change name to create_dataset_cifar10

model_zoo/alexnet/eval.py

@@ -20,7 +20,7 @@ python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
 
 import argparse
 from src.config import alexnet_cfg as cfg
-from src.dataset import create_dataset_mnist
+from src.dataset import create_dataset_cifar10
 from src.alexnet import AlexNet
 import mindspore.nn as nn
 from mindspore import context
@@ -50,7 +50,7 @@ if __name__ == "__main__":
     print("============== Starting Testing ==============")
     param_dict = load_checkpoint(args.ckpt_path)
     load_param_into_net(network, param_dict)
-    ds_eval = create_dataset_mnist(args.data_path,
+    ds_eval = create_dataset_cifar10(args.data_path,
                                      cfg.batch_size,
                                      status="test")
     acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)

model_zoo/alexnet/src/dataset.py

@@ -23,7 +23,7 @@ from mindspore.common import dtype as mstype
 from .config import alexnet_cfg as cfg
 
 
-def create_dataset_mnist(data_path, batch_size=32, repeat_size=1, status="train"):
+def create_dataset_cifar10(data_path, batch_size=32, repeat_size=1, status="train"):
     """
     create dataset for train or test
     """

model_zoo/alexnet/train.py

@@ -20,7 +20,7 @@ python train.py --data_path /YourDataPath
 
 import argparse
 from src.config import alexnet_cfg as cfg
-from src.dataset import create_dataset_mnist
+from src.dataset import create_dataset_cifar10
 from src.generator_lr import get_lr
 from src.alexnet import AlexNet
 import mindspore.nn as nn
@@ -43,7 +43,7 @@ if __name__ == "__main__":
 
     context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
 
-    ds_train = create_dataset_mnist(args.data_path, cfg.batch_size, cfg.epoch_size)
+    ds_train = create_dataset_cifar10(args.data_path, cfg.batch_size, cfg.epoch_size)
     network = AlexNet(cfg.num_classes)
     loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
     lr = Tensor(get_lr(0, cfg.learning_rate, cfg.epoch_size, ds_train.get_dataset_size()))