171 lines
7.8 KiB
Python
171 lines
7.8 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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from argparse import Namespace
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import numpy as np
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from generator import generate, generator
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from extensions.back.MatMulNormalizer import SmartReshape_HC_Reshape_MatMul, PullTransposeThroughFQUp
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from extensions.ops.MatMul import MatMul
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from extensions.ops.fakequantize import FakeQuantize
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from extensions.ops.transpose import Transpose
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from mo.front.common.partial_infer.utils import int64_array
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from mo.ops.reshape import Reshape
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from unit_tests.utils.graph import build_graph, regular_op_with_shaped_data, valued_const_with_data, \
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result, connect, connect_data
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from unit_tests.utils.graph import regular_op_with_empty_data as op_with_empty_data
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@generator
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class SmartReshape_HC_Reshape_MatMulTest(unittest.TestCase):
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@generate(
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*[
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([1, 20, 30], [30, 40], [20, -1], False, False, [-1, 30]),
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([1, 20, 30], [40, 30], [20, -1], False, True, [-1, 30]),
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([1, 30, 20], [30, 40], [-1, 20], True, False, [30, -1]),
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([1, 30, 20], [40, 30], [-1, 20], True, True, [30, -1]),
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]
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)
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def test_reshape_on_the_A_input(self,
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in1_shape, in2_shape, reshape_pattern, transpose_a, transpose_b, updated_pattern):
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nodes = {
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**regular_op_with_shaped_data('in_1', in1_shape, dict(type='Parameter', op='Parameter')),
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**regular_op_with_shaped_data('in_2', in2_shape, dict(type='Parameter', op='Parameter')),
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**valued_const_with_data('dim', int64_array(reshape_pattern)),
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**op_with_empty_data('reshape',
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dict(type='Reshape', op='Reshape', infer=Reshape.infer, need_shape_inference=True)),
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**op_with_empty_data('matmul',
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dict(type='MatMul', op='MatMul', infer=MatMul.infer, need_shape_inference=True,
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transpose_a=transpose_a, transpose_b=transpose_b, dim_attrs={})),
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**result(),
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}
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edges = [
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*connect('in_1:0', '0:reshape'),
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*connect('dim:0', '1:reshape'),
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*connect('reshape:0', '0:matmul'),
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*connect('in_2:0', '1:matmul'),
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*connect('matmul:0', 'output'),
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]
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graph = build_graph(nodes_attrs=nodes, edges=edges, cli=Namespace(static_shape=True))
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graph.clean_up()
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SmartReshape_HC_Reshape_MatMul().find_and_replace_pattern(graph)
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graph.clean_up()
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graph_ref = build_graph(nodes_attrs=nodes, edges=edges, update_attributes={
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'dim': {'value': int64_array(updated_pattern)}, 'dim_d': {'value': int64_array(updated_pattern)}})
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graph_ref.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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@generate(*[
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([20, 30], [1, 30, 40], [-1, 40], False, False, [30, -1]),
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([20, 30], [1, 40, 30], [40, -1], False, True, [-1, 30]),
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([30, 20], [1, 30, 40], [-1, 40], True, False, [30, -1]),
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([30, 20], [1, 40, 30], [40, -1], True, True, [-1, 30]),
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])
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def test_reshape_on_the_B_input(self,
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in1_shape, in2_shape, reshape_pattern, transpose_a, transpose_b, updated_pattern):
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nodes = {
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**regular_op_with_shaped_data('in_1', in1_shape, dict(type='Parameter', op='Parameter')),
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**regular_op_with_shaped_data('in_2', in2_shape, dict(type='Parameter', op='Parameter')),
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**valued_const_with_data('dim', int64_array(reshape_pattern)),
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**op_with_empty_data('reshape',
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dict(type='Reshape', op='Reshape', infer=Reshape.infer, need_shape_inference=True)),
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**op_with_empty_data('matmul',
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dict(type='MatMul', op='MatMul', infer=MatMul.infer, need_shape_inference=True,
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transpose_a=transpose_a, transpose_b=transpose_b, dim_attrs={})),
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**result(),
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}
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edges = [
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*connect('in_1:0', '0:matmul'),
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*connect('in_2:0', '0:reshape'),
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*connect('dim:0', '1:reshape'),
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*connect('reshape:0', '1:matmul'),
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*connect('matmul:0', 'output'),
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]
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graph = build_graph(nodes_attrs=nodes, edges=edges, cli=Namespace(static_shape=True))
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graph.clean_up()
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SmartReshape_HC_Reshape_MatMul().find_and_replace_pattern(graph)
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graph.clean_up()
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graph_ref = build_graph(nodes_attrs=nodes, edges=edges, update_attributes={
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'dim': {'value': int64_array(updated_pattern)}, 'dim_d': {'value': int64_array(updated_pattern)}})
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graph_ref.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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class FQTransposePullerTest(unittest.TestCase):
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def nodes(self, input_shape, transpose_shape, fq_shape):
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return {
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**regular_op_with_shaped_data('input', input_shape, dict(type='Parameter', op='Parameter')),
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**valued_const_with_data('il', np.array([[[[0]]]])),
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**valued_const_with_data('ih', np.array([[[[255]]]])),
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**valued_const_with_data('ol', np.array([[[[0]]]])),
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**valued_const_with_data('oh', np.array([[[[255]]]])),
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**regular_op_with_shaped_data('FQ', fq_shape, dict(type='FakeQuantize', op='FakeQuantize', infer=FakeQuantize.infer)),
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**valued_const_with_data('order', int64_array([0, 2, 3, 1])),
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**regular_op_with_shaped_data('transpose', transpose_shape, dict(type='Transpose', op='Transpose', infer=Transpose.infer)),
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**regular_op_with_shaped_data('relu', fq_shape, dict(type='Relu', op='Relu')),
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**result(),
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}
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def test_positive(self):
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nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 3, 224, 224])
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edges = [
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*connect('input', '0:FQ'),
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*connect('il', '1:FQ'),
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*connect('ih', '2:FQ'),
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*connect('ol', '3:FQ'),
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*connect('oh', '4:FQ'),
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*connect('FQ:0', '0:transpose'),
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*connect('order:0', '1:transpose'),
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*connect('transpose:0', 'output'),
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]
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graph = build_graph(nodes_attrs=nodes, edges=edges, nodes_with_edges_only=True)
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PullTransposeThroughFQUp().find_and_replace_pattern(graph)
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graph.clean_up()
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nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 224, 224, 3])
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edges = [
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*connect('input', '0:transpose'),
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*connect('order:0', '1:transpose'),
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*connect('transpose', '0:FQ'),
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*connect('il', '1:FQ'),
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*connect('ih', '2:FQ'),
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*connect('ol', '3:FQ'),
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*connect('oh', '4:FQ'),
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*connect('FQ:0', 'output'),
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]
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graph_ref = build_graph(nodes_attrs=nodes, edges=edges, nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_negative(self):
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nodes = self.nodes([1, 3, 224, 224], [1, 224, 224, 3], [1, 3, 224, 224])
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edges = [
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*connect('input', '0:FQ'),
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*connect('il', '1:FQ'),
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*connect('ih', '2:FQ'),
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*connect('ol', '3:FQ'),
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*connect('oh', '4:FQ'),
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*connect('FQ:0', '0:transpose'),
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*connect_data('FQ:0', 'relu'),
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*connect('order:0', '1:transpose'),
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*connect('transpose:0', 'output'),
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]
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graph = build_graph(nodes_attrs=nodes, edges=edges, nodes_with_edges_only=True)
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graph_ref = graph.copy()
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PullTransposeThroughFQUp().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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