124 lines
5.6 KiB
Python
124 lines
5.6 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# 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.ops.fakequantize import FakeQuantize, broadcastable
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from mo.graph.graph import Node
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from unit_tests.utils.graph import build_graph
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class TestBroadcastable(unittest.TestCase):
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def test_matching(self):
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self.assertTrue(broadcastable([1, 2, 3], [1, 2, 3]))
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def test_incomplete(self):
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self.assertTrue(broadcastable([1, 1, 1], [1, 2, 3]))
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self.assertTrue(broadcastable([2, 3], [1, 2, 3]))
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self.assertTrue(broadcastable([1, 3], [1, 2, 3]))
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self.assertTrue(broadcastable([1, 1], [1, 2, 3]))
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self.assertTrue(broadcastable([], [1, 2, 3]))
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self.assertTrue(broadcastable([1], [1, 2, 3]))
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def test_reverse_incomplete(self):
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self.assertFalse(broadcastable([1, 2, 3], [1, 1, 1]))
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self.assertFalse(broadcastable([1, 2, 3], [2, 3]))
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self.assertFalse(broadcastable([1, 2, 3], [1, 3]))
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self.assertFalse(broadcastable([1, 2, 3], [1, 1]))
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self.assertFalse(broadcastable([1, 2, 3], []))
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self.assertFalse(broadcastable([1, 2, 3], [1]))
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def test_invalid(self):
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self.assertFalse(broadcastable([3, 2, 1], [1, 2, 3]))
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self.assertFalse(broadcastable([5], [6]))
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self.assertFalse(broadcastable([5], [1]))
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self.assertFalse(broadcastable([64], [1, 55, 56, 56]))
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nodes_attributes = {'node_in_1': {'op': 'Identity', 'kind': 'op'},
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'node_in_2': {'op': 'Identity', 'kind': 'op'},
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'node_in_3': {'op': 'Identity', 'kind': 'op'},
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'node_in_4': {'op': 'Identity', 'kind': 'op'},
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'node_in_5': {'op': 'Identity', 'kind': 'op'},
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'quantize': {'op': 'FakeQuantize', 'kind': 'op', 'levels': 2},
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'node_out_1': {'op': 'Identity', 'kind': 'op'},
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'op_output': {'kind': 'op', 'op': 'Result'}
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}
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class TestFakeQuantizeOp(unittest.TestCase):
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def test_shape_only(self):
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graph = build_graph(nodes_attributes,
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[('node_in_1', 'quantize'),
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('node_in_2', 'quantize'),
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('node_in_3', 'quantize'),
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('node_in_4', 'quantize'),
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('node_in_5', 'quantize'),
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('quantize', 'node_out_1'),
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('node_out_1', 'op_output')
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],
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{'node_out_1': {'shape': None},
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'node_in_1': {'shape': np.array([1, 3, 10, 20])},
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'node_in_2': {'shape': np.array([1, 3, 10, 20])},
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'node_in_3': {'shape': np.array([1, 3, 10, 20])},
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'node_in_4': {'shape': np.array([1, 3, 10, 20])},
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'node_in_5': {'shape': np.array([1, 3, 10, 20])},
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})
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quantize_node = Node(graph, 'quantize')
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FakeQuantize.infer(quantize_node)
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quantize_shape = np.array([1, 3, 10, 20])
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res_shape = graph.node['node_out_1']['shape']
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for i in range(0, len(quantize_shape)):
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self.assertEqual(quantize_shape[i], res_shape[i])
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def test_shape_and_value(self):
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graph = build_graph(nodes_attributes,
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[('node_in_1', 'quantize'),
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('node_in_2', 'quantize'),
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('node_in_3', 'quantize'),
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('node_in_4', 'quantize'),
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('node_in_5', 'quantize'),
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('quantize', 'node_out_1'),
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('node_out_1', 'op_output')
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],
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{
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'node_out_1': {
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'shape': None,
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'value': None,
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},
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'node_in_1': {
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'shape': np.array([4]),
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'value': np.array([5, 17, 0, 100], dtype=np.float32),
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},
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'node_in_2': {
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'shape': np.array([4]),
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'value': np.array([0, 12, 12, 12], dtype=np.float32),
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},
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'node_in_3': {
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'shape': np.array([4]),
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'value': np.array([10, 20, 20, 20], dtype=np.float32),
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},
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'node_in_4': {
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'shape': np.array([4]),
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'value': np.array([0, 0, 0, 0], dtype=np.float32),
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},
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'node_in_5': {
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'shape': np.array([4]),
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'value': np.array([1, 1, 1, 1], dtype=np.float32),
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},
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})
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exp_node = Node(graph, 'quantize')
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FakeQuantize.infer(exp_node)
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quantize_shape = np.array([4])
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quantize_value = np.array([1, 1, 0, 1], dtype=np.float32)
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res_shape = graph.node['node_out_1']['shape']
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res_value = graph.node['node_out_1']['value']
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for i in range(0, len(quantize_shape)):
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self.assertEqual(quantize_shape[i], res_shape[i])
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for i in range(0, len(quantize_value)):
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self.assertAlmostEqual(quantize_value[i], res_value[i], places=6)
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