Ilya Churaev
0c9abf43a9
* Updated copyright headers
* Revert "Fixed linker warnings in docs snippets on Windows (#15119)"
This reverts commit 372699ec49.
295 lines
12 KiB
Python
295 lines
12 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from common.layer_test_class import check_ir_version
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from common.onnx_layer_test_class import OnnxRuntimeLayerTest
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from unit_tests.utils.graph import build_graph
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class TestReshape(OnnxRuntimeLayerTest):
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def create_reshape_net(self, input_shape, output_shape, ir_version):
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"""
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ONNX net IR net
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Input->Reshape->Output => Input->Reshape
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"""
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#
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# Create ONNX model
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#
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import onnx
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from onnx import helper
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from onnx import TensorProto
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, input_shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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node_shape_def = onnx.helper.make_node(
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'Constant',
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inputs=[],
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outputs=['shape'],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.INT64,
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dims=[len(output_shape)],
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vals=output_shape,
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),
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)
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node_reshape_def = onnx.helper.make_node(
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'Reshape',
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inputs=['input', 'shape'],
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outputs=['output']
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)
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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[node_shape_def, node_reshape_def],
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'test_reshape_model',
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[input],
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[output],
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)
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_reshape_model')
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#
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# Create reference IR net
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# Please, specify 'type': 'Input' for input node
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# Moreover, do not forget to validate ALL layer attributes!!!
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#
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ref_net = None
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if check_ir_version(10, None, ir_version):
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nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter'},
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'input_data': {'shape': input_shape, 'kind': 'data'},
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'input_data_1': {'shape': [len(output_shape)], 'value': output_shape,
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'kind': 'data'},
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'const_1': {'kind': 'op', 'type': 'Const'},
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'const_data_1': {'shape': [len(output_shape)], 'value': None, 'kind': 'data'},
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# 'value': output_shape,
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'reshape': {'kind': 'op', 'type': 'Reshape'},
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'reshape_data': {'shape': output_shape, 'kind': 'data'},
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'result': {'kind': 'op', 'type': 'Result'}
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}
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ref_net = build_graph(nodes_attributes,
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[('input', 'input_data'),
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('input_data_1', 'const_1'),
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('const_1', 'const_data_1'),
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('const_data_1', 'reshape'),
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('input_data', 'reshape'),
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('reshape', 'reshape_data'),
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('reshape_data', 'result')
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])
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return onnx_net, ref_net
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def create_reshape_net_const(self, input_shape, output_shape, ir_version):
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"""
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ONNX net IR net
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Input->Concat(+reshaped const)->Output => Input->Concat(+const)
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"""
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#
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# Create ONNX model
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#
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import onnx
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from onnx import helper
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from onnx import TensorProto
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import numpy as np
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concat_axis = 0
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concat_output_shape = output_shape.copy()
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concat_output_shape[concat_axis] *= 2
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, output_shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, concat_output_shape)
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const_number = np.prod(input_shape)
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constant = np.random.randint(-127, 127, const_number).astype(float)
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node_const_def = onnx.helper.make_node(
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'Constant',
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inputs=[],
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outputs=['const1'],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.FLOAT,
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dims=input_shape,
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vals=constant,
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),
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)
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node_shape_def = onnx.helper.make_node(
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'Constant',
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inputs=[],
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outputs=['shape'],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.INT64,
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dims=[len(output_shape)],
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vals=output_shape,
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),
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)
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node_reshape_def = onnx.helper.make_node(
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'Reshape',
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inputs=['const1', 'shape'],
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outputs=['reshape1']
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)
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node_concat_def = onnx.helper.make_node(
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'Concat',
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inputs=['input', 'reshape1'],
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outputs=['output'],
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axis=concat_axis
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)
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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[node_const_def, node_shape_def, node_reshape_def, node_concat_def],
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'test_reshape_model',
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[input],
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[output],
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)
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_reshape_model')
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#
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# Create reference IR net
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# Please, specify 'type': 'Input' for input node
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# Moreover, do not forget to validate ALL layer attributes!!!
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#
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ref_net = None
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if check_ir_version(10, None, ir_version):
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nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter'},
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'input_data': {'shape': output_shape, 'kind': 'data'},
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'input_const_data': {'kind': 'data', 'value': constant},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'shape': output_shape, 'value': None, 'kind': 'data'},
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'concat': {'kind': 'op', 'type': 'Concat', 'axis': concat_axis},
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'concat_data': {'shape': concat_output_shape, 'kind': 'data'},
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'result': {'kind': 'op', 'type': 'Result'}
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}
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ref_net = build_graph(nodes_attributes,
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[('input', 'input_data'),
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('input_const_data', 'const'),
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('const', 'const_data'),
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('input_data', 'concat'),
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('const_data', 'concat'),
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('concat', 'concat_data'),
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('concat_data', 'result'),
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])
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return onnx_net, ref_net
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test_data_5D = [dict(input_shape=[4, 6, 8, 10, 12], output_shape=[4, 6, 8, 120]),
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dict(input_shape=[4, 6, 8, 10, 12], output_shape=[4, 6, 80, 12]),
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dict(input_shape=[4, 6, 8, 10, 12], output_shape=[4, 48, 10, 12]),
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dict(input_shape=[4, 6, 8, 10, 12], output_shape=[24, 8, 10, 12]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[2, 2, 6, 8, 10]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[4, 2, 3, 8, 10]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[4, 6, 2, 4, 10]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[4, 6, 8, 2, 5])]
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test_data_5D_precommit = [dict(input_shape=[2, 4, 6, 8, 10], output_shape=[8, 6, 8, 10])]
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test_data_4D = [dict(input_shape=[4, 6, 8, 10], output_shape=[24, 8, 10]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[4, 48, 10]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[4, 6, 80]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[192, 10]),
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dict(input_shape=[4, 6, 8, 10], output_shape=[4, 480]),
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dict(input_shape=[4, 6, 8], output_shape=[2, 2, 6, 8]),
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dict(input_shape=[4, 6, 8], output_shape=[4, 2, 3, 8]),
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dict(input_shape=[4, 6, 8], output_shape=[4, 6, 2, 4]),
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dict(input_shape=[4, 6], output_shape=[2, 2, 2, 3])]
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test_data_4D_precommit = [dict(input_shape=[2, 4, 6, 8], output_shape=[48, 8])]
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test_data_3D = [dict(input_shape=[4, 6, 8], output_shape=[24, 8]),
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dict(input_shape=[4, 6, 8], output_shape=[4, 48]),
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dict(input_shape=[4, 6], output_shape=[2, 2, 6]),
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dict(input_shape=[4, 6], output_shape=[4, 2, 3]),
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dict(input_shape=[4, 6], output_shape=[2, 4, 3])]
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test_data_3D_precommit = [dict(input_shape=[2, 4, 6], output_shape=[8, 6])]
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@pytest.mark.parametrize("params", test_data_5D_precommit)
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@pytest.mark.precommit
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def test_reshape_5D_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_4D_precommit)
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@pytest.mark.precommit
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def test_reshape_4D_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_3D_precommit)
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@pytest.mark.precommit
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def test_reshape_3D_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_5D)
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_reshape_5D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_4D)
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@pytest.mark.nightly
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def test_reshape_4D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_3D)
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@pytest.mark.nightly
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def test_reshape_3D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_5D)
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@pytest.mark.nightly
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def test_reshape_const_5D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net_const(**params, ir_version=ir_version), ie_device,
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precision, ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_4D)
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@pytest.mark.nightly
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def test_reshape_const_4D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net_const(**params, ir_version=ir_version), ie_device,
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precision, ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_3D)
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@pytest.mark.nightly
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def test_reshape_const_3D(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reshape_net_const(**params, ir_version=ir_version), ie_device,
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precision, ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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