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tests/test_conv2d.py

@@ -8,7 +8,9 @@
 
 class TestUtils:
     def test_conv2d_no_bias(self):
-        """Tests a 2D convolutional layer without bias using THOP profiling with predefined input dimensions and convolution parameters."""
+        """Tests a 2D convolutional layer without bias using THOP profiling with predefined input dimensions and
+        convolution parameters.
+        """
         n, in_c, ih, iw = 1, 3, 32, 32  # torch.randint(1, 10, (4,)).tolist()
         out_c, kh, kw = 12, 5, 5
         s, p, d, g = 1, 1, 1, 1

tests/test_relu.py

@@ -7,7 +7,9 @@
 
 class TestUtils:
     def test_relu(self):
-        """Tests the ReLU activation function to ensure it has zero FLOPs and checks parameter count using THOP profiling."""
+        """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)
         net = nn.ReLU()

tests/test_utils.py

@@ -12,7 +12,9 @@ def test_clever_format_returns_formatted_number(self):
         assert clever_nums == "1.00B"
 
     def test_clever_format_returns_formatted_numbers(self):
-        """Tests that the clever_format function correctly formats a list of numbers as strings with a '1.00B' pattern."""
+        """Tests that the clever_format function correctly formats a list of numbers as strings with a '1.00B'
+        pattern.
+        """
         nums = [1, 2]
         format = "%.2f"
         clever_nums = utils.clever_format(nums, format)

thop/fx_profile.py

@@ -129,7 +129,9 @@ def null_print(*args, **kwargs):
 
 
 def fx_profile(mod: nn.Module, input: th.Tensor, verbose=False):
-    """Profiles the given torch.nn Module to calculate total FLOPs for each operation and prints detailed node information if verbose."""
+    """Profiles the given torch.nn Module to calculate total FLOPs for each operation and prints detailed node
+    information if verbose.
+    """
     gm: torch.fx.GraphModule = symbolic_trace(mod)
     g = gm.graph
     ShapeProp(gm).propagate(input)
@@ -226,7 +228,9 @@ def __init__(self):
             self.myop = MyOP()
 
         def forward(self, x):
-            """Applies two linear transformations to the input tensor, clamps the second, then combines and processes with MyOP operator."""
+            """Applies two linear transformations to the input tensor, clamps the second, then combines and processes
+            with MyOP operator.
+            """
             out1 = self.linear1(x)
             out2 = self.linear2(x).clamp(min=0.0, max=1.0)
             return self.myop(out1 + out2)

thop/onnx_profile.py

@@ -55,7 +55,9 @@ def create_dict(self, weight, input, output):
         return diction
 
     def nodes_counter(self, diction, node):
-        """Count nodes of a specific type in an ONNX graph, returning the count and associated node operation details."""
+        """Count nodes of a specific type in an ONNX graph, returning the count and associated node operation
+        details.
+        """
         if node.op_type not in onnx_operators:
             print("Sorry, we haven't add ", node.op_type, "into dictionary.")
             return 0, None, None

thop/profile.py

@@ -66,7 +66,9 @@
 
 
 def profile_origin(model, inputs, custom_ops=None, verbose=True, report_missing=False):
-    """Profiles a PyTorch model's operations and parameters by applying custom or default hooks and returns total operations and parameters."""
+    """Profiles a PyTorch model's operations and parameters by applying custom or default hooks and returns total
+    operations and parameters.
+    """
     handler_collection = []
     types_collection = set()
     if custom_ops is None:

thop/rnn_hooks.py

@@ -57,7 +57,9 @@ def count_gru_cell(m: nn.GRUCell, x: torch.Tensor, y: torch.Tensor):
 
 
 def _count_lstm_cell(input_size, hidden_size, bias=True):
-    """Calculates the total operations for an LSTM cell during inference given input size, hidden size, and optional bias."""
+    """Calculates the total operations for an LSTM cell during inference given input size, hidden size, and optional
+    bias.
+    """
     total_ops = 0
 
     # i = \sigma(W_{ii} x + b_{ii} + W_{hi} h + b_{hi}) \\
@@ -80,7 +82,9 @@ def _count_lstm_cell(input_size, hidden_size, bias=True):
 
 
 def count_lstm_cell(m: nn.LSTMCell, x: torch.Tensor, y: torch.Tensor):
-    """Count the number of operations for a single LSTM cell in a given batch, updating the model's total operations count."""
+    """Count the number of operations for a single LSTM cell in a given batch, updating the model's total operations
+    count.
+    """
     total_ops = _count_lstm_cell(m.input_size, m.hidden_size, m.bias)
 
     batch_size = x[0].size(0)
@@ -166,7 +170,9 @@ def count_gru(m: nn.GRU, x, y):
 
 
 def count_lstm(m: nn.LSTM, x, y):
-    """Calculate the total operations for LSTM layers in a network, accounting for input size, hidden size, bias, and bidirectionality."""
+    """Calculate the total operations for LSTM layers in a network, accounting for input size, hidden size, bias, and
+    bidirectionality.
+    """
     bias = m.bias
     input_size = m.input_size
     hidden_size = m.hidden_size

thop/utils.py

@@ -7,6 +7,7 @@
 
 def colorful_print(fn_print, color=COLOR_RED):
     """A decorator to print text in the specified terminal color by wrapping the given print function."""
+
     def actual_call(*args, **kwargs):
         print(f"\033[{color}", end="")
         fn_print(*args, **kwargs)

thop/vision/basic_hooks.py

@@ -64,7 +64,9 @@ def count_convNd_ver2(m: _ConvNd, x, y: torch.Tensor):
 
 
 def count_normalization(m: nn.modules.batchnorm._BatchNorm, x, y):
-    """Calculate and add the FLOPs for a batch normalization layer, considering elementwise operations and possible affine parameters."""
+    """Calculate and add the FLOPs for a batch normalization layer, considering elementwise operations and possible
+    affine parameters.
+    """
     # https://github.com/Lyken17/pytorch-OpCounter/issues/124
     # y = (x - mean) / sqrt(eps + var) * weight + bias
     x = x[0]
@@ -146,7 +148,9 @@ def count_upsample(m, x, y):
 
 # nn.Linear
 def count_linear(m, x, y):
-    """Counts total operations for nn.Linear layers by calculating multiplications and additions based on input and output elements."""
+    """Counts total operations for nn.Linear layers by calculating multiplications and additions based on input and
+    output elements.
+    """
     total_mul = m.in_features
     # total_add = m.in_features - 1
     # total_add += 1 if m.bias is not None else 0

thop/vision/calc_func.py

@@ -65,7 +65,9 @@ def calculate_relu(input_size: torch.Tensor):
 
 
 def calculate_softmax(batch_size, nfeatures):
-    """Calculate the number of FLOPs required for a softmax activation function based on batch size and number of features."""
+    """Calculate the number of FLOPs required for a softmax activation function based on batch size and number of
+    features.
+    """
     total_exp = nfeatures
     total_add = nfeatures - 1
     total_div = nfeatures

thop/vision/onnx_counter.py

@@ -44,7 +44,9 @@ def onnx_counter_add(diction, node):
 
 
 def onnx_counter_conv(diction, node):
-    """Calculates MACs, output size, and name for an ONNX convolution node based on input tensor dimensions and node attributes."""
+    """Calculates MACs, output size, and name for an ONNX convolution node based on input tensor dimensions and node
+    attributes.
+    """
     # bias,kernelsize,outputsize
     dim_bias = 0
     input_count = 0
@@ -125,7 +127,9 @@ def onnx_counter_relu(diction, node):
 
 
 def onnx_counter_reducemean(diction, node):
-    """Compute MACs, output size, and name for the ReduceMean ONNX node, adjusting dimensions based on the 'axes' and 'keepdims' attributes."""
+    """Compute MACs, output size, and name for the ReduceMean ONNX node, adjusting dimensions based on the 'axes' and
+    'keepdims' attributes.
+    """
     keep_dim = 0
     for attr in node.attribute:
         if "axes" in attr.name:
@@ -330,7 +334,9 @@ def onnx_counter_concat(diction, node):
 
 
 def onnx_counter_clip(diction, node):
-    """Calculate MACs, output size, and output name for an ONNX node clip operation using provided dimensions and input size."""
+    """Calculate MACs, output size, and output name for an ONNX node clip operation using provided dimensions and input
+    size.
+    """
     macs = calculate_zero_ops()
     output_name = node.output[0]
     input_size = diction[node.input[0]]