3b5f3d1957
* add placeholder for python version of first snippet * fix problem with placeholder * fix wrong file name * fix fragment name * update python snippets * move imports to the top of the code fragments
108 lines
3.1 KiB
Python
108 lines
3.1 KiB
Python
# Copyright (C) 2018-2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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#
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#! [ov_imports]
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from openvino.runtime import Core, Layout, Type
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from openvino.preprocess import ColorFormat, PrePostProcessor, ResizeAlgorithm
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#! [ov_imports]
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#! [imports]
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import openvino.inference_engine as ie
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#! [imports]
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#include "inference_engine.hpp"
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model_path = ''
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tensor_name = ''
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core = Core()
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model = core.read_model(model=model_path)
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#! [ov_mean_scale]
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ppp = PrePostProcessor(model)
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input = ppp.input(tensor_name)
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# we only need to know where is C dimension
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input.model().set_layout(Layout('...C'))
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# specify scale and mean values, order of operations is important
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input.preprocess().mean([116.78]).scale([57.21, 57.45, 57.73])
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# insert preprocessing operations to the 'model'
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model = ppp.build()
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#! [ov_mean_scale]
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#! [ov_conversions]
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ppp = PrePostProcessor(model)
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input = ppp.input(tensor_name)
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input.tensor().set_layout(Layout('NCHW')).set_element_type(Type.u8)
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input.model().set_layout(Layout('NCHW'))
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# layout and precision conversion is inserted automatically,
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# because tensor format != model input format
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model = ppp.build()
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#! [ov_conversions]
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#! [ov_color_space]
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ppp = PrePostProcessor(model)
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input = ppp.input(tensor_name)
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input.tensor().set_color_format(ColorFormat.NV12_TWO_PLANES)
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# add NV12 to BGR conversion
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input.preprocess().convert_color(ColorFormat.BGR)
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# and insert operations to the model
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model = ppp.build()
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#! [ov_color_space]
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#! [ov_image_scale]
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ppp = PrePostProcessor(model)
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input = ppp.input(tensor_name)
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# need to specify H and W dimensions in model, others are not important
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input.model().set_layout(Layout('??HW'))
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# scale to model shape
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input.preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR, 448, 448)
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# and insert operations to the model
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model = ppp.build()
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#! [ov_image_scale]
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model_path = ''
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operation_name = ''
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core = Core()
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network = core.ReadNetwork(model_path)
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#! [mean_scale]
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preProcess = network.getInputsInfo()[operation_name].getPreProcess()
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preProcess.init(3)
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preProcess[0].meanValue = 116.78
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preProcess[1].meanValue = 116.78
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preProcess[2].meanValue = 116.78
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preProcess[0].stdScale = 57.21
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preProcess[1].stdScale = 57.45
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preProcess[2].stdScale = 57.73
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preProcess.setVariant(ie.MEAN_VALUE)
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#! [mean_scale]
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#! [conversions]
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inputInfo = network.getInputsInfo()[operation_name]
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inputInfo.setPrecision(ie.Precision.U8)
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inputInfo.setLayout(ie.Layout.NHWC)
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# model input layout is always NCHW in Inference Engine
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# for shapes with 4 dimensions
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#! [conversions]
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#! [color_space]
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preProcess = network.getInputsInfo()[operation_name].getPreProcess()
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# Inference Engine supposes NV12 as two inputs which need to be passed
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# as InferenceEngine::NV12Blob composed of two Y and UV planes
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preProcess.setColorFormat(ie.NV12)
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#! [color_space]
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#! [image_scale]
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preProcess = network.getInputsInfo()[operation_name].getPreProcess()
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# Inference Engine supposes input for resize is always in NCHW layout
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# while for OpenVINO Runtime API 2.0 `H` and `W` dimensions must be specified
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# Also, current code snippet supposed resize from dynamic shapes
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preProcess.setResizeAlgorithm(ie.ResizeAlgorithm.RESIZE_BILINEAR)
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#! [image_scale]
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