103 lines
3.0 KiB
Python
103 lines
3.0 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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#
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from utils import get_model, get_ngraph_model
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#! [ov_imports]
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from openvino 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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tensor_name="input"
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core = Core()
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model = get_model([1,32,32,3])
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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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model = get_model()
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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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model = get_model([1, 3, 448, 448])
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#! [ov_image_scale]
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ppp = PrePostProcessor(model)
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input = ppp.input("input")
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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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import openvino.inference_engine as ie
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import ngraph as ng
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operation_name = "data"
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core = ie.IECore()
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network = get_ngraph_model()
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#! [mean_scale]
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preprocess_info = network.input_info[operation_name].preprocess_info
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preprocess_info.init(3)
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preprocess_info[0].mean_value = 116.78
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preprocess_info[1].mean_value = 116.78
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preprocess_info[2].mean_value = 116.78
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preprocess_info[0].std_scale = 57.21
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preprocess_info[1].std_scale = 57.45
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preprocess_info[2].std_scale = 57.73
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preprocess_info.mean_variant = ie.MeanVariant.MEAN_VALUE
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#! [mean_scale]
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#! [conversions]
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input_info = network.input_info[operation_name]
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input_info.precision = "U8"
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input_info.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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#! [image_scale]
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preprocess_info = network.input_info[operation_name].preprocess_info
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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_info.resize_algorithm = ie.ResizeAlgorithm.RESIZE_BILINEAR
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