add label smooth and remove hccl setting in eval

example/mobilenetv2_imagenet2012/config.py

@@ -27,6 +27,7 @@ config = ed({
     "lr": 0.4,
     "momentum": 0.9,
     "weight_decay": 4e-5,
+    "label_smooth": 0.1,
     "loss_scale": 1024,
     "save_checkpoint": True,
     "save_checkpoint_epochs": 1,

example/mobilenetv2_imagenet2012/dataset.py

@@ -53,8 +53,8 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
 
     # define map operations
     decode_op = C.Decode()
-    resize_crop_op = C.RandomResizedCrop(resize_height, scale=(0.2, 1.0))
-    horizontal_flip_op = C.RandomHorizontalFlip()
+    resize_crop_op = C.RandomResizedCrop(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
+    horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)
 
     resize_op = C.Resize((256, 256))
     center_crop = C.CenterCrop(resize_width)

example/mobilenetv2_imagenet2012/eval.py

@@ -38,8 +38,6 @@ context.set_context(enable_loop_sink=True)
 context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
-    context.set_context(enable_hccl=False)
-
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
     net = mobilenet_v2()
 

example/mobilenetv2_imagenet2012/train.py

@@ -28,6 +28,10 @@ from mindspore.model_zoo.mobilenet import mobilenet_v2
 from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.nn.optim.momentum import Momentum
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+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.train.model import Model, ParallelMode
 
@@ -54,6 +58,35 @@ context.set_context(enable_task_sink=True)
 context.set_context(enable_loop_sink=True)
 context.set_context(enable_mem_reuse=True)
 
+class CrossEntropyWithLabelSmooth(_Loss):
+    """
+    CrossEntropyWith LabelSmooth.
+
+    Args:
+        smooth_factor (float): smooth factor, default=0.
+        num_classes (int): num classes
+
+    Returns:
+        None.
+
+    Examples:
+        >>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
+    """
+
+    def __init__(self, smooth_factor=0., num_classes=1000):
+        super(CrossEntropyWithLabelSmooth, self).__init__()
+        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)
+        self.ce = nn.SoftmaxCrossEntropyWithLogits()
+        self.mean = P.ReduceMean(False)
+        self.cast = P.Cast()
+
+    def construct(self, logit, label):
+        one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1], self.on_value, self.off_value)
+        out_loss = self.ce(logit, one_hot_label)
+        out_loss = self.mean(out_loss, 0)
+        return out_loss
 
 class Monitor(Callback):
     """
@@ -63,7 +96,7 @@ class Monitor(Callback):
         lr_init (numpy array): train lr
 
     Returns:
-        None.
+        None
 
     Examples:
         >>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
@@ -122,7 +155,10 @@ if __name__ == '__main__':
     for _, cell in net.cells_and_names():
         if isinstance(cell, nn.Dense):
             cell.add_flags_recursive(fp32=True)
-    loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
+    if config.label_smooth > 0:
+        loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth, num_classes=config.num_classes)
+    else:
+        loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
 
     print("train args: ", args_opt, "\ncfg: ", config,
           "\nparallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))