84 lines
3.6 KiB
Python
84 lines
3.6 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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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.ops.op import Op
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class CTCLoss(Op):
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op = 'CTCLoss'
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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': 'opset4',
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'type_infer': self.type_infer,
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'infer': self.infer,
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'in_ports_count': 5,
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'out_ports_count': 1,
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'preprocess_collapse_repeated': False,
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'ctc_merge_repeated': True,
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'unique': False
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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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return [('preprocess_collapse_repeated', lambda node: bool_to_str(node, 'preprocess_collapse_repeated')),
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('ctc_merge_repeated', lambda node: bool_to_str(node, 'ctc_merge_repeated')),
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('unique', lambda node: bool_to_str(node, 'unique'))]
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@staticmethod
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def type_infer(node):
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logits_type = node.in_port(0).get_data_type()
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logit_length_type = node.in_port(1).get_data_type()
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labels_type = node.in_port(2).get_data_type()
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label_length_type = node.in_port(3).get_data_type()
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blank_index_type = labels_type
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if not node.in_port(4).disconnected():
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blank_index_type = node.in_port(4).get_data_type()
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assert logit_length_type == label_length_type and logit_length_type in [np.int64, np.int32], \
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'Inputs with logits and labels lengths for node {} must be the same and int32 or int64, {} and {} found'.format(
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node.soft_get('name'), logit_length_type, label_length_type)
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assert labels_type == blank_index_type and labels_type in [np.int64, np.int32], \
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'Inputs with labels and blank index for node {} must be the same and int32 or int64, {} and {} found'.format(
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node.soft_get('name'), labels_type, blank_index_type)
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node.out_port(0).set_data_type(logits_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 [4, 5], \
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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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logit_length_shape = node.in_port(1).data.get_shape()
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labels_shape = node.in_port(2).data.get_shape()
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label_length_shape = node.in_port(3).data.get_shape()
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blank_index_shape = int64_array([])
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if len(node.in_nodes()) == 5:
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blank_index_shape = node.in_port(4).data.get_shape()
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# check shapes of input tensors
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assert len(logits_shape) == 3 and len(logit_length_shape) == 1 and len(labels_shape) == 2\
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and len(label_length_shape) == 1 and len(blank_index_shape) == 0, \
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'Incorrect rank of some input tensor for {} node'.format(node_name)
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assert logits_shape[0] == logit_length_shape[0] and logits_shape[0] == labels_shape[0]\
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and logits_shape[0] == label_length_shape[0], \
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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[1] == labels_shape[1], \
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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[0]
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node.out_port(0).data.set_shape(int64_array([batch_size]))
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