286 lines
11 KiB
Python
286 lines
11 KiB
Python
# Copyright (C) 2018-2023 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 OnnxRuntimeLayerTest
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from unit_tests.utils.graph import build_graph
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class TestGather(OnnxRuntimeLayerTest):
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def create_net(self, shape, axis, indices, output_shape, ir_version):
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"""
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ONNX net IR net
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Input->Gather->Output => Input->Gather
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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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indices = np.array(indices)
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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_indices_def = onnx.helper.make_node(
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'Constant',
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inputs=[],
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outputs=['indices'],
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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=indices.shape,
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vals=indices.flatten(),
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),
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)
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args = dict()
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if axis:
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args['axis'] = axis
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else:
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axis = 0
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node_def = onnx.helper.make_node(
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'Gather',
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inputs=['input', 'indices'],
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outputs=['output'],
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**args
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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_indices_def, 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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#
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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_const_data': {'kind': 'data', 'value': indices.flatten()},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'shape': indices.shape, 'kind': 'data', 'value': None},
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'input_axis_const_data': {'kind': 'data', 'value': [axis]},
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'axis_const': {'kind': 'op', 'type': 'Const'},
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'axis_const_data': {'shape': [], 'kind': 'data', 'value': None},
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'node': {'kind': 'op', 'type': 'Gather'},
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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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[('input', 'input_data'),
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('input_const_data', 'const'),
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('const', 'const_data'),
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('input_axis_const_data', 'axis_const'),
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('axis_const', 'axis_const_data'),
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('input_data', 'node'),
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('const_data', 'node'),
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('axis_const_data', 'node'),
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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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def create_net_const(self, shape, axis, indices, output_shape, ir_version):
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"""
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ONNX net IR net
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Input->Concat(+gathered 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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indices = np.array(indices)
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concat_axis = 0
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input_shape = output_shape.copy()
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concat_output_shape = output_shape.copy()
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concat_output_shape[concat_axis] = 2 * concat_output_shape[concat_axis]
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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, concat_output_shape)
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constant = np.random.randint(-127, 127, shape).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=constant.shape,
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vals=constant.flatten(),
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),
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)
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node_indices_def = onnx.helper.make_node(
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'Constant',
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inputs=[],
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outputs=['indices'],
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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=indices.shape,
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vals=indices.flatten(),
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),
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)
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args = dict()
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if axis:
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args['axis'] = axis
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node_def = onnx.helper.make_node(
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'Gather',
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inputs=['const1', 'indices'],
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outputs=['gather'],
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**args
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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', 'gather'],
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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_indices_def, node_def, node_concat_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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#
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constant = np.take(constant, indices, axis=axis if axis else 0)
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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_const_data': {'kind': 'data', 'value': constant.flatten()},
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'const': {'kind': 'op', 'type': 'Const'},
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'const_data': {'shape': constant.shape, 'kind': 'data', 'value': None},
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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_precommit = [
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dict(shape=[6, 8, 10, 12], axis=2, indices=[[0, 2, 4], [5, 7, 9]],
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output_shape=[6, 8, 2, 3, 12]),
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dict(shape=[4, 6, 8, 10, 12], axis=1, indices=[2, 5], output_shape=[4, 2, 8, 10, 12]),
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dict(shape=[4, 6, 8, 10, 12], axis=-1, indices=[5, 8], output_shape=[4, 6, 8, 10, 2]),
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dict(shape=[6, 8, 10, 12], axis=-1, indices=[[[2, -1], [3, 2]], [[5, -1], [3, -2]]],
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output_shape=[6, 8, 10, 2, 2, 2])
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]
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test_data = [dict(shape=[10, 12], axis=0, indices=[3, 6], output_shape=[2, 12]),
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dict(shape=[10, 12], axis=-1, indices=[4, 7], output_shape=[10, 2]),
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dict(shape=[10, 12], axis=None, indices=[[0, 1, 3, 4], [5, 6, 8, 9]],
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output_shape=[2, 4, 12]),
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dict(shape=[10, 12], axis=1, indices=[[0, 1, 3, 4, 5], [6, 7, 9, 10, 11]],
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output_shape=[10, 2, 5]),
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dict(shape=[8, 10, 12], axis=0, indices=[3, 6], output_shape=[2, 10, 12]),
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dict(shape=[8, 10, 12], axis=-1, indices=[5, 8], output_shape=[8, 10, 2]),
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dict(shape=[8, 10, 12], axis=None, indices=[[0, 1], [3, 4], [6, 7]],
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output_shape=[3, 2, 10, 12]),
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dict(shape=[8, 10, 12], axis=1, indices=[[0, 2, 4], [5, 7, 9]],
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output_shape=[8, 2, 3, 12]),
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dict(shape=[6, 8, 10, 12], axis=-1, indices=[5, 8], output_shape=[6, 8, 10, 2]),
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dict(shape=[6, 8, 10, 12], axis=None, indices=[[0, 1, 2], [3, 4, 5]],
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output_shape=[2, 3, 8, 10, 12]),
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dict(shape=[6, 8, 10, 12], axis=2, indices=[[0, 2, 4], [5, 7, 9]],
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output_shape=[6, 8, 2, 3, 12]),
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dict(shape=[4, 6, 8, 10, 12], axis=0, indices=[1, 3],
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output_shape=[2, 6, 8, 10, 12]),
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dict(shape=[4, 6, 8, 10, 12], axis=1, indices=[2, 5],
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output_shape=[4, 2, 8, 10, 12]),
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dict(shape=[4, 6, 8, 10, 12], axis=-1, indices=[5, 8],
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output_shape=[4, 6, 8, 10, 2])]
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.precommit
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def test_gather(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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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_gather(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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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_gather_const(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net_const(**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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test_data_negative_indices = [
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dict(shape=[10, 12], axis=0, indices=[3, -1, -4], output_shape=[3, 12]),
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dict(shape=[6, 10, 14, 12], axis=1, indices=[[0, -1, 3, -4], [-5, 6, -7, 8]],
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output_shape=[6, 2, 4, 14, 12]),
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dict(shape=[8, 10, 14, 12], axis=1, indices=[[-2, 2, -4], [5, -7, 9]],
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output_shape=[8, 2, 3, 14, 12]),
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dict(shape=[6, 8, 10, 12], axis=-1, indices=[[[2, -1], [3, 2]], [[5, -1], [3, -2]]],
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output_shape=[6, 8, 10, 2, 2, 2])]
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@pytest.mark.parametrize("params", test_data_negative_indices)
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@pytest.mark.nightly
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def test_gather_nightly_negative_indices(self, params, ie_device, precision, ir_version,
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temp_dir, use_old_api):
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if ie_device == 'GPU':
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pytest.skip('GREEN_SUITE')
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self._test(*self.create_net(**params, 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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