f82533005b
* [OV2.0] Preprocessing documentation - first draft * Small update * Added ov::Layout overview * Fix code style * Preprocessing details - ~50% done * Corrected links * Fixed comments, added more docs * Minor updates * Couple more links * Fixed comments * Remove 'future' link
172 lines
5.7 KiB
Python
172 lines
5.7 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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from openvino.preprocess import ResizeAlgorithm, ColorFormat
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from openvino.runtime import Layout, Type
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xml_path = ''
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input_name = ''
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# ! [ov:preprocess:create]
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from openvino.preprocess import PrePostProcessor
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from openvino.runtime import Core
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core = Core()
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model = core.read_model(model=xml_path)
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ppp = PrePostProcessor(model)
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# ! [ov:preprocess:create]
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# ! [ov:preprocess:tensor]
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from openvino.preprocess import ColorFormat
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from openvino.runtime import Layout, Type
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ppp.input(input_name).tensor() \
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.set_element_type(Type.u8) \
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.set_shape([1, 480, 640, 3]) \
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.set_layout(Layout('NHWC')) \
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.set_color_format(ColorFormat.BGR)
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# ! [ov:preprocess:tensor]
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# ! [ov:preprocess:model]
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# `model's input` already `knows` it's shape and data type, no need to specify them here
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ppp.input(input_name).model().set_layout(Layout('NCHW'))
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# ! [ov:preprocess:model]
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# ! [ov:preprocess:steps]
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from openvino.preprocess import ResizeAlgorithm
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ppp.input(input_name).preprocess() \
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.convert_element_type(Type.f32) \
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.convert_color(ColorFormat.RGB) \
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.resize(ResizeAlgorithm.RESIZE_LINEAR) \
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.mean([100.5, 101, 101.5]) \
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.scale([50., 51., 52.])
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# .convert_layout(Layout('NCHW')); # Not needed, such conversion will be added implicitly
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# ! [ov:preprocess:steps]
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# ! [ov:preprocess:build]
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print(f'Dump preprocessor: {ppp}')
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model = ppp.build()
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# ! [ov:preprocess:build]
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# ! [ov:preprocess:input_index]
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ppp.input(1) # Gets 2nd input in a model
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ppp.output(2) # Gets output with index=2 (3rd one) in a model
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# ! [ov:preprocess:input_index]
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# ! [ov:preprocess:input_name]
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ppp.input('image')
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ppp.output('result')
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# ! [ov:preprocess:input_name]
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# ! [ov:preprocess:input_1]
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# no index/name is needed if model has one input
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ppp.input().preprocess().scale(50.)
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# same for output
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ppp.output() \
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.postprocess().convert_element_type(Type.u8)
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# ! [ov:preprocess:input_1]
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# ! [ov:preprocess:mean_scale]
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ppp.input('input').preprocess().mean(128).scale(127)
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# ! [ov:preprocess:mean_scale]
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# ! [ov:preprocess:mean_scale_array]
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# Suppose model's shape is {1, 3, 224, 224}
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# N=1, C=3, H=224, W=224
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ppp.input('input').model().set_layout(Layout('NCHW'))
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# Mean/Scale has 3 values which matches with C=3
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ppp.input('input').preprocess() \
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.mean([103.94, 116.78, 123.68]).scale([57.21, 57.45, 57.73])
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# ! [ov:preprocess:mean_scale_array]
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# ! [ov:preprocess:convert_element_type]
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# First define data type for your tensor
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ppp.input('input').tensor().set_element_type(Type.u8)
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# Then define preprocessing step
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ppp.input('input').preprocess().convert_element_type(Type.f32)
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# If conversion is needed to `model's` element type, 'f32' can be omitted
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ppp.input('input').preprocess().convert_element_type()
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# ! [ov:preprocess:convert_element_type]
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# ! [ov:preprocess:convert_layout]
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# First define layout for your tensor
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ppp.input('input').tensor().set_layout(Layout('NHWC'))
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# Then define layout of model
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ppp.input('input').model().set_layout(Layout('NCHW'))
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print(ppp) # Will print 'implicit layout conversion step'
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# ! [ov:preprocess:convert_layout]
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# ! [ov:preprocess:convert_layout_2]
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ppp.input('input').tensor().set_shape([1, 480, 640, 3])
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# Model expects shape {1, 3, 480, 640}
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ppp.input('input').preprocess()\
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.convert_layout([0, 3, 1, 2])
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# 0 -> 0; 3 -> 1; 1 -> 2; 2 -> 3
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# ! [ov:preprocess:convert_layout_2]
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# ! [ov:preprocess:resize_1]
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ppp.input('input').tensor().set_shape([1, 3, 960, 1280])
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ppp.input('input').model().set_layout(Layout('??HW'))
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ppp.input('input').preprocess()\
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.resize(ResizeAlgorithm.RESIZE_LINEAR, 480, 640)
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# ! [ov:preprocess:resize_1]
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# ! [ov:preprocess:resize_2]
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ppp.input('input').tensor().set_shape([1, 3, 960, 1280])
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# Model accepts {1, 3, 480, 640} shape, thus last dimensions are 'H' and 'W'
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ppp.input('input').model().set_layout(Layout('??HW'))
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# Resize to model's dimension
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ppp.input('input').preprocess().resize(ResizeAlgorithm.RESIZE_LINEAR)
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# ! [ov:preprocess:resize_2]
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# ! [ov:preprocess:convert_color_1]
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ppp.input('input').tensor().set_color_format(ColorFormat.BGR)
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ppp.input('input').preprocess().convert_color(ColorFormat.RGB)
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# ! [ov:preprocess:convert_color_1]
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# ! [ov:preprocess:convert_color_2]
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# This will split original `input` to 2 separate inputs: `input/y' and 'input/uv'
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ppp.input('input').tensor()\
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.set_color_format(ColorFormat.NV12_TWO_PLANES)
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ppp.input('input').preprocess()\
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.convert_color(ColorFormat.RGB)
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print(ppp) # Dump preprocessing steps to see what will happen
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# ! [ov:preprocess:convert_color_2]
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# ! [ov:preprocess:custom]
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# It is possible to insert some custom operations
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import openvino.runtime.opset8 as ops
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from openvino.runtime import Output
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from openvino.runtime.utils.decorators import custom_preprocess_function
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@custom_preprocess_function
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def custom_abs(output: Output):
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# Custom nodes can be inserted as Preprocessing steps
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return ops.abs(output)
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ppp.input("input_image").preprocess() \
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.custom(custom_abs)
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# ! [ov:preprocess:custom]
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# ! [ov:preprocess:postprocess]
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# Model's output has 'NCHW' layout
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ppp.output('result_image').model().set_layout(Layout('NCHW'))
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# Set target user's tensor to U8 type + 'NHWC' layout
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# Precision & layout conversions will be done implicitly
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ppp.output('result_image').tensor()\
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.set_layout(Layout("NHWC"))\
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.set_element_type(Type.u8)
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# Also it is possible to insert some custom operations
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import openvino.runtime.opset8 as ops
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from openvino.runtime import Output
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from openvino.runtime.utils.decorators import custom_preprocess_function
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@custom_preprocess_function
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def custom_abs(output: Output):
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# Custom nodes can be inserted as Post-processing steps
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return ops.abs(output)
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ppp.output("result_image").postprocess()\
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.custom(custom_abs)
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# ! [ov:preprocess:postprocess]
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