add fashion dataset (#28411)

python/paddle/tests/test_datasets.py

@@ -134,6 +134,52 @@ class TestMNISTTrain(unittest.TestCase):
             mnist = MNIST(mode='train', transform=transform, backend=1)
 
 
+class TestFASHIONMNISTTest(unittest.TestCase):
+    def test_main(self):
+        transform = T.Transpose()
+        mnist = FashionMNIST(mode='test', transform=transform)
+        self.assertTrue(len(mnist) == 10000)
+
+        for i in range(len(mnist)):
+            image, label = mnist[i]
+            self.assertTrue(image.shape[0] == 1)
+            self.assertTrue(image.shape[1] == 28)
+            self.assertTrue(image.shape[2] == 28)
+            self.assertTrue(label.shape[0] == 1)
+            self.assertTrue(0 <= int(label) <= 9)
+
+
+class TestFASHIONMNISTTrain(unittest.TestCase):
+    def test_main(self):
+        transform = T.Transpose()
+        mnist = FashionMNIST(mode='train', transform=transform)
+        self.assertTrue(len(mnist) == 60000)
+
+        for i in range(len(mnist)):
+            image, label = mnist[i]
+            self.assertTrue(image.shape[0] == 1)
+            self.assertTrue(image.shape[1] == 28)
+            self.assertTrue(image.shape[2] == 28)
+            self.assertTrue(label.shape[0] == 1)
+            self.assertTrue(0 <= int(label) <= 9)
+
+        # test cv2 backend
+        mnist = FashionMNIST(mode='train', transform=transform, backend='cv2')
+        self.assertTrue(len(mnist) == 60000)
+
+        for i in range(len(mnist)):
+            image, label = mnist[i]
+            self.assertTrue(image.shape[0] == 1)
+            self.assertTrue(image.shape[1] == 28)
+            self.assertTrue(image.shape[2] == 28)
+            self.assertTrue(label.shape[0] == 1)
+            self.assertTrue(0 <= int(label) <= 9)
+            break
+
+        with self.assertRaises(ValueError):
+            mnist = FashionMNIST(mode='train', transform=transform, backend=1)
+
+
 class TestFlowersTrain(unittest.TestCase):
     def test_main(self):
         flowers = Flowers(mode='train')

python/paddle/vision/datasets/cifar.py

@@ -161,7 +161,7 @@ class Cifar10(Dataset):
         image = image.transpose([1, 2, 0])
 
         if self.backend == 'pil':
-            image = Image.fromarray(image)
+            image = Image.fromarray(image.astype('uint8'))
         if self.transform is not None:
             image = self.transform(image)
 

python/paddle/vision/datasets/mnist.py

@@ -24,17 +24,7 @@ import paddle
 from paddle.io import Dataset
 from paddle.dataset.common import _check_exists_and_download
 
-__all__ = ["MNIST"]
-
-URL_PREFIX = 'https://dataset.bj.bcebos.com/mnist/'
-TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
-TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
-TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
-TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
-TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
-TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
-TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
-TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
+__all__ = ["MNIST", "FashionMNIST"]
 
 
 class MNIST(Dataset):
@@ -70,6 +60,16 @@ class MNIST(Dataset):
                 print(sample[0].size, sample[1])
 
     """
+    NAME = 'mnist'
+    URL_PREFIX = 'https://dataset.bj.bcebos.com/mnist/'
+    TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
+    TEST_IMAGE_MD5 = '9fb629c4189551a2d022fa330f9573f3'
+    TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
+    TEST_LABEL_MD5 = 'ec29112dd5afa0611ce80d1b7f02629c'
+    TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
+    TRAIN_IMAGE_MD5 = 'f68b3c2dcbeaaa9fbdd348bbdeb94873'
+    TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
+    TRAIN_LABEL_MD5 = 'd53e105ee54ea40749a09fcbcd1e9432'
 
     def __init__(self,
                  image_path=None,
@@ -93,18 +93,18 @@ class MNIST(Dataset):
         self.image_path = image_path
         if self.image_path is None:
             assert download, "image_path is not set and downloading automatically is disabled"
-            image_url = TRAIN_IMAGE_URL if mode == 'train' else TEST_IMAGE_URL
-            image_md5 = TRAIN_IMAGE_MD5 if mode == 'train' else TEST_IMAGE_MD5
+            image_url = self.TRAIN_IMAGE_URL if mode == 'train' else self.TEST_IMAGE_URL
+            image_md5 = self.TRAIN_IMAGE_MD5 if mode == 'train' else self.TEST_IMAGE_MD5
             self.image_path = _check_exists_and_download(
-                image_path, image_url, image_md5, 'mnist', download)
+                image_path, image_url, image_md5, self.NAME, download)
 
         self.label_path = label_path
         if self.label_path is None:
             assert download, "label_path is not set and downloading automatically is disabled"
-            label_url = TRAIN_LABEL_URL if self.mode == 'train' else TEST_LABEL_URL
-            label_md5 = TRAIN_LABEL_MD5 if self.mode == 'train' else TEST_LABEL_MD5
+            label_url = self.TRAIN_LABEL_URL if self.mode == 'train' else self.TEST_LABEL_URL
+            label_md5 = self.TRAIN_LABEL_MD5 if self.mode == 'train' else self.TEST_LABEL_MD5
             self.label_path = _check_exists_and_download(
-                label_path, label_url, label_md5, 'mnist', download)
+                label_path, label_url, label_md5, self.NAME, download)
 
         self.transform = transform
 
@@ -175,3 +175,48 @@ class MNIST(Dataset):
 
     def __len__(self):
         return len(self.labels)
+
+
+class FashionMNIST(MNIST):
+    """
+    Implementation `Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ dataset.
+
+    Args:
+        image_path(str): path to image file, can be set None if
+            :attr:`download` is True. Default None
+        label_path(str): path to label file, can be set None if
+            :attr:`download` is True. Default None
+        mode(str): 'train' or 'test' mode. Default 'train'.
+        download(bool): whether to download dataset automatically if
+            :attr:`image_path` :attr:`label_path` is not set. Default True
+        backend(str, optional): Specifies which type of image to be returned: 
+            PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}. 
+            If this option is not set, will get backend from ``paddle.vsion.get_image_backend`` ,
+            default backend is 'pil'. Default: None.
+            
+    Returns:
+        Dataset: Fashion-MNIST Dataset.
+
+    Examples:
+        
+        .. code-block:: python
+
+            from paddle.vision.datasets import FashionMNIST
+
+            mnist = FashionMNIST(mode='test')
+
+            for i in range(len(mnist)):
+                sample = mnist[i]
+                print(sample[0].size, sample[1])
+    """
+
+    NAME = 'fashion-mnist'
+    URL_PREFIX = 'https://dataset.bj.bcebos.com/fashion_mnist/'
+    TEST_IMAGE_URL = URL_PREFIX + 't10k-images-idx3-ubyte.gz'
+    TEST_IMAGE_MD5 = 'bef4ecab320f06d8554ea6380940ec79'
+    TEST_LABEL_URL = URL_PREFIX + 't10k-labels-idx1-ubyte.gz'
+    TEST_LABEL_MD5 = 'bb300cfdad3c16e7a12a480ee83cd310'
+    TRAIN_IMAGE_URL = URL_PREFIX + 'train-images-idx3-ubyte.gz'
+    TRAIN_IMAGE_MD5 = '8d4fb7e6c68d591d4c3dfef9ec88bf0d'
+    TRAIN_LABEL_URL = URL_PREFIX + 'train-labels-idx1-ubyte.gz'
+    TRAIN_LABEL_MD5 = '25c81989df183df01b3e8a0aad5dffbe'