update lenet and alexnet

chapter03/lenet/lenet.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """LeNet."""
-import mindspore.ops.operations as P
 import mindspore.nn as nn
 from mindspore.common.initializer import TruncatedNormal
 

chapter04/alexnet/alexnet.py

@@ -14,7 +14,6 @@
 # ============================================================================
 """Alexnet."""
 from config import alexnet_cfg as cfg
-import mindspore.ops.operations as P
 import mindspore.nn as nn
 from mindspore.common.initializer import TruncatedNormal
 

chapter04/alexnet/main.py

@@ -17,9 +17,9 @@ AlexNet example tutorial
 Usage:
      python alexnet.py
 with --device_target=GPU: After 20 epoch training, the accuracy is up to 80%
+with --device_target=Ascend: After 10 epoch training, the accuracy is up to 81%
 """
 
-import os
 import argparse
 from config import alexnet_cfg as cfg
 from alexnet import AlexNet
@@ -35,7 +35,7 @@ from mindspore.nn.metrics import Accuracy
 from mindspore.common import dtype as mstype
 
 
-def create_dataset(data_path, batch_size=32, repeat_size=1):
+def create_dataset(data_path, batch_size=32, repeat_size=1, mode="train"):
     """
     create dataset for train or test
     """
@@ -46,21 +46,23 @@ def create_dataset(data_path, batch_size=32, repeat_size=1):
     resize_op = CV.Resize((cfg.image_height, cfg.image_width))
     rescale_op = CV.Rescale(rescale, shift)
     normalize_op = CV.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
-    random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
-    random_horizontal_op = CV.RandomHorizontalFlip()
+    if mode == "train":
+        random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])
+        random_horizontal_op = CV.RandomHorizontalFlip()
     channel_swap_op = CV.HWC2CHW()
     typecast_op = C.TypeCast(mstype.int32)
     cifar_ds = cifar_ds.map(input_columns="label", operations=typecast_op)
-    cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
-    cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
+    if mode == "train":
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_crop_op)
+        cifar_ds = cifar_ds.map(input_columns="image", operations=random_horizontal_op)
     cifar_ds = cifar_ds.map(input_columns="image", operations=resize_op)
     cifar_ds = cifar_ds.map(input_columns="image", operations=rescale_op)
     cifar_ds = cifar_ds.map(input_columns="image", operations=normalize_op)
     cifar_ds = cifar_ds.map(input_columns="image", operations=channel_swap_op)
 
     cifar_ds = cifar_ds.shuffle(buffer_size=cfg.buffer_size)
-    cifar_ds = cifar_ds.repeat(repeat_size)
     cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)
+    cifar_ds = cifar_ds.repeat(repeat_size)
     return cifar_ds
 
 
@@ -88,7 +90,8 @@ if __name__ == "__main__":
         print("============== Starting Training ==============")
         ds_train = create_dataset(args.data_path,
                                   cfg.batch_size,
-                                  repeat_size)
+                                  repeat_size,
+                                  args.mode)
         config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps,
                                      keep_checkpoint_max=cfg.keep_checkpoint_max)
         ckpoint_cb = ModelCheckpoint(prefix="checkpoint_alexnet",  directory=args.ckpt_path, config=config_ck)
@@ -98,7 +101,7 @@ if __name__ == "__main__":
         print("============== Starting Testing ==============")
         param_dict = load_checkpoint(args.ckpt_path)
         load_param_into_net(network, param_dict)
-        ds_eval = create_dataset(args.data_path)
+        ds_eval = create_dataset(args.data_path, mode=args.mode)
         acc = model.eval(ds_eval, dataset_sink_mode=args.dataset_sink_mode)
         print("============== Accuracy:{} ==============".format(acc))
     else: