[mxnet2.0] gluon 2.0 data: deprecate dataset transform= argument

docs/python_docs/python/tutorials/packages/gluon/blocks/save_load_params.md

@@ -180,7 +180,7 @@ def verify_loaded_model(net):
         return data.astype(np.float32)/255, label.astype(np.float32)
 
     # Load ten random images from the test dataset
-    sample_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False, transform=transform),
+    sample_data = mx.gluon.data.DataLoader(mx.gluon.data.vision.MNIST(train=False).transform(transform),
                                   10, shuffle=True)
 
     for data, label in sample_data:

example/distributed_training/README.md

@@ -117,7 +117,7 @@ We can then create a `DataLoader` using the `SplitSampler` like shown below:
 
 ```python
 # Load the training data
-train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=True, transform=transform),
+train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=True).transform(transform),
                                       batch_size,
                                       sampler=SplitSampler(50000, store.num_workers, store.rank))
 ```
@@ -141,7 +141,7 @@ def train_batch(batch, ctx, net, trainer):
     # Split and load data into multiple GPUs
     data = batch[0]
     data = gluon.utils.split_and_load(data, ctx)
-    
+
     # Split and load label into multiple GPUs
     label = batch[1]
     label = gluon.utils.split_and_load(label, ctx)
@@ -204,7 +204,7 @@ python ~/mxnet/tools/launch.py -n 2 -s 2 -H hosts \
 Let's take a look at the `hosts` file.
 
 ```
-~/dist$ cat hosts 
+~/dist$ cat hosts
 d1
 d2
 ```
@@ -232,7 +232,7 @@ Last login: Wed Jan 31 18:06:45 2018 from 72.21.198.67
 Note that no authentication information was provided to login to the host. This can be done using multiple methods. One easy way is to specify the ssh certificates in `~/.ssh/config`. Example:
 
 ```
-~$ cat ~/.ssh/config 
+~$ cat ~/.ssh/config
 Host d1
     HostName ec2-34-201-108-233.compute-1.amazonaws.com
     port 22
@@ -269,4 +269,3 @@ Epoch 4: Test_acc 0.687900
 ```
 
 Note that the output from all hosts are merged and printed to the console.
-

example/distributed_training/cifar10_dist.py

@@ -86,11 +86,11 @@ def __len__(self):
 
 
 # Load the training data
-train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=True, transform=transform), batch_size,
+train_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=True).transform(transform), batch_size,
                                    sampler=SplitSampler(50000, store.num_workers, store.rank))
 
 # Load the test data
-test_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=False, transform=transform),
+test_data = gluon.data.DataLoader(gluon.data.vision.CIFAR10(train=False).transform(transform),
                                   batch_size, shuffle=False)
 
 # Use ResNet from model zoo

example/gluon/data.py

@@ -78,15 +78,15 @@ def get_imagenet_iterator(root, batch_size, num_workers, data_shape=224, dtype='
     train_dir = os.path.join(root, 'train')
     train_transform, val_transform = get_imagenet_transforms(data_shape, dtype)
     logging.info("Loading image folder %s, this may take a bit long...", train_dir)
-    train_dataset = ImageFolderDataset(train_dir, transform=train_transform)
+    train_dataset = ImageFolderDataset(train_dir).transform_first(train_transform)
     train_data = DataLoader(train_dataset, batch_size, shuffle=True,
                             last_batch='discard', num_workers=num_workers)
     val_dir = os.path.join(root, 'val')
     if not os.path.isdir(os.path.expanduser(os.path.join(root, 'val', 'n01440764'))):
         user_warning = 'Make sure validation images are stored in one subdir per category, a helper script is available at https://git.io/vNQv1'
         raise ValueError(user_warning)
     logging.info("Loading image folder %s, this may take a bit long...", val_dir)
-    val_dataset = ImageFolderDataset(val_dir, transform=val_transform)
+    val_dataset = ImageFolderDataset(val_dir).transform(val_transform)
     val_data = DataLoader(val_dataset, batch_size, last_batch='keep', num_workers=num_workers)
     return DataLoaderIter(train_data, dtype), DataLoaderIter(val_data, dtype)
 
@@ -118,8 +118,8 @@ def transform(image, label):
         return transposed, label
 
     training_path, testing_path = get_caltech101_data()
-    dataset_train = ImageFolderDataset(root=training_path, transform=transform)
-    dataset_test = ImageFolderDataset(root=testing_path, transform=transform)
+    dataset_train = ImageFolderDataset(root=training_path).transform(transform)
+    dataset_test = ImageFolderDataset(root=testing_path).transform(transform)
 
     train_data = DataLoader(dataset_train, batch_size, shuffle=True, num_workers=num_workers)
     test_data = DataLoader(dataset_test, batch_size, shuffle=False, num_workers=num_workers)

example/gluon/dc_gan/dcgan.py

@@ -143,20 +143,20 @@ def get_dataset(dataset_name):
     # mnist
     if dataset == "mnist":
         train_data = gluon.data.DataLoader(
-            gluon.data.vision.MNIST('./data', train=True, transform=transformer),
+            gluon.data.vision.MNIST('./data', train=True).transform(transformer),
             batch_size, shuffle=True, last_batch='discard')
 
         val_data = gluon.data.DataLoader(
-            gluon.data.vision.MNIST('./data', train=False, transform=transformer),
+            gluon.data.vision.MNIST('./data', train=False).transform(transformer),
             batch_size, shuffle=False)
     # cifar10
     elif dataset == "cifar10":
         train_data = gluon.data.DataLoader(
-            gluon.data.vision.CIFAR10('./data', train=True, transform=transformer),
+            gluon.data.vision.CIFAR10('./data', train=True).transform(transformer),
             batch_size, shuffle=True, last_batch='discard')
 
         val_data = gluon.data.DataLoader(
-            gluon.data.vision.CIFAR10('./data', train=False, transform=transformer),
+            gluon.data.vision.CIFAR10('./data', train=False).transform(transformer),
             batch_size, shuffle=False)
 
     return train_data, val_data

example/gluon/mnist/mnist.py

@@ -60,11 +60,11 @@ def transformer(data, label):
     return data, label
 
 train_data = gluon.data.DataLoader(
-    gluon.data.vision.MNIST('./data', train=True, transform=transformer),
+    gluon.data.vision.MNIST('./data', train=True).transform(transformer),
     batch_size=opt.batch_size, shuffle=True, last_batch='discard')
 
 val_data = gluon.data.DataLoader(
-    gluon.data.vision.MNIST('./data', train=False, transform=transformer),
+    gluon.data.vision.MNIST('./data', train=False).transform(transformer),
     batch_size=opt.batch_size, shuffle=False)
 
 # train

example/gluon/sn_gan/data.py

@@ -38,5 +38,5 @@ def transformer(data, label):
 def get_training_data(batch_size):
     """ helper function to get dataloader"""
     return gluon.data.DataLoader(
-        CIFAR10(train=True, transform=transformer),
+        CIFAR10(train=True).transform(transformer),
         batch_size=batch_size, shuffle=True, last_batch='discard')

example/restricted-boltzmann-machine/binary_rbm_gluon.py

@@ -62,8 +62,8 @@ def get_non_auxiliary_params(rbm):
 def data_transform(data, label):
     return data.astype(np.float32) / 255, label.astype(np.float32)
 
-mnist_train_dataset = mx.gluon.data.vision.MNIST(train=True, transform=data_transform)
-mnist_test_dataset = mx.gluon.data.vision.MNIST(train=False, transform=data_transform)
+mnist_train_dataset = mx.gluon.data.vision.MNIST(train=True).transform(data_transform)
+mnist_test_dataset = mx.gluon.data.vision.MNIST(train=False).transform(data_transform)
 img_height = mnist_train_dataset[0][0].shape[0]
 img_width = mnist_train_dataset[0][0].shape[1]
 num_visible = img_width * img_height
@@ -139,4 +139,4 @@ def data_transform(data, label):
 plt.axvline(showcase_num_samples_w * img_width, color='y')
 plt.show(s)
 
-print("Done")
+print("Done")

python/mxnet/gluon/data/dataset.py

@@ -314,6 +314,10 @@ class _DownloadedDataset(Dataset):
     """Base class for MNIST, cifar10, etc."""
     def __init__(self, root, transform):
         super(_DownloadedDataset, self).__init__()
+        if transform is not None:
+            raise DeprecationWarning(
+                'Directly apply transform to dataset is deprecated. '
+                'Please use dataset.transform() or dataset.transform_first() instead...')
         self._transform = transform
         self._data = None
         self._label = None

python/mxnet/gluon/data/vision/datasets.py

@@ -47,6 +47,7 @@ class MNIST(dataset._DownloadedDataset):
     train : bool, default True
         Whether to load the training or testing set.
     transform : function, default None
+        DEPRECATED FUNCTION ARGUMENTS.
         A user defined callback that transforms each sample. For example::
 
             transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -110,6 +111,7 @@ class FashionMNIST(MNIST):
     train : bool, default True
         Whether to load the training or testing set.
     transform : function, default None
+        DEPRECATED FUNCTION ARGUMENTS.
         A user defined callback that transforms each sample. For example::
 
             transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -142,6 +144,7 @@ class CIFAR10(dataset._DownloadedDataset):
     train : bool, default True
         Whether to load the training or testing set.
     transform : function, default None
+        DEPRECATED FUNCTION ARGUMENTS.
         A user defined callback that transforms each sample. For example::
 
             transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -207,6 +210,7 @@ class CIFAR100(CIFAR10):
     train : bool, default True
         Whether to load the training or testing set.
     transform : function, default None
+        DEPRECATED FUNCTION ARGUMENTS.
         A user defined callback that transforms each sample. For example::
 
             transform=lambda data, label: (data.astype(np.float32)/255, label)
@@ -243,13 +247,18 @@ class ImageRecordDataset(dataset.RecordFileDataset):
         If 0, always convert images to greyscale. \
         If 1, always convert images to colored (RGB).
     transform : function, default None
+        DEPRECATED FUNCTION ARGUMENTS.
         A user defined callback that transforms each sample. For example::
 
             transform=lambda data, label: (data.astype(np.float32)/255, label)
 
     """
     def __init__(self, filename, flag=1, transform=None):
         super(ImageRecordDataset, self).__init__(filename)
+        if transform is not None:
+            raise DeprecationWarning(
+                'Directly apply transform to dataset is deprecated. '
+                'Please use dataset.transform() or dataset.transform_first() instead...')
         self._flag = flag
         self._transform = transform
 
@@ -281,6 +290,7 @@ class ImageFolderDataset(dataset.Dataset):
         If 0, always convert loaded images to greyscale (1 channel).
         If 1, always convert loaded images to colored (3 channels).
     transform : callable, default None
+        DEPRECATED FUNCTION ARGUMENTS.
         A function that takes data and label and transforms them::
 
             transform = lambda data, label: (data.astype(np.float32)/255, label)
@@ -295,6 +305,10 @@ class ImageFolderDataset(dataset.Dataset):
     def __init__(self, root, flag=1, transform=None):
         self._root = os.path.expanduser(root)
         self._flag = flag
+        if transform is not None:
+            raise DeprecationWarning(
+                'Directly apply transform to dataset is deprecated. '
+                'Please use dataset.transform() or dataset.transform_first() instead...')
         self._transform = transform
         self._exts = ['.jpg', '.jpeg', '.png']
         self._list_images(self._root)