torch.Tensor -> torch.tensor

model_compression_toolkit/core/pytorch/back2framework/instance_builder.py

@@ -33,7 +33,7 @@ def node_builder(n: BaseNode) -> Module:
 
     framework_attr = copy.copy(n.framework_attr)
     node_instance = n.type(**framework_attr)
-    node_instance.load_state_dict({k: torch.Tensor(v) for k, v in n.weights.items()}, strict=False)
+    node_instance.load_state_dict({k: torch.tensor(v) for k, v in n.weights.items()}, strict=False)
     set_model(node_instance)
     return node_instance
 

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:

tests/pytorch_tests/model_tests/feature_models/scalar_tensor_test.py

@@ -0,0 +1,44 @@
+# Copyright 2023 Sony Semiconductor Israel, Inc. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+import torch
+from tests.pytorch_tests.model_tests.base_pytorch_test import BasePytorchTest
+
+
+class ScalarTensorNet(torch.nn.Module):
+    def __init__(self):
+        super(ScalarTensorNet, self).__init__()
+        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):
+        x = self.conv1(x)
+        for scalar in self.scalars:
+            x = x + scalar
+        return x
+
+
+class ScalarTensorTest(BasePytorchTest):
+    """
+    This test checks that we build a correct graph when the input graph contains a tensor with a single integer value.
+    """
+
+    def __init__(self, unit_test):
+        super().__init__(unit_test)
+
+    def create_inputs_shape(self):
+        return [[self.val_batch_size, 3, 32, 32]]
+
+    def create_feature_network(self, input_shape):
+        return ScalarTensorNet()

tests/pytorch_tests/model_tests/test_feature_models_runner.py

@@ -39,6 +39,7 @@
     MixedPercisionActivationSearch4BitFunctional, MixedPercisionActivationMultipleInputs
 from tests.pytorch_tests.model_tests.feature_models.relu_bound_test import ReLUBoundToPOTNetTest, \
     HardtanhBoundToPOTNetTest
+from tests.pytorch_tests.model_tests.feature_models.scalar_tensor_test import ScalarTensorTest
 from tests.pytorch_tests.model_tests.feature_models.second_moment_correction_test import ConvSecondMomentNetTest, \
     ConvTSecondMomentNetTest, MultipleInputsConvSecondMomentNetTest, ValueSecondMomentTest
 from tests.pytorch_tests.model_tests.feature_models.symmetric_activation_test import SymmetricActivationTest
@@ -253,6 +254,12 @@ def test_scale_equalization(self):
         # and with zero padding.
         ScaleEqualizationReluFuncConvTransposeWithZeroPadNetTest(self).run_test()
 
+    def test_scalar_tensor(self):
+        """
+        This test checks that we support scalar tensors initialized as torch.tensor(x) where x is int
+        """
+        ScalarTensorTest(self).run_test()
+
     def test_layer_name(self):
         """
         This test checks that we build a correct graph and correctly reconstruct the model