remove rescale op in dataset and add fp16 in eval

example/mobilenetv2_imagenet2012/dataset.py

@@ -63,7 +63,7 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32):
     if do_train:
         trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
     else:
-        trans = [decode_op, resize_op, center_crop, rescale_op, normalize_op, change_swap_op]
+        trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]
 
     type_cast_op = C2.TypeCast(mstype.int32)
 

example/mobilenetv2_imagenet2012/eval.py

@@ -24,6 +24,7 @@ from mindspore.model_zoo.mobilenet import mobilenet_v2
 from mindspore.train.model import Model
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+from mindspore.common import dtype as mstype
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
@@ -39,7 +40,8 @@ context.set_context(enable_mem_reuse=True)
 
 if __name__ == '__main__':
     loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
-    net = mobilenet_v2()
+    net = mobilenet_v2(num_classes=config.num_classes)
+    net.to_float(mstype.float16)
 
     dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=False, batch_size=config.batch_size)
     step_size = dataset.get_dataset_size()

example/mobilenetv2_imagenet2012/train.py

@@ -151,7 +151,7 @@ if __name__ == '__main__':
 
     epoch_size = config.epoch_size
     net = mobilenet_v2(num_classes=config.num_classes)
-    net.add_flags_recursive(fp16=True)
+    net.to_float(mstype.float16)
     for _, cell in net.cells_and_names():
         if isinstance(cell, nn.Dense):
             cell.add_flags_recursive(fp32=True)