Update demo

demo/image_classification/train.py

-import paddle.fluid as fluid
+import paddle
 import paddlehub as hub
-from paddle.fluid.dygraph.parallel import ParallelEnv
+from paddle.distributed import ParallelEnv
 from paddlehub.finetune.trainer import Trainer
 from paddlehub.datasets.flowers import Flowers
 from paddlehub.process.transforms import Compose, Resize, Normalize
 from paddlehub.module.cv_module import ImageClassifierModule
 
 if __name__ == '__main__':
-    with fluid.dygraph.guard(fluid.CUDAPlace(ParallelEnv().dev_id)):
-        transforms = Compose([Resize((224, 224)), Normalize()])
-        flowers = Flowers(transforms)
-        flowers_validate = Flowers(transforms, mode='val')
+    paddle.disable_static(paddle.CUDAPlace(ParallelEnv().dev_id))
+    transforms = Compose([Resize((224, 224)), Normalize()])
+    flowers = Flowers(transforms)
+    flowers_validate = Flowers(transforms, mode='val')
 
-        model = hub.Module(directory='mobilenet_v2_animals', class_dim=flowers.num_classes)
-        # model = hub.Module(name='mobilenet_v2_animals', class_dim=flowers.num_classes)
+    model = hub.Module(name='mobilenet_v2_imagenet', class_dim=flowers.num_classes)
 
-        optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001, parameter_list=model.parameters())
-        trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
+    optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
+    trainer = Trainer(model, optimizer, checkpoint_dir='test_ckpt_img_cls')
 
-        trainer.train(flowers, epochs=100, batch_size=32, eval_dataset=flowers_validate, save_interval=1)
+    trainer.train(flowers, epochs=100, batch_size=32, eval_dataset=flowers_validate, save_interval=1)