zhutian
46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import tensorflow as tf
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from onnx_tf.handlers.backend_handler import BackendHandler
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from onnx_tf.handlers.handler import onnx_op
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@onnx_op("SequenceErase")
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class SequenceErase(BackendHandler):
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@classmethod
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def chk_pos_in_bounds(cls, input_seq, pos):
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"""
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Check the position is in-bounds with respect to the sequence.
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Accepted range for 'position' is in [-n, n - 1], where n is the
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number of tensors in 'input_sequence'.
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:param input_seq: input sequence
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:param pos: position of the output tensor
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:return: True if position is in-bounds
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"""
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seq_length = tf.shape(input_seq.to_sparse(), out_type=pos.dtype)[0]
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cond1 = tf.greater_equal(pos, tf.negative(seq_length))
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cond2 = tf.less_equal(pos, seq_length - 1)
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# pos >= -n and pos < n
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return tf.reduce_all(tf.logical_and(cond1, cond2))
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@classmethod
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def version_11(cls, node, **kwargs):
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tensor_dict = kwargs["tensor_dict"]
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input_sequence = tensor_dict[node.inputs[0]]
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seq_length = tf.shape(input_sequence.to_sparse())[0]
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position = tensor_dict[node.inputs[1]] if len(
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node.inputs) == 2 else seq_length - 1
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# check whether position is in-bounds and assert if not
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result = cls.chk_pos_in_bounds(input_sequence, position)
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assert_pos = tf.Assert(tf.equal(result, True), [result])
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with tf.control_dependencies([assert_pos]):
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s1 = input_sequence[:position]
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s2 = input_sequence[position + 1:]
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return [tf.concat([s1, s2], axis=0)]
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