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added unit-tests for DivisionToZeroFP16Resolver.py, some improvements
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115
model-optimizer/unit_tests/extensions/middle/DivisionToZeroFP16Resolver_test.py
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| # Copyright (C) 2018-2021 Intel Corporation | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
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| import unittest | ||
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| import numpy as np | ||
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| from extensions.middle.DivisionToZeroFP16Resolver import DivisionToZeroFP16Resolver | ||
| from mo.front.common.partial_infer.utils import shape_array | ||
| from mo.graph.graph import Node | ||
| from unit_tests.utils.graph import build_graph, result, regular_op_with_empty_data, connect, shaped_parameter, \ | ||
| valued_const_with_data | ||
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| class ChangeOutputTypeAttributesTests(unittest.TestCase): | ||
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| def test_division_maximum(self): | ||
| self.build_and_test_division_graph(eps=np.array(1e-12), pow_value=np.array(-1), preventing_type='Maximum') | ||
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| def test_division_add(self): | ||
| self.build_and_test_division_graph(eps=np.array(1e-12), pow_value=np.array(-1), preventing_type='Add') | ||
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| def test_division_arbitrary_negative_pow_1(self): | ||
| self.build_and_test_division_graph(eps=np.array(1e-12), pow_value=np.array(-1/2), preventing_type='Add') | ||
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| def test_division_arbitrary_negative_pow_2(self): | ||
| self.build_and_test_division_graph(eps=np.array(1e-12), pow_value=np.array(-0.2), preventing_type='Add') | ||
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| def test_division_eps_as_array_1(self): | ||
| self.build_and_test_division_graph(eps=np.array([1e-12, 1e-12]), pow_value=np.array(-1), preventing_type='Add') | ||
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| def test_division_eps_as_array_2(self): | ||
| self.build_and_test_division_graph(eps=np.array([1e-12]), pow_value=np.array(-1), preventing_type='Add') | ||
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| # in this case graph should not be changed so eps will be left unchanged 1e-2 | ||
| # in that case build_and_test_division_graph will raise AssertionError and it's expected | ||
| def test_division_graph_not_changed_1(self): | ||
| try: | ||
| self.build_and_test_division_graph(eps=np.array(1e-2), pow_value=np.array(-1), preventing_type='Maximum') | ||
| raise Exception | ||
| except AssertionError: | ||
| pass | ||
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| # if at least one value is greater than FP16 smallest normal value | ||
| # graph should not be changed so eps will be left unchanged ([1e-2, 1e-12]) | ||
| # in that case build_and_test_division_graph will raise AssertionError and it's expected | ||
| def test_division_graph_not_changed_2(self): | ||
| try: | ||
| self.build_and_test_division_graph(eps=np.array([1e-2, 1e-12]), pow_value=np.array(-1), preventing_type='Maximum') | ||
| raise Exception | ||
| except AssertionError: | ||
| pass | ||
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| def build_and_test_division_graph(self, eps, pow_value, preventing_type): | ||
| nodes = { | ||
| **shaped_parameter('input_1', shape_array((1, 3, 10, 10))), | ||
| **shaped_parameter('input_2', shape_array((1, 3, 10, 10))), | ||
| **regular_op_with_empty_data(preventing_type, {'type': preventing_type, 'op': preventing_type}), | ||
| **regular_op_with_empty_data('negative_pow', {'type': 'Pow', 'op': 'Pow'}), | ||
| **regular_op_with_empty_data('mul', {'type': 'Mul', 'op': 'Mul'}), | ||
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| **valued_const_with_data('negative_pow_const', pow_value), | ||
| **valued_const_with_data('eps', eps), | ||
| **result('res'), | ||
| } | ||
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| edges = [ | ||
| *connect('input_2', '0:' + preventing_type), | ||
| *connect('eps', '1:' + preventing_type), | ||
| *connect(preventing_type, '0:negative_pow'), | ||
| *connect('negative_pow_const', '1:negative_pow'), | ||
| *connect('negative_pow', '1:mul'), | ||
| *connect('input_1', '0:mul'), | ||
| *connect('mul', 'res'), | ||
| ] | ||
| graph = build_graph(nodes, edges) | ||
| graph.graph['cmd_params'].compress_fp16 = True | ||
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| DivisionToZeroFP16Resolver().find_and_replace_pattern(graph) | ||
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| self.assertTrue(np.all(Node(graph, 'eps').value == np.finfo(np.float16).tiny)) | ||
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| def test_l2_norm(self): | ||
| nodes = { | ||
| **shaped_parameter('input', shape_array((1, 3, 10, 10))), | ||
| **regular_op_with_empty_data('square', {'type': 'Pow', 'op': 'Pow'}), | ||
| **regular_op_with_empty_data('sum', {'type': 'ReduceSum', 'op': 'ReduceSum'}), | ||
| **regular_op_with_empty_data('max', {'type': 'Maximum', 'op': 'Maximum'}), | ||
| **regular_op_with_empty_data('rsqrt', {'type': 'Pow', 'op': 'Pow'}), | ||
| **regular_op_with_empty_data('l2norm', {'type': 'Mul', 'op': 'Mul'}), | ||
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| **valued_const_with_data('rsqrt_pow_const', np.array(-1 / 2)), | ||
| **valued_const_with_data('square_pow', np.array(2)), | ||
| **valued_const_with_data('eps', np.array(1e-12)), | ||
| **result('res'), | ||
| } | ||
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| edges = [ | ||
| *connect('input:0', '0:square'), | ||
| *connect('square_pow', '1:square'), | ||
| *connect('square', 'sum'), | ||
| *connect('sum', '0:max'), | ||
| *connect('eps', '1:max'), | ||
| *connect('max', '0:rsqrt'), | ||
| *connect('rsqrt_pow_const', '1:rsqrt'), | ||
| *connect('rsqrt', '0:l2norm'), | ||
| *connect('input:0', '1:l2norm', skip_data=True), | ||
| *connect('l2norm', 'res'), | ||
| ] | ||
| graph = build_graph(nodes, edges) | ||
| graph.graph['cmd_params'].compress_fp16 = True | ||
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| DivisionToZeroFP16Resolver().find_and_replace_pattern(graph) | ||
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| self.assertTrue(np.all(Node(graph, 'eps').value == np.finfo(np.float16).tiny)) |