85 lines
3.7 KiB
Python
85 lines
3.7 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.middle.FakeSplitOutputs import AddFakeOutputsToSplit, AddFakeOutputsToVariadicSplit
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from mo.front.common.partial_infer.elemental import copy_shape_infer
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from mo.graph.graph import Node
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from mo.middle.passes.eliminate import graph_clean_up
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from unit_tests.utils.graph import build_graph
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nodes_attributes = {
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'placeholder_1': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter', 'shape': np.array([1, 227, 227, 3])},
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# VariadicSplit operation
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'variadic_split': {'type': 'VariadicSplit', 'kind': 'op', 'op': 'VariadicSplit'},
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'split': {'type': 'Split', 'kind': 'op', 'op': 'Split', 'num_splits': 3, 'axis': 3},
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# Test operation
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'last': {'type': None, 'value': None, 'kind': 'op', 'op': None, 'infer': copy_shape_infer},
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'res': {'type': 'Result', 'kind': 'op', 'op': 'Result'},
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# Data nodes
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'placeholder_data': {'kind': 'data', 'value': None, 'shape': np.array([1, 227, 227, 3])},
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'variadic_split_data_1': {'kind': 'data', 'value': None, 'shape': np.array([1, 2, 227, 3])},
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'split_data_1': {'kind': 'data', 'value': None, 'shape': np.array([1, 227, 227, 1])},
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'last_data': {'kind': 'data', 'value': None, 'shape': np.array([1, 227, 227, 3])},
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'axis_const': {'kind': 'op', 'op': 'Const'},
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'axis_const_data': {'value': np.int64(1), 'shape': None, 'kind': 'data'},
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'split_dim_const': {'kind': 'op', 'op': 'Const'},
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'split_dim_const_data': {'value': np.array([1, 2, 3]), 'shape': None, 'kind': 'data'},
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}
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class SplitSaveEmptyBranchesTest(unittest.TestCase):
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def test_variadic_split_non_zero(self):
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graph = build_graph(nodes_attributes,
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[('placeholder_1', 'placeholder_data'), ('placeholder_data', 'variadic_split'),
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('variadic_split', 'variadic_split_data_1'), ('variadic_split_data_1', 'last'),
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('last', 'last_data'), ('last_data', 'res'),
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('axis_const', 'axis_const_data'),
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('split_dim_const', 'split_dim_const_data'),
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('axis_const_data', 'variadic_split', {'in': 1}),
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('split_dim_const_data', 'variadic_split', {'in': 2}),
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], nodes_with_edges_only=True)
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node = Node(graph, 'variadic_split')
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# extractor should do it
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node['out_ports_count'] = 3
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for p in range(len(node.out_edges()), node.out_ports_count):
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node.add_output_port(p)
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replacer = AddFakeOutputsToVariadicSplit()
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replacer.find_and_replace_pattern(graph)
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for n in graph.get_op_nodes():
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n['need_shape_inference'] = False
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graph_clean_up(graph)
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self.assertTrue(len(node.out_edges()) == 3)
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def test_split(self):
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graph = build_graph(nodes_attributes,
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[('placeholder_1', 'placeholder_data'), ('placeholder_data', 'split'),
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('split', 'split_data_1'), ('split_data_1', 'last'),
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('last', 'last_data'), ('last_data', 'res'),
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], nodes_with_edges_only=True)
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node = Node(graph, 'split')
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# extractor should do it
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node['out_ports_count'] = node.num_splits
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for p in range(len(node.out_edges()), node.out_ports_count):
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node.add_output_port(p)
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replacer = AddFakeOutputsToSplit()
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replacer.find_and_replace_pattern(graph)
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for n in graph.get_op_nodes():
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n['need_shape_inference'] = False
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graph_clean_up(graph)
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self.assertTrue(len(node.out_edges()) == node.num_splits)
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