210-ct-scan-live-inference: Live Inference and Benchmark CT-scan Data with OpenVINO


Thu Jun 16 2022 14:44:32 GMT+0000 (UTC)

Saved by @OpenVINOtoolkit #python #openvino #openvino-notebooks #live-inference #ct-scan #deeplearning #accelerated-inference

# Imports
import os
import sys
import zipfile
from pathlib import Path

import numpy as np
from monai.transforms import LoadImage
from openvino.inference_engine import IECore

from models.custom_segmentation import SegmentationModel
from notebook_utils import benchmark_model, download_file, show_live_inference

# Settings
# The directory that contains the IR model (xml and bin) files
MODEL_PATH = "pretrained_model/quantized_unet_kits19.xml"
# Uncomment the next line to use the FP16 model instead of the quantized model
# MODEL_PATH = "pretrained_model/unet_kits19.xml"

# Benchmark Model Performance
ie = IECore()
# By default, benchmark on MULTI:CPU,GPU if a GPU is available, otherwise on CPU.
device = "MULTI:CPU,GPU" if "GPU" in ie.available_devices else "CPU"
# Uncomment one of the options below to benchmark on other devices
# device = "GPU"
# device = "CPU"
# device = "AUTO"

# Benchmark model
benchmark_model(model_path=MODEL_PATH, device=device, seconds=15)

# Download and Prepare Data
# Directory that contains the CT scan data. This directory should contain subdirectories
# case_00XXX where XXX is between 000 and 299
BASEDIR = Path("kits19_frames_1")
# The CT scan case number. For example: 16 for data from the case_00016 directory
# Currently only 117 is supported
CASE = 117

case_path = BASEDIR / f"case_{CASE:05d}"

if not case_path.exists():
    filename = download_file(
    with zipfile.ZipFile(filename, "r") as zip_ref:
    os.remove(filename)  # remove zipfile
    print(f"Downloaded and extracted data for case_{CASE:05d}")
    print(f"Data for case_{CASE:05d} exists")

# Load model
ie = IECore()
segmentation_model = SegmentationModel(
    ie=ie, model_path=Path(MODEL_PATH), sigmoid=True, rotate_and_flip=True
image_paths = sorted(case_path.glob("imaging_frames/*jpg"))

print(f"{case_path.name}, {len(image_paths)} images")

# Show Live Inference
# Possible options for device include "CPU", "GPU", "AUTO", "MULTI"
device = "MULTI:CPU,GPU" if "GPU" in ie.available_devices else "CPU"
reader = LoadImage(image_only=True, dtype=np.uint8)

    ie=ie, image_paths=image_paths, model=segmentation_model, device=device, reader=reader

This tutorial is part of a series on how to train, optimize, quantize and show live inference on a medical segmentation model. The goal is to accelerate inference on a kidney segmentation model. The UNet model is trained from scratch; the data is from Kits19. This tutorial shows how to - Benchmark performance of the model - Show live inference with OpenVINO's async API and MULTI plugin To learn how this model was quantized, please see the Convert and Quantize a UNet Model and Show Live Inference tutorial. This notebook needs a quantized OpenVINO IR model. We provide a pretrained model trained for 20 epochs with the full Kits-19 frames dataset, which has an F1 score on the validation set of 0.9. The training code is available in the PyTorch Monai Training notebook. It also needs images from the Kits19 dataset, converted to 2D images. For demonstration purposes, this tutorial will download one converted CT scan to use for inference. If you have not yet installed OpenVINO™, please follow the Installation Guide to install all required dependencies. https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide Link to .bin and .xml files: https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/210-ct-scan-live-inference/pretrained_model