fix CIFAR MNIST UCIHousing dataset. test=develop (#27368)

* fix CIFAR & MNIST dataset. test=develop

python/paddle/tests/test_dataset_cifar.py

@@ -27,8 +27,10 @@ class TestCifar10Train(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 50000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 9)
 
 
@@ -41,8 +43,10 @@ class TestCifar10Test(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 10000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 9)
 
 
@@ -55,8 +59,10 @@ class TestCifar100Train(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 50000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 99)
 
 
@@ -69,8 +75,10 @@ class TestCifar100Test(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 10000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 99)
 
 

python/paddle/tests/test_datasets.py

@@ -103,12 +103,14 @@ class TestMNISTTest(unittest.TestCase):
 
 class TestMNISTTrain(unittest.TestCase):
     def test_main(self):
-        mnist = MNIST(mode='train', chw_format=False)
+        mnist = MNIST(mode='train')
         self.assertTrue(len(mnist) == 60000)
 
         for i in range(len(mnist)):
             image, label = mnist[i]
-            self.assertTrue(image.shape[0] == 784)
+            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)
 

python/paddle/text/datasets/uci_housing.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import six
 import numpy as np
 
+import paddle
 from paddle.io import Dataset
 from paddle.dataset.common import _check_exists_and_download
 
@@ -88,6 +89,8 @@ class UCIHousing(Dataset):
         # read dataset into memory
         self._load_data()
 
+        self.dtype = paddle.get_default_dtype()
+
     def _load_data(self, feature_num=14, ratio=0.8):
         data = np.fromfile(self.data_file, sep=' ')
         data = data.reshape(data.shape[0] // feature_num, feature_num)
@@ -103,7 +106,8 @@ class UCIHousing(Dataset):
 
     def __getitem__(self, idx):
         data = self.data[idx]
-        return np.array(data[:-1]), np.array(data[-1:])
+        return np.array(data[:-1]).astype(self.dtype), \
+                np.array(data[-1:]).astype(self.dtype)
 
     def __len__(self):
         return len(self.data)

python/paddle/vision/datasets/cifar.py

@@ -139,6 +139,7 @@ class Cifar10(Dataset):
 
     def __getitem__(self, idx):
         image, label = self.data[idx]
+        image = np.reshape(image, [3, 32, 32])
         if self.transform is not None:
             image = self.transform(image)
         return image, label

python/paddle/vision/datasets/mnist.py

@@ -44,8 +44,6 @@ class MNIST(Dataset):
             :attr:`download` is True. Default None
         label_path(str): path to label file, can be set None if
             :attr:`download` is True. Default None
-        chw_format(bool): If set True, the output shape is [1, 28, 28],
-            otherwise, output shape is [1, 784]. Default True.
         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
@@ -70,14 +68,12 @@ class MNIST(Dataset):
     def __init__(self,
                  image_path=None,
                  label_path=None,
-                 chw_format=True,
                  mode='train',
                  transform=None,
                  download=True):
         assert mode.lower() in ['train', 'test'], \
                 "mode should be 'train' or 'test', but got {}".format(mode)
         self.mode = mode.lower()
-        self.chw_format = chw_format
         self.image_path = image_path
         if self.image_path is None:
             assert download, "image_path is not set and downloading automatically is disabled"
@@ -139,10 +135,6 @@ class MNIST(Dataset):
                                                       cols)).astype('float32')
                     offset_img += struct.calcsize(fmt_images)
 
-                    images = images / 255.0
-                    images = images * 2.0
-                    images = images - 1.0
-
                     for i in range(buffer_size):
                         self.images.append(images[i, :])
                         self.labels.append(
@@ -150,7 +142,6 @@ class MNIST(Dataset):
 
     def __getitem__(self, idx):
         image, label = self.images[idx], self.labels[idx]
-        if self.chw_format:
         image = np.reshape(image, [1, 28, 28])
         if self.transform is not None:
             image = self.transform(image)