!49904 add_ops_ctc_loss_docs_master

Merge pull request !49904 from yide12/code_docs_nn_master

docs/api/api_python/mindspore.ops.rst

@@ -59,6 +59,7 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
     mindspore.ops.binary_cross_entropy_with_logits
     mindspore.ops.cosine_embedding_loss
     mindspore.ops.cross_entropy
+    mindspore.ops.ctc_loss
     mindspore.ops.gaussian_nll_loss
     mindspore.ops.hinge_embedding_loss
     mindspore.ops.huber_loss

docs/api/api_python/ops/mindspore.ops.func_ctc_loss.rst

@@ -0,0 +1,34 @@
+mindspore.ops.ctc_loss
+======================
+
+.. py:function:: mindspore.ops.ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reduction="mean", zero_infinity=False)
+
+    计算CTC（Connectist Temporal Classification）损失和梯度。
+
+    关于CTCLoss算法详细介绍，请参考 `Connectionist Temporal Classification: Labeling Unsegmented Sequence Data withRecurrent Neural Networks <http://www.cs.toronto.edu/~graves/icml_2006.pdf>`_ 。
+
+    参数：
+        - **log_probs** (Tensor) - 输入Tensor，shape :math:`(T, N, C)` 。其中T表示输入长度，N表示批次大小，C是分类数，包含空白。
+        - **targets** (Tensor) - 目标Tensor，shape :math:`(N, S)` 。其中S表示最大目标长度。
+        - **input_lengths** (Union[tuple, Tensor]) - shape为N的Tensor或tuple。表示输入长度。
+        - **target_lengths** (Union[tuple, Tensor]) - shape为N的Tensor或tuple。表示目标长度。
+        - **blank** (int) - 空白标签。默认值：0。
+        - **reduction** (str) - 对输出应用归约方法。可选值为"none"、"mean"或"sum"。默认值："mean"。
+        - **zero_infinity** (bool) - 是否设置无限损失和相关梯度为零。默认值：False。
+
+    返回：
+        - **neg_log_likelihood** (Tensor) - 对每一个输入节点可微调的损失值，shape是 :math:`(N)`。
+        - **log_alpha** (Tensor) - shape为 :math:`(N, T, 2 * S + 1)` 的输入到输出的轨迹概率。
+
+    异常：
+        - **TypeError** - `zero_infinity` 不是布尔值， `reduction` 不是字符串。
+        - **TypeError** - `log_probs` 的数据类型不是float或double。
+        - **TypeError** - `targets` 、 `input_lengths` 或 `target_lengths` 数据类型不是int32或int64。
+        - **ValueError** - `log_probs` 的秩不是3。
+        - **ValueError** - `targets` 的秩不是2。
+        - **ValueError** - `input_lengths` 的shape大小不等于N。N是 `log_probs` 的批次大小。
+        - **ValueError** - `target_lengths` 的shape大小不等于N。N是 `log_probs` 的批次大小。
+        - **ValueError** - `targets` 、 `input_lengths` 或 `target_lengths` 的数据类型是不同的。
+        - **ValueError** - `blank` 值不介于0到C之间。C是 `log_probs` 的分类数。
+        - **RuntimeError** - `input_lengths` 的值大于T。T是 `log_probs` 的长度。
+        - **RuntimeError** - `target_lengths[i]` 的取值范围不在0到 `input_length[i]` 之间。

docs/api/api_python_en/mindspore.ops.rst

@@ -59,6 +59,7 @@ Loss Functions
     mindspore.ops.binary_cross_entropy_with_logits
     mindspore.ops.cosine_embedding_loss
     mindspore.ops.cross_entropy
+    mindspore.ops.ctc_loss
     mindspore.ops.gaussian_nll_loss
     mindspore.ops.hinge_embedding_loss
     mindspore.ops.huber_loss

mindspore/python/mindspore/nn/loss/loss.py

@@ -2302,7 +2302,7 @@ class CTCLoss(LossBase):
     Recurrent Neural Networks <http://www.cs.toronto.edu/~graves/icml_2006.pdf>`_ .
 
     Args:
-        blank (int): The blank tag. Default: 0.
+        blank (int): The blank label. Default: 0.
         reduction (str): Implements the reduction method to the output with 'none', 'mean', or 'sum'. Default: 'mean'.
         zero_infinity (bool): Whether to set infinite loss and correlation gradient to 0. Default: False.
 

mindspore/python/mindspore/ops/function/nn_func.py

@@ -3974,26 +3974,27 @@ def ctc_loss(log_probs, targets, input_lengths, target_lengths, blank=0, reducti
         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.
         blank (int): The blank label. Default: 0.
-        reduction (string): Apply specific reduction method to the output: 'none', 'mean', or 'sum'. Default: 'mean'.
-        zero_infinity (bool): Whether to set infinite loss and correlation gradient to zero. Default: False.
+        reduction (string): Implements the reduction method to the output with 'none', 'mean', or 'sum'.
+            Default: 'mean'.
+        zero_infinity (bool): Whether to set infinite loss and correlation gradient to 0. Default: False.
 
     Returns:
-        neg_log_likelihood (Tensor), A loss value with shape (N), which is differentiable with respect to
+        neg_log_likelihood (Tensor), A loss value with shape :math:`(N)` , which is differentiable with respect to
         each input node.
 
-        log_alpha (Tensor), The probability of possible trace of input to target with shape (N, T, 2 * S + 1).
+        log_alpha (Tensor), The probability of possible trace of input to target with shape :math:`(N, T, 2 * S + 1)` .
 
     Raises:
-        TypeError: If `zero_infinity` is not a bool, reduction is not string.
-        TypeError: If the dtype of `log_probs` or `grad_out` is not float or double.
+        TypeError: If `zero_infinity` is not a bool, `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.
-        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).
+        ValueError: If the shape of `input_lengths` does not match N. N is batch size of `log_probs` .
+        ValueError: If the shape of `target_lengths` does not match N. N is batch size of `log_probs` .
+        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). C is number of classes of `log_probs` .
+        RuntimeError: If any value of `input_lengths` is larger than T. T is the length of `log_probs`.
         RuntimeError: If any target_lengths[i] is not in range [0, input_length[i]].
 
     Supported Platforms: