e2534eb9d6
* first snippet * part1 * update model state snippet * add temp dir * CPU snippets update (#134) * snippets CPU 1/6 * snippets CPU 2/6 * snippets CPU 3/6 * snippets CPU 4/6 * snippets CPU 5/6 * snippets CPU 6/6 * make module TODO: REMEMBER ABOUT EXPORTING PYTONPATH ON CIs ETC * Add static model creation in snippets for CPU * export_comp_model done * leftovers * apply comments * apply comments -- properties * small fixes * add serialize * rempve debug info * return IENetwork instead of Function * apply comments * revert precision change in common snippets * update opset * [PyOV] Edit docs for the rest of plugins (#136) * modify main.py * GNA snippets * GPU snippets * AUTO snippets * MULTI snippets * HETERO snippets * Added properties * update gna * more samples * Update docs/OV_Runtime_UG/model_state_intro.md * Update docs/OV_Runtime_UG/model_state_intro.md --------- Co-authored-by: Jan Iwaszkiewicz <jan.iwaszkiewicz@intel.com> Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
74 lines
2.2 KiB
Python
74 lines
2.2 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 as ov
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#! [py_frontend_extension_ThresholdedReLU_header]
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import openvino.runtime.opset12 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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from utils import get_path_to_extension_library
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path_to_extension_lib = get_path_to_extension_library()
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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(path_to_extension_lib)
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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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