Onboard onto ruff

profiler.py

@@ -2,10 +2,10 @@
 import pstats
 import random
 
-import torchvision  # type: ignore[import]  # pylint: disable=unused-import  # noqa
-from tqdm import trange  # pylint: disable=unused-import  # noqa
+import torchvision  # type: ignore[import]  # pylint: disable=unused-import  # noqa: F401, E501
+from tqdm import trange  # pylint: disable=unused-import  # noqa: F401
 
-from torchinfo import summary  # pylint: disable=unused-import  # noqa
+from torchinfo import summary  # pylint: disable=unused-import  # noqa: F401
 
 
 def profile() -> None:

ruff.toml

@@ -0,0 +1,45 @@
+select = ["ALL"]
+ignore = [
+    "ANN101",  # Missing type annotation for `self` in method
+    "ANN102",  # Missing type annotation for `cls` in classmethod
+	"ANN401",  # Dynamically typed expressions (typing.Any) are disallowed
+	"C901",    # function is too complex (12 > 10)
+	"COM812",  # Trailing comma missing
+	"D",       # Docstring rules
+	"EM101",   # Exception must not use a string literal, assign to variable first
+	"EM102",   # Exception must not use an f-string literal, assign to variable first
+	"ERA001",  # Found commented-out code
+	"FBT001",  # Boolean positional arg in function definition
+	"FBT002",  # Boolean default value in function definition
+	"FBT003",  # Boolean positional value in function call
+    "PLR0911", # Too many return statements (11 > 6)
+	"PLR2004", # Magic value used in comparison, consider replacing 2 with a constant variable
+    "PLR0912", # Too many branches
+	"PLR0913", # Too many arguments to function call
+	"PLR0915", # Too many statements
+	"PTH123",  # `open()` should be replaced by `Path.open()`
+	"S101",    # Use of `assert` detected
+	"S311",    # Standard pseudo-random generators are not suitable for cryptographic purposes
+	"T201",    # print() found
+	"TCH001",  # Move application import into a type-checking block
+    "TCH003",  # Move standard library import into a type-checking block
+	"TRY003",  # Avoid specifying long messages outside the exception class
+
+	# torchinfo-specific ignores
+    "N803",    # Argument name `A_i` should be lowercase
+    "N806",    # Variable `G` in function should be lowercase
+	"BLE001",  # Do not catch blind exception: `Exception`
+    "PLW0602", # Using global for `_cached_forward_pass` but no assignment is done
+    "PLW0603", # Using the global statement to update `_cached_forward_pass` is discouraged
+    "PLW2901", # `for` loop variable `name` overwritten by assignment target
+    "SIM108",  # [*] Use ternary operator `model_mode = Mode.EVAL if mode is None else Mode(mode)` instead of `if`-`else`-block
+    "SLF001",  # Private member accessed: `_modules`
+    "TCH002",  # Move third-party import into a type-checking block
+    "TRY004",  # Prefer `TypeError` exception for invalid type
+    "TRY301",  # Abstract `raise` to an inner function
+]
+target-version = "py37"
+exclude = ["tests"]
+
+[flake8-pytest-style]
+fixture-parentheses = false

tests/half_precision_test.py

@@ -1,6 +1,5 @@
 import pytest
 import torch
-from pytest import approx
 
 from tests.fixtures.models import LinearModel, LSTMNet, SingleInputNet
 from torchinfo import summary
@@ -38,12 +37,12 @@ def test_linear_model_half() -> None:
         x = x.type(torch.float16).cuda()
         results_half = summary(model, input_data=x)
 
-        assert ModelStatistics.to_megabytes(results_half.total_param_bytes) == approx(
-            ModelStatistics.to_megabytes(results.total_param_bytes) / 2
-        )
-        assert ModelStatistics.to_megabytes(results_half.total_output_bytes) == approx(
-            ModelStatistics.to_megabytes(results.total_output_bytes) / 2
-        )
+        assert ModelStatistics.to_megabytes(
+            results_half.total_param_bytes
+        ) == pytest.approx(ModelStatistics.to_megabytes(results.total_param_bytes) / 2)
+        assert ModelStatistics.to_megabytes(
+            results_half.total_output_bytes
+        ) == pytest.approx(ModelStatistics.to_megabytes(results.total_output_bytes) / 2)
 
     @staticmethod
     def test_lstm_half() -> None:

torchinfo/layer_info.py

@@ -203,10 +203,9 @@ def calculate_num_params(self) -> None:
 
             # kernel_size for inner layer parameters
             ksize = list(param.size())
-            if name == "weight":
-                # to make [in_shape, out_shape, ksize, ksize]
-                if len(ksize) > 1:
-                    ksize[0], ksize[1] = ksize[1], ksize[0]
+            # to make [in_shape, out_shape, ksize, ksize]
+            if name == "weight" and len(ksize) > 1:
+                ksize[0], ksize[1] = ksize[1], ksize[0]
 
             # RNN modules have inner weights such as weight_ih_l0
             # Don't show parameters for the overall model, show for individual layers