SoftmaxCrossEntropyWithLogic api adapt

model_zoo/official/cv/googlenet/eval.py

@@ -29,6 +29,7 @@ from src.config import cifar_cfg, imagenet_cfg
 from src.dataset import create_dataset_cifar10, create_dataset_imagenet
 
 from src.googlenet import GoogleNet
+from src.CrossEntropySmooth import CrossEntropySmooth
 
 set_seed(1)
 
@@ -43,7 +44,7 @@ if __name__ == '__main__':
     if args_opt.dataset_name == 'cifar10':
         cfg = cifar_cfg
         dataset = create_dataset_cifar10(cfg.data_path, 1, False)
-        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
+        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
         net = GoogleNet(num_classes=cfg.num_classes)
         opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, cfg.momentum,
                        weight_decay=cfg.weight_decay)
@@ -54,8 +55,8 @@ if __name__ == '__main__':
         dataset = create_dataset_imagenet(cfg.val_data_path, 1, False)
         if not cfg.use_label_smooth:
             cfg.label_smooth_factor = 0.0
-        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean",
-                                                smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
+        loss = CrossEntropySmooth(sparse=True, reduction="mean",
+                                  smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
         net = GoogleNet(num_classes=cfg.num_classes)
         model = Model(net, loss_fn=loss, metrics={'top_1_accuracy', 'top_5_accuracy'})
 

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

@@ -0,0 +1,38 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""define loss function for network"""
+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.ops import functional as F
+from mindspore.ops import operations as P
+
+
+class CrossEntropySmooth(_Loss):
+    """CrossEntropy"""
+    def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000):
+        super(CrossEntropySmooth, self).__init__()
+        self.onehot = P.OneHot()
+        self.sparse = sparse
+        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
+        self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
+        self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction)
+
+    def construct(self, logit, label):
+        if self.sparse:
+            label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
+        loss = self.ce(logit, label)
+        return loss

model_zoo/official/cv/googlenet/train.py

@@ -36,6 +36,7 @@ from mindspore.common import set_seed
 from src.config import cifar_cfg, imagenet_cfg
 from src.dataset import create_dataset_cifar10, create_dataset_imagenet
 from src.googlenet import GoogleNet
+from src.CrossEntropySmooth import CrossEntropySmooth
 
 set_seed(1)
 
@@ -148,7 +149,7 @@ if __name__ == '__main__':
                        learning_rate=Tensor(lr),
                        momentum=cfg.momentum,
                        weight_decay=cfg.weight_decay)
-        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean', is_grad=False)
+        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
 
     elif args_opt.dataset_name == 'imagenet':
         lr = lr_steps_imagenet(cfg, batch_num)
@@ -188,8 +189,8 @@ if __name__ == '__main__':
                        loss_scale=cfg.loss_scale)
         if not cfg.use_label_smooth:
             cfg.label_smooth_factor = 0.0
-        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean",
-                                                smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
+        loss = CrossEntropySmooth(sparse=True, reduction="mean",
+                                  smooth_factor=cfg.label_smooth_factor, num_classes=cfg.num_classes)
 
         if cfg.is_dynamic_loss_scale == 1:
             loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)