chore: Apply linting

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

cpp/src/compile_spec.cpp

@@ -294,7 +294,9 @@ CompileSpec::Input::Input(at::Tensor tensor) {
   this->shape = tensor.sizes().vec();
   this->dtype = tensor.scalar_type();
   this->explicit_set_dtype = true;
-  TRTORCH_ASSERT(tensor.is_contiguous(at::MemoryFormat::ChannelsLast) || tensor.is_contiguous(at::MemoryFormat::Contiguous), "Tensor does not have a supported contiguous memory format, supported formats are contiguous or channel_last");
+  TRTORCH_ASSERT(
+      tensor.is_contiguous(at::MemoryFormat::ChannelsLast) || tensor.is_contiguous(at::MemoryFormat::Contiguous),
+      "Tensor does not have a supported contiguous memory format, supported formats are contiguous or channel_last");
   at::MemoryFormat frmt;
   if (tensor.is_contiguous(at::MemoryFormat::Contiguous)) {
     frmt = at::MemoryFormat::Contiguous;

py/trtorch/Input.py

@@ -199,7 +199,13 @@ def _parse_format(format: Any) -> _types.TensorFormat:
 
     @classmethod
     def _from_tensor(cls, t: torch.Tensor):
-        if not any([t.is_contiguous(memory_format=torch.contiguous_format), t.is_contiguous(memory_format=torch.channels_last)]):
-            raise ValueError("Tensor does not have a supported contiguous memory format, supported formats are contiguous or channel_last")
-        frmt = torch.contiguous_format if t.is_contiguous(memory_format=torch.contiguous_format) else torch.channels_last
-        return cls(shape=t.shape, dtype=t.dtype, format=frmt)
+        if not any([
+                t.is_contiguous(memory_format=torch.contiguous_format),
+                t.is_contiguous(memory_format=torch.channels_last)
+        ]):
+            raise ValueError(
+                "Tensor does not have a supported contiguous memory format, supported formats are contiguous or channel_last"
+            )
+        frmt = torch.contiguous_format if t.is_contiguous(
+            memory_format=torch.contiguous_format) else torch.channels_last
+        return cls(shape=t.shape, dtype=t.dtype, format=frmt)

py/trtorch/_compile_spec.py

@@ -175,7 +175,8 @@ def _parse_compile_spec(compile_spec: Dict[str, Any]) -> trtorch._C.CompileSpec:
 
     if "inputs" in compile_spec:
         if not all([isinstance(i, torch.Tensor) or isinstance(i, trtorch.Input) for i in compile_spec["inputs"]]):
-            raise KeyError("Input specs should be either trtorch.Input or torch.Tensor, found types: {}".format([typeof(i) for i in compile_spec["inputs"]]))
+            raise KeyError("Input specs should be either trtorch.Input or torch.Tensor, found types: {}".format(
+                [typeof(i) for i in compile_spec["inputs"]]))
 
         inputs = [trtorch.Input._from_tensor(i) if isinstance(i, torch.Tensor) else i for i in compile_spec["inputs"]]
         info.inputs = [i._to_internal() for i in inputs]

tests/cpp/test_example_tensors.cpp

@@ -21,4 +21,3 @@ INSTANTIATE_TEST_SUITE_P(
     CompiledModuleForwardIsCloseSuite,
     CppAPITests,
     testing::Values(PathAndInSize({"tests/modules/resnet18_traced.jit.pt", {{1, 3, 224, 224}}, 2e-5})));
-

tests/py/test_api.py

@@ -88,16 +88,13 @@ def test_from_torch_tensor(self):
         self.assertTrue(same < 2e-2)
 
     def test_device(self):
-        compile_spec = {
-            "inputs": [self.input],
-            "device": trtorch.Device("gpu:0"),
-            "enabled_precisions": {torch.float}
-        }
+        compile_spec = {"inputs": [self.input], "device": trtorch.Device("gpu:0"), "enabled_precisions": {torch.float}}
 
         trt_mod = trtorch.compile(self.scripted_model, compile_spec)
         same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
         self.assertTrue(same < 2e-2)
 
+
 class TestCompileHalf(ModelTestCase):
 
     def setUp(self):