93 lines
3.8 KiB
Python
93 lines
3.8 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import unittest
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import numpy as np
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from generator import generator, generate
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from extensions.ops.MatMul import MatMul
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node
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from mo.utils.unittest.graph import build_graph_with_attrs
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@generator
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class TestMatMul(unittest.TestCase):
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nodes = [
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('A', {'type': 'Parameter', 'kind': 'op'}),
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('A_d', {'kind': 'data'}),
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('B', {'type': 'Parameter', 'kind': 'op'}),
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('B_d', {'kind': 'data', 'dim_attrs': []}),
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('mat_mul', {'type': 'MatMul', 'kind': 'op'}),
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('mat_mul_d', {'kind': 'data', 'value': None, 'shape': None}),
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('op_output', {'kind': 'op', 'op': 'Result'}),
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]
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edges = [
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('A', 'A_d'),
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('B', 'B_d'),
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('A_d', 'mat_mul', {'in': 0}),
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('B_d', 'mat_mul', {'in': 1}),
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('mat_mul', 'mat_mul_d'),
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('mat_mul_d', 'op_output'),
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]
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@generate(*[
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([1024], [1024, 1000], [1, 1000], False, False),
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([1, 1024], [1024, 1000], [1, 1000], False, False),
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([1, 1024], [1000, 1024], [1, 1000], False, True),
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([1024], [1024, 1000], [1, 1000], False, False),
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([10, 1024], [1024, 1000], [10, 1000], False, False),
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([5, 10, 1024], [1024, 1000], [5, 10, 1000], False, False),
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([5, 10, 1024], [5, 1024, 1000], [5, 10, 1000], False, False),
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([5, 10, 1024], [1, 1024, 1000], [5, 10, 1000], False, False),
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([5, 10, 1024], [1, 1000, 1024], [5, 10, 1000], False, True),
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])
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def test_positive_matmul_infer(self, A_shape, B_shape, C_shape, transpose_a, transpose_b):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[
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('A_d', {'shape': int64_array(A_shape)}),
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('B_d', {'shape': int64_array(B_shape)}),
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('mat_mul', {'transpose_a': transpose_a, 'transpose_b': transpose_b}),
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])
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node = Node(graph, 'mat_mul')
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MatMul.infer(node)
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msg = "MatMul infer failed for case: A_shape={}, B_shape={}, transpose_a={}, transpose_b={}" \
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"expexted_shape={}, actual_shape={}"
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self.assertTrue(np.array_equal(graph.node['mat_mul_d']['shape'], int64_array(C_shape)),
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msg.format(A_shape, B_shape, transpose_a, transpose_b, C_shape,
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graph.node['mat_mul_d']['shape']))
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@generate(*[
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(None, [1024, 1000]),
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(1, [1024, 1000]),
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([], [1024, 1000]),
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([1024, 1000], [1024, 1000]),
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([5, 10, 1024], [3, 1024, 1000]),
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])
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def test_negative_matmul_infer(self, A_shape, B_shape):
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graph = build_graph_with_attrs(nodes_with_attrs=self.nodes, edges_with_attrs=self.edges,
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update_nodes_attributes=[
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('A_d', {'shape': np.array(A_shape)}),
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('B_d', {'shape': int64_array(B_shape)}),
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])
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node = Node(graph, 'mat_mul')
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self.assertRaises(AssertionError, MatMul.infer, node)
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