
"\n",
"- `remove_initializer_from_input`\n",
" - This function removes any initializers from the input graph of the ONNX model.\n",
" - Without removing the initializers from the input graph, users will not be able to run inference.\n",
" - Adapted from the `onnxruntime` [github](https://github.com/microsoft/onnxruntime/blob/main/tools/python/remove_initializer_from_input.py). \n",
" - Users specify:\n",
" - `model_path` (str): path to ONNX model,\n",
" - `new_path` (str): path to save adjusted model w/o initializers\n",
" - We will run this function on all models placed in our model zoo, so users will not have to run it unless they are working with their own local models.\n",
" \n",
" <br> \n",
" \n",
"- `check_onnx_clean`\n",
" - Checks if the initializers are in the input graph\n",
" - Returns `True` and a `ValueError` if there are initializers in the input graph\n",
" - Adapted from the `onnxruntime` [github](https://github.com/microsoft/onnxruntime/blob/main/tools/python/remove_initializer_from_input.py). \n",
" - Users specify:\n",
" - `model_path` (str): path to ONNX model\n",
"\n",
" <br> \n",
"\n",
" - `convert_pytorch_onnx` \n",
" - Converts a PyTorch `.pt` file to `.onnx`\n",
" - Wrapper function of the [PyTorch](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html) function to handle the conversion.\n",
" - Users specify:\n",
" - model_path (torch.nn.Module Model): Pytorch model to be converted,\n",
" - dummy_tensor (torch.tensor): dummy input tensor that is an example of what will be passed into the model,\n",
" - model_name (str): name of ONNX model created with .onnx at the end,\n",
" - opset_version (int): which opset version you want to use to export\n",
" - input_name (str): name assigned to dummy_tensor\n",
" - Note that the model class must be defined before loading the `.pt` file and set to eval before calling this function. \n",
"\n",
"### Inference Classes\n",
"\n",
"<br> \n",
"\n",
"- `InferenceBase`\n",
" - This class inherits from `pathml.preprocessing.transforms.transform`, similar to all of the preprocessing transformations. Inheriting from `transforms.transform` allows us to use the existing `Pipeline` function in PathML which users should be familar with. \n",
" - This is the base class for all Inference classes for ONNX modeling\n",
" - Each instance of a class also comes with a `model_card` which specifies certain details of the model in dictionary form. The default parameters are:\n",
" - ```python \n",
" self.model_card = {'name' : None, 'num_classes' : None,'model_type' : None, 'notes' : None, 'model_input_notes': None, 'model_output_notes' : None,'citation': None } \n",
" ``` \n",
" - Model cards are where important information about the model should be kept. Since they are in dictionary form, the user can add keys and values as they see fit. \n",
" - This class also has getter and setter functions to adjust the `model_card`. Certain functions include `get_model_card`, `set_name`, `set_num_classes`, etc. \n",
" \n",
" <br> \n",
" \n",
"- `Inference` \n",
" - This class is for when the user wants to use an ONNX model stored locally. \n",
" - Calls the `check_onnx_clean` function to check if the model is clean.\n",
" - Users specify:\n",
" - `model_path` (str): path to ONNX model,\n",
" - `input_name` (str): name of input for ONNX model, *defaults to `data`* \n",
" - `num_classes` (int): number of outcome classes, \n",
" - `model_type` (str): type of model (classification, segmentation) \n",
" - `local` (bool): if you are using a local model or a remote model, *defaults to `True`* \n",
" \n",
" <br> \n",
" \n",
"- `HaloAIInference`\n",
" - This class inherits from `Inference`\n",
" - HaloAI ONNX models always return 20 prediction maps: this class will subset and return the necessary ones. \n",
"\n",
"<br> \n",
"\n",
"- `RemoteTestHoverNet` \n",
" - This class inherits from `Inference` and is the test class for public models hosted on `HuggingFace`. \n",
" - `local` is automatically set to `False` \n",
" - Our current test model is a HoverNet from [TIAToolbox](https://github.com/TissueImageAnalytics/tiatoolbox)\n",
" - Pocock J, Graham S, Vu QD, Jahanifar M, Deshpande S, Hadjigeorghiou G, Shephard A, Bashir RM, Bilal M, Lu W, Epstein D. TIAToolbox as an end-to-end library for advanced tissue image analytics. Communications medicine. 2022 Sep 24;2(1):120.\n",
" - Its `model_card` is:\n",
" - ```python \n",
" {'name': 'Tiabox HoverNet Test','num_classes': 5,'model_type': 'Segmentation','notes': None, 'model_input_notes': 'Accepts tiles of 256 x 256', 'model_output_notes': None, 'citation': 'Pocock J, Graham S, Vu QD, Jahanifar M, Deshpande S, Hadjigeorghiou G, Shephard A, Bashir RM, Bilal M, Lu W, Epstein D. TIAToolbox as an end-to-end library for advanced tissue image analytics. Communications medicine. 2022 Sep 24;2(1):120.'}\n",
" ```\n",
"\n",
"<br> \n",
"\n",
"- `RemoteMesmer` \n",
" - This class inherits from `Inference` and is hosted on `HuggingFace`. \n",
" - `local` is automatically set to `False` \n",
" - This model is from [Deepcell](https://github.com/vanvalenlab/deepcell-tf/blob/master/deepcell/applications/mesmer.py)\n",
" - Greenwald NF, Miller G, Moen E, Kong A, Kagel A, Dougherty T, Fullaway CC, McIntosh BJ, Leow KX, Schwartz MS, Pavelchek C. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nature biotechnology. 2022 Apr;40(4):555-65.\n",
" - Its `model_card` is:\n",
" - ```python \n",
" {'name': \"Deepcell's Mesmer\", 'num_classes': 3, 'model_type': 'Segmentation','notes': None, 'model_input_notes': 'Accepts tiles of 256 x 256', 'model_output_notes': None, 'citation': 'Greenwald NF, Miller G, Moen E, Kong A, Kagel A, Dougherty T, Fullaway CC, McIntosh BJ, Leow KX, Schwartz MS, Pavelchek C. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nature biotechnology. 2022 Apr;40(4):555-65.'}\n",
" ```"
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"source": [
"## Try it Yourself!\n",
"\n",
"- What you need:\n",
" - An ONNX model stored locally\n",
" - An image with which you want to run inference stored locally\n",
" - PathML already downloaded \n",
"\n",
"- Make sure to define the `Inference` class and `remove_initializer_from_input` above in the previous seciton if you have not downloaded the latest version of PathML.\n",
"\n",
"- You will need to define the following variables: \n",
" - `slide_path`: 'PATH TO SLIDE'\n",
" - `model_path`: 'PATH TO ONNX MODEL'\n",
" - `new_path`: 'PATH TO SAVE FIXED ONNX MODEL'\n",
" - `num_classes`: 'NUMBER OF CLASSES IN YOUR DATASET'\n",
" - `tile_size`: 'TILE SIZE THAT YOUR ONNX MODEL ACCEPTS'\n",
" \n",
"- The code in the cell below assumes you want the images passed in as is. If you need to select channels, you will need to add another `transform` method to do so before the inference transform. The following code provides an example if you want to subset into the first channel of an image. *Remember that PathML reads images in as XYZCT.* \n",
"\n",
"```python \n",
"class convert_format(Transforms.Transform):\n",
" def F(self, image):\n",
" # orig = (1280, 1280, 1, 6, 1) = (XYZCT)\n",
" image = image[:, :, :, 0, ...] # this will make the tile (1280, 1280, 1, 1)\n",
" return image\n",
"\n",
" def apply(self, tile):\n",
" tile.image = self.F(tile.image)\n",
" \n",
"convert = convert_format()\n",
"inference = Inference(\n",
" model_path = 'PATH TO LOCAL MODEL', \n",
" input_name = 'data', \n",
" num_classes = 'NUMBER OF CLASSES' , \n",
" model_type = 'CLASSIFICATION OR SEGMENTATION', \n",
" local = True)\n",
"\n",
"transformation_list = [convert, inference] \n",
"\n",
"```"
]
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"### Converting a Pytorch Model to ONNX Using the `convert_pytorch_onnx` Function\n",
"\n",
"Note the following:\n",
"- Similar to PyTorch, you will need to define and create an instance of you model class before loading the `.pt` file. Then you will need to set it to eval mode before calling the conversion function. The code to do these steps is below."
]
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"source": [
"# Define your model class\n",
"num_input, num_output, batch_size = 10, 1, 1\n",
"\n",
"\n",
"class SimpleModel(torch.nn.Module):\n",
" def __init__(self):\n",
" super(SimpleModel, self).__init__()\n",
" self.linear = torch.nn.Linear(num_input, num_output)\n",
" torch.nn.init.xavier_uniform_(self.linear.weight)\n",
"\n",
" def forward(self, x):\n",
" y = self.linear(x)\n",
" return y\n",
"\n",
"\n",
"# Define your model var\n",
"model = SimpleModel()\n",
"\n",
"# Export model as .pt if you haven't already done so\n",
"# If you have already exported a .pt file, you will still need to define a model class, initialize it, and set it to eval mode.\n",
"# If you saved your model using `torch.jit.script`, you will not need to define your model class and instead load it using `torch.jit.load` then set it to eval mode.\n",
"torch.save(model, \"test.pt\")\n",
"\n",
"# Load .pt file\n",
"model_test = torch.load(\"test.pt\")\n",
"# Set model to eval mode\n",
"model_test.eval()\n",
"\n",
"# Define a dummy tensor (this is an example of what the ONNX should expect during inference)\n",
"x = torch.randn(batch_size, num_input)\n",
"\n",
"# Run conversion function\n",
"convert_pytorch_onnx(\n",
" model=model_test, dummy_tensor=x, model_name=\"NAME_OF_OUTPUT_MODEL_HERE.onnx\"\n",
")"
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"source": [
"### Local ONNX Model Using the `Inference` Class"
]
},
{
"cell_type": "code",
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"source": [
"# Define slide path\n",
"slide_path = \"PATH TO SLIDE\"\n",
"\n",
"# Set path to model\n",
"model_path = \"PATH TO ONNX MODEL\"\n",
"# Define path to export fixed model\n",
"new_path = \"PATH TO SAVE NEW ONNX MODEL\"\n",
"\n",
"\n",
"# Fix the ONNX model\n",
"remove_initializer_from_input(model_path, new_path)\n",
"\n",
"inference = Inference(\n",
" model_path=new_path,\n",
" input_name=\"data\",\n",
" num_classes=\"NUMBER OF CLASSES\",\n",
" model_type=\"CLASSIFICATION OR SEGMENTATION\",\n",
" local=True,\n",
")\n",
"\n",
"transformation_list = [inference]\n",
"\n",
"# Initialize pathml.core.slide_data.SlideData object\n",
"wsi = SlideData(slide_path)\n",
"\n",
"# Set up PathML pipeline\n",
"pipeline = Pipeline(transformation_list)\n",
"\n",
"# Run Inference\n",
"# Level is equal to 0 for highest resolution (Note that this is the default setting)\n",
"wsi.run(pipeline, tile_size=\"TILE SIZE THAT YOUR ONNX MODEL ACCEPTS\", level=0)"
]
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"source": [
"### Local ONNX Model Using the `HaloAIInference` Class"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d2eedbf1-be61-440e-a044-6dce4c8de04e",
"metadata": {},
"outputs": [],
"source": [
"# Define slide path\n",
"slide_path = \"PATH TO SLIDE\"\n",
"\n",
"# Set path to model\n",
"model_path = \"PATH TO ONNX MODEL\"\n",
"# Define path to export fixed model\n",
"new_path = \"PATH TO SAVE NEW ONNX MODEL\"\n",
"\n",
"\n",
"# Fix the ONNX model\n",
"remove_initializer_from_input(model_path, new_path)\n",
"\n",
"inference = HaloAIInference(\n",
" model_path=new_path,\n",
" input_name=\"data\",\n",
" num_classes=\"NUMBER OF CLASSES\",\n",
" model_type=\"CLASSIFICATION OR SEGMENTATION\",\n",
" local=True,\n",
")\n",
"\n",
"transformation_list = [inference]\n",
"\n",
"# Initialize pathml.core.slide_data.SlideData object\n",
"wsi = SlideData(slide_path)\n",
"\n",
"# Set up PathML pipeline\n",
"pipeline = Pipeline(transformation_list)\n",
"\n",
"# Run Inference\n",
"# Level is equal to 0 for highest resolution (Note that this is the default setting)\n",
"wsi.run(pipeline, tile_size=\"TILE SIZE THAT YOUR ONNX MODEL ACCEPTS\", level=0)"
]
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"### Remote ONNX Using our `RemoteTestHoverNet` Class\n",
"- Uses a Hovernet from [TIAToolbox](https://github.com/TissueImageAnalytics/tiatoolbox) \n",
"- This version of Hovernet was trained on the [MoNuSAC](https://monusac-2020.grand-challenge.org/) dataset.\n",
"- Note that the purpose of this model is to illustrate how PathML will handle future remote models. We plan on release more public models to our model zoo on HuggingFace in the future.\n",
"- Citation for model:\n",
" - Pocock J, Graham S, Vu QD, Jahanifar M, Deshpande S, Hadjigeorghiou G, Shephard A, Bashir RM, Bilal M, Lu W, Epstein D. TIAToolbox as an end-to-end library for advanced tissue image analytics. Communications medicine. 2022 Sep 24;2(1):120.\n",
"- Make sure your image has 3 channels! \n",
"- When the `RemoteTestHoverNet` is first initialized, it downloads the HoverNet from HuggingFace and saves it locally on your own system as `temp.onnx`. \n",
" - **You will need to remove it manually by calling the `remove()` method** An example of how to call this method is in the last line in the code below. "
]
},
{
"cell_type": "code",
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"source": [
"# Define slide path\n",
"slide_path = \"PATH TO SLIDE\"\n",
"\n",
"inference = RemoteTestHoverNet()\n",
"\n",
"# Create a transformation list\n",
"transformation_list = [inference]\n",
"\n",
"# Initialize pathml.core.slide_data.SlideData object\n",
"wsi = SlideData(slide_path)\n",
"\n",
"# Set up PathML pipeline\n",
"pipeline = Pipeline(transformation_list)\n",
"\n",
"# Run Inference\n",
"wsi.run(pipeline, tile_size=256)\n",
"\n",
"# DELETE ONNX MODEL DOWNLOADED FROM HUGGINGFACE\n",
"inference.remove()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "318ae957-73d8-4c7f-b87c-b012750eda10",
"metadata": {},
"source": [
"## Iterate over the tiles\n",
"\n",
"Now that you have your tiles saved to your SlideData object, you can now iterate over them.\n",
"\n",
"For example, if you wanted to check the shape of the tiles you could run the following code: \n",
"\n",
"```python\n",
"for tile in wsi.tiles: \n",
" print(tile.image.shape) \n",
"```\n",
"\n",
"To see how to use these tiles to make visualizations, see below."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "251a9099-8e6f-4e4c-b685-7087191fe9fe",
"metadata": {},
"source": [
"## Full Example With Vizualization of Output\n",
"\n",
"The `RemoteTestHoverNet()` uses a pretrained HoverNet from TIAToolBox trained on the [MoNuSAC](https://monusac-2020.grand-challenge.org/) dataset. **The model was trained to accept tiles of 256x256 to create a prediction matrix of size 164x164 with 9 channels.** The first 5 channels correspond to the Nuclei Types (TP), the next two channels correspond to the Nuclei Pixels (NP), and the last two channels correspond to the Hover (HV). The documention for these channels can be found here on TIAToolBox's [website](https://tia-toolbox.readthedocs.io/en/v1.0.1/_modules/tiatoolbox/models/architecture/hovernet.html#HoVerNet.infer_batch). \n",
"\n",
"In this example we use an taken from the [MoNuSAC](https://monusac-2020.grand-challenge.org/) dataset. See citation in the `References` section."
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "925d4ebd-3803-409a-82be-780115ffb152",
"metadata": {},
"source": [
"### Run Code as Demonstrated Above\n",
"\n",
"Note that to run the following code, you will need to download and save the image titled `TCGA-5P-A9K0-01Z-00-DX1_1.svs` in the same directory as the notebook."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "23951050-b47f-4b38-b0b6-786081fc69f0",
"metadata": {},
"outputs": [],
"source": [
"# Define slide path\n",
"slide_path = \"TCGA-5P-A9K0-01Z-00-DX1_1.svs\"\n",
"\n",
"inference = RemoteTestHoverNet()\n",
"\n",
"# Create a transformation list\n",
"transformation_list = [inference]\n",
"\n",
"# Initialize pathml.core.slide_data.SlideData object\n",
"wsi = SlideData(slide_path)\n",
"\n",
"# Set up PathML pipeline\n",
"pipeline = Pipeline(transformation_list)\n",
"\n",
"# Run Inference\n",
"wsi.run(pipeline, tile_size=256, tile_stride=164, tile_pad=True)\n",
"\n",
"# DELETE ONNX MODEL DOWNLOADED FROM HUGGINGFACE\n",
"inference.remove()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "2921a180-20bc-4ce1-960d-7005892f4585",
"metadata": {},
"source": [
"Let's look at the first tile which comes from the top left corner (0,0) and Nucleus Pixel predictions."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a607bb7d-de3e-4444-8829-75d7da9505fb",
"metadata": {},
"outputs": [],
"source": [
"for tile in wsi.tiles:\n",
" # Create empty numpy array\n",
" a = np.empty((2, 164, 164), dtype=object)\n",
" # Get Nucleus Predictions\n",
" classes = tile.image[0, 5:7, :, :]\n",
" a = classes\n",
" # Take the argmax to make the predictions binary\n",
" image = np.argmax(a, axis=0)\n",
" # Multiple values by 255 to make the array image friendly\n",
" image = image * (255 / 1)\n",
" # Make a grey scale image\n",
" img = Image.fromarray(image.astype(\"uint8\"), \"L\")\n",
" # Save Image\n",
" img.save(\"test_array_1.png\")\n",
" # Can break after one iteration since we are using at the tile at (0, 0).\n",
" break"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "aa6fbb49-7173-4a65-9b1f-e7b90a5228c5",
"metadata": {},
"source": [
"Lets visualize the tile vs the tile predictions. Since the model uses a 256x256 tile to create a prediction map of size 164x164, we need to take our tile located at (0,0) and crop it down to the center 164x164 pixes. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e29e98f3-c04c-4d77-8681-c837181bf415",
"metadata": {},
"outputs": [],
"source": [
"prediction_dim = 164\n",
"tile_dim = 256\n",
"crop_amount = int((256 - 164) / 2)\n",
"wsi = SlideData(slide_path)\n",
"\n",
"generator = wsi.generate_tiles(shape=(tile_dim, tile_dim), level=0)\n",
"\n",
"for tile in generator:\n",
" # Extract array from tile\n",
" image = tile.image\n",
" # Crop tile\n",
" image = image[\n",
" crop_amount : crop_amount + prediction_dim,\n",
" crop_amount : crop_amount + prediction_dim,\n",
" ]\n",
" # Convert array to image\n",
" img = Image.fromarray(image)\n",
" # Save Image\n",
" img.save(\"raw_tile.png\")\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "98ab9eb0-455d-4353-b760-3d65820e81de",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1100x800 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set figure sice\n",
"plt.rcParams[\"figure.figsize\"] = 11, 8\n",
"\n",
"# Read images\n",
"img_A = matplotlib.image.imread(\"raw_tile.png\")\n",
"img_B = matplotlib.image.imread(\"test_array_1.png\")\n",
"\n",
"# Set up plots\n",
"fig, ax = plt.subplots(1, 2)\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"ax[0].imshow(img_A)\n",
"ax[1].imshow(img_B, cmap=\"gray\")\n",
"ax[0].set_title(\"Original Image\")\n",
"ax[1].set_title(\"Model Predictions\")\n",
"plt.tight_layout()\n",
"\n",
"# Get rid of tick marks\n",
"for a in ax.ravel():\n",
" a.set_xticks([])\n",
" a.set_yticks([])\n",
"\n",
"# Show images\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"id": "fc5c89ae-400e-4380-a717-12800fb77d97",
"metadata": {},
"source": [
"## References\n",
"\n",
"- Pocock J, Graham S, Vu QD, Jahanifar M, Deshpande S, Hadjigeorghiou G, Shephard A, Bashir RM, Bilal M, Lu W, Epstein D. TIAToolbox as an end-to-end library for advanced tissue image analytics. Communications medicine. 2022 Sep 24;2(1):120.\n",
"\n",
"- R. Verma, et al. \"MoNuSAC2020: A Multi-organ Nuclei Segmentation and Classification Challenge.\" IEEE Transactions on Medical Imaging (2021).\n",
"\n",
"- https://github.com/microsoft/onnxruntime/blob/main/tools/python/remove_initializer_from_input.py\n",
"\n",
"- https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html"
]
}
],
"metadata": {
"environment": {
"kernel": "james_test2",
"name": "pytorch-gpu.1-13.m105",
"type": "gcloud",
"uri": "gcr.io/deeplearning-platform-release/pytorch-gpu.1-13:m105"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|