136 lines
6.1 KiB
Python
136 lines
6.1 KiB
Python
#!/usr/bin/env python
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"""
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Copyright (C) 2018-2019 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from __future__ import print_function
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import sys
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import os
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from argparse import ArgumentParser, SUPPRESS
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import cv2
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import numpy as np
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import logging as log
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from time import time
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from openvino.inference_engine import IENetwork, IECore
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def build_argparser():
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parser = ArgumentParser(add_help=False)
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args = parser.add_argument_group('Options')
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args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
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args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.", required=True,
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type=str)
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args.add_argument("-i", "--input", help="Required. Path to a folder with images or path to an image files",
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required=True,
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type=str, nargs="+")
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args.add_argument("-l", "--cpu_extension",
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help="Optional. Required for CPU custom layers. "
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"MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the"
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" kernels implementations.", type=str, default=None)
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args.add_argument("-d", "--device",
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help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL, MYRIAD or HETERO: is "
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"acceptable. The sample will look for a suitable plugin for device specified. Default "
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"value is CPU",
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default="CPU", type=str)
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args.add_argument("--labels", help="Optional. Path to a labels mapping file", default=None, type=str)
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args.add_argument("-nt", "--number_top", help="Optional. Number of top results", default=10, type=int)
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return parser
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def main():
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log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
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args = build_argparser().parse_args()
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model_xml = args.model
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model_bin = os.path.splitext(model_xml)[0] + ".bin"
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# Plugin initialization for specified device and load extensions library if specified
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log.info("Creating Inference Engine")
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ie = IECore()
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if args.cpu_extension and 'CPU' in args.device:
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ie.add_extension(args.cpu_extension, "CPU")
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# Read IR
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log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
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net = IENetwork(model=model_xml, weights=model_bin)
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if "CPU" in args.device:
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supported_layers = ie.query_network(net, "CPU")
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not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
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if len(not_supported_layers) != 0:
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log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
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format(args.device, ', '.join(not_supported_layers)))
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log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
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"or --cpu_extension command line argument")
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sys.exit(1)
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assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
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assert len(net.outputs) == 1, "Sample supports only single output topologies"
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log.info("Preparing input blobs")
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input_blob = next(iter(net.inputs))
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out_blob = next(iter(net.outputs))
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net.batch_size = len(args.input)
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# Read and pre-process input images
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n, c, h, w = net.inputs[input_blob].shape
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images = np.ndarray(shape=(n, c, h, w))
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for i in range(n):
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image = cv2.imread(args.input[i])
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if image.shape[:-1] != (h, w):
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log.warning("Image {} is resized from {} to {}".format(args.input[i], image.shape[:-1], (h, w)))
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image = cv2.resize(image, (w, h))
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image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW
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images[i] = image
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log.info("Batch size is {}".format(n))
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# Loading model to the plugin
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log.info("Loading model to the plugin")
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exec_net = ie.load_network(network=net, device_name=args.device)
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# Start sync inference
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log.info("Starting inference in synchronous mode")
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res = exec_net.infer(inputs={input_blob: images})
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# Processing output blob
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log.info("Processing output blob")
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res = res[out_blob]
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log.info("Top {} results: ".format(args.number_top))
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if args.labels:
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with open(args.labels, 'r') as f:
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labels_map = [x.split(sep=' ', maxsplit=1)[-1].strip() for x in f]
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else:
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labels_map = None
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classid_str = "classid"
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probability_str = "probability"
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for i, probs in enumerate(res):
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probs = np.squeeze(probs)
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top_ind = np.argsort(probs)[-args.number_top:][::-1]
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print("Image {}\n".format(args.input[i]))
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print(classid_str, probability_str)
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print("{} {}".format('-' * len(classid_str), '-' * len(probability_str)))
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for id in top_ind:
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det_label = labels_map[id] if labels_map else "{}".format(id)
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label_length = len(det_label)
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space_num_before = (len(classid_str) - label_length) // 2
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space_num_after = len(classid_str) - (space_num_before + label_length) + 2
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space_num_before_prob = (len(probability_str) - len(str(probs[id]))) // 2
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print("{}{}{}{}{:.7f}".format(' ' * space_num_before, det_label,
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' ' * space_num_after, ' ' * space_num_before_prob,
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probs[id]))
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print("\n")
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log.info("This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n")
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if __name__ == '__main__':
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sys.exit(main() or 0)
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