replaced random_sample with randn

tests/layer_tests/pytorch_tests/test_adaptive_avg_pool.py

@@ -34,7 +34,7 @@ def forward(self, input_shape):
     @pytest.mark.precommit_ts_backend
     @pytest.mark.precommit_fx_backend
     def test_adaptive_avg_pool3d(self, ie_device, precision, ir_version, input_shape, output_size):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(output_size), ie_device, precision, ir_version)
 
 
@@ -64,7 +64,7 @@ def forward(self, input_tensor):
     @pytest.mark.precommit_ts_backend
     @pytest.mark.precommit_fx_backend
     def test_adaptive_avg_pool2d(self, ie_device, precision, ir_version, input_shape, output_size):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(output_size), ie_device, precision, ir_version)
 
 
@@ -94,7 +94,7 @@ def forward(self, input_tensor):
     @pytest.mark.precommit_ts_backend
     @pytest.mark.precommit_fx_backend
     def test_adaptive_avg_pool1d(self, ie_device, precision, ir_version, input_shape, output_size):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(output_size), ie_device, precision, ir_version)
 
 

tests/layer_tests/pytorch_tests/test_adaptive_max_pool.py

@@ -52,7 +52,7 @@ def forward(self, input_tensor):
     @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
                        reason='Ticket - 122715')
     def test_adaptive_max_pool3d(self, ie_device, precision, ir_version, input_shape, output_size, return_indices):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(output_size, return_indices), ie_device, precision, ir_version)
 
 
@@ -97,7 +97,7 @@ def forward(self, input_tensor):
     @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
                        reason='Ticket - 122715')
     def test_adaptive_max_pool2d(self, ie_device, precision, ir_version, input_shape, output_size, return_indices):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(output_size, return_indices), ie_device, precision, ir_version)
 
 
@@ -144,5 +144,5 @@ def forward(self, input_tensor):
     @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
                        reason='Ticket - 122715')
     def test_adaptive_max_pool1d(self, ie_device, precision, ir_version, input_shape, output_size, return_indices):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(output_size, return_indices), ie_device, precision, ir_version)

tests/layer_tests/pytorch_tests/test_add.py

@@ -43,7 +43,7 @@ def forward2(self, lhs, rhs):
     @pytest.mark.precommit_fx_backend
     @pytest.mark.parametrize("op_type", ["add", "add_"])
     def test_add(self, ie_device, precision, ir_version, alpha, input_shape_rhs, op_type):
-        self.input_rhs = np.random.random_sample(input_shape_rhs).astype(np.float32)
+        self.input_rhs = np.random.randn(*input_shape_rhs).astype(np.float32)
         self._test(*self.create_model(alpha, op_type), ie_device, precision, ir_version, use_convert_model=True)
 
 

tests/layer_tests/pytorch_tests/test_argsort.py

@@ -74,7 +74,7 @@ def forward(self, input_tensor):
     def test_argsort(self, tensor_stable_pair, descending, ie_device, precision, ir_version):
         input_shape, stable = tensor_stable_pair
         if type(input_shape) is list:
-            self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+            self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         else:
             self.input_tensor = input_shape
         dims = len(self.input_tensor.shape)

tests/layer_tests/pytorch_tests/test_floor_divide.py

@@ -55,11 +55,11 @@ def forward(self, input_tensor, other_tensor):
                        reason='Ticket - 122715')
     def test_floor_divide(self, input_tensor, other_tensor, ie_device, precision, ir_version):
         if type(input_tensor) is list:
-            self.input_tensor = np.random.random_sample(input_tensor).astype(np.float32)
+            self.input_tensor = np.random.randn(*input_tensor).astype(np.float32)
         else:
             self.input_tensor = input_tensor
         if type(other_tensor) is list:
-            self.other_tensor = np.random.random_sample(other_tensor).astype(np.float32)
+            self.other_tensor = np.random.randn(*other_tensor).astype(np.float32)
         else:
             self.other_tensor = other_tensor
         self._test(*self.create_model(), ie_device, precision, ir_version, trace_model=True, use_convert_model=True)

tests/layer_tests/pytorch_tests/test_index_put_.py

@@ -54,7 +54,7 @@ def forward(self, input_tensor, values):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_index_put_single_indices(self, ie_device, precision, ir_version, input_data, indices, accumulate):
-        self.input_tensor = np.random.random_sample(input_data["input_shape"]).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_data["input_shape"]).astype(np.float32)
         self.values = input_data["values"]
         self._test(*self.create_model(indices, accumulate), ie_device, precision, ir_version)
 
@@ -107,7 +107,7 @@ def forward(self, input_tensor, values):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_index_put_many_indices(self, ie_device, precision, ir_version, input_data, indices, accumulate):
-        self.input_tensor = np.random.random_sample(input_data["input_shape"]).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_data["input_shape"]).astype(np.float32)
         self.values = input_data["values"]
         self._test(*self.create_model(indices, accumulate), ie_device, precision, ir_version)
 
@@ -158,7 +158,7 @@ def forward(self, input_tensor, values, indices_0, indices_1):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_nonzero_index_put_(self, ie_device, precision, ir_version, input_data, indices, accumulate):
-        self.input_tensor = np.random.random_sample(input_data["input_shape"]).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_data["input_shape"]).astype(np.float32)
         self.values = input_data["values"]
         for i in range(len(indices)):
             if type(indices[i]) is list:

tests/layer_tests/pytorch_tests/test_len.py

@@ -42,13 +42,13 @@ def forward(self, input_tensor):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_len(self, ie_device, precision, ir_version, input_tensor):
-        self.input_tensor = np.random.random_sample(input_tensor).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_tensor).astype(np.float32)
         self._test(*self.create_model(), ie_device, precision, ir_version)
 
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_len_int_list(self, ie_device, precision, ir_version, input_tensor):
-        self.input_tensor = np.random.random_sample(input_tensor).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_tensor).astype(np.float32)
         self._test(*self.create_model_int_list(),
                    ie_device, precision, ir_version, use_convert_model=True)
 

tests/layer_tests/pytorch_tests/test_narrow.py

@@ -38,7 +38,7 @@ def forward(self, input_tensor, dim: int, start, length: int):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_narrow(self, input_shape, dim, start, length, ie_device, precision, ir_version):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self.dim = dim
         self.start = start
         self.length = length

tests/layer_tests/pytorch_tests/test_remainder.py

@@ -31,7 +31,7 @@ def forward(self, lhs, rhs):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_remainder(self, ie_device, precision, ir_version, input_shape_rhs):
-        self.input_rhs = np.random.random_sample(input_shape_rhs).astype(np.float32)
+        self.input_rhs = np.random.randn(*input_shape_rhs).astype(np.float32)
         self._test(*self.create_model(), ie_device, precision, ir_version, use_convert_model=True)
 
 

tests/layer_tests/pytorch_tests/test_roi_align.py

@@ -54,7 +54,7 @@ def forward(self, input_tensor, rois):
     @pytest.mark.precommit
     def test_roi_align(self, ie_device, precision, ir_version, input_shape, boxes, output_size,
                        spatial_scale, sampling_ratio, aligned):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self.boxes = boxes
         self._test(*self.create_model(output_size, spatial_scale, sampling_ratio, aligned),
                    ie_device, precision, ir_version, trace_model=True)

tests/layer_tests/pytorch_tests/test_rsub.py

@@ -44,7 +44,7 @@ def test_rsub(self, ie_device, precision, ir_version, input_data):
         self.input_data = []
         for input in input_data:
             if type(input) is list:
-                self.input_data.append(np.random.random_sample(input).astype(np.float32))
+                self.input_data.append(np.random.randn(*input).astype(np.float32))
             else:
                 self.input_data.append(input)
         self._test(*self.create_model(second_type="float"), ie_device, precision, ir_version, use_convert_model=True)
@@ -59,7 +59,7 @@ def test_rsub(self, ie_device, precision, ir_version, input_data):
         self.input_data = []
         for input in input_data:
             if type(input) is list:
-                self.input_data.append(np.random.random_sample(input).astype(np.float32))
+                self.input_data.append(np.random.randn(*input).astype(np.float32))
             else:
                 self.input_data.append(input)
         self._test(*self.create_model(second_type="int"), ie_device, precision, ir_version)

tests/layer_tests/pytorch_tests/test_sort.py

@@ -81,7 +81,7 @@ def forward(self, input_tensor):
     def test_sort(self, input_shape, descending, stable, ie_device, precision, ir_version):
         self.input_tensor = []
         if type(input_shape) is list:
-            self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+            self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         else:
             self.input_tensor = input_shape
         dims = len(self.input_tensor.shape)

tests/layer_tests/pytorch_tests/test_stack.py

@@ -40,8 +40,8 @@ def forward(self, x, y):
     @pytest.mark.precommit
     def test_stack2D(self, input_shape, dim, ie_device, precision, ir_version):
         self.input_tensors = [
-            np.random.random_sample(input_shape).astype(np.float32),
-            np.random.random_sample(input_shape).astype(np.float32),
+            np.random.randn(*input_shape).astype(np.float32),
+            np.random.randn(*input_shape).astype(np.float32),
         ]
         self._test(*self.create_model(dim), ie_device, precision, ir_version)
 
@@ -79,8 +79,8 @@ def forward(self, x, y, z):
     @pytest.mark.precommit
     def test_stack3D(self, input_shape, dim, ie_device, precision, ir_version):
         self.input_tensors = [
-            np.random.random_sample(input_shape).astype(np.float32),
-            np.random.random_sample(input_shape).astype(np.float32),
-            np.random.random_sample(input_shape).astype(np.float32)
+            np.random.randn(*input_shape).astype(np.float32),
+            np.random.randn(*input_shape).astype(np.float32),
+            np.random.randn(*input_shape).astype(np.float32)
         ]
         self._test(*self.create_model(dim), ie_device, precision, ir_version)

tests/layer_tests/pytorch_tests/test_sub.py

@@ -52,7 +52,7 @@ def _forward_inplace(self, x, y, alpha: float):
     def test_sub(self, ie_device, precision, ir_version, input_shapes, inplace):
         self.input_data = []
         for input_shape in input_shapes:
-            self.input_data.append(np.random.random_sample(input_shape).astype(np.float32))
+            self.input_data.append(np.random.randn(*input_shape).astype(np.float32))
         self._test(*self.create_model(inplace), ie_device, precision, ir_version, use_convert_model=True)
 
 

tests/layer_tests/pytorch_tests/test_topk.py

@@ -63,5 +63,5 @@ def forward(self, input_tensor):
     @pytest.mark.precommit
     @pytest.mark.skipif(os.getenv("GITHUB_ACTIONS") == 'true', reason="Ticket - 115085")
     def test_topK(self, input_shape, k, dim, largest, sort, ie_device, precision, ir_version):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(k, dim, largest, sort), ie_device, precision, ir_version)

tests/layer_tests/pytorch_tests/test_unfold.py

@@ -39,6 +39,6 @@ def forward(self, input_tensor):
     @pytest.mark.nightly
     @pytest.mark.precommit
     def test_unfold(self, ie_device, precision, ir_version, dimension, size, step, input_shape):
-        self.input_tensor = np.random.random_sample(input_shape).astype(np.float32)
+        self.input_tensor = np.random.randn(*input_shape).astype(np.float32)
         self._test(*self.create_model(dimension, size, step),
                    ie_device, precision, ir_version)

tests/layer_tests/pytorch_tests/test_view.py

@@ -38,7 +38,7 @@ def test_view_list_construct(self, ie_device, precision, ir_version, input_shape
         self.input_data = []
         for input_shape in input_shapes:
             if type(input_shape) is list:
-                self.input_data.append(np.random.random_sample(input_shape).astype(np.float32))
+                self.input_data.append(np.random.randn(*input_shape).astype(np.float32))
             else:
                 self.input_data.append(input_shape)
         self._test(*self.create_model(), ie_device, precision, ir_version)
@@ -70,7 +70,7 @@ def test_view_dtype(self, ie_device, precision, ir_version, input_shapes):
         self.input_data = []
         for input_shape in input_shapes:
             if type(input_shape) is list:
-                self.input_data.append(np.random.random_sample(input_shape).astype(np.float32))
+                self.input_data.append(np.random.randn(*input_shape).astype(np.float32))
             else:
                 self.input_data.append(input_shape)
         self._test(*self.create_model(), ie_device, precision, ir_version)
@@ -103,7 +103,7 @@ def test_view_size(self, ie_device, precision, ir_version, input_shapes):
         self.input_data = []
         for input_shape in input_shapes:
             if type(input_shape) is list:
-                self.input_data.append(np.random.random_sample(input_shape).astype(np.float32))
+                self.input_data.append(np.random.randn(*input_shape).astype(np.float32))
             else:
                 self.input_data.append(input_shape)
         self._test(*self.create_model(), ie_device, precision, ir_version)
@@ -146,7 +146,7 @@ def test_view(self, ie_device, precision, ir_version, input_shapes):
         self.input_data = []
         for input_shape in input_shapes:
             if type(input_shape) is list:
-                self.input_data.append(np.random.random_sample(input_shape).astype(np.float32))
+                self.input_data.append(np.random.randn(*input_shape).astype(np.float32))
             else:
                 self.input_data.append(input_shape)
         self._test(*self.create_model(), ie_device, precision, ir_version)