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Diffstat (limited to 'lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py')
| -rw-r--r-- | lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py | 135 |
1 files changed, 135 insertions, 0 deletions
diff --git a/lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py b/lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py new file mode 100644 index 00000000..1906e9fe --- /dev/null +++ b/lib/oeqa/runtime/files/dldt-inference-engine/classification_sample.py @@ -0,0 +1,135 @@ +#!/usr/bin/env python3 +""" + Copyright (C) 2018-2019 Intel Corporation + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" +from __future__ import print_function +import sys +import os +from argparse import ArgumentParser, SUPPRESS +import cv2 +import numpy as np +import logging as log +from time import time +from openvino.inference_engine import IENetwork, IECore + + +def build_argparser(): + parser = ArgumentParser(add_help=False) + args = parser.add_argument_group('Options') + args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.') + args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.", required=True, + type=str) + args.add_argument("-i", "--input", help="Required. Path to a folder with images or path to an image files", + required=True, + type=str, nargs="+") + args.add_argument("-l", "--cpu_extension", + help="Optional. Required for CPU custom layers. " + "MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the" + " kernels implementations.", type=str, default=None) + args.add_argument("-d", "--device", + help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL, MYRIAD or HETERO: is " + "acceptable. The sample will look for a suitable plugin for device specified. Default " + "value is CPU", + default="CPU", type=str) + args.add_argument("--labels", help="Optional. Path to a labels mapping file", default=None, type=str) + args.add_argument("-nt", "--number_top", help="Optional. Number of top results", default=10, type=int) + + return parser + + +def main(): + log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout) + args = build_argparser().parse_args() + model_xml = args.model + model_bin = os.path.splitext(model_xml)[0] + ".bin" + + # Plugin initialization for specified device and load extensions library if specified + log.info("Creating Inference Engine") + ie = IECore() + if args.cpu_extension and 'CPU' in args.device: + ie.add_extension(args.cpu_extension, "CPU") + # Read IR + log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin)) + net = IENetwork(model=model_xml, weights=model_bin) + + if "CPU" in args.device: + supported_layers = ie.query_network(net, "CPU") + not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers] + if len(not_supported_layers) != 0: + log.error("Following layers are not supported by the plugin for specified device {}:\n {}". + format(args.device, ', '.join(not_supported_layers))) + log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l " + "or --cpu_extension command line argument") + sys.exit(1) + + assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies" + assert len(net.outputs) == 1, "Sample supports only single output topologies" + + log.info("Preparing input blobs") + input_blob = next(iter(net.inputs)) + out_blob = next(iter(net.outputs)) + net.batch_size = len(args.input) + + # Read and pre-process input images + n, c, h, w = net.inputs[input_blob].shape + images = np.ndarray(shape=(n, c, h, w)) + for i in range(n): + image = cv2.imread(args.input[i]) + if image.shape[:-1] != (h, w): + log.warning("Image {} is resized from {} to {}".format(args.input[i], image.shape[:-1], (h, w))) + image = cv2.resize(image, (w, h)) + image = image.transpose((2, 0, 1)) # Change data layout from HWC to CHW + images[i] = image + log.info("Batch size is {}".format(n)) + + # Loading model to the plugin + log.info("Loading model to the plugin") + exec_net = ie.load_network(network=net, device_name=args.device) + + # Start sync inference + log.info("Starting inference in synchronous mode") + res = exec_net.infer(inputs={input_blob: images}) + + # Processing output blob + log.info("Processing output blob") + res = res[out_blob] + log.info("Top {} results: ".format(args.number_top)) + if args.labels: + with open(args.labels, 'r') as f: + labels_map = [x.split(sep=' ', maxsplit=1)[-1].strip() for x in f] + else: + labels_map = None + classid_str = "classid" + probability_str = "probability" + for i, probs in enumerate(res): + probs = np.squeeze(probs) + top_ind = np.argsort(probs)[-args.number_top:][::-1] + print("Image {}\n".format(args.input[i])) + print(classid_str, probability_str) + print("{} {}".format('-' * len(classid_str), '-' * len(probability_str))) + for id in top_ind: + det_label = labels_map[id] if labels_map else "{}".format(id) + label_length = len(det_label) + space_num_before = (len(classid_str) - label_length) // 2 + space_num_after = len(classid_str) - (space_num_before + label_length) + 2 + space_num_before_prob = (len(probability_str) - len(str(probs[id]))) // 2 + print("{}{}{}{}{:.7f}".format(' ' * space_num_before, det_label, + ' ' * space_num_after, ' ' * space_num_before_prob, + probs[id])) + print("\n") + log.info("This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n") + +if __name__ == '__main__': + sys.exit(main() or 0) |