Failed MO shape infer after SplitConcatPairToInterpolate transformation, when Concat has more than one producer (#4502)
* Fixes in the MO transformation SplitConcatPairToInterpolate. * Small fix. * Small fix. * Added test for the case when inputs of Concat are two Splits. * Added docstring to the function get_concat_after_split. * Some fixes. * Small fix.
This commit is contained in:
Vladimir Gavrilov
GitHub
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commit
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@ -1,5 +1,5 @@
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"""
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Copyright (c) 2020 Intel Corporation
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Copyright (c) 2018-2021 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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@ -32,6 +32,19 @@ from mo.ops.strided_slice import StridedSlice
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def get_concat_after_split(split: Node) -> Optional[Node]:
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"""
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This function gets consumers of the 'split' node, checks that the following conditions are fulfilled:
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1) 'split' node has only one consumer;
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2) for any output port of 'split', number of corresponding input ports of the consumer is the same;
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3) for any output port 'i' of the 'split', corresponding input ports of the consumer are
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[i * m, ..., i * m + (m - 1)], where 'm' is the same for all 'i';
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4) the consumer operation is 'Concat';
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5) 'split' is a unique producer for this 'Concat';
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and, if all these conditions are fulfilled, returns the above mentioned 'Concat' node. Otherwise, if some of these
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conditions is false, this functions returns None.
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:param split: Split node
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:return: Concat node, if all conditions are fulfilled, or None otherwise
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"""
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# If number of output nodes of 'split' is not equal to 1, then the transformation is not applicable.
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split_outputs = [d.node for _, p in split.out_ports().items() for d in p.get_connection().get_destinations()]
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names_of_split_outputs = set([n.name for n in split_outputs])
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@ -53,7 +66,17 @@ def get_concat_after_split(split: Node) -> Optional[Node]:
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dest = split.out_port(0).get_destinations()[0].node
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# The transformation is applicable, only if next node is Concat.
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return dest if dest.soft_get('type') == 'Concat' else None
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if dest.soft_get('type') != 'Concat':
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return
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# The transformation is applicable, only if Split is a unique producer for Concat.
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dest_inputs = [p.get_source().node for p in dest.in_ports().values() if not p.disconnected()]
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names_of_concat_inputs = set([n.soft_get('name', n.id) for n in dest_inputs])
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expected_number_of_unique_inputs = 1 if dest.has_valid('axis') else 2
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if len(names_of_concat_inputs) != expected_number_of_unique_inputs:
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return
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return dest
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def get_interpolate_pattern(split: Node) -> dict:
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@ -503,6 +503,81 @@ ref_graph_node_attrs_for_3d_spatial_case_2 = {
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}
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graph_node_attrs_when_there_are_two_splits_one_concat = {
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'placeholder1': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder1_data': {
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'value': None,
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'shape': int64_array([1, 13, 13, 3, 2]),
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'kind': 'data',
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'data_type': None
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},
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'placeholder2': {'type': 'Parameter', 'kind': 'op', 'op': 'Parameter'},
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'placeholder2_data': {
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'value': None,
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'shape': int64_array([1, 13, 13, 3, 2]),
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'kind': 'data',
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'data_type': None
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},
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'split1': {'type': 'Split', 'kind': 'op', 'op': 'Split', 'num_splits': 2},
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'split1_axis_const': {
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'kind': 'op',
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'value': np.array(4, dtype=np.int64),
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'op': 'Const',
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'type': 'Const'
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},
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'split1_axis_const_data': {
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'value': np.array(4, dtype=np.int64),
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'shape': np.array(4, dtype=np.int64).shape,
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'kind': 'data'
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},
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'split2': {'type': 'Split', 'kind': 'op', 'op': 'Split', 'num_splits': 2},
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'split2_axis_const': {
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'kind': 'op',
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'value': np.array(4, dtype=np.int64),
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'op': 'Const',
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'type': 'Const'
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},
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'split2_axis_const_data': {
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'value': np.array(4, dtype=np.int64),
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'shape': np.array(4, dtype=np.int64).shape,
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'kind': 'data'
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},
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'split1_data_0': {'value': None, 'shape': int64_array([1, 13, 13, 3, 1]), 'kind': 'data'},
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'split1_data_1': {'value': None, 'shape': int64_array([1, 13, 13, 3, 1]), 'kind': 'data'},
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'split2_data_0': {'value': None, 'shape': int64_array([1, 13, 13, 3, 1]), 'kind': 'data'},
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'split2_data_1': {'value': None, 'shape': int64_array([1, 13, 13, 3, 1]), 'kind': 'data'},
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'concat': {'type': 'Concat', 'kind': 'op', 'axis': 4},
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'concat_data': {'value': None, 'shape': int64_array([1, 13, 13, 3, 4]), 'kind': 'data'},
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'abs': {'type': 'Abs', 'kind': 'op', 'op': 'Abs'},
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'abs_data': {'value': None, 'shape': int64_array([1, 13, 13, 3, 4]), 'kind': 'data'},
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'output': {'kind': 'op', 'op': 'Result'},
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}
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graph_edges_when_there_are_two_splits_one_concat = [
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('placeholder1', 'placeholder1_data'),
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('placeholder2', 'placeholder2_data'),
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('placeholder1_data', 'split1', {'in': 0}),
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('split1_axis_const', 'split1_axis_const_data'),
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('split1_axis_const_data', 'split1', {'in': 1}),
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('split1', 'split1_data_0', {'out': 0}),
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('split1', 'split1_data_1', {'out': 1}),
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('placeholder2_data', 'split2', {'in': 0}),
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('split2_axis_const', 'split2_axis_const_data'),
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('split2_axis_const_data', 'split2', {'in': 1}),
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('split2', 'split2_data_0', {'out': 0}),
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('split2', 'split2_data_1', {'out': 1}),
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('split1_data_0', 'concat', {'in': 0}),
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('split1_data_1', 'concat', {'in': 1}),
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('split2_data_0', 'concat', {'in': 2}),
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('split2_data_1', 'concat', {'in': 3}),
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('concat', 'concat_data'),
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('concat_data', 'abs'),
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('abs', 'abs_data'),
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('abs_data', 'output')
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]
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class SplitConcatPairToInterpolateTest(unittest.TestCase):
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def test_spatial_2d_split_concat_1(self):
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graph = build_graph(
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@ -601,3 +676,16 @@ class SplitConcatPairToInterpolateTest(unittest.TestCase):
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SplitConcatPairToInterpolate().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, ref_graph, 'output')
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self.assertTrue(flag, resp)
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def test_two_splits_one_concat(self):
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graph = build_graph(
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nodes_attrs=graph_node_attrs_when_there_are_two_splits_one_concat,
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edges=graph_edges_when_there_are_two_splits_one_concat
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)
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ref_graph = build_graph(
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nodes_attrs=graph_node_attrs_when_there_are_two_splits_one_concat,
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edges=graph_edges_when_there_are_two_splits_one_concat
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)
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SplitConcatPairToInterpolate().find_and_replace_pattern(graph)
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(flag, resp) = compare_graphs(graph, ref_graph, 'output')
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self.assertTrue(flag, resp)
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