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68 lines
2.4 KiB
68 lines
2.4 KiB
/**
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* Copyright 2019-2020 Huawei Technologies Co., Ltd
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*
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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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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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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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#ifndef GE_OP_CTC_OPS_H
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#define GE_OP_CTC_OPS_H
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#include "graph/operator.h"
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#include "graph/operator_reg.h"
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namespace ge {
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/**
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*@brief Calculates the CTC Loss (log probability) for each batch entry. \n
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Also calculates the gradient.
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*@par Inputs:
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*@li inputs: 3-D, shape: `(max_time x batch_size x num_classes)`, the logits.
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*@li labels_indices: The indices of a `SparseTensor<int32, 2>`. \n
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`labels_indices(i, :) == [b, t]` means `labels_values(i)` stores the id for \n
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`(batch b, time t)`.
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*@li labels_values: The values (labels) associated with the given batch and time.
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*@li sequence_length: A vector containing sequence lengths (batch).
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*@par Outputs:
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*@li loss: A vector (batch) containing log-probabilities.
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*@li gradient: The gradient of `loss`. 3-D, shape: `(max_time x \n
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batch_size x num_classes)`.
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*@par Attributes:
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*@li preprocess_collapse_repeated: Scalar, if true then repeated labels are collapsed prior to \n
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the CTC calculation.If not specified, defaults to false
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*@li ctc_merge_repeated: Scalar. If set to false, *during* CTC calculation \n
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repeated non-blank labels will not be merged and are interpreted as \n
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individual labels. This is a simplified version of CTC. \n
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If not specified, defaults to true
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*@par Third-party framework compatibility
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* Compatible with TensorFlow CTCLoss operator.
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*/
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REG_OP(CTCLoss)
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.INPUT(inputs, TensorType({DT_FLOAT, DT_DOUBLE}))
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.INPUT(labels_indices, TensorType({DT_INT64}))
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.INPUT(labels_values, TensorType({DT_INT32}))
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.INPUT(sequence_length, TensorType({DT_INT32}))
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.OUTPUT(loss, TensorType({DT_FLOAT, DT_DOUBLE}))
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.OUTPUT(gradient, TensorType({DT_FLOAT, DT_DOUBLE}))
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.ATTR(preprocess_collapse_repeated, Bool, false)
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.ATTR(ctc_merge_repeated, Bool, true)
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.ATTR(ignore_longer_outputs_than_inputs, Bool, false)
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.OP_END_FACTORY_REG(CTCLoss)
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} // namespace ge
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#endif //GE_OP_CTC_OPS_H
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