[fbsync] using np.random.RandomState(seed) instead of `np.random.se…

…ed(seed)` (#4250)

Summary: Co-authored-by: Vincent Moens <vmoens@fb.com>

Reviewed By: NicolasHug

Differential Revision: D30417196

fbshipit-source-id: f53bc950aea4935c164939cab0e14b266e3dd1cb

test/common_utils.py

@@ -44,7 +44,6 @@ def get_tmp_dir(src=None, **kwargs):
 def set_rng_seed(seed):
     torch.manual_seed(seed)
     random.seed(seed)
-    np.random.seed(seed)
 
 
 class MapNestedTensorObjectImpl(object):

test/test_datasets.py

@@ -444,8 +444,9 @@ def inject_fake_data(self, tmpdir, config):
         )
 
     def _create_batch_file(self, root, name, num_images):
+        np_rng = np.random.RandomState(0)
         data = datasets_utils.create_image_or_video_tensor((num_images, 32 * 32 * 3))
-        labels = np.random.randint(0, self._VERSION_CONFIG["num_categories"], size=num_images).tolist()
+        labels = np_rng.randint(0, self._VERSION_CONFIG["num_categories"], size=num_images).tolist()
         self._create_binary_file(root, name, {"data": data, self._VERSION_CONFIG["labels_key"]: labels})
 
     def _create_meta_file(self, root):

test/test_image.py

@@ -272,9 +272,10 @@ def test_write_file_non_ascii():
     (105, 105),
 ])
 def test_read_1_bit_png(shape):
+    np_rng = np.random.RandomState(0)
     with get_tmp_dir() as root:
         image_path = os.path.join(root, f'test_{shape}.png')
-        pixels = np.random.rand(*shape) > 0.5
+        pixels = np_rng.rand(*shape) > 0.5
         img = Image.fromarray(pixels)
         img.save(image_path)
         img1 = read_image(image_path)
@@ -292,9 +293,10 @@ def test_read_1_bit_png(shape):
     ImageReadMode.GRAY,
 ])
 def test_read_1_bit_png_consistency(shape, mode):
+    np_rng = np.random.RandomState(0)
     with get_tmp_dir() as root:
         image_path = os.path.join(root, f'test_{shape}.png')
-        pixels = np.random.rand(*shape) > 0.5
+        pixels = np_rng.rand(*shape) > 0.5
         img = Image.fromarray(pixels)
         img.save(image_path)
         img1 = read_image(image_path, mode)

test/test_transforms.py

@@ -200,18 +200,19 @@ class TestToTensor:
     def test_to_tensor(self, channels):
         height, width = 4, 4
         trans = transforms.ToTensor()
+        np_rng = np.random.RandomState(0)
 
         input_data = torch.ByteTensor(channels, height, width).random_(0, 255).float().div_(255)
         img = transforms.ToPILImage()(input_data)
         output = trans(img)
         torch.testing.assert_close(output, input_data)
 
-        ndarray = np.random.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
+        ndarray = np_rng.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
         output = trans(ndarray)
         expected_output = ndarray.transpose((2, 0, 1)) / 255.0
         torch.testing.assert_close(output.numpy(), expected_output, check_dtype=False)
 
-        ndarray = np.random.rand(height, width, channels).astype(np.float32)
+        ndarray = np_rng.rand(height, width, channels).astype(np.float32)
         output = trans(ndarray)
         expected_output = ndarray.transpose((2, 0, 1))
         torch.testing.assert_close(output.numpy(), expected_output, check_dtype=False)
@@ -225,22 +226,24 @@ def test_to_tensor(self, channels):
     def test_to_tensor_errors(self):
         height, width = 4, 4
         trans = transforms.ToTensor()
+        np_rng = np.random.RandomState(0)
 
         with pytest.raises(TypeError):
-            trans(np.random.rand(1, height, width).tolist())
+            trans(np_rng.rand(1, height, width).tolist())
 
         with pytest.raises(ValueError):
-            trans(np.random.rand(height))
+            trans(np_rng.rand(height))
 
         with pytest.raises(ValueError):
-            trans(np.random.rand(1, 1, height, width))
+            trans(np_rng.rand(1, 1, height, width))
 
     @pytest.mark.parametrize('dtype', [torch.float16, torch.float, torch.double])
     def test_to_tensor_with_other_default_dtypes(self, dtype):
+        np_rng = np.random.RandomState(0)
         current_def_dtype = torch.get_default_dtype()
 
         t = transforms.ToTensor()
-        np_arr = np.random.randint(0, 255, (32, 32, 3), dtype=np.uint8)
+        np_arr = np_rng.randint(0, 255, (32, 32, 3), dtype=np.uint8)
         img = Image.fromarray(np_arr)
 
         torch.set_default_dtype(dtype)
@@ -253,19 +256,20 @@ def test_to_tensor_with_other_default_dtypes(self, dtype):
     def test_pil_to_tensor(self, channels):
         height, width = 4, 4
         trans = transforms.PILToTensor()
+        np_rng = np.random.RandomState(0)
 
         input_data = torch.ByteTensor(channels, height, width).random_(0, 255)
         img = transforms.ToPILImage()(input_data)
         output = trans(img)
         torch.testing.assert_close(input_data, output)
 
-        input_data = np.random.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
+        input_data = np_rng.randint(low=0, high=255, size=(height, width, channels)).astype(np.uint8)
         img = transforms.ToPILImage()(input_data)
         output = trans(img)
         expected_output = input_data.transpose((2, 0, 1))
         torch.testing.assert_close(output.numpy(), expected_output)
 
-        input_data = torch.as_tensor(np.random.rand(channels, height, width).astype(np.float32))
+        input_data = torch.as_tensor(np_rng.rand(channels, height, width).astype(np.float32))
         img = transforms.ToPILImage()(input_data)  # CHW -> HWC and (* 255).byte()
         output = trans(img)  # HWC -> CHW
         expected_output = (input_data * 255).byte()
@@ -280,12 +284,13 @@ def test_pil_to_tensor(self, channels):
     def test_pil_to_tensor_errors(self):
         height, width = 4, 4
         trans = transforms.PILToTensor()
+        np_rng = np.random.RandomState(0)
 
         with pytest.raises(TypeError):
-            trans(np.random.rand(1, height, width).tolist())
+            trans(np_rng.rand(1, height, width).tolist())
 
         with pytest.raises(TypeError):
-            trans(np.random.rand(1, height, width))
+            trans(np_rng.rand(1, height, width))
 
 
 def test_randomresized_params():
@@ -1180,10 +1185,11 @@ def test_random_grayscale():
     """Unit tests for random grayscale transform"""
 
     # Test Set 1: RGB -> 3 channel grayscale
+    np_rng = np.random.RandomState(0)
     random_state = random.getstate()
     random.seed(42)
     x_shape = [2, 2, 3]
-    x_np = np.random.randint(0, 256, x_shape, np.uint8)
+    x_np = np_rng.randint(0, 256, x_shape, np.uint8)
     x_pil = Image.fromarray(x_np, mode='RGB')
     x_pil_2 = x_pil.convert('L')
     gray_np = np.array(x_pil_2)
@@ -1206,7 +1212,7 @@ def test_random_grayscale():
     random_state = random.getstate()
     random.seed(42)
     x_shape = [2, 2, 3]
-    x_np = np.random.randint(0, 256, x_shape, np.uint8)
+    x_np = np_rng.randint(0, 256, x_shape, np.uint8)
     x_pil = Image.fromarray(x_np, mode='RGB')
     x_pil_2 = x_pil.convert('L')
     gray_np = np.array(x_pil_2)

test/test_transforms_video.py

@@ -131,7 +131,8 @@ def test_to_tensor_video(self):
         trans = transforms.ToTensorVideo()
 
         with pytest.raises(TypeError):
-            trans(np.random.rand(numFrames, height, width, 1).tolist())
+            np_rng = np.random.RandomState(0)
+            trans(np_rng.rand(numFrames, height, width, 1).tolist())
             trans(torch.rand((numFrames, height, width, 1), dtype=torch.float))
 
         with pytest.raises(ValueError):