Anton Chetverikov
c1a606d507
* Removechanges added by mistake * Update transformation * Refactor tests, add more cases * Rename variable * Refactor and rename transformation * Update tests, add more cases
421 lines
20 KiB
Python
421 lines
20 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 extensions.middle.L2NormFusing import L2NormToNorm
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from mo.front.common.partial_infer.utils import int64_array
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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_with_attrs
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# A list with nodes attributes used to build various graphs.
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nodes = [
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('l2_normalize_mul', dict(kind='op', op='Mul', name='l2_norm_name')),
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('l2_normalize_mul_data', dict(kind='data')),
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('maximum', dict(kind='op', op='Maximum')),
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('maximum_data', dict(kind='data')),
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('maximum_y_const', dict(kind='op', op='Const', value=np.array(12.e-13, dtype=np.float32))),
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('maximum_y_data', dict(kind='data', value=np.array(12.e-13, dtype=np.float32))),
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('rsqrt_pow', dict(kind='data', value=-0.5)),
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('rsqrt', dict(kind='op', op='Pow')),
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('rsqrt_data', dict(kind='data')),
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('square_pow', dict(kind='op', op='Const', value=2.)),
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('square_pow_data', dict(kind='data', value=2.)),
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('square', dict(kind='op', op='Pow')),
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('sum', dict(kind='op', op='ReduceSum')),
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('sum_data', dict(kind='data')),
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('sum_axes', dict(kind='op', op='Const')),
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# nodes added after replacement
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('normalize_node', dict(kind='op', op='NormalizeL2')),
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('weights_node', dict(kind='op', op='Const')),
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('result', dict(kind='op', op='Result'))
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]
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edges = [
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('input', 'input_data', {'out': 0}),
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('input_data', 'square', {'in': 0}),
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('square_pow', 'square_pow_data', {'out': 0}),
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('square_pow_data', 'square', {'in': 1}),
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('square', 'square_data'),
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('square_data', 'sum'),
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('sum_axes', 'sum_axes_data'),
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('sum_axes_data', 'sum'),
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('sum', 'sum_data'),
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('maximum_y_const', 'maximum_y_data'),
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('maximum_y_data', 'maximum'),
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('sum_data', 'maximum'),
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('maximum', 'maximum_data'),
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('maximum_data', 'rsqrt', {'in': 0}),
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('rsqrt_pow', 'rsqrt', {'in': 1}),
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('rsqrt', 'rsqrt_data'),
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('rsqrt_data', 'l2_normalize_mul'),
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('input_data', 'l2_normalize_mul'),
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('l2_normalize_mul', 'l2_normalize_mul_data'),
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('l2_normalize_mul_data', 'result'),
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]
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edges_after_replacement = [
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('input', 'input_data', {'out': 0}),
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('input_data', 'normalize_node'),
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('weights_node', 'weights_node_data'),
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('weights_node_data', 'normalize_node'),
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('normalize_node', 'l2_normalize_mul_data'),
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('l2_normalize_mul_data', 'result'),
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]
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class L2NormToNormTest(unittest.TestCase):
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def test_2D(self):
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input_shape = int64_array([1, 300])
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axes = int64_array([1])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=axes.shape)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('weights_node_data', dict(kind='data', value=axes.sort())),
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], edges_after_replacement, nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
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self.assertTrue(flag, resp)
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def test_2D_scalar_axis(self):
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input_shape = int64_array([1, 300])
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axes = int64_array(1)
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('weights_node_data', dict(kind='data', value=int64_array([axes]).sort())),
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], edges_after_replacement, nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
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self.assertTrue(flag, resp)
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def test_3D(self):
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input_shape = int64_array([1, 300, 300])
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axes = int64_array([1, 2])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('weights_node_data', dict(kind='data', value=axes.sort())),
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], edges_after_replacement, nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
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self.assertTrue(flag, resp)
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def test_4D(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([1, 2, 3])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('weights_node_data', dict(kind='data', value=axes.sort())),
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], edges_after_replacement, nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
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self.assertTrue(flag, resp)
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def test_4D_mixed_axes(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([3, 1, 2])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('weights_node_data', dict(kind='data', value=axes.sort())),
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], edges_after_replacement, nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
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self.assertTrue(flag, resp)
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def test_4D_multiple_consumers(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([1, 2, 3])
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weights_value = np.ones(shape=int64_array([input_shape[-1]]), dtype=np.float32)
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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('result_2', dict(kind='op', op='Result'))
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], edges + [('input_data', 'result_2')], nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('weights_node_data', dict(kind='data', value=axes.sort())),
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('result_2', dict(kind='op', op='Result'))
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], edges_after_replacement + [('input_data', 'result_2')], nodes_with_edges_only=True)
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(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
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self.assertTrue(graph.node[graph.get_nodes_with_attributes(type='NormalizeL2')[0]]['name'] == 'l2_norm_name')
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self.assertTrue(flag, resp)
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def test_1D_negative(self):
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input_shape = int64_array([300])
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axes = int64_array([0])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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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_2D_negative(self):
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input_shape = int64_array([1, 300])
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axes = int64_array([0])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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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_3D_negative(self):
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input_shape = int64_array([1, 300, 300])
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axes = int64_array([2])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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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_4D_negative_1(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([0, 1, 2])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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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_4D_negative_2(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([2])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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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_4D_negative_3(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([2, 1])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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graph.stage = 'middle'
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L2NormToNorm().find_and_replace_pattern(graph)
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graph_ref = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
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('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
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('square_data', dict(kind='data', shape=input_shape)),
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('sum_axes_data', dict(kind='data', value=axes, shape=None)),
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], edges, nodes_with_edges_only=True)
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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_4D_negative_4(self):
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input_shape = int64_array([1, 300, 300, 3])
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axes = int64_array([2, 0])
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graph = build_graph_with_attrs(nodes + [
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('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
|
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
|
('square_data', dict(kind='data', shape=input_shape)),
|
|
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
|
], edges, nodes_with_edges_only=True)
|
|
graph.stage = 'middle'
|
|
|
|
L2NormToNorm().find_and_replace_pattern(graph)
|
|
|
|
graph_ref = build_graph_with_attrs(nodes + [
|
|
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
|
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
|
('square_data', dict(kind='data', shape=input_shape)),
|
|
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
|
], edges, nodes_with_edges_only=True)
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
|
self.assertTrue(flag, resp)
|
|
|
|
def test_5D_negative(self):
|
|
input_shape = int64_array([1, 300, 300, 300, 3])
|
|
axes = int64_array([1, 2, 3, 4])
|
|
|
|
graph = build_graph_with_attrs(nodes + [
|
|
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
|
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
|
('square_data', dict(kind='data', shape=input_shape)),
|
|
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
|
], edges, nodes_with_edges_only=True)
|
|
graph.stage = 'middle'
|
|
|
|
L2NormToNorm().find_and_replace_pattern(graph)
|
|
|
|
graph_ref = build_graph_with_attrs(nodes + [
|
|
('input', dict(kind='op', shape=input_shape, op='Parameter', data_type=np.float32)),
|
|
('input_data', dict(kind='data', shape=input_shape, data_type=np.float32)),
|
|
('square_data', dict(kind='data', shape=input_shape)),
|
|
('sum_axes_data', dict(kind='data', value=axes, shape=None)),
|
|
], edges, nodes_with_edges_only=True)
|
|
|
|
(flag, resp) = compare_graphs(graph, graph_ref, 'result', check_op_attrs=True)
|
|
self.assertTrue(flag, resp)
|