fix

configs/EfficientNet/EfficientNetB0.yaml

@@ -46,9 +46,10 @@ TRAIN:
             channel_first: False
         - RandCropImage:
             size: 224
+            interpolation: 2
         - RandFlipImage:
             flip_code: 1
-        - AA:
+        - AutoArgument:
         - NormalizeImage:
             scale: 1./255.
             mean: [0.485, 0.456, 0.406]

ppcls/data/imaug/operators.py

@@ -172,7 +172,7 @@ class RandFlipImage(object):
         else:
             return img
 
-class AA(object):
+class AutoArgument(object):
      
     def __init__(self):
         self.policy = ImageNetPolicy()

tools/ema_clean.py

@@ -23,6 +23,12 @@ import shutil
 import sys
 
 def main():
+"""
+Usage: when training with flag use_ema, and evaluating EMA model, should clean the saved model at first.
+       To generate clean model:
+    
+       python ema_clean.py ema_model_dir cleaned_model_dir
+"""
     cleaned_model_dir = sys.argv[1]
     ema_model_dir = sys.argv[2]
     if not os.path.exists(cleaned_model_dir):

tools/program.py

@@ -36,6 +36,8 @@ from ppcls.utils import logger
 from paddle.fluid.incubate.fleet.collective import fleet
 from paddle.fluid.incubate.fleet.collective import DistributedStrategy
 
+import ema
+
 
 def create_feeds(image_shape, use_mix=None):
     """
@@ -102,7 +104,6 @@ def create_model(architecture, image, classes_num, is_train):
     name = architecture["name"]
     params = architecture.get("params", {})
     params['is_test'] = not is_train
-    print(params)
     model = architectures.__dict__[name](**params)
     out = model.net(input=image, class_dim=classes_num)
     return out