Ruslan Nugmanov
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
193 lines
9.5 KiB
Python
193 lines
9.5 KiB
Python
# Copyright (C) 2018-2022 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 TestReduce(OnnxRuntimeLayerTest):
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def create_reduce(self, shape, reshapped_shape, op, axes, keep_dims, ir_version):
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"""
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ONNX net IR net
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Input->Reduce Operation (axes)->Output => Input->Reduce Operation
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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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if op not in ['ReduceMin', 'ReduceMax', 'ReduceMean', 'ReduceProd', 'ReduceSum']:
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raise ValueError("Operation has to be either Reduce(Min or Max or Mean or Sum or Prod")
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output_shape = shape.copy()
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for axis in axes:
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output_shape[axis] = 1
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if not keep_dims:
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output_shape = [dim for dim in output_shape if dim != 1]
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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node_def = onnx.helper.make_node(
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op,
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inputs=['input'],
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outputs=['output'],
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axes=axes,
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keepdims=keep_dims
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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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[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_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': shape, 'kind': 'data'},
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'input_data_1': {'shape': [len(axes)], 'value': axes, 'kind': 'data'},
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'const_1': {'kind': 'op', 'type': 'Const'},
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'const_data_1': {'shape': [len(axes)], 'kind': 'data'},
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'reduce': {'kind': 'op', 'type': op, 'keep_dims': keep_dims},
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'reduce_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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('input_data', 'reduce'),
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('const_data_1', 'reduce'),
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('reduce', 'reduce_data'),
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('reduce_data', 'result')
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])
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return onnx_net, ref_net
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test_data_precommit = [
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dict(shape=[2, 4, 6], reshapped_shape=[2, 1, 4 * 6, 1], axes=[1, 2]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[2, 1, 4 * 6 * 8, 1], axes=[1, 2, 3]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 4, 6 * 8 * 10, 1], axes=[2, 3, 4])
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]
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test_data = [
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dict(shape=[2, 4, 6], reshapped_shape=[1, 1, 2, 4 * 6], axes=[0]),
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dict(shape=[2, 4, 6], reshapped_shape=[2, 1, 4, 6], axes=[1]),
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dict(shape=[2, 4, 6], reshapped_shape=[2, 4, 6, 1], axes=[2]),
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dict(shape=[2, 4, 6], reshapped_shape=[1, 1, 2 * 4, 6], axes=[0, 1]),
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dict(shape=[2, 4, 6], reshapped_shape=[2, 1, 4 * 6, 1], axes=[1, 2]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[1, 1, 2, 4 * 6 * 8], axes=[0]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[2, 1, 4, 6 * 8], axes=[1]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[2, 4, 6, 8], axes=[2]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[2, 4 * 6, 8, 1], axes=[3]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[1, 1, 2 * 4, 6 * 8], axes=[0, 1]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[2, 1, 4 * 6, 8], axes=[1, 2]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[2, 4, 6 * 8, 1], axes=[2, 3]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[1, 1, 2 * 4 * 6, 8], axes=[0, 1, 2]),
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dict(shape=[2, 4, 6, 8], reshapped_shape=[2, 1, 4 * 6 * 8, 1], axes=[1, 2, 3]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[1, 1, 2, 4 * 6 * 8 * 10], axes=[0]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 1, 4, 6 * 8 * 10], axes=[1]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 4, 6, 8 * 10], axes=[2]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 4 * 6, 8, 10], axes=[3]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 4 * 6 * 8, 10, 1], axes=[4]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[1, 1, 2 * 4, 6 * 8 * 10], axes=[0, 1]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 1, 4 * 6, 8 * 10], axes=[1, 2]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 4, 6 * 8, 10], axes=[2, 3]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 4 * 6, 8 * 10, 1], axes=[3, 4]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[1, 1, 2 * 4 * 6, 8 * 10], axes=[0, 1, 2]),
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dict(shape=[2, 4, 6, 8, 10], reshapped_shape=[2, 4, 6 * 8 * 10, 1], axes=[2, 3, 4])
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]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.precommit
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def test_reduce_max_precommit(self, params, keep_dims, ie_device, precision, ir_version,
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temp_dir, use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceMax', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.nightly
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def test_reduce_max(self, params, keep_dims, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceMax', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.nightly
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def test_reduce_sum(self, params, keep_dims, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceSum', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.nightly
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def test_reduce_prod(self, params, keep_dims, ie_device, precision, ir_version, temp_dir,
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use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceProd', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.precommit
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def test_reduce_mean_precommit(self, params, keep_dims, ie_device, precision, ir_version,
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temp_dir, use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceMean', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_reduce_mean(self, params, keep_dims, ie_device, precision, ir_version, temp_dir,
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use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceMean', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.precommit
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def test_reduce_min_precommit(self, params, keep_dims, ie_device, precision, ir_version,
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temp_dir, use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceMin', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.parametrize("keep_dims", [True, False])
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@pytest.mark.nightly
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def test_reduce_min(self, params, keep_dims, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_reduce(**params, op='ReduceMin', keep_dims=keep_dims,
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ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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