use paddle.get_default_dtype in vision datasets. test=develop (#27426)

python/paddle/vision/datasets/cifar.py

@@ -19,6 +19,7 @@ import numpy as np
 import six
 from six.moves import cPickle as pickle
 
+import paddle
 from paddle.io import Dataset
 from paddle.dataset.common import _check_exists_and_download
 
@@ -113,6 +114,8 @@ class Cifar10(Dataset):
         # read dataset into memory
         self._load_data()
 
+        self.dtype = paddle.get_default_dtype()
+
     def _init_url_md5_flag(self):
         self.data_url = CIFAR10_URL
         self.data_md5 = CIFAR10_MD5
@@ -142,7 +145,7 @@ class Cifar10(Dataset):
         image = np.reshape(image, [3, 32, 32])
         if self.transform is not None:
             image = self.transform(image)
-        return image, label
+        return image.astype(self.dtype), np.array(label).astype('int64')
 
     def __len__(self):
         return len(self.data)

python/paddle/vision/datasets/flowers.py

@@ -21,6 +21,7 @@ import numpy as np
 import scipy.io as scio
 from PIL import Image
 
+import paddle
 from paddle.io import Dataset
 from paddle.dataset.common import _check_exists_and_download
 
@@ -104,6 +105,8 @@ class Flowers(Dataset):
         # read dataset into memory
         self._load_anno()
 
+        self.dtype = paddle.get_default_dtype()
+
     def _load_anno(self):
         self.name2mem = {}
         self.data_tar = tarfile.open(self.data_file)
@@ -124,7 +127,7 @@ class Flowers(Dataset):
         if self.transform is not None:
             image = self.transform(image)
 
-        return image, label.astype('int64')
+        return image.astype(self.dtype), label.astype('int64')
 
     def __len__(self):
         return len(self.indexes)

python/paddle/vision/datasets/folder.py

@@ -15,6 +15,7 @@
 import os
 import sys
 
+import paddle
 from paddle.io import Dataset
 from paddle.utils import try_import
 
@@ -143,6 +144,8 @@ class DatasetFolder(Dataset):
         self.samples = samples
         self.targets = [s[1] for s in samples]
 
+        self.dtype = paddle.get_default_dtype()
+
     def _find_classes(self, dir):
         """
         Finds the class folders in a dataset.

python/paddle/vision/datasets/mnist.py

@@ -19,6 +19,7 @@ import gzip
 import struct
 import numpy as np
 
+import paddle
 from paddle.io import Dataset
 from paddle.dataset.common import _check_exists_and_download
 
@@ -95,6 +96,8 @@ class MNIST(Dataset):
         # read dataset into memory
         self._parse_dataset()
 
+        self.dtype = paddle.get_default_dtype()
+
     def _parse_dataset(self, buffer_size=100):
         self.images = []
         self.labels = []
@@ -145,7 +148,7 @@ class MNIST(Dataset):
         image = np.reshape(image, [1, 28, 28])
         if self.transform is not None:
             image = self.transform(image)
-        return image, label
+        return image.astype(self.dtype), label.astype('int64')
 
     def __len__(self):
         return len(self.labels)

python/paddle/vision/datasets/voc2012.py

@@ -19,6 +19,7 @@ import tarfile
 import numpy as np
 from PIL import Image
 
+import paddle
 from paddle.io import Dataset
 from paddle.dataset.common import _check_exists_and_download
 
@@ -96,6 +97,8 @@ class VOC2012(Dataset):
         # read dataset into memory
         self._load_anno()
 
+        self.dtype = paddle.get_default_dtype()
+
     def _load_anno(self):
         self.name2mem = {}
         self.data_tar = tarfile.open(self.data_file)
@@ -127,7 +130,7 @@ class VOC2012(Dataset):
         label = np.array(label)
         if self.transform is not None:
             data = self.transform(data)
-        return data, label
+        return data.astype(self.dtype), label.astype(self.dtype)
 
     def __len__(self):
         return len(self.data)