Code Refactor ruff check --fix --extend-select I (#25)

tests/test_relu.py

@@ -9,8 +9,7 @@ def test_relu(self):
         """Tests the ReLU activation function to ensure it has zero FLOPs and checks parameter count using THOP
         profiling.
         """
-        n, in_c, out_c = 1, 100, 200
-        data = torch.randn(n, in_c)
+        n, in_c, _out_c = 1, 100, 200
         net = nn.ReLU()
         flops, params = profile(net, inputs=(torch.randn(n, in_c),))
         print(flops, params)

thop/fx_profile.py

@@ -33,10 +33,10 @@ def count_matmul(input_shapes, output_shapes):
 
 def count_fn_linear(input_shapes, output_shapes, *args, **kwargs):
     """Calculates total operations (FLOPs) for a linear layer given input and output shapes."""
-    mul_flops = count_matmul(input_shapes, output_shapes)
+    flops = count_matmul(input_shapes, output_shapes)
     if "bias" in kwargs:
-        add_flops = output_shapes[0].numel()
-    return mul_flops
+        flops += output_shapes[0].numel()
+    return flops
 
 
 from .vision.calc_func import calculate_conv
@@ -131,7 +131,6 @@ def fx_profile(mod: nn.Module, input: th.Tensor, verbose=False):
     information if verbose.
     """
     gm: torch.fx.GraphModule = symbolic_trace(mod)
-    g = gm.graph
     ShapeProp(gm).propagate(input)
 
     fprint = null_print

thop/vision/basic_hooks.py

@@ -10,7 +10,6 @@
 
 def count_parameters(m, x, y):
     """Calculate and update the total number of parameters in a given PyTorch model."""
-    total_params = sum(torch.DoubleTensor([p.numel()]) for p in m.parameters())
     m.total_params[0] = calculate_parameters(m.parameters())
 
 
@@ -22,10 +21,7 @@ def zero_ops(m, x, y):
 def count_convNd(m: _ConvNd, x, y: torch.Tensor):
     """Calculate and add the number of convolutional operations (FLOPs) to the model's total operations count."""
     x = x[0]
-
-    kernel_ops = torch.zeros(m.weight.size()[2:]).numel()  # Kw x Kh
-    bias_ops = 1 if m.bias is not None else 0
-
+
     m.total_ops += calculate_conv2d_flops(
         input_size=list(x.shape),
         output_size=list(y.shape),
@@ -95,9 +91,6 @@ def count_prelu(m, x, y):
 def count_relu(m, x, y):
     """Calculate and update the total operation counts for a ReLU layer."""
     x = x[0]
-
-    nelements = x.numel()
-
     m.total_ops += calculate_relu_flops(list(x.shape))