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!45515 [MS][DOC]add CTCLoss v2 doc 

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1
docs/api/api_python/mindspore.ops.rst
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@ -84,6 +84,7 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
mindspore.ops.BCEWithLogitsLoss
mindspore.ops.BinaryCrossEntropy
mindspore.ops.CTCLoss
mindspore.ops.CTCLossV2
mindspore.ops.KLDivLoss
mindspore.ops.L2Loss
mindspore.ops.NLLLoss

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docs/api/api_python/ops/mindspore.ops.CTCLossV2.rst Normal file
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@ -0,0 +1,37 @@
mindspore.ops.CTCLossV2
=======================
.. py:class:: mindspore.ops.CTCLossV2(blank=0, reduction="none", zero_infinity=False)
计算CTC(Connectionist Temporal Classification)损失和梯度。
CTC算法是在 `Connectionist Temporal Classification: Labeling Unsegmented Sequence Data with Recurrent Neural Networks <http://www.cs.toronto.edu/~graves/icml_2006.pdf>`_ 中提出的。
参数：
- **blank** (int，可选) - 空白标签。默认值：0。
- **reduction** (str，可选) - 对输出应用特定的缩减方法。目前仅支持“none”，不区分大小写。默认值：“none”。
- **zero_infinity** (bool，可选) - 是否将无限损失和相关梯度设置为零。默认值：False。
输入：
- **log_probs** (Tensor) - 输入Tensor，其shape为 :math:`(T, C, N)` 的三维Tensor。 :math:`T` 表示输入长度， :math:`N` 表示批大小， :math:`C` 表示类别数，包含空白标签。
- **targets** (Tensor) - 标签序列。其shape为 :math:`(N, S)` 的三维Tensor。 :math:`S` 表示最大标签长度。
- **input_lengths** (Union(Tuple, Tensor)) - 输入的长度。其shape为 :math:`(N)` 。
- **target_lengths** (Union(Tuple, Tensor)) - 标签的长度。其shape为 :math:`(N)` 。
输出：
- **neg_log_likelihood** (Tensor) - 相对于每个输入节点可微分的损失值。
- **log_alpha** (Tensor) - 输入到目标的可能跟踪概率。
异常：
- **TypeError** - 如果 `zero_infinity` 不是bool类型。
- **TypeError** - 如果 `reduction` 不是string类型。
- **TypeError** - 如果 `log_probs` 的dtype不是float类型或double类型。
- **TypeError** - 如果 `targets`、 `input_lengths` 或 `target_lengths` 的dtype不是int32类型或int64类型。
- **ValueError** - 如果 `log_probs` 的秩不等于2。
- **ValueError** - 如果 `targets` 的秩不等于2。
- **ValueError** - 如果 `input_lengths` 的shape与批大小 :math:`N` 不匹配。
- **ValueError** - 如果 `targets` 的shape与批大小 :math:`N` 不匹配。
- **TypeError** - 如果 `targets`、 `input_lengths` 或 `target_lengths` 的类型不同。
- **ValueError** - 如果 `blank` 的数值不是介于0和 :math:`C` 之间。
- **RuntimeError** - `labels_indices` 的数据类型不是int64。
- **RuntimeError** - 如果任何 `target_lengths[i]` 不在范围 [0， `input_length[i]` ] 范围内。

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docs/api/api_python_en/mindspore.ops.rst
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@ -84,6 +84,7 @@ Loss Function
mindspore.ops.BCEWithLogitsLoss
mindspore.ops.BinaryCrossEntropy
mindspore.ops.CTCLoss
mindspore.ops.CTCLossV2
mindspore.ops.KLDivLoss
mindspore.ops.L2Loss
mindspore.ops.NLLLoss

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mindspore/python/mindspore/ops/operations/nn_ops.py
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@ -8002,34 +8002,38 @@ class CTCLossV2(Primitive):
Recurrent Neural Networks <http://www.cs.toronto.edu/~graves/icml_2006.pdf>`_.
Args:
blank (int): The blank label. Default: 0.
reduction (string): Apply specific reduction method to the output. Currently only support 'none',
blank (int, optional): The blank label. Default: 0.
reduction (string, optional): Apply specific reduction method to the output. Currently only support 'none',
not case sensitive. Default: "none".
zero_infinity (bool): Whether to set infinite loss and correlation gradient to zero. Default: False.
zero_infinity (bool, optional): Whether to set infinite loss and correlation gradient to zero. Default: False.
Inputs:
- **log_probs** (Tensor) - A tensor of shape (T, N, C), where T is input length, N is batch size and C is number
of classes (including blank).
- **targets** (Tensor) - A tensor of shape (N, S), where S is max target length, means the target sequences.
- **input_lengths** (Union(Tuple, Tensor)) - A tuple or Tensor of shape(N). It means the lengths of the input.
- **target_lengths** (Union(Tuple, Tensor)) - A tuple or Tensor of shape(N). It means the lengths of the target.
- **log_probs** (Tensor) - A tensor of shape :math:`(T, C, N)`, where :math:`T` is input length, :math:`N` is
batch size and :math:`C` is number of classes (including blank).
- **targets** (Tensor) - A tensor of shape :math:`(N, S)`, where :math:`S` is max target length,
means the target sequences.
- **input_lengths** (Union(Tuple, Tensor)) - A tuple or Tensor of shape :math:`(N)`.
It means the lengths of the input.
- **target_lengths** (Union(Tuple, Tensor)) - A tuple or Tensor of shape :math:`(N)`.
It means the lengths of the target.
Outputs:
- **neg_log_likelihood** (Tensor) - A loss value which is differentiable with respect to each input node.
- **log_alpha** (Tensor) - The probability of possible trace of input to target.
Raises:
TypeError: If `zero_infinity` is not a bool, reduction is not string.
TypeError: If `zero_infinity` is not a bool.
TypeError: If `reduction` is not string.
TypeError: If the dtype of `log_probs` is not float or double.
TypeError: If the dtype of `targets`, `input_lengths` or `target_lengths` is not int32 or int64.
ValueError: If the rank of `log_probs` is not 3.
ValueError: If the rank of `targets` is not 2.
ValueError: If the shape of `input_lengths` does not match {batch_size|N}.
ValueError: If the shape of `target_lengths` does not match {batch_size|N}.
TypeError: If the types of `targets`, `input_lengths`, `grad_out` or `target_lengths` are different.
TypeError: If the types of `targets`, `input_lengths` or `target_lengths` are different.
ValueError: If the value of `blank` is not in range [0, num_labels|C).
RuntimeError: If any value of `input_lengths` is larger than (num_labels|C).
RuntimeError: If any target_lengths[i] is not in range [0, input_length[i]].
RuntimeError: If any `target_lengths[i]` is not in range [0, `input_length[i]` ].
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``