Add load pretrained model in SE-ResNeXt

fluid/image_classification/se_resnext.py

@@ -100,7 +100,11 @@ 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 +133,9 @@ 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_params(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])
@@ -157,4 +164,4 @@ def train(learning_rate, batch_size, num_passes, model_save_dir='model'):
 
 
 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)