fix naset performance

official/cv/nasnet/src/nasnet_a_mobile.py

@@ -18,9 +18,7 @@ import numpy as np
 from mindspore import context
 from mindspore import Tensor
 import mindspore.nn as nn
-
 from mindspore.nn.loss.loss import LossBase
-
 import mindspore.ops.operations as P
 import mindspore.ops.functional as F
 import mindspore.ops.composite as C
@@ -61,9 +59,8 @@ def _clip_grad(clip_type, clip_value, grad):
 
 class CrossEntropy(LossBase):
     """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
-    def __init__(self, smooth_factor=0, num_classes=1000, factor=0.4):
+    def __init__(self, smooth_factor=0, num_classes=1000):
         super(CrossEntropy, self).__init__()
-        self.factor = factor
         self.onehot = P.OneHot()
         self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
         self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
@@ -71,14 +68,11 @@ class CrossEntropy(LossBase):
         self.mean = P.ReduceMean(False)
 
     def construct(self, logits, label):
-        logit, aux = logits
+        logit = logits[0]
         one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
         loss_logit = self.ce(logit, one_hot_label)
         loss_logit = self.mean(loss_logit, 0)
-        one_hot_label_aux = self.onehot(label, F.shape(aux)[1], self.on_value, self.off_value)
-        loss_aux = self.ce(aux, one_hot_label_aux)
-        loss_aux = self.mean(loss_aux, 0)
-        return loss_logit + self.factor*loss_aux
+        return loss_logit
 
 
 class AuxLogits(nn.Cell):
@@ -896,7 +890,7 @@ class NASNetAMobileWithLoss(nn.Cell):
         super(NASNetAMobileWithLoss, self).__init__()
         self.network = NASNetAMobile(config.num_classes, is_training)
         self.loss = CrossEntropy(smooth_factor=config.label_smooth_factor,
-                                 num_classes=config.num_classes, factor=config.aux_factor)
+                                 num_classes=config.num_classes)
         self.cast = P.Cast()
 
     def construct(self, data, label):