Adding a test and fixing another torch.Tensor instance to torch.tensor.

In general, torch.Tensor is for uninitialized tensors, because all of our tensors are with values and the datatype is inferred (not explicit) we should use torch.tensor.
sony · Dec 5, 2023 · 69ec6ac · 69ec6ac
1 parent cbace9c
commit 69ec6ac
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Showing 2 changed files with 3 additions and 3 deletions.
diff --git a/model_compression_toolkit/core/pytorch/back2framework/pytorch_model_builder.py b/model_compression_toolkit/core/pytorch/back2framework/pytorch_model_builder.py
@@ -233,7 +233,7 @@ def _add_modules(self):
                 if node.type == BufferHolder:
                     self.get_submodule(node.name). \
                         register_buffer(node.name,
-                                        torch.Tensor(node.get_weights_by_keys(BUFFER)).to(get_working_device()))
+                                        torch.tensor(node.get_weights_by_keys(BUFFER)).to(get_working_device()))
 
             # Add activation quantization modules if an activation holder is configured for this node
             if node.is_activation_quantization_enabled() and self.get_activation_quantizer_holder is not None:

diff --git a/tests/pytorch_tests/model_tests/feature_models/scalar_tensor_test.py b/tests/pytorch_tests/model_tests/feature_models/scalar_tensor_test.py
@@ -22,7 +22,7 @@ def __init__(self):
         self.conv1 = torch.nn.Conv2d(3, 3, kernel_size=1, stride=1)
         self.scalars = [torch.tensor(i) for i in range(-5, 6)]
 
-    def forward(self, x, y):
+    def forward(self, x):
         x = self.conv1(x)
         for scalar in self.scalars:
             x = x + scalar
@@ -38,7 +38,7 @@ def __init__(self, unit_test):
         super().__init__(unit_test)
 
     def create_inputs_shape(self):
-        return [[self.val_batch_size, 3, 32, 32], [self.val_batch_size, 3, 32, 32]]
+        return [[self.val_batch_size, 3, 32, 32]]
 
     def create_feature_network(self, input_shape):
         return ScalarTensorNet()