271 lines
12 KiB
Python
271 lines
12 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.split import AttributedSplit, AttributedVariadicSplit, VariadicSplit
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node
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from mo.utils.ir_engine.compare_graphs import compare_graphs
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from unit_tests.utils.graph import build_graph
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from generator import generator, generate
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class TestSplitOp(unittest.TestCase):
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nodes = {
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'input': {'kind': 'op'},
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'split_input_data': {'kind': 'data', 'shape': None, 'value': None},
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'split_op': {'kind': 'op', 'axis': None, 'num_splits': None, 'op': 'AttributedSplit'},
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'split_output_0_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_0': {'kind': 'op'},
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'split_output_1_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_1': {'kind': 'op'},
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}
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edges = [
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('input', 'split_input_data'),
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('split_input_data', 'split_op'),
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('split_op', 'split_output_0_data'),
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('split_output_0_data', 'output_0'),
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('split_op', 'split_output_1_data'),
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('split_output_1_data', 'output_1'),
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]
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def test_split_shape_infer(self):
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# test configuration
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input_shape = [2, 10]
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input_value = None
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axis = 1
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num_splits = 2
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output_shape = [2, 5]
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output_value = [None, None]
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# action
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graph = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array(input_shape),
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'value': input_value},
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'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
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}
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)
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split_op = Node(graph, 'split_op')
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AttributedSplit.infer(split_op)
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# reference
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graph_ref = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array(input_shape),
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'value': input_value},
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'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
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'split_output_0_data': {'shape': int64_array(output_shape),
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'value': output_value[0]},
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'split_output_1_data': {'shape': int64_array(output_shape),
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'value': output_value[1]},
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}
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)
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# check
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(flag, resp) = compare_graphs(graph, graph_ref, 'split_input_data')
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self.assertTrue(flag, resp)
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def test_split_value_infer(self):
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# test configuration
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input_shape = [2, 10]
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input_value = [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]]
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axis = 1
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num_splits = 2
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output_shape = [2, 5]
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output_value = [[[0, 1, 2, 3, 4], [10, 11, 12, 13, 14]], [[5, 6, 7, 8, 9], [15, 16, 17, 18, 19]]]
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# action
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graph = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array(input_shape),
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'value': int64_array(input_value)},
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'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
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}
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)
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split_op = Node(graph, 'split_op')
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AttributedSplit.infer(split_op)
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# reference
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graph_ref = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array(input_shape),
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'value': int64_array(input_value)},
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'split_op': {'axis': np.array(axis), 'num_splits': np.array(num_splits)},
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'split_output_0_data': {'shape': int64_array(output_shape),
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'value': int64_array(output_value[0])},
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'split_output_1_data': {'shape': int64_array(output_shape),
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'value': int64_array(output_value[1])},
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}
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)
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# check
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(flag, resp) = compare_graphs(graph, graph_ref, 'split_input_data')
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self.assertTrue(flag, resp)
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class TestAttributedVariadicSplitOp(unittest.TestCase):
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nodes = {
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'input': {'kind': 'op'},
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'split_input_data': {'kind': 'data', 'shape': None, 'value': None},
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'split_op': {'kind': 'op', 'axis': None, 'split_lengths': None, 'op': 'AttributedVariadicSplit'},
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'split_output_0_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_0': {'kind': 'op'},
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'split_output_1_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_1': {'kind': 'op'},
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'split_output_2_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_2': {'kind': 'op'},
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}
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edges = [
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('input', 'split_input_data'),
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('split_input_data', 'split_op'),
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('split_op', 'split_output_0_data'),
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('split_output_0_data', 'output_0'),
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('split_op', 'split_output_1_data'),
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('split_output_1_data', 'output_1'),
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('split_op', 'split_output_2_data'),
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('split_output_2_data', 'output_2'),
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]
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def test_splitv_zero(self):
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graph = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array([2, 12, 25, 30])},
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'split_op': {'axis': np.array(2), 'split_lengths': np.array([2, 13, 10, 0]),
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'out_ports_count': 4},
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}
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)
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node = Node(graph, 'split_op')
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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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AttributedVariadicSplit.infer(node)
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self.assertTrue(len(node.out_edges()) == 3)
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self.assertTrue(np.all(node.split_lengths == np.array([2, 13, 10])))
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def test_splitv_zero_not_last(self):
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graph = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array([2, 12, 25, 30])},
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'split_op': {'axis': np.array(2), 'split_lengths': np.array([2, 13, 0, 10]),
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'out_ports_count': 4},
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}
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)
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node = Node(graph, 'split_op')
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# extractor should do it
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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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node.out_port(2).get_connection().set_source(node.out_port(3))
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AttributedVariadicSplit.infer(node)
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self.assertTrue(node.out_port(3).disconnected())
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self.assertTrue(np.all(node.split_lengths == np.array([2, 13, 10])))
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def test_splitv_2_zero_not_last(self):
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graph = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array([2, 12, 25, 30])},
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'split_op': {'axis': np.array(2), 'split_lengths': np.array([2, 13, 0, 0, 10]),
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'out_ports_count': 5},
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}
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)
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node = Node(graph, 'split_op')
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# extractor should do it
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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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node.out_port(2).get_connection().set_source(node.out_port(4))
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AttributedVariadicSplit.infer(node)
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self.assertTrue(node.out_port(4).disconnected())
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self.assertTrue(node.out_port(3).disconnected())
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self.assertTrue(np.all(node.split_lengths == np.array([2, 13, 10])))
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@generator
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class TestVariadicSplitOp(unittest.TestCase):
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nodes = {
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'input': {'kind': 'op'},
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'split_input_data': {'kind': 'data', 'shape': None, 'value': None},
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'split_axis': {'kind': 'op', 'op': 'Const'},
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'split_axis_data': {'kind': 'data', 'shape': None, 'value': None},
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'split_lengths': {'kind': 'op', 'op': 'Const'},
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'split_lengths_data': {'kind': 'data', 'shape': None, 'value': None},
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'split_op': {'kind': 'op', 'op': 'VariadicSplit'},
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'split_output_0_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_0': {'kind': 'op'},
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'split_output_1_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_1': {'kind': 'op'},
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'split_output_2_data': {'kind': 'data', 'shape': None, 'value': None},
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'output_2': {'kind': 'op'},
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}
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edges = [
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('input', 'split_input_data'),
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('split_input_data', 'split_op'),
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('split_axis', 'split_axis_data'),
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('split_axis_data', 'split_op'),
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('split_lengths', 'split_lengths_data'),
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('split_lengths_data', 'split_op'),
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('split_op', 'split_output_0_data'),
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('split_output_0_data', 'output_0'),
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('split_op', 'split_output_1_data'),
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('split_output_1_data', 'output_1'),
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('split_op', 'split_output_2_data'),
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('split_output_2_data', 'output_2'),
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]
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@generate(*[int64_array(2),
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int64_array([2])])
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def test_variadic_split_axis(self, axis):
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lengths = int64_array([2, 13, 10])
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graph = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array([2, 12, 25, 30])},
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'split_axis_data': {'value': axis},
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'split_lengths_data': {'value': lengths},
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'split_op': {'out_ports_count': 4},
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}
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)
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node = Node(graph, 'split_op')
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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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VariadicSplit.infer(node)
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ont_nodes_count = len(node.out_edges())
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self.assertTrue(ont_nodes_count == 3)
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for out in range(ont_nodes_count):
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self.assertTrue(np.all(node.out_node(out).shape == int64_array([2, 12, lengths[out], 30])))
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@generate(*[int64_array([[2], [2]]),
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int64_array([2, 2])])
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def test_negative_variadic_split_axis(self, axis):
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lengths = int64_array([2, 13, 10])
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graph = build_graph(self.nodes, self.edges,
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{
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'split_input_data': {'shape': int64_array([2, 12, 25, 30])},
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'split_axis_data': {'value': axis},
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'split_lengths_data': {'value': lengths},
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'split_op': {'out_ports_count': 4},
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}
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)
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node = Node(graph, 'split_op')
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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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try:
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VariadicSplit.infer(node)
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except AssertionError as e:
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self.assertTrue(e.args[0] == 'VariadicSplit `axis` should be scalar or tensor with shape [1], '
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'but it`s not for node split_op')
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