9de27f16bc
* Publishing R5 content * Updated ade revision * updated readme * add possibility to build CPU plugin with Intel MKL package
137 lines
6.1 KiB
Python
137 lines
6.1 KiB
Python
#!/usr/bin/env python
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"""
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Copyright (c) 2018 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
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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, IEPlugin
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def build_argparser():
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parser = ArgumentParser()
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parser.add_argument("-m", "--model", help="Path to an .xml file with a trained model.", required=True, type=str)
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parser.add_argument("-i", "--input", help="Path to a folder with images or path to an image files", required=True,
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type=str, nargs="+")
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parser.add_argument("-l", "--cpu_extension",
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help="MKLDNN (CPU)-targeted custom layers.Absolute path to a shared library with the kernels "
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"impl.", type=str, default=None)
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parser.add_argument("-pp", "--plugin_dir", help="Path to a plugin folder", type=str, default=None)
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parser.add_argument("-d", "--device",
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help="Specify the target device to infer on; CPU, GPU, FPGA or MYRIAD is acceptable. Sample "
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"will look for a suitable plugin for device specified (CPU by default)", default="CPU",
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type=str)
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parser.add_argument("--labels", help="Labels mapping file", default=None, type=str)
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parser.add_argument("-nt", "--number_top", help="Number of top results", default=10, type=int)
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parser.add_argument("-ni", "--number_iter", help="Number of inference iterations", default=1, type=int)
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parser.add_argument("-pc", "--perf_counts", help="Report performance counters", default=False, action="store_true")
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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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plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
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if args.cpu_extension and 'CPU' in args.device:
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plugin.add_cpu_extension(args.cpu_extension)
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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 plugin.device == "CPU":
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supported_layers = plugin.get_supported_layers(net)
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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(plugin.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 = plugin.load(network=net)
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del net
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# Start sync inference
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log.info("Starting inference ({} iterations)".format(args.number_iter))
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infer_time = []
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for i in range(args.number_iter):
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t0 = time()
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infer_request_handle = exec_net.start_async(request_id=0, inputs={input_blob: images})
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infer_request_handle.wait()
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infer_time.append((time() - t0) * 1000)
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log.info("Average running time of one iteration: {} ms".format(np.average(np.asarray(infer_time))))
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if args.perf_counts:
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perf_counts = infer_request_handle.get_perf_counts()
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log.info("Performance counters:")
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print("{:<70} {:<15} {:<15} {:<15} {:<10}".format('name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
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for layer, stats in perf_counts.items():
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print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(layer, stats['layer_type'], stats['exec_type'],
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stats['status'], stats['real_time']))
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# Processing output blob
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log.info("Processing output blob")
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res = infer_request_handle.outputs[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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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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for id in top_ind:
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det_label = labels_map[id] if labels_map else "#{}".format(id)
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print("{:.7f} {}".format(probs[id], det_label))
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print("\n")
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del exec_net
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del plugin
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if __name__ == '__main__':
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sys.exit(main() or 0)
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