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infere_ovino21.4.py
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infere_ovino21.4.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""
@23/02/01 this is originally hello_classification.py
"""
import sys, os
import signal
signal.signal(signal.SIGINT, signal.SIG_DFL)
import argparse
import logging as log
#import cv2, time
import numpy as np
from matplotlib import pyplot as plt
from openvino.inference_engine import IECore, StatusCode
def parse_args() -> argparse.Namespace:
"""Parse and return command line arguments"""
parser = argparse.ArgumentParser(add_help=False)
args = parser.add_argument_group('Options')
# fmt: off
args.add_argument('-h', '--help', action='help',
help='Show this help message and exit.')
args.add_argument('-m', '--model', type=str,
help='Required. Path to an .xml or .onnx file with a trained model.')
args.add_argument('-i', '--input', type=str, nargs='+',
help='Required. Path to an image file(s).')
args.add_argument('-l', '--extension', type=str, default=None,
help='Optional. Required by the CPU Plugin for executing the custom operation on a CPU. '
'Absolute path to a shared library with the kernels implementations.')
args.add_argument('-c', '--config', type=str, default=None,
help='Optional. Required by GPU or VPU Plugins for the custom operation kernel. '
'Absolute path to operation description file (.xml).')
args.add_argument('-d', '--device', default='CPU', type=str,
help='Optional. Specify the target device to infer on; CPU, GPU, MYRIAD, HDDL or HETERO: '
'is acceptable. The sample will look for a suitable plugin for device specified. '
'Default value is CPU.')
args.add_argument('--labels', default=None, type=str, help='Optional. Path to a labels mapping file.')
args.add_argument('-nt', '--number_top', default=10, type=int, help='Optional. Number of top results.')
# fmt: on
return parser.parse_args()
def main():
log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
args = parse_args()
# ---------------------------Step 1. Initialize inference engine core--------------------------------------------------
log.info('Creating Inference Engine')
ie = IECore()
if args.extension and args.device == 'CPU':
log.info(f'Loading the {args.device} extension: {args.extension}')
ie.add_extension(args.extension, args.device)
if args.config and args.device in ('GPU', 'MYRIAD', 'HDDL'):
log.info(f'Loading the {args.device} configuration: {args.config}')
ie.set_config({'CONFIG_FILE': args.config}, args.device)
# ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation or ONNX format---------------
log.info(f'Reading the network: {args.model}')
# (.xml and .bin files) or (.onnx file)
if 1: # add for my test
# int8 model via onnx runtime
# quantize/dequantize linear layers
args.model = "model.test-int8-Alex.onnx-rt-int8.cut.onnx"
# int8 model via nncf package ovino21.4
# fake-quantized layer
#args.model = "model.test-int8-Alex.ovino-nncrf-int8.ovino21.4_mo.cut" + ".xml"
# image
fig = "Zebra-1.jpg"
#fig = "Accordion-842.jpg"
np_save_name = "save_np_ovino21.4_" + fig + "_" + args.model
net = ie.read_network(model="sample-model/"+args.model)
if len(net.input_info) != 1:
log.error('Sample supports only single input topologies')
return -1
if len(net.outputs) != 1:
log.error('Sample supports only single output topologies')
return -1
# ---------------------------Step 3. Configure input & output----------------------------------------------------------
log.info('Configuring input and output blobs')
# Get names of input and output blobs
input_blob = next(iter(net.input_info))
out_blob = next(iter(net.outputs))
# Set input and output precision manually
#net.input_info[input_blob].precision = 'U8'
net.input_info[input_blob].precision = 'FP32'
net.outputs[out_blob].precision = 'FP32'
iterate = 1 # just 1 input file
# Get a number of input images
num_of_input = iterate #len(args.input)
# Get a number of classes recognized by a model
num_of_classes = max(net.outputs[out_blob].shape)
# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
log.info('Loading the model to the plugin')
exec_net = ie.load_network(network=net, device_name=args.device, num_requests=num_of_input)
print(exec_net)
# ---------------------------Step 5. Create infer request--------------------------------------------------------------
# load_network() method of the IECore class with a specified number of requests (default 1) returns an ExecutableNetwork
# instance which stores infer requests. So you already created Infer requests in the previous step.
# ---------------------------Step 6. Prepare input---------------------------------------------------------------------
input_data = []
_, _, h, w = net.input_info[input_blob].input_data.shape
if 0: # here is original code
for i in range(num_of_input):
#image = cv2.imread(args.input[i])
image = cv2.imread(fig)
if image.shape[:-1] != (h, w):
log.warning(f'Image {args.input[i]} is resized from {image.shape[:-1]} to {(h, w)}')
image = cv2.resize(image, (w, h))
# Change data layout from HWC to CHW
image = image.transpose((2, 0, 1))
# Add N dimension to transform to NCHW
image = np.expand_dims(image, axis=0)
input_data.append(image)
else: # add for just my test
# add for normalization of figure
from PIL import Image, ImageFilter
import torchvision
normalize_GoogleOpenImage = torchvision.transforms.Normalize(
mean=[0.4900, 0.4252, 0.3741],
std=[0.2741, 0.2673, 0.2739]
)
transform_resize_totensor_donorm = torchvision.transforms.Compose([
torchvision.transforms.Resize(2048,interpolation=torchvision.transforms.InterpolationMode.BILINEAR),
torchvision.transforms.ToTensor(),
normalize_GoogleOpenImage,
])
#img = Image.open("sample-image/"+fig)
img = Image.open("sample-image/"+fig).convert('RGB')
img = transform_resize_totensor_donorm( img )
#print(type(img), img.shape)
#<class 'torch.Tensor'> torch.Size([3, 2048, 2048])
#print(img)
#img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0) # add batch dimension
print(type(img), img.shape)
print("\ninput image = \n", img[0:1,0:1,0:1,0:9], "\n\n")
input_data.append(img)
# ---------------------------Step 7. Do inference----------------------------------------------------------------------
for i in range(iterate):
#log.info('Starting inference in asynchronous mode')
#exec_net.requests[i].async_infer({input_blob: input_data[0]})
#log.info('Starting inference in synchronous mode')
exec_net.infer(inputs={input_blob: input_data[0]})
# ---------------------------Step 8. Process output--------------------------------------------------------------------
# Generate a label list
if args.labels:
with open(args.labels, 'r') as f:
labels = [line.split(',')[0].strip() for line in f]
# Create a list to control a order of output
output_queue = list(range(num_of_input))
while True:
for i in output_queue:
# Immediately returns a inference status without blocking or interrupting
infer_status = exec_net.requests[i].wait(0)
if infer_status == StatusCode.RESULT_NOT_READY:
continue
log.info(f'Infer request {i} returned {infer_status}')
if infer_status != StatusCode.OK:
return -2
# Read infer request results from buffer
res = exec_net.requests[i].output_blobs[out_blob].buffer
#print("Read infer request results from buffer = ", res.shape)
# Read infer request results from buffer = (1, 256, 255, 255)
if 1: # add for my output
print(res.shape)
print("\noutput features = \n", res[0:1,0:1,0:1,0:9], "\n\n")
fig = plt.figure("")
plt.imshow(res[0][0]) # b,c,h,w
#plt.show()
np.save(np_save_name, res[0])
plt.show()
"""
# Change a shape of a numpy.ndarray with results to get another one with one dimension
probs = res.reshape(num_of_classes)
# Get an array of args.number_top class IDs in descending order of probability
top_n_idexes = np.argsort(probs)[-args.number_top :][::-1]
header = 'classid probability'
header = header + ' label' if args.labels else header
log.info(f'Image path: {args.input[i]}')
log.info(f'Top {args.number_top} results: ')
log.info(header)
log.info('-' * len(header))
for class_id in top_n_idexes:
probability_indent = ' ' * (len('classid') - len(str(class_id)) + 1)
label_indent = ' ' * (len('probability') - 8) if args.labels else ''
label = labels[class_id] if args.labels else ''
log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}{label_indent}{label}')
log.info('')
"""
output_queue.remove(i)
if len(output_queue) == 0:
break
# ----------------------------------------------------------------------------------------------------------------------
log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
return 0
if __name__ == '__main__':
sys.exit(main())