From 86d81af5ef43e9bc0d9684ba91f96871a6cd8d6f Mon Sep 17 00:00:00 2001
From: LielinJiang <50691816+LielinJiang@users.noreply.github.com>
Date: Mon, 11 Jan 2021 20:06:49 +0800
Subject: [PATCH] reduce unittest time of test_datasets (#30275)

---
 python/paddle/tests/test_datasets.py | 56 ++++++++++++++--------------
 1 file changed, 28 insertions(+), 28 deletions(-)

diff --git a/python/paddle/tests/test_datasets.py b/python/paddle/tests/test_datasets.py
index 3aa21ae2db2..89fa01cbceb 100644
--- a/python/paddle/tests/test_datasets.py
+++ b/python/paddle/tests/test_datasets.py
@@ -94,13 +94,13 @@ class TestMNISTTest(unittest.TestCase):
         mnist = MNIST(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)
+        i = np.random.randint(0, len(mnist) - 1)
+        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 TestMNISTTrain(unittest.TestCase):
@@ -109,13 +109,13 @@ class TestMNISTTrain(unittest.TestCase):
         mnist = MNIST(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)
+        i = np.random.randint(0, len(mnist) - 1)
+        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 = MNIST(mode='train', transform=transform, backend='cv2')
@@ -140,13 +140,13 @@ class TestFASHIONMNISTTest(unittest.TestCase):
         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)
+        i = np.random.randint(0, len(mnist) - 1)
+        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):
@@ -155,13 +155,13 @@ class TestFASHIONMNISTTrain(unittest.TestCase):
         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)
+        i = np.random.randint(0, len(mnist) - 1)
+        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')
-- 
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