990f4e2919
* Implement reshapeable CTCGreedyDecoderPlusSparseToDense transformation and test Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix consts (after code-review #1) Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Add CTCGreedyDecoderTransformation with more generic pattern Also it adds new middle-replacer for transforming sequence length to a mask along with tests. Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Do fixes after review #2 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix after review #3 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com> * Fix after review #4 Signed-off-by: Roman Kazantsev <roman.kazantsev@intel.com>
75 lines
2.9 KiB
Python
75 lines
2.9 KiB
Python
"""
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Copyright (C) 2018-2020 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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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from mo.front.common.partial_infer.utils import int64_array
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from mo.graph.graph import Node, Graph
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from mo.ops.op import Op
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class CTCGreedyDecoderOp(Op):
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op = 'CTCGreedyDecoder'
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'type': self.op,
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'op': self.op,
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'version': 'opset1',
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'infer': self.infer,
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'reinterp_shape': True,
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'in_ports_count': 2,
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'out_ports_count': 1
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}
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super().__init__(graph, mandatory_props, attrs)
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def supported_attrs(self):
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return [
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'ctc_merge_repeated'
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]
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@staticmethod
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def infer(node: Node):
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node_name = node.soft_get('name', node.id)
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connected_in_ports = [port for port in node.in_ports().values() if not port.disconnected()]
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assert len(connected_in_ports) == 2, \
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"Incorrect number of inputs for {} node".format(node_name)
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logits_shape = node.in_port(0).data.get_shape()
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sequence_mask_shape = node.in_port(1).data.get_shape()
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# check shapes of input tensors
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assert len(logits_shape) == 3, \
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'Incorrect rank of logits for {} node'.format(node_name)
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if node.has_valid('use_mask_format') and node.use_mask_format is True:
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# it is a case when CTCGreedyDecoder still uses an original format for sequence_length
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assert len(sequence_mask_shape) == 1, \
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'Incorrect rank of sequence length tensor for {} node'.format(node_name)
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assert logits_shape[1] == sequence_mask_shape[0], \
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'Batch dimensions of input tensors must be the same for {} node'.format(node_name)
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else:
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# it is a case when CTCGreedyDecoder uses a sequence mask
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assert len(sequence_mask_shape) == 2, \
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'Incorrect rank of sequence length tensor for {} node'.format(node_name)
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assert logits_shape[1] == sequence_mask_shape[1], \
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'Batch dimensions of input tensors must be the same for {} node'.format(node_name)
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assert logits_shape[0] == sequence_mask_shape[0], \
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'Time dimensions of input tensors must be the same for {} node'.format(node_name)
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batch_size = logits_shape[1]
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time_size = logits_shape[0]
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node.out_port(0).data.set_shape(int64_array([batch_size, time_size, 1, 1]))
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