116 lines
4.2 KiB
Python
Executable File
116 lines
4.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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import sys
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import cv2
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import numpy as np
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from openvino.preprocess import PrePostProcessor, ResizeAlgorithm
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from openvino.runtime import Core, Layout, Type
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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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# Parsing and validation of input arguments
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if len(sys.argv) != 4:
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log.info('Usage: <path_to_model> <path_to_image> <device_name>')
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return 1
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model_path = sys.argv[1]
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image_path = sys.argv[2]
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device_name = sys.argv[3]
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# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
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log.info('Creating OpenVINO Runtime Core')
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core = Core()
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# --------------------------- Step 2. Read a model --------------------------------------------------------------------
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log.info(f'Reading the network: {model_path}')
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# (.xml and .bin files) or (.onnx file)
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model = core.read_model(model_path)
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if len(model.inputs) != 1:
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log.error('Sample supports only single input topologies')
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return -1
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if len(model.outputs) != 1:
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log.error('Sample supports only single output topologies')
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return -1
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# --------------------------- Step 3. Set up input --------------------------------------------------------------------
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# Read input image
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image = cv2.imread(image_path)
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# Add N dimension
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input_tensor = np.expand_dims(image, 0)
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# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
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ppp = PrePostProcessor(model)
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_, h, w, _ = input_tensor.shape
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# 1) Set input tensor information:
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# - input() provides information about a single model input
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# - precision of tensor is supposed to be 'u8'
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# - layout of data is 'NHWC'
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# - set static spatial dimensions to input tensor to resize from
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ppp.input().tensor() \
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.set_element_type(Type.u8) \
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.set_layout(Layout('NHWC')) \
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.set_spatial_static_shape(h, w) # noqa: ECE001, N400
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# 2) Adding explicit preprocessing steps:
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# - apply linear resize from tensor spatial dims to model spatial dims
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ppp.input().preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
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# 3) Here we suppose model has 'NCHW' layout for input
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ppp.input().model().set_layout(Layout('NCHW'))
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# 4) Set output tensor information:
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# - precision of tensor is supposed to be 'f32'
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ppp.output().tensor().set_element_type(Type.f32)
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# 5) Apply preprocessing modifing the original 'model'
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model = ppp.build()
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# --------------------------- Step 5. Loading model to the device -----------------------------------------------------
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log.info('Loading the model to the plugin')
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compiled_model = core.compile_model(model, device_name)
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# --------------------------- Step 6. Create infer request and do inference synchronously -----------------------------
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log.info('Starting inference in synchronous mode')
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results = compiled_model.infer_new_request({0: input_tensor})
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# --------------------------- Step 7. Process output ------------------------------------------------------------------
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predictions = next(iter(results.values()))
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# Change a shape of a numpy.ndarray with results to get another one with one dimension
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probs = predictions.reshape(-1)
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# Get an array of 10 class IDs in descending order of probability
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top_10 = np.argsort(probs)[-10:][::-1]
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header = 'class_id probability'
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log.info(f'Image path: {image_path}')
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log.info('Top 10 results: ')
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log.info(header)
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log.info('-' * len(header))
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for class_id in top_10:
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probability_indent = ' ' * (len('class_id') - len(str(class_id)) + 1)
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log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}')
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log.info('')
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# ----------------------------------------------------------------------------------------------------------------------
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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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return 0
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if __name__ == '__main__':
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sys.exit(main())
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