Merge pull request #601 from will-am/fix_seresnext

Refine SE-ResNeXt

fluid/image_classification/se_resnext.py

 import os
-
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
 import reader
@@ -100,7 +99,11 @@ def SE_ResNeXt(input, class_dim, infer=False):
     return out
 
 
-def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
+def train(learning_rate,
+          batch_size,
+          num_passes,
+          init_model=None,
+          model_save_dir='model'):
     class_dim = 1000
     image_shape = [3, 224, 224]
 
@@ -129,6 +132,9 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
     exe = fluid.Executor(place)
     exe.run(fluid.default_startup_program())
 
+    if init_model is not None:
+        fluid.io.load_persistables(exe, init_model)
+
     train_reader = paddle.batch(reader.train(), batch_size=batch_size)
     test_reader = paddle.batch(reader.test(), batch_size=batch_size)
     feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
@@ -145,16 +151,18 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
 
         test_accuracy.reset(exe)
         for data in test_reader():
-            out, acc = exe.run(inference_program,
-                               feed=feeder.feed(data),
-                               fetch_list=[avg_cost] + test_accuracy.metrics)
+            loss, acc = exe.run(inference_program,
+                                feed=feeder.feed(data),
+                                fetch_list=[avg_cost] + test_accuracy.metrics)
         test_pass_acc = test_accuracy.eval(exe)
         print("End pass {0}, train_acc {1}, test_acc {2}".format(
             pass_id, pass_acc, test_pass_acc))
 
         model_path = os.path.join(model_save_dir, str(pass_id))
-        fluid.io.save_inference_model(model_path, ['image'], [out], exe)
+        if not os.path.isdir(model_path):
+            os.makedirs(model_path)
+        fluid.io.save_persistables(exe, model_path)
 
 
 if __name__ == '__main__':
-    train(learning_rate=0.1, batch_size=8, num_passes=100)
+    train(learning_rate=0.1, batch_size=8, num_passes=100, init_model=None)