76 lines
2.4 KiB
Python
76 lines
2.4 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.front.binary_quantize_normalization import BinaryFakeQuantizeNormalization
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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
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graph_nodes = {
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'0': {'name': 'input', 'kind': 'op', 'op': 'Parameter'},
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'1': {'name': 'mi_i', 'kind': 'op', 'op': 'Const'},
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'2': {'name': 'ma_i', 'kind': 'op', 'op': 'Const'},
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'3': {'name': 'mi_o', 'kind': 'op', 'op': 'Const'},
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'4': {'name': 'mi_o', 'kind': 'op', 'op': 'Const'},
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'add': {'kind': 'op', 'op': 'Add'},
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'const': {'kind': 'op', 'op': 'Const', 'value': np.array(0.5)},
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'mul': {'kind': 'op', 'op': 'Mul'},
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'quantize': {'name': 'quantize', 'levels': 2, 'kind': 'op', 'op': 'FakeQuantize'},
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'output': {'name': 'output1', 'kind': 'op', 'op': 'Result', 'type': 'Result'},
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}
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graph_edges = [
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('0', 'quantize', {'in': 0}),
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('1', 'quantize', {'in': 1}),
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('2', 'quantize', {'in': 2}),
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('3', 'quantize', {'in': 3}),
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('4', 'quantize', {'in': 4}),
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('quantize', 'output'),
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]
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graph_ref_edges = [
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('0', 'quantize', {'in': 0}),
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('1', 'add'),
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('2', 'add'),
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('add', 'mul'),
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('const', 'mul'),
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('mul', 'quantize', {'in': 1, 'out': 0}),
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('mul', 'quantize', {'in': 2, 'out': 0}),
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('3', 'quantize', {'in': 3}),
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('4', 'quantize', {'in': 4}),
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('quantize', 'output'),
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]
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class TestBinaryQuantizeNormalization(unittest.TestCase):
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def test_binary_quantize_normalizer(self):
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graph = build_graph(graph_nodes, graph_edges, nodes_with_edges_only=True)
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graph.stage = 'front'
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BinaryFakeQuantizeNormalization().find_and_replace_pattern(graph)
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graph.clean_up()
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graph_ref = build_graph(graph_nodes, graph_ref_edges)
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graph_ref.clean_up()
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(flag, resp) = compare_graphs(graph, graph_ref, 'output')
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self.assertTrue(flag, resp)
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