Tutorial: Brain Segmentation PyTorch

3.1

This tutorial shows how to import and use a PyTorch model in AIAA with Triton backend.

  1. Follow Running AIAA to start your server.

  2. Follow Convert PyTorch trained network to convert the example PyTorch model.

  3. Write your own transforms that are missing from Clara Train API Below are two specific transforms that you need for this tutorial. Put them inside a file called “transforms.py” and put that inside /workspace/lib/ You can also download the file here: transforms.py.

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import logging import numpy as np import cv2 class MyLabelNPSqueeze(object): def __init__(self, label_in='model', label_out='model', dtype='uint8'): self.key_label_in = label_in self.key_label_out = label_out self.dtype = dtype def __call__(self, data): logger = logging.getLogger(self.__class__.__name__) label = data[self.key_label_in] logger.debug('Input Label Shape:{}'.format(label.shape)) label = np.squeeze(label).astype(self.dtype) logger.debug('Output Label Shape:{}'.format(label.shape)) data[self.key_label_out] = label return data class MyOpenCVWriter(object): def __init__(self, image_in='model'): self.key_image_in = image_in def __call__(self, output_file, data): logger = logging.getLogger(self.__class__.__name__) # convert 0-1 back to 0-255 image = data[self.key_image_in] * 255 logger.debug('Saving Image{}to:{}'.format(image.shape, output_file)) cv2.imwrite(output_file, image) return output_file


  1. Prepare your configuration:

Now you have models and transforms ready, you should write a config.json file for AIAA to understand. Open a file named segmentation_2d_brain.json and write your configuration as follows. You can also download it here: segmentation_2d_brain.json.

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{ "version": "3", "type": "segmentation", "labels": [ "brain" ], "description": "2D segmentation training and evaluation examples to identify and segment the brain based on Unet model given in https://pytorch.org/hub/mateuszbuda_brain-segmentation-pytorch_unet/", "pre_transforms": [ { "name": "LoadPng", "args": { "fields": "image" } }, { "name": "ConvertToChannelsFirst", "args": { "fields": "image" } }, { "name": "NormalizeNonzeroIntensities", "args": { "fields": "image" } } ], "inference": { "image": "image", "name" : "TRTISInference", "args": { "batch_size": 1 }, "node_mapping": { "INPUT__0": "image", "OUTPUT__0": "model" }, "trtis": { "platform": "pytorch_libtorch", "max_batch_size": 1, "input": [ { "name": "INPUT__0", "data_type": "TYPE_FP32", "dims": [3, 256, 256] } ], "output": [ { "name": "OUTPUT__0", "data_type": "TYPE_FP32", "dims": [1, 256, 256] } ], "instance_group": [ { "count": 1, "gpus": [ 0 ], "kind": "KIND_AUTO" } ] } }, "post_transforms": [ { "name": "ThresholdValues", "args": { "fields": "model", "threshold": 0.5, "dtype": "uint8" } }, { "name": "MyLabelNPSqueeze", "path": "transforms.MyLabelNPSqueeze", "args": { "label_in": "model", "label_out": "model" } } ], "writer": { "name": "MyOpenCVWriter", "path": "transforms.MyOpenCVWriter", "args": { "image_in": "model" } } }


  1. Upload the model:

Use the command below to load it in AIAA.

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# Import Model curl -X PUT "http://127.0.0.1:$LOCAL_PORT/admin/model/segmentation_2d_brain" \ -F "config=@segmentation_2d_brain.json;type=application/json" \ -F "data=@unet.pt"


  1. Call the APIs:

Now the model is loaded in AIAA and ready to serve. Type following commands in your linux machine and the result will be stored as result.png.

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# download example wget https://github.com/mateuszbuda/brain-segmentation-pytorch/raw/master/assets/TCGA_CS_4944.png # call segmentation API curl -v -X POST "http://127.0.0.1:$LOCAL_PORT/v1/segmentation?model=segmentation_2d_brain&output=image" \ -H "accept: multipart/form-data" \ -H "Content-Type: multipart/form-data" \ -F "params={}" \ -F "image=@TCGA_CS_4944.png" \ -o result.png


  1. Verify the result. The input image is on the left while the result is on the right.

TCGA_CS_4944.png


TCGA_CS_4944_result.png

© Copyright 2020, NVIDIA. Last updated on Feb 2, 2023.