d86019d104
* Refactored LeakyRelu transformation * Added unit test for LeakyRelu transformation + removed duplicate test function valued_const
200 lines
7.8 KiB
Python
200 lines
7.8 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 extensions.back.ConvolutionNormalizer import PullReshapeThroughFQ, V7ConvolutionWithGroupsResolver, \
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V10ConvolutionWithGroupsResolver
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from extensions.ops.fakequantize import FakeQuantize
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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 mo.utils.unittest.graph import build_graph, result, regular_op_with_shaped_data, regular_op_with_empty_data, \
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valued_const_with_data, connect
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def graph_template(weights_initial_shape, new_reshape_shape, limits_initial_shape, limits_new_shape=None):
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limits_new_shape = limits_initial_shape if limits_new_shape is None else limits_new_shape
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core_connections = [
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*connect('input:0', '0:convolution'),
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*connect('convolution:0', '0:output'),
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]
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core_nodes = lambda weights_shape, limit_shape, reshape_shape: {
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**regular_op_with_shaped_data('input', None, {'type': 'Parameter', 'op': 'Parameter'}),
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**valued_const_with_data('weights', np.ones(weights_shape)),
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**valued_const_with_data('dim', int64_array(reshape_shape)),
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**regular_op_with_shaped_data('reshape', reshape_shape, {'type': 'Reshape', 'infer': Reshape.infer, 'op': 'Reshape'}),
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**valued_const_with_data('il', np.ones(limit_shape)),
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**valued_const_with_data('ih', np.ones(limit_shape)),
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**valued_const_with_data('ol', np.ones(limit_shape)),
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**valued_const_with_data('oh', np.ones(limit_shape)),
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**regular_op_with_shaped_data('FQ', weights_shape, {'type': 'FakeQuantize', 'infer': FakeQuantize.infer,
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'stop_value_propagation': True, 'levels': 2, 'op': 'FakeQuantize'}),
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**regular_op_with_shaped_data('convolution', None, {'type': 'Convolution', 'op': 'Convolution'}),
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**result(),
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}
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nodes_before = core_nodes(weights_initial_shape, limits_initial_shape, new_reshape_shape)
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edges_before = [
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*connect('weights:0', '0:FQ'),
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*connect('il:0', '1:FQ'),
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*connect('ih:0', '2:FQ'),
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*connect('ol:0', '3:FQ'),
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*connect('oh:0', '4:FQ'),
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*connect('FQ:0', '0:reshape'),
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*connect('dim:0', '1:reshape'),
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*connect('reshape:0', '1:convolution'),
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*core_connections,
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]
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graph = build_graph(nodes_attrs=nodes_before, edges=edges_before, nodes_with_edges_only=True)
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nodes_after = core_nodes(new_reshape_shape, limits_new_shape, [])
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edges_after = [
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*connect('weights:0', '0:FQ'),
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*connect('il:0', '1:FQ'),
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*connect('ih:0', '2:FQ'),
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*connect('ol:0', '3:FQ'),
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*connect('oh:0', '4:FQ'),
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*connect('FQ:0', '1:convolution'),
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*core_connections,
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]
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graph_ref = build_graph(nodes_attrs=nodes_after, edges=edges_after, nodes_with_edges_only=True)
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return graph, graph_ref
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class TestPullReshapeThroughFQ(unittest.TestCase):
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def test_v7_weights_reshape(self):
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graph, graph_ref = graph_template([3, 8, 7, 7], [24, 1, 7, 7], [1, 1, 1, 1])
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PullReshapeThroughFQ().find_and_replace_pattern(graph)
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graph.clean_up()
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graph_ref.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_reshape_reducing_tensor_rank(self):
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graph, graph_ref = graph_template([3, 8, 7, 7], [24, 7, 7], [1, 1, 1])
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PullReshapeThroughFQ().find_and_replace_pattern(graph)
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graph.clean_up()
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graph_ref.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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class TestV7ConvolutionWithGroupsResolver(unittest.TestCase):
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def test_v7_group_convolution_resolver(self):
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nodes = {
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**regular_op_with_shaped_data('input', None, {'type': 'Parameter'}),
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**valued_const_with_data('weights', np.ones([3, 8, 7, 7])),
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**valued_const_with_data('dim', int64_array([24, -1, 7, 7])),
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**regular_op_with_empty_data('reshape', {'type': 'Reshape'}),
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**regular_op_with_shaped_data('convolution', None, {'type': 'Convolution', 'group': 3, 'output': 24}),
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**result(),
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}
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graph = build_graph(nodes, [
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*connect('input', '0:convolution'),
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*connect('weights', '1:convolution'),
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*connect('convolution', 'output'),
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], nodes_with_edges_only=True)
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V7ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
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graph_ref = build_graph(nodes, [
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*connect('input', '0:convolution'),
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*connect('weights', '0:reshape'),
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*connect('dim', '1:reshape'),
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*connect('reshape', '1:convolution'),
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*connect('convolution', 'output'),
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], nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_v7_group_convolution_resolver_weight_are_in_the_right_layout(self):
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nodes = {
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**regular_op_with_shaped_data('input', None, {'type': 'Parameter'}),
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**valued_const_with_data('weights', np.ones([24, 1, 7, 7])),
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**regular_op_with_shaped_data('convolution', None, {'type': 'Convolution', 'group': 3, 'output': 24}),
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**result(),
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}
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edges = [
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*connect('input', '0:convolution'),
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*connect('weights', '1:convolution'),
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*connect('convolution', 'output'),
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]
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graph = build_graph(nodes, edges)
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V7ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
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graph_ref = build_graph(nodes, edges)
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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class TestV10ConvolutionWithGroupsResolver(unittest.TestCase):
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def test_v10_group_convolution_resolver(self):
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nodes = {
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**regular_op_with_shaped_data('input', [1, 3, 224, 224], {'type': 'Parameter'}),
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**valued_const_with_data('weights', np.ones([3, 8, 7, 7])),
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**valued_const_with_data('dim', int64_array([3, 8, 1, 7, 7])),
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**regular_op_with_empty_data('reshape', {'type': 'Reshape'}),
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**regular_op_with_shaped_data('convolution', None, {'type': 'Convolution', 'group': 3, 'output': 24}),
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**result(),
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}
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graph = build_graph(nodes, [
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*connect('input', '0:convolution'),
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*connect('weights', '1:convolution'),
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*connect('convolution', 'output'),
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], nodes_with_edges_only=True)
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V10ConvolutionWithGroupsResolver().find_and_replace_pattern(graph)
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nodes['convolution']['type'] = 'GroupConvolution'
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del nodes['convolution']['group']
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graph_ref = build_graph(nodes, [
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*connect('input', '0:convolution'),
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*connect('weights', '0:reshape'),
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*connect('dim', '1:reshape'),
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*connect('reshape', '1:convolution'),
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*connect('convolution', 'output'),
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], nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, last_node='output', check_op_attrs=True)
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self.assertTrue(flag, resp)
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