change _Loss to Loss

mindspore/nn/loss/loss.py

@@ -15,6 +15,7 @@
 """loss"""
 import mindspore
 import mindspore.common.dtype as mstype
+from mindspore import log
 from mindspore.common.tensor import Tensor
 from mindspore.common.parameter import Parameter
 from mindspore.ops import operations as P
@@ -27,13 +28,18 @@ from mindspore._checkparam import Validator as validator
 from mindspore._checkparam import Rel
 from ... import context
 
-
-class _Loss(Cell):
+class Loss(Cell):
     """
     Base class for other losses.
+
+    Other losses derived from this could use method `self.get_loss` to apply reduction to loss values.
+
+    Args:
+        reduction (str): Type of reduction to be applied to loss. The optional values are "mean", "sum", and "none".
+            Default: "mean".
     """
     def __init__(self, reduction='mean'):
-        super(_Loss, self).__init__()
+        super(Loss, self).__init__()
         if reduction is None:
             reduction = 'none'
 
@@ -79,13 +85,27 @@ class _Loss(Cell):
     def construct(self, base, target):
         raise NotImplementedError
 
+
+class _Loss(Loss):
+    """
+    Base class for other losses.
+    """
+    def __init__(self, reduction='mean'):
+        log.warning("'_Loss' is deprecated from version 1.3 and "
+                    "will be removed in a future version, use 'Loss' instead.")
+        super(_Loss, self).__init__()
+
+    def construct(self, base, target):
+        raise NotImplementedError
+
+
 @constexpr
 def _check_input_type(param_name, input_data, allow_dtype, cls_name):
     if input_data is not None and not isinstance(input_data, allow_dtype):
         raise TypeError(f"For '{cls_name}', the '{param_name}' should be '{allow_dtype}', "
                         f"but got '{F.typeof(input_data)}'")
 
-class L1Loss(_Loss):
+class L1Loss(Loss):
     r"""
     L1Loss creates a criterion to measure the mean absolute error (MAE) between :math:`x` and :math:`y` element-wise,
     where :math:`x` is the input Tensor and :math:`y` is the target Tensor.
@@ -135,7 +155,7 @@ class L1Loss(_Loss):
         return self.get_loss(x)
 
 
-class MSELoss(_Loss):
+class MSELoss(Loss):
     r"""
     MSELoss creates a criterion to measure the mean squared error (squared L2-norm) between :math:`x` and :math:`y`
     element-wise, where :math:`x` is the input and :math:`y` is the target.
@@ -181,7 +201,7 @@ class MSELoss(_Loss):
         return self.get_loss(x)
 
 
-class RMSELoss(_Loss):
+class RMSELoss(Loss):
     r"""
     RMSELoss creates a standard to measure the root mean square error between :math:`x` and :math:`y`
     element-wise, where :math:`x` is the input and :math:`y` is the target.
@@ -222,7 +242,7 @@ class RMSELoss(_Loss):
         return rmse_loss
 
 
-class MAELoss(_Loss):
+class MAELoss(Loss):
     r"""
     MAELoss creates a standard to measure the average absolute error between :math:`x` and :math:`y`
     element-wise, where :math:`x` is the input and :math:`y` is the target.
@@ -270,7 +290,7 @@ class MAELoss(_Loss):
         return self.get_loss(x)
 
 
-class SmoothL1Loss(_Loss):
+class SmoothL1Loss(Loss):
     r"""
     A loss class for learning region proposals.
 
@@ -332,7 +352,7 @@ class SmoothL1Loss(_Loss):
         return self.smooth_l1_loss(base, target)
 
 
-class SoftmaxCrossEntropyWithLogits(_Loss):
+class SoftmaxCrossEntropyWithLogits(Loss):
     r"""
     Computes softmax cross entropy between logits and labels.
 
@@ -419,7 +439,7 @@ def _check_label_dtype(labels_dtype, cls_name):
     validator.check_type_name("labels", labels_dtype, [mstype.int32, mstype.int64], cls_name)
 
 
-class DiceLoss(_Loss):
+class DiceLoss(Loss):
     r"""
     The Dice coefficient is a set similarity loss. It is used to calculate the similarity between two samples. The
     value of the Dice coefficient is 1 when the segmentation result is the best and 0 when the segmentation result
@@ -493,7 +513,7 @@ def _check_weights(weight_shape, label_shape):
         raise ValueError("The weight shape[0] should be equal to label.shape[1].")
 
 
-class MultiClassDiceLoss(_Loss):
+class MultiClassDiceLoss(Loss):
     r"""
     When there are multiple classifications, label is transformed into multiple binary classifications by one hot.
     For each channel section in the channel, it can be regarded as a binary classification problem, so it can be
@@ -572,7 +592,7 @@ class MultiClassDiceLoss(_Loss):
         return total_loss/label.shape[1]
 
 
-class SampledSoftmaxLoss(_Loss):
+class SampledSoftmaxLoss(Loss):
     r"""
     Computes the sampled softmax training loss.
 
@@ -795,7 +815,7 @@ class SampledSoftmaxLoss(_Loss):
         return out_logits, out_labels
 
 
-class BCELoss(_Loss):
+class BCELoss(Loss):
     r"""
     BCELoss creates a criterion to measure the binary cross entropy between the true labels and predicted labels.
 
@@ -876,7 +896,7 @@ def _check_reduced_shape_valid(ori_shape, reduced_shape, axis, cls_name):
     validator.check_reduce_shape(ori_shape, reduced_shape, axis, cls_name)
 
 
-class CosineEmbeddingLoss(_Loss):
+class CosineEmbeddingLoss(Loss):
     r"""
     Computes the similarity between two tensors using cosine distance.
 
@@ -951,7 +971,7 @@ class CosineEmbeddingLoss(_Loss):
         return self.get_loss(output_unreduced)
 
 
-class BCEWithLogitsLoss(_Loss):
+class BCEWithLogitsLoss(Loss):
     r"""
     Adds sigmoid activation function to input `predict`, and uses the given logits to compute binary cross entropy
     between the target and the output.
@@ -1065,7 +1085,7 @@ def _check_input_dtype(targets_dtype, cls_name):
                                                          mstype.float32], cls_name)
 
 
-class FocalLoss(_Loss):
+class FocalLoss(Loss):
     r"""
     The loss function proposed by Kaiming team in their paper ``Focal Loss for Dense Object Detection`` improves the
     effect of image object detection. It is a loss function to solve the imbalance of categories and the difference of

mindspore/train/callback/_summary_collector.py

@@ -34,7 +34,7 @@ from mindspore.train.callback import Callback, ModelCheckpoint
 from mindspore.train import lineage_pb2
 from mindspore.train.callback._dataset_graph import DatasetGraph
 from mindspore.nn.optim.optimizer import Optimizer
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.train._utils import check_value_type
 
 HYPER_CONFIG_ENV_NAME = "MINDINSIGHT_HYPER_CONFIG"
@@ -907,7 +907,7 @@ class SummaryCollector(Callback):
             network = cb_params.eval_network
 
         for _, cell in network.cells_and_names():
-            if isinstance(cell, _Loss):
+            if isinstance(cell, Loss):
                 loss_fn = cell
                 break
         return loss_fn

model_zoo/official/cv/crnn/src/loss.py

@@ -14,13 +14,13 @@
 # ============================================================================
 """CTC Loss."""
 import numpy as np
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore import Tensor, Parameter
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 
 
-class CTCLoss(_Loss):
+class CTCLoss(Loss):
     """
      CTCLoss definition
 

model_zoo/official/cv/crnn_seq2seq_ocr/src/attention_ocr.py

@@ -24,7 +24,7 @@ import mindspore.nn as nn
 import mindspore.ops.operations as P
 import mindspore.common.dtype as mstype
 from mindspore import context, Tensor
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import composite as C
 from mindspore.ops import functional as F
 from mindspore.context import ParallelMode
@@ -34,7 +34,7 @@ from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from src.seq2seq import Encoder, Decoder
 
 
-class NLLLoss(_Loss):
+class NLLLoss(Loss):
     def __init__(self, reduction='mean'):
         super(NLLLoss, self).__init__(reduction)
         self.one_hot = P.OneHot()

model_zoo/official/cv/efficientnet/src/loss.py

@@ -13,14 +13,14 @@
 # limitations under the License.
 # ============================================================================
 """define loss function for network."""
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
 from mindspore import Tensor
 import mindspore.nn as nn
 
-class LabelSmoothingCrossEntropy(_Loss):
+class LabelSmoothingCrossEntropy(Loss):
 
     def __init__(self, smooth_factor=0.1, num_classes=1000):
         super(LabelSmoothingCrossEntropy, self).__init__()

model_zoo/official/cv/googlenet/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/official/cv/mobilenetv1/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/official/cv/mobilenetv2/src/models.py

@@ -19,12 +19,12 @@ from mindspore import nn
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.train.callback import Callback
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from src.mobilenetV2 import MobileNetV2Backbone, MobileNetV2Head, mobilenet_v2
 
-class CrossEntropyWithLabelSmooth(_Loss):
+class CrossEntropyWithLabelSmooth(Loss):
     """
     CrossEntropyWith LabelSmooth.
 

model_zoo/official/cv/mobilenetv2_quant/src/utils.py

@@ -20,7 +20,7 @@ import numpy as np
 from mindspore.train.callback import Callback
 from mindspore import Tensor
 from mindspore import nn
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
@@ -78,7 +78,7 @@ class Monitor(Callback):
             np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))
 
 
-class CrossEntropyWithLabelSmooth(_Loss):
+class CrossEntropyWithLabelSmooth(Loss):
     """
     CrossEntropyWith LabelSmooth.
 

model_zoo/official/cv/mobilenetv3/train.py

@@ -24,7 +24,7 @@ from mindspore import Tensor
 from mindspore import nn
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
@@ -69,7 +69,7 @@ else:
     raise ValueError("Unsupported device_target.")
 
 
-class CrossEntropyWithLabelSmooth(_Loss):
+class CrossEntropyWithLabelSmooth(Loss):
     """
     CrossEntropyWith LabelSmooth.
 

model_zoo/official/cv/nasnet/src/loss.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """define evaluation loss function for network."""
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -21,7 +21,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class CrossEntropy_Val(_Loss):
+class CrossEntropy_Val(Loss):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits, will be used in inference process"""
     def __init__(self, smooth_factor=0, num_classes=1000):
         super(CrossEntropy_Val, self).__init__()

model_zoo/official/cv/nasnet/src/nasnet_a_mobile.py

@@ -17,7 +17,7 @@ import numpy as np
 
 from mindspore import Tensor
 import mindspore.nn as nn
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 import mindspore.ops.operations as P
 import mindspore.ops.functional as F
 import mindspore.ops.composite as C
@@ -57,7 +57,7 @@ def _clip_grad(clip_type, clip_value, grad):
     return new_grad
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
     def __init__(self, smooth_factor=0, num_classes=1000, factor=0.4):
         super(CrossEntropy, self).__init__()

model_zoo/official/cv/resnet/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/official/cv/resnet152/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/official/cv/resnet50_quant/src/crossentropy.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """define loss function for network"""
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -21,7 +21,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
 
     def __init__(self, smooth_factor=0, num_classes=1001):

model_zoo/official/cv/resnext101/src/crossentropy.py

@@ -15,14 +15,14 @@
 """
 define loss function for network.
 """
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     """
     the redefined loss function with SoftmaxCrossEntropyWithLogits.
     """

model_zoo/official/cv/resnext50/src/crossentropy.py

@@ -15,14 +15,14 @@
 """
 define loss function for network.
 """
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     """
     the redefined loss function with SoftmaxCrossEntropyWithLogits.
     """

model_zoo/official/cv/shufflenetv2/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/official/cv/simple_pose/src/network_define.py

@@ -15,11 +15,11 @@
 import mindspore.nn as nn
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.common import dtype as mstype
 
 
-class JointsMSELoss(_Loss):
+class JointsMSELoss(Loss):
     def __init__(self, use_target_weight):
         super(JointsMSELoss, self).__init__()
         self.criterion = nn.MSELoss(reduction='mean')

model_zoo/official/cv/tinydarknet/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/official/cv/unet3d/src/loss.py

@@ -16,10 +16,10 @@
 import mindspore.nn as nn
 from mindspore import dtype as mstype
 from mindspore.ops import operations as P
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from src.model_utils.config import config
 
-class SoftmaxCrossEntropyWithLogits(_Loss):
+class SoftmaxCrossEntropyWithLogits(Loss):
     def __init__(self):
         super(SoftmaxCrossEntropyWithLogits, self).__init__()
         self.transpose = P.Transpose()

model_zoo/official/cv/vgg16/src/crossentropy.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """define loss function for network"""
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -21,7 +21,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
 
     def __init__(self, smooth_factor=0., num_classes=1001):

model_zoo/official/cv/warpctc/src/loss.py

@@ -14,13 +14,13 @@
 # ============================================================================
 """CTC Loss."""
 import numpy as np
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore import Tensor, Parameter
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 
 
-class CTCLoss(_Loss):
+class CTCLoss(Loss):
     """
      CTCLoss definition
 

model_zoo/official/nlp/gru/src/loss.py

@@ -14,10 +14,10 @@
 # ============================================================================
 """NLLLoss cell"""
 import mindspore.ops.operations as P
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 
-class NLLLoss(_Loss):
+class NLLLoss(Loss):
     '''
        NLLLoss function
     '''

model_zoo/research/cv/FaceQualityAssessment/src/loss.py

@@ -17,7 +17,7 @@ import mindspore.nn as nn
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore import Tensor
 
 eps = 1e-24
@@ -41,7 +41,7 @@ class log_softmax(nn.Cell):
         return result
 
 
-class CEWithIgnoreIndex3D(_Loss):
+class CEWithIgnoreIndex3D(Loss):
     '''CEWithIgnoreIndex3D'''
     def __init__(self):
         super(CEWithIgnoreIndex3D, self).__init__()

model_zoo/research/cv/FaceRecognitionForTracking/src/loss.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Face Recognition loss."""
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -23,7 +23,7 @@ import mindspore.nn as nn
 eps = 1e-24
 
 
-class CrossEntropyNew(_Loss):
+class CrossEntropyNew(Loss):
     '''CrossEntropyNew'''
     def __init__(self, smooth_factor=0., num_classes=1000):
         super(CrossEntropyNew, self).__init__()
@@ -42,7 +42,7 @@ class CrossEntropyNew(_Loss):
         return loss
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     '''CrossEntropy'''
     def __init__(self):
         super(CrossEntropy, self).__init__()
@@ -106,7 +106,7 @@ class CrossEntropyWithIgnoreIndex(nn.Cell):
 eps = 1e-24
 
 
-class CEWithIgnoreIndex3D(_Loss):
+class CEWithIgnoreIndex3D(Loss):
     '''CEWithIgnoreIndex3D'''
     def __init__(self):
         super(CEWithIgnoreIndex3D, self).__init__()

model_zoo/research/cv/GENet_Res50/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/research/cv/ManiDP/src/loss.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """define loss function for network."""
 
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -22,7 +22,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class LabelSmoothingCrossEntropy(_Loss):
+class LabelSmoothingCrossEntropy(Loss):
     """cross-entropy with label smoothing"""
 
     def __init__(self, smooth_factor=0.1, num_classes=1000):

model_zoo/research/cv/glore_res50/src/loss.py

@@ -16,7 +16,7 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 import mindspore.ops as ops
@@ -57,7 +57,7 @@ class SoftmaxCrossEntropyExpand(nn.Cell):  # pylint: disable=missing-docstring
         return loss
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
 
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):

model_zoo/research/cv/hardnet/src/EntropyLoss.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/research/cv/renas/src/loss.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """define loss function for network."""
 
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -22,7 +22,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class LabelSmoothingCrossEntropy(_Loss):
+class LabelSmoothingCrossEntropy(Loss):
     """cross-entropy with label smoothing"""
 
     def __init__(self, smooth_factor=0.1, num_classes=1000):

model_zoo/research/cv/resnetv2/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

model_zoo/research/cv/tinynet/src/loss.py

@@ -14,7 +14,7 @@
 # ============================================================================
 """define loss function for network."""
 
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -22,7 +22,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class LabelSmoothingCrossEntropy(_Loss):
+class LabelSmoothingCrossEntropy(Loss):
     """cross-entropy with label smoothing"""
 
     def __init__(self, smooth_factor=0.1, num_classes=1000):

model_zoo/research/cv/vgg19/src/crossentropy.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """define loss function for network"""
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
@@ -21,7 +21,7 @@ from mindspore.common import dtype as mstype
 import mindspore.nn as nn
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
 
     def __init__(self, smooth_factor=0., num_classes=1001):

model_zoo/research/nlp/dscnn/src/loss.py

@@ -14,14 +14,14 @@
 # ===========================================================================
 """DSCNN loss."""
 import mindspore.nn as nn
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     '''Build CrossEntropy Loss.'''
     def __init__(self, smooth_factor=0., num_classes=1000):
         super(CrossEntropy, self).__init__()

tests/st/auto_parallel/resnet50_expand_loss.py

@@ -23,7 +23,7 @@ import mindspore.ops.functional as F
 from mindspore import Tensor
 from mindspore.common.initializer import TruncatedNormal
 from mindspore.communication.management import init
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.ops import operations as P
 from mindspore.parallel import set_algo_parameters
@@ -245,7 +245,7 @@ def resnet50(class_num=10):
                   class_num)
 
 
-class SoftmaxCrossEntropyExpand(_Loss):
+class SoftmaxCrossEntropyExpand(Loss):
     def __init__(self, sparse=False):
         super(SoftmaxCrossEntropyExpand, self).__init__()
         self.exp = P.Exp()
@@ -307,15 +307,15 @@ class DataGenerator():
         data = (self.generate_data(shape)).astype(np.float32)
         stra = [1] * len(shape)
         stra[0] = device_num
-        datas = self.get_parallel_blocks(data, stra)
-        return Tensor(data), Tensor(datas[rank_id])
+        data_parallel = self.get_parallel_blocks(data, stra)
+        return Tensor(data), Tensor(data_parallel[rank_id])
 
     def label_data(self, shape):
         data = (self.generate_data(shape) * 1000 / np.prod(shape)).astype(np.int32)
         stra = [1] * len(shape)
         stra[0] = device_num
-        datas = self.get_parallel_blocks(data, stra)
-        return Tensor(data), Tensor(datas[rank_id])
+        data_parallel = self.get_parallel_blocks(data, stra)
+        return Tensor(data), Tensor(data_parallel[rank_id])
 
 
 class Dataset():

tests/st/networks/models/resnet50/src/CrossEntropySmooth.py

@@ -16,12 +16,12 @@
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.common import dtype as mstype
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import functional as F
 from mindspore.ops import operations as P
 
 
-class CrossEntropySmooth(_Loss):
+class CrossEntropySmooth(Loss):
     """CrossEntropy"""
     def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
         super(CrossEntropySmooth, self).__init__()

tests/st/ops/gpu/test_loss.py

@@ -18,11 +18,11 @@ import pytest
 
 from mindspore import Tensor
 from mindspore.ops import operations as P
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.nn.loss.loss import L1Loss
 import mindspore.context as context
 
-class WeightedLoss(_Loss):
+class WeightedLoss(Loss):
     def __init__(self, reduction='mean', weights=1.0):
         super(WeightedLoss, self).__init__(reduction)
         self.abs = P.Abs()
@@ -72,7 +72,7 @@ def test_weighted_loss_float32():
 def test_weighted_loss_float64():
     weighted_loss(np.float64)
 
-class CustomLoss(_Loss):
+class CustomLoss(Loss):
     def __init__(self, reduction='mean'):
         super(CustomLoss, self).__init__(reduction)
         self.abs = P.Abs()

tests/st/ops/gpu/test_sampled_softmax_loss_op.py

@@ -142,7 +142,7 @@ def test_sampledsoftmaxloss_reduction_invalid():
     with pytest.raises(ValueError):
         nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, reduction="invalid")
 
-    # reduction can be None, as defined in _Loss
+    # reduction can be None, as defined in Loss
     # with pytest.raises(ValueError):
     #     nn.SampledSoftmaxLoss(num_sampled=4, num_classes=7, reduction=None)  #
 

tests/st/quantization/mobilenetv2_quant/utils.py

@@ -20,7 +20,7 @@ import numpy as np
 from mindspore.train.callback import Callback
 from mindspore import Tensor
 from mindspore import nn
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
@@ -87,7 +87,7 @@ class Monitor(Callback):
             run_context.request_stop()
 
 
-class CrossEntropyWithLabelSmooth(_Loss):
+class CrossEntropyWithLabelSmooth(Loss):
     """
     CrossEntropyWith LabelSmooth.
 

tests/st/quantization/resnet50_quant/utils.py

@@ -20,7 +20,7 @@ import numpy as np
 from mindspore.train.callback import Callback
 from mindspore import Tensor
 from mindspore import nn
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
 from mindspore.common import dtype as mstype
@@ -85,7 +85,7 @@ class Monitor(Callback):
             run_context.request_stop()
 
 
-class CrossEntropy(_Loss):
+class CrossEntropy(Loss):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
 
     def __init__(self, smooth_factor=0, num_classes=1001):

tests/ut/python/parallel/test_auto_parallel_resnet.py

@@ -23,7 +23,7 @@ from mindspore import context
 from mindspore.common.api import _executor
 from mindspore.common.initializer import TruncatedNormal
 from mindspore.communication.management import init
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.ops import operations as P
 from mindspore.parallel import _cost_model_context as cost_model_context
@@ -215,7 +215,7 @@ def resnet50(class_num=10):
                   class_num)
 
 
-class SoftmaxCrossEntropyExpand(_Loss):
+class SoftmaxCrossEntropyExpand(Loss):
     def __init__(self, sparse=False):
         super(SoftmaxCrossEntropyExpand, self).__init__()
         self.exp = P.Exp()

tests/ut/python/parallel/test_auto_star_elimination.py

@@ -20,7 +20,7 @@ from mindspore import Tensor, Parameter
 from mindspore import context
 from mindspore.common import dtype as mstype
 from mindspore.common.api import _executor
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.ops import composite as C
 from mindspore.ops import operations as P
 from tests.ut.python.ops.test_math_ops import VirtualLoss
@@ -59,7 +59,7 @@ class CustomMatMul(nn.Cell):
         return out
 
 
-class MarginCE(_Loss):
+class MarginCE(Loss):
     def __init__(self):
         super(MarginCE, self).__init__()
         self.fc = CustomMatMul(transpose_b=True)

tests/ut/python/parallel/test_gather_v2_primitive.py

@@ -22,7 +22,7 @@ from mindspore.common import dtype as mstype
 from mindspore.common.parameter import ParameterTuple
 from mindspore.communication.management import init
 from mindspore.nn import Dense, Cell
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.nn.optim import Momentum
 from mindspore.ops import composite as C
 from mindspore.ops import functional as F
@@ -64,7 +64,7 @@ class Dataset():
         return self
 
 
-class GatherV2(_Loss):
+class GatherV2(Loss):
     def __init__(self, index_dim, strategy, index_size=16):
         super(GatherV2, self).__init__()
         self.pow = P.Pow()
@@ -195,7 +195,7 @@ def test_strategy3():
     net_trains(criterion, rank)
 
 
-class GatherV2Axis1(_Loss):
+class GatherV2Axis1(Loss):
     def __init__(self, index_dim, strategy, index_size=16):
         super(GatherV2Axis1, self).__init__()
         self.pow = P.Pow()

tests/ut/python/parallel/test_one_dev.py

@@ -22,7 +22,7 @@ from mindspore import context
 import mindspore.common.dtype as mstype
 from mindspore.common.api import _executor
 from mindspore.common.parameter import Parameter
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import Loss
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
@@ -68,7 +68,7 @@ class AllToAllNet(nn.Cell):
         return x
 
 
-class SoftmaxCrossEntropyWithLogits(_Loss):
+class SoftmaxCrossEntropyWithLogits(Loss):
     def __init__(self,
                  sparse=False,
                  reduction='none'):