178 lines
8.9 KiB
Python
178 lines
8.9 KiB
Python
"""
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Copyright (C) 2018-2021 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.middle.FusedBatchNormTraining import FusedBatchNormTraining
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from mo.front.common.partial_infer.utils import int64_array
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from mo.middle.passes.eliminate import shape_inference
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from mo.middle.passes.eliminate_test import build_graph
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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nodes_attributes = {
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'placeholder': {'value': None, 'shape': int64_array([3, 10, 11, 5]), 'type': 'Parameter', 'kind': 'op',
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'op': 'Parameter'},
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'placeholder_data': {'shape': int64_array([3, 10, 11, 5]), 'value': None, 'kind': 'data'},
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'scale': {'value': np.array([2, 3.5, 4.5, 5.1, 2.6], dtype=np.float32), 'shape': int64_array([5]), 'kind': 'op',
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'op': 'Const'},
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'scale_data': {'value': np.array([2, 3.5, 4.5, 5.1, 2.6], dtype=np.float32), 'shape': int64_array([5]),
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'kind': 'data'},
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'offset': {'value': np.array([1, 2.5, 3.5, 4.1, 5.6], dtype=np.float32), 'shape': int64_array([5]), 'kind': 'op',
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'op': 'Const'},
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'offset_data': {'value': np.array([1, 2.5, 3.5, 4.1, 5.6], dtype=np.float32), 'shape': int64_array([5]),
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'kind': 'data'},
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'mean': {'value': None, 'shape': int64_array([]), 'kind': 'op', 'op': 'Const'},
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'mean_data': {'value': None, 'shape': int64_array([]), 'kind': 'data'},
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'variance': {'value': None, 'shape': int64_array([]), 'kind': 'op', 'op': 'Const'},
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'variance_data': {'value': None, 'shape': int64_array([]), 'kind': 'data'},
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'batchnorm': {'value': None, 'shape': int64_array([3, 10, 11, 5]), 'type': None, 'kind': 'op',
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'op': 'FusedBatchNorm', 'is_training': True, 'eps': 1e-3},
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'batchnorm_data': {'value': None, 'shape': int64_array([3, 10, 11, 5]), 'kind': 'data'},
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'result': {'kind': 'op', 'op': 'Result'},
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# nodes after transformation
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'bn_mean': {'value': np.zeros([5]), 'shape': int64_array([5]), 'kind': 'op', 'op': 'Const'},
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'bn_mean_data': {'value': np.zeros([5]), 'shape': int64_array([5]), 'kind': 'data'},
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'bn_variance': {'value': np.ones([5]), 'shape': int64_array([5]), 'kind': 'op', 'op': 'Const'},
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'bn_variance_data': {'value': np.ones([5]), 'shape': int64_array([5]), 'kind': 'data'},
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'shapeof': {'type': 'ShapeOf', 'value': None, 'kind': 'op', 'op': 'ShapeOf'},
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'shapeof_data': {'value': int64_array([3, 10, 11, 5]), 'shape': int64_array([4]), 'kind': 'data'},
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'reshape_to_orig': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
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'reshape_to_orig_data': {'value': None, 'shape': None, 'kind': 'data'},
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'start': {'kind': 'op', 'op': 'Const'},
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'start_data': {'value': None, 'shape': None, 'kind': 'data'},
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'stop': {'kind': 'op', 'op': 'Const'},
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'stop_data': {'value': None, 'shape': None, 'kind': 'data'},
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'step': {'kind': 'op', 'op': 'Const'},
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'step_data': {'value': None, 'shape': None, 'kind': 'data'},
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'mvn_axes': {'kind': 'op', 'op': 'Range'},
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'mvn_axes_data': {'value': None, 'shape': None, 'kind': 'data'},
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'mvn': {'type': 'MVN', 'value': None, 'kind': 'op', 'op': 'MVN', 'eps': 1e-3},
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'mvn_data': {'value': None, 'shape': None, 'kind': 'data'},
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'reshape_1': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
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'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
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'reshape_1_const': {'type': 'Const', 'kind': 'op', 'op': 'Const', 'value': int64_array([1, -1, 0, 0])},
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'reshape_1_const_data': {'kind': 'data', 'value': None, 'shape': None},
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}
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@generator
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class FusedBatchNormTrainingTest(unittest.TestCase):
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@generate(*[
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'FusedBatchNorm', 'FusedBatchNormV2', 'FusedBatchNormV3',
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])
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def test_transformation(self, op: str):
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graph = build_graph(nodes_attributes,
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[('placeholder', 'placeholder_data', {}),
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('scale', 'scale_data'),
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('offset', 'offset_data'),
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('mean', 'mean_data'),
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('variance', 'variance_data'),
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('placeholder_data', 'batchnorm', {'in': 0}),
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('scale_data', 'batchnorm', {'in': 1}),
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('offset_data', 'batchnorm', {'in': 2}),
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('mean_data', 'batchnorm', {'in': 3}),
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('variance_data', 'batchnorm', {'in': 4}),
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('batchnorm', 'batchnorm_data'),
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('batchnorm_data', 'result'),
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],
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{}, nodes_with_edges_only=True)
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graph.nodes['batchnorm']['op'] = op
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graph_ref = build_graph(nodes_attributes,
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[('placeholder', 'placeholder_data', {}),
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('scale', 'scale_data'),
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('offset', 'offset_data'),
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('bn_mean', 'bn_mean_data'),
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('bn_variance', 'bn_variance_data'),
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('scale_data', 'batchnorm', {'in': 1}),
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('offset_data', 'batchnorm', {'in': 2}),
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('bn_mean_data', 'batchnorm', {'in': 3}),
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('bn_variance_data', 'batchnorm', {'in': 4}),
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('placeholder_data', 'reshape_1', {'in': 0}),
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('reshape_1_const', 'reshape_1_const_data'),
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('reshape_1_const_data', 'reshape_1', {'in': 1}),
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('reshape_1', 'reshape_1_data', {}),
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('reshape_1_data', 'mvn', {'in': 0}),
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('mvn', 'mvn_data'),
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('mvn_data', 'reshape_to_orig', {'in': 0}),
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('start', 'start_data'),
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('start_data', 'mvn_axes'),
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('stop', 'stop_data'),
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('stop_data', 'mvn_axes'),
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('step', 'step_data'),
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('step_data', 'mvn_axes'),
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('mvn_axes', 'mvn_axes_data'),
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('mvn_axes_data', 'mvn'),
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('placeholder_data', 'shapeof', {'in': 0}),
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('shapeof', 'shapeof_data'),
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('shapeof_data', 'reshape_to_orig', {'in': 1}),
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('reshape_to_orig', 'reshape_to_orig_data'),
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('reshape_to_orig_data', 'batchnorm', {'in': 0}),
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('batchnorm', 'batchnorm_data'),
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('batchnorm_data', 'result'),
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],
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{'batchnorm': {'is_training': False},
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}, nodes_with_edges_only=True)
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FusedBatchNormTraining().find_and_replace_pattern(graph)
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shape_inference(graph)
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graph_ref.nodes['batchnorm']['op'] = op
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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def test_non_training(self):
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graph = build_graph(nodes_attributes,
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[('placeholder', 'placeholder_data', {}),
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('scale', 'scale_data'),
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('offset', 'offset_data'),
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('mean', 'mean_data'),
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('variance', 'variance_data'),
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('placeholder_data', 'batchnorm', {'in': 0}),
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('scale_data', 'batchnorm', {'in': 1}),
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('offset_data', 'batchnorm', {'in': 2}),
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('mean_data', 'batchnorm', {'in': 3}),
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('variance_data', 'batchnorm', {'in': 4}),
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('batchnorm', 'batchnorm_data'),
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('batchnorm_data', 'result'),
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],
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{'batchnorm': {'is_training': False}}, nodes_with_edges_only=True)
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graph_ref = graph.copy()
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FusedBatchNormTraining().find_and_replace_pattern(graph)
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shape_inference(graph)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(flag, resp)
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