6f7e9cd786
* Update docs for frontend extensions * Apply suggestions from code review Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> * fix order in openvino_framework_map * no discard return value * add note of openvino_contrib repo * update example for PT * note * add paragraph of named inputs and outputs Signed-off-by: Mateusz Tabaka <mateusz.tabaka@intel.com> * title underline too short * review comments * remove m_ prefix from CustomOp attr names --------- Signed-off-by: Mateusz Tabaka <mateusz.tabaka@intel.com> Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> Co-authored-by: Michal Lukaszewski <michal.lukaszewski@intel.com>
70 lines
2.1 KiB
Python
70 lines
2.1 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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import openvino.runtime as ov
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#! [py_frontend_extension_ThresholdedReLU_header]
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import openvino.runtime.opset8 as ops
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from openvino.frontend import ConversionExtension
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#! [py_frontend_extension_ThresholdedReLU_header]
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#! [add_extension]
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# Not implemented
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#! [add_extension]
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#! [add_frontend_extension]
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# Not implemented
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#! [add_frontend_extension]
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#! [add_extension_lib]
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core = ov.Core()
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# Load extensions library to ov.Core
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core.add_extension("libopenvino_template_extension.so")
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#! [add_extension_lib]
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#! [py_frontend_extension_MyRelu]
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from openvino.frontend import OpExtension
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core.add_extension(OpExtension("Relu", "MyRelu"))
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#! [py_frontend_extension_MyRelu]
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#! [py_frontend_extension_ThresholdedReLU]
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def conversion(node):
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input_node = node.get_input(0)
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input_type = input_node.get_element_type()
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greater = ops.greater(input_node, ops.constant([node.get_attribute("alpha")], input_type))
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casted = ops.convert(greater, input_type.get_type_name())
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return ops.multiply(input_node, casted).outputs()
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core.add_extension(ConversionExtension("ThresholdedRelu", conversion))
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#! [py_frontend_extension_ThresholdedReLU]
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#! [py_frontend_extension_aten_hardtanh]
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import torch
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from openvino.frontend import ConversionExtension, NodeContext
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from openvino.tools.mo import convert_model
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class HardTanh(torch.nn.Module):
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def __init__(self, min_val, max_val):
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super(HardTanh, self).__init__()
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self.min_val = min_val
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self.max_val = max_val
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def forward(self, inp):
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return torch.nn.functional.hardtanh(inp, self.min_val, self.max_val)
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def convert_hardtanh(node: NodeContext):
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inp = node.get_input(0)
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min_value = node.get_values_from_const_input(1)
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max_value = node.get_values_from_const_input(2)
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return ops.clamp(inp, min_value, max_value).outputs()
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model = HardTanh(min_val=0.1, max_val=2.0)
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hardtanh_ext = ConversionExtension("aten::hardtanh", convert_hardtanh)
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ov_model = convert_model(input_model=model, extensions=[hardtanh_ext])
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#! [py_frontend_extension_aten_hardtanh]
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