fix

fluid/image_classification/dist_train/dist_train.py

@@ -33,10 +33,12 @@ def parse_args():
     parser.add_argument(
         '--model',
         type=str,
-        default='resnet_dist',
+        default='DistResNet',
         help='The model to run.')
     parser.add_argument(
         '--batch_size', type=int, default=32, help='The minibatch size per device.')
+    parser.add_argument(
+        '--multi_batch_repeat', type=int, default=1, help='Batch merge repeats.')
     parser.add_argument(
         '--learning_rate', type=float, default=0.1, help='The learning rate.')
     parser.add_argument(
@@ -124,7 +126,7 @@ def get_model(args, is_train, main_prog, startup_prog):
                 name="train_reader" if is_train else "test_reader",
                 use_double_buffer=True)
             input, label = fluid.layers.read_file(pyreader)
-            model_def = models.__dict__[args.model](is_train)
+            model_def = models.__dict__[args.model](layers=50, is_train=is_train)
             predict = model_def.net(input, class_dim=class_dim)
 
             cost = fluid.layers.cross_entropy(input=predict, label=label)

fluid/image_classification/models/__init__.py

@@ -3,6 +3,8 @@ from .mobilenet import MobileNet
 from .googlenet import GoogleNet
 from .vgg import VGG11, VGG13, VGG16, VGG19
 from .resnet import ResNet50, ResNet101, ResNet152
+from .resnet_dist import DistResNet
 from .inception_v4 import InceptionV4
 from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_32x4d
 from .dpn import DPN68, DPN92, DPN98, DPN107, DPN131
+import learning_rate

fluid/image_classification/models/resnet_dist.py

@@ -5,7 +5,7 @@ import paddle
 import paddle.fluid as fluid
 import math
 
-__all__ = ["ResNet", "ResNet50", "ResNet101", "ResNet152"]
+__all__ = ["DistResNet"]
 
 train_parameters = {
     "input_size": [3, 224, 224],
@@ -20,7 +20,7 @@ train_parameters = {
 }
 
 
-class ResNet():
+class DistResNet():
     def __init__(self, layers=50, is_train=True):
         self.params = train_parameters
         self.layers = layers
@@ -119,18 +119,3 @@ class ResNet():
         short = self.shortcut(input, num_filters * 4, stride)
 
         return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
-
-
-def ResNet50():
-    model = ResNet(layers=50)
-    return model
-
-
-def ResNet101():
-    model = ResNet(layers=101)
-    return model
-
-
-def ResNet152():
-    model = ResNet(layers=152)
-    return model