add download datast

demo/colorization/train.py

@@ -2,7 +2,7 @@ import paddle
 import paddlehub as hub
 import paddlehub.process.transforms as T
 from paddlehub.finetune.trainer import Trainer
-from paddlehub.datasets.colorizedataset import Colorizedataset
+from paddlehub.datasets.Canvas import Canvas
 
 if __name__ == '__main__':
 
@@ -13,7 +13,7 @@ if __name__ == '__main__':
                           stay_rgb=True,
                           is_permute=False)
 
-    color_set = Colorizedataset(transform=transform, mode='train')
+    color_set = Canvas(transform=transform, mode='train')
     optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
     trainer = Trainer(model, optimizer, checkpoint_dir='img_colorization_ckpt')
     trainer.train(color_set, epochs=101, batch_size=2, eval_dataset=color_set, log_interval=10, save_interval=10)

demo/style_transfer/train.py

@@ -2,14 +2,14 @@ import paddle
 import paddlehub as hub
 import paddlehub.process.transforms as T
 from paddlehub.finetune.trainer import Trainer
-from paddlehub.datasets.styletransfer import StyleTransferData
+from paddlehub.datasets.MiniCOCO import MiniCOCO
 
 if __name__ == "__main__":
     model = hub.Module(name='msgnet')
     transform = T.Compose([T.Resize(
         (256, 256), interp='LINEAR'), T.CenterCrop(crop_size=256)], T.SetType(datatype='float32'))
 
-    styledata = StyleTransferData(transform)
+    styledata = MiniCOCO(transform)
     optimizer = paddle.optimizer.Adam(learning_rate=0.0001, parameters=model.parameters())
     trainer = Trainer(model, optimizer, checkpoint_dir='img_style_transfer_ckpt')
     trainer.train(styledata, epochs=5, batch_size=16, eval_dataset=styledata, log_interval=1, save_interval=1)

paddlehub/datasets/colorizedataset.py → paddlehub/datasets/Canvas.py

@@ -21,11 +21,14 @@ import paddle
 from paddlehub.process.functional import get_img_file
 from paddlehub.env import DATA_HOME
 from typing import Callable
+from paddlehub.utils.download import download_data
 
 
-class Colorizedataset(paddle.io.Dataset):
+@download_data(url='https://paddlehub.bj.bcebos.com/dygraph/datasets/canvas.tar.gz')
+class Canvas(paddle.io.Dataset):
     """
-    Dataset for colorization.
+    Dataset for colorization. It contains 1193 and 400 pictures for Monet and Vango paintings style, respectively.
+    We collected data from https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/.
 
     Args:
        transform(callmethod) : The method of preprocess images.
@@ -34,6 +37,7 @@ class Colorizedataset(paddle.io.Dataset):
     Returns:
         DataSet: An iterable object for data iterating
     """
+
     def __init__(self, transform: Callable, mode: str = 'train'):
         self.mode = mode
         self.transform = transform

paddlehub/datasets/styletransfer.py → paddlehub/datasets/MiniCOCO.py

@@ -19,11 +19,14 @@ from typing import Callable
 import paddle
 from paddlehub.process.functional import get_img_file
 from paddlehub.env import DATA_HOME
+from paddlehub.utils.download import download_data
 
 
-class StyleTransferData(paddle.io.Dataset):
+@download_data(url='https://paddlehub.bj.bcebos.com/dygraph/datasets/minicoco.tar.gz')
+class MiniCOCO(paddle.io.Dataset):
     """
-    Dataset for Style transfer.
+    Dataset for Style transfer. The dataset contains 2001 images for training set and 200 images for testing set.
+    They are derived form COCO2014. Meanwhile, it contains 21 different style pictures in file "21styles".
 
     Args:
        transform(callmethod) : The method of preprocess images.
@@ -32,6 +35,7 @@ class StyleTransferData(paddle.io.Dataset):
     Returns:
         DataSet: An iterable object for data iterating
     """
+
     def __init__(self, transform: Callable, mode: str = 'train'):
         self.mode = mode
         self.transform = transform

paddlehub/utils/download.py

+import os
+
+from paddlehub.env import DATA_HOME
+from paddle.utils.download import get_path_from_url
+
+
+def download_data(url):
+    save_name = os.path.basename(url).split('.')[0]
+    output_path = os.path.join(DATA_HOME, save_name)
+
+    if not os.path.exists(output_path):
+        get_path_from_url(url, DATA_HOME)
+
+    def _wrapper(Dataset):
+        return Dataset
+
+    return _wrapper