pytorch · ppwwyyxx · Feb 2, 2023 · Feb 17, 2023
diff --git a/test/quantization/core/test_quantized_op.py b/test/quantization/core/test_quantized_op.py
@@ -3007,7 +3007,7 @@ def test_qlinear(self, batch_size, input_channels, output_channels,
         # W_scale = 1.0
         # W_zp = 0
         W_scales = np.ones(output_channels)
-        W_zps = np.zeros(output_channels).astype(np.int)
+        W_zps = np.zeros(output_channels).astype(int)
         W_value_min = -128
         W_value_max = 127
         W_q0 = np.round(
@@ -3571,9 +3571,9 @@ def _test_qlinear_impl(self, batch_size, input_channels, output_channels, use_bi
             # xnnpack forces W_zp to 0 when using symmetric quantization
             # ONEDNN only supports symmetric quantization of weight
             if dtype == torch.qint8 or qengine_is_onednn():
-                W_zps = np.zeros(output_channels).astype(np.int)
+                W_zps = np.zeros(output_channels).astype(int)
             else:
-                W_zps = np.round(np.random.rand(output_channels) * 100 - 50).astype(np.int)
+                W_zps = np.round(np.random.rand(output_channels) * 100 - 50).astype(int)
             # when using symmetric quantization
             # special restriction for xnnpack fully connected op weight
             # [-127, 127] instead of [-128, 127]

diff --git a/test/test_reductions.py b/test/test_reductions.py
@@ -1434,7 +1434,7 @@ def test_prod_bool(self, device):
         vals = [[True, True], [True, False], [False, False], []]
         for val in vals:
             result = torch.prod(torch.tensor(val, device=device), dtype=torch.bool).item()
-            expect = np.prod(np.array(val), dtype=np.bool)
+            expect = np.prod(np.array(val), dtype=bool)
             self.assertEqual(result, expect)
 
             result = torch.prod(torch.tensor(val, device=device)).item()

diff --git a/test/test_tensor_creation_ops.py b/test/test_tensor_creation_ops.py
@@ -1444,14 +1444,14 @@ def test_linlogspace_mem_overlap(self, device):
     def test_ctor_with_numpy_array(self, device):
         correct_dtypes = [
             np.double,
-            np.float,
+            float,
             np.float16,
             np.int64,
             np.int32,
             np.int16,
             np.int8,
             np.uint8,
-            np.bool,
+            bool,
         ]
 
         incorrect_byteorder = '>' if sys.byteorder == 'little' else '<'

diff --git a/test/test_tensorboard.py b/test/test_tensorboard.py
@@ -807,7 +807,7 @@ def test_caffe2_simple_model(self):
         model = ModelHelper(name="mnist")
         # how come those inputs don't break the forward pass =.=a
         workspace.FeedBlob("data", np.random.randn(1, 3, 64, 64).astype(np.float32))
-        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))
+        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(int))
 
         with core.NameScope("conv1"):
             conv1 = brew.conv(model, "data", 'conv1', dim_in=1, dim_out=20, kernel=5)
@@ -842,7 +842,7 @@ def test_caffe2_simple_model(self):
     def test_caffe2_simple_cnnmodel(self):
         model = cnn.CNNModelHelper("NCHW", name="overfeat")
         workspace.FeedBlob("data", np.random.randn(1, 3, 64, 64).astype(np.float32))
-        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(np.int))
+        workspace.FeedBlob("label", np.random.randn(1, 1000).astype(int))
         with core.NameScope("conv1"):
             conv1 = model.Conv("data", "conv1", 3, 96, 11, stride=4)
             relu1 = model.Relu(conv1, conv1)

diff --git a/test/test_torch.py b/test/test_torch.py
@@ -6367,7 +6367,7 @@ def test_parsing_intlist(self):
         # fail parse with float variables
         self.assertRaises(TypeError, lambda: torch.ones((torch.tensor(3.), torch.tensor(4))))
         # fail parse with numpy floats
-        self.assertRaises(TypeError, lambda: torch.ones((np.float(3.), torch.tensor(4))))
+        self.assertRaises(TypeError, lambda: torch.ones((3., torch.tensor(4))))
         self.assertRaises(TypeError, lambda: torch.ones((np.array(3.), torch.tensor(4))))
 
         # fail parse with > 1 element variables

diff --git a/torch/utils/tensorboard/summary.py b/torch/utils/tensorboard/summary.py
@@ -380,6 +380,7 @@ def make_histogram(values, bins, max_bins=None):
         limits = new_limits
 
     # Find the first and the last bin defining the support of the histogram:
+
     cum_counts = np.cumsum(np.greater(counts, 0))
     start, end = np.searchsorted(cum_counts, [0, cum_counts[-1] - 1], side="right")
     start = int(start)