Hello
I have an issue when trying to run inference python script on FPGA or HETERO although it is running fine on CPU
Linux: ubuntu 16.04
Python: 3.5
openvino: 2019 R1
<INSTALL_DIR>/deployment_tools/model_optimizer/mo_onnx.py --input_model alexnet.onnx --input_shape "[1,3, 224, 224]" --mean_values "[0.485, 0.456, 0.406]" --scale_values "[58.395, 57.12 , 57.375]" --data_type "FP32" --output_dir .
script.py -m alexnet -i RPW.png -d FPGA
script.py -m alexnet -i RPW.png -d HETERO
on the other hand the squeezenet example in openvino samples is working on HETERO and FPGA normally.
The code:
import sys import time from pathlib import Path import cv2 import matplotlib.pyplot as plt import numpy as np import torch import argparse import os from openvino.inference_engine import IENetwork, IEPlugin parser = argparse.ArgumentParser() parser.add_argument("-m") parser.add_argument("-i") parser.add_argument("-d") args = parser.parse_args() model=os.path.abspath(args.m) imageName=os.path.abspath(args.i) deviceName=args.d BASE_MODEL_NAME = model model_path=model + ".pytorch" onnx_path = model + ".onnx" ir_path = model + ".xml" if not os.path.exists(onnx_path):
dummy_input = torch.randn(1, 3, 224, 224) # For the Fastseg model, setting do_constant_folding to False is required # for PyTorch>1.5.1
torch.onnx.export(
model,
dummy_input,
onnx_path,
opset_version=11,
do_constant_folding=False,
) def normalize(image): """ Normalize the image to the given mean and standard deviation for CityScapes models. """
image = image.astype(np.float32)
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
image /= 255.0
image -= mean
image /= std
return image image = cv2.cvtColor(cv2.imread(imageName), cv2.COLOR_BGR2RGB) resized_image = cv2.resize(image, (224, 224)) input_image = np.expand_dims(np.transpose(resized_image, (2, 0, 1)), 0) net_onnx= IENetwork(model="alexnet.xml", weights="alexnet.bin") plugin = IEPlugin(device=deviceName) exec_net_onnx = plugin.load(network=net_onnx) input_layer_onnx = next(iter(exec_net_onnx.inputs)) output_layer_onnx = next(iter(exec_net_onnx.outputs)) # Run the Inference on the Input image... res_onnx = exec_net_onnx.infer( inputs={input_layer_onnx: input_image} ) res_onnx = res_onnx[output_layer_onnx] #print(res_onnx) result_mask_onnx = np.squeeze(np.argmax(res_onnx, axis=1)).astype(np.uint8) #print(result_mask_onnx) if (result_mask_onnx == 0):
print("Your image is not an RPW") elif (result_mask_onnx == 1):
print("Your image is RPW") #show_image_and_result(image, result_mask_onnx)Link Copied
