236778aeec
* refactor of renaming libraries in layer tests * 1. adds check for old API and new FE usafe 2. refactor of api_2 arg * fix for tf_NMS test preprocessing * take libs path from LD_LIBRARY_PATH env * convert str to Path object * use wheels path to libs * print lib paths * print lib paths * use ov_frontend_path env * also check if file to rename exists * removes redundant prints * copy instead of rename * 1. copy instead of rename 2. adds some details to readme
131 lines
4.9 KiB
Python
131 lines
4.9 KiB
Python
# Copyright (C) 2018-2022 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 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 Caffe2OnnxLayerTest
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from unit_tests.utils.graph import build_graph
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class TestScatters(Caffe2OnnxLayerTest):
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op = None
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def create_net(self, input_shape, indices_shape, updates_shape, output_shape,
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axis, ir_version):
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"""
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ONNX net IR net
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Input->Scatter->Output => Parameter->ScatterElementsUpdate->Result
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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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data = helper.make_tensor_value_info('data', TensorProto.FLOAT, input_shape)
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indices = helper.make_tensor_value_info('indices', TensorProto.INT64, indices_shape)
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updates = helper.make_tensor_value_info('updates', TensorProto.FLOAT, indices_shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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params = {'axis': axis} if axis is not None else {}
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node_def = onnx.helper.make_node(
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self.op,
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inputs=['data', 'indices', 'updates'],
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outputs=['output'],
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**params,
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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_def],
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'test_model',
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[data, indices, updates],
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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_model')
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#
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# Create reference IR net
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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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# comparison in these tests starts from input node, as we have 3 of them IREngine gets confused
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# and takes the first input node in inputs list sorted by lexicographical order
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'1_input': {'kind': 'op', 'type': 'Parameter'},
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'input_data': {'shape': input_shape, 'kind': 'data'},
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'2_indices': {'kind': 'op', 'type': 'Parameter'},
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'indices_data': {'shape': indices_shape, 'kind': 'data'},
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'3_updates': {'kind': 'op', 'type': 'Parameter'},
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'updates_data': {'shape': updates_shape, 'kind': 'data'},
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'const_indata': {'kind': 'data',
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'value': np.int64(axis) if axis is not None else np.int64(0)},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'kind': 'data'},
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'node': {'kind': 'op', 'type': 'ScatterElementsUpdate'},
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'node_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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[
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('1_input', 'input_data'),
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('input_data', 'node', {'in': 0}),
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('2_indices', 'indices_data'),
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('indices_data', 'node', {'in': 1}),
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('3_updates', 'updates_data'),
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('updates_data', 'node', {'in': 2}),
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('const_indata', 'const'),
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('const', 'const_data'),
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('const_data', 'node', {'in': 3}),
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('node', 'node_data'),
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('node_data', 'result')
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])
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return onnx_net, ref_net
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test_data = [
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dict(input_shape=[1, 5], indices_shape=[1, 2], updates_shape=[1, 2],
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axis=1, output_shape=[1, 5]),
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dict(input_shape=[1, 256, 200, 272], indices_shape=[1, 256, 200, 272],
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updates_shape=[1, 256, 200, 272],
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axis=None, output_shape=[1, 256, 200, 272])]
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class TestScatter(TestScatters):
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op = 'Scatter'
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_scatter(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_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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class TestScatterElements(TestScatters):
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op = 'ScatterElements'
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_scatter_elements(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_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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