iliya mironov
f670b7cb3a
* Add CTCGreedyDecoder mo support * Update copiright * Update bom file * Add transformation * Fix code style * Fix according to review * Add CTCGreedyDecoder v6 to ConvertPrecision * Hot fix * Add replasment for ctc_greedy_decoder * Fix test * Fix * Update ie transform * Draft ctc lost replaser * Add ctcloss replaser * Update * Refactoring code * Update transformation * Update decoder * Remove comments * Convert seq mask from int to float * Fix unit test * Add dynamic tests * Refactoring code * Fix py code style * update style * Disable ctcgreedydecoder transform for mkldnn plugin * Add some comments * Add transfor code comments * Enable transform from differend plagins * Fix mo * fix tests * Fix comment * Fix convert precition * Update comment * Fix prcition * Refactoring according to reviw * Add ir reder extender * Rename transformation * Update bom file * Fix mo replacer * Fix tests * Move transform to decomp * Add check blank_index * Rafactoring ctcloss * Change dinemic rank check * Fix ctclos extractor * Remove comment * Fix code style * Refactoring pattern matcher for transformation CTCGreedyDecoder * Disavle transform for vpu * Refactoring according to review * Refactoring code * Disable transformation for cldnn * Remove unused code * Reverse transfomation * Fix code style * Hot fix transform * Fix unit tests * Update transform * Enable transform in common pipline * Fix names replasments for mo transformations * Hot fix * Fix
93 lines
3.6 KiB
Python
93 lines
3.6 KiB
Python
"""
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Copyright (C) 2017-2021 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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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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import numpy as np
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.extractor import bool_to_str
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from mo.graph.graph import Node, Graph
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from mo.middle.passes.convert_data_type import np_data_type_to_destination_type
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from mo.ops.op import Op
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from mo.utils.error import Error
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class CTCGreedyDecoderSeqLenOp(Op):
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op = 'CTCGreedyDecoderSeqLen'
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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': 'opset6',
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'infer': self.infer,
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'type_infer': self.type_infer,
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'in_ports_count': 3,
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'out_ports_count': 2,
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'merge_repeated': True,
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'classes_index_type': np.int32,
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'sequence_length_type': np.int32
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}
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super().__init__(graph, mandatory_props, attrs)
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def backend_attrs(self):
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version = self.get_opset()
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if version == 'opset6':
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return [('classes_index_type', lambda node: np_data_type_to_destination_type(node.classes_index_type)),
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('sequence_length_type', lambda node: np_data_type_to_destination_type(node.sequence_length_type)),
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('merge_repeated', lambda node: bool_to_str(node, 'merge_repeated'))]
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else:
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raise Error('Unknown opset version "{}"'.format(version))
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@staticmethod
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def type_infer(node):
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opset = node.get_opset()
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if opset == 'opset6':
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node.out_port(0).set_data_type(node.classes_index_type)
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node.out_port(1).set_data_type(node.sequence_length_type)
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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) in [2, 3], \
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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_len_shape = node.in_port(1).data.get_shape()
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if len(node.in_nodes()) == 3:
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blank_index_shape = node.in_port(2).data.get_shape()
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assert len(blank_index_shape) == 1, \
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'Incorrect rank of blank_index for {} node'.format(node_name)
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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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assert len(sequence_len_shape) == 1, \
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'Incorrect rank of sequence length tensor for {} node'.format(node_name)
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assert logits_shape[0] == sequence_len_shape[0], \
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'Batch dimensions of input tensors must be the same for {} node'.format(node_name)
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batch_size = logits_shape[0]
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time_size = logits_shape[1]
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if node.is_out_port_connected(0):
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node.out_port(0).data.set_shape(int64_array([batch_size, time_size]))
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if node.is_out_port_connected(1):
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node.out_port(1).data.set_shape(int64_array([batch_size]))
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