Maciej Smyk
9ea3553d5d
Recreation of BDTI PRs for master. Recreated PRs: Docs: Update Dynamic Shapes documentation #15216 Docs: Edits to Performance Hints and Cumulative Throughput documentation #14793 Docs: Update Devices pages to state improved INT8 performance with 11th & 12th gen devices #12067
115 lines
3.0 KiB
Python
115 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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import numpy as np
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#! [import]
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import openvino.runtime as ov
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#! [import]
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#! [reshape_undefined]
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Core = ov.Core()
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model = core.read_model(“model.xml”)
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# Set first dimension to be dynamic while keeping others static
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model.reshape([-1, 3, 224, 224])
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# Or, set third and fourth dimensions as dynamic
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model.reshape([1, 3, -1, -1])
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#! [reshape_undefined]
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#! [reshape_bounds]
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# Example 1 - set first dimension as dynamic (no bounds) and third and fourth dimensions to range of 112..448
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model.reshape([-1, 3, (112, 448), (112, 448)])
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# Example 2 - Set first dimension to a range of 1..8 and third and fourth dimensions to range of 112..448
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model.reshape([(1, 8), 3, (112, 448), (112, 448)])
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#! [reshape_bounds]
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model = core.read_model("model.xml")
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#! [print_dynamic]
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# Print output partial shape
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print(model.output().partial_shape)
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# Print input partial shape
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print(model.input().partial_shape)
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#! [print_dynamic]
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#! [detect_dynamic]
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model = core.read_model("model.xml")
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if model.input(0).partial_shape.is_dynamic():
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# input is dynamic
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pass
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if model.output(0).partial_shape.is_dynamic():
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# output is dynamic
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pass
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if model.output(0).partial_shape[1].is_dynamic():
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# 1-st dimension of output is dynamic
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pass
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#! [detect_dynamic]
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executable = core.compile_model(model)
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infer_request = executable.create_infer_request()
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#! [set_input_tensor]
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# For first inference call, prepare an input tensor with 1x128 shape and run inference request
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Input_data1 = np.ones(shape=[1,128])
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infer_request.infer([input_data1])
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# Get resulting outputs
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Output_tensor1 = infer_request.get_output_tensor()
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Output_data1 = output_tensor.data[:]
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# For second inference call, prepare a 1x200 input tensor and run inference request
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Input_data2 = np.ones(shape=[1,200])
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infer_request.infer([input_data2])
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# Get resulting outputs
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Output_tensor2 = infer_request.get_output_tensor()
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Output_data2 = output_tensor.data[:]
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#! [set_input_tensor]
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infer_request = executable.create_infer_request()
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#! [get_input_tensor]
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# Get the tensor, shape is not initialized
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input_tensor = infer_request.get_input_tensor()
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# Set shape is required
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input_tensor.shape = [1, 128]
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# ... write values to input_tensor
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infer_request.infer()
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output_tensor = infer_request.get_output_tensor()
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data1 = output_tensor.data[:]
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# The second inference call, repeat steps:
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# Set a new shape, may reallocate tensor memory
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input_tensor.shape = [1, 200]
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# ... write values to input_tensor
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infer_request.infer()
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data2 = output_tensor.data[:]
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#! [get_input_tensor]
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#! [check_inputs]
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core = ov.Core()
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model = core.read_model("model.xml")
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# Print model input layer info
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for input_layer in model.inputs:
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print(input_layer.names, input_layer.partial_shape)
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#! [check_inputs]
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#! [reshape_multiple_inputs]
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# Assign dynamic shapes to second dimension in every input layer
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shapes = {}
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for input_layer in model.inputs:
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shapes[input_layer] = input_layer.partial_shape
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shapes[input_layer][1] = -1
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model.reshape(shapes)
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#! [reshape_multiple_inputs] |