Add pyproject.toml and Fix Ruff issues (#36)

* Add pyproject.toml and fix ruff issues

* open file using Path

* fix ruff issues

.pre-commit-config.yaml

@@ -8,6 +8,8 @@ repos:
       - id: end-of-file-fixer
       - id: check-yaml
       - id: check-added-large-files
+      - id: debug-statements
+      - id: detect-private-key
 
   - repo: https://github.com/pre-commit/mirrors-mypy
     rev: "v1.7.0"

docs/examples/graph/plot_basic_dense.py

@@ -1,29 +1,31 @@
-"""
-Basic Dense
+"""Basic Dense
 =======================================
 
 Visualization of basic dense model
-"""
+"""  # noqa: D205
 
-import torch.nn as nn
-import visualtorch
 import matplotlib.pyplot as plt
+import torch
+import visualtorch
+from torch import nn
 
 
 class SimpleDense(nn.Module):
-    def __init__(self):
-        super(SimpleDense, self).__init__()
+    """Simple Dense Model."""
+
+    def __init__(self) -> None:
+        super().__init__()
         self.h0 = nn.Linear(4, 8)
         self.h1 = nn.Linear(8, 8)
         self.h2 = nn.Linear(8, 4)
         self.out = nn.Linear(4, 2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Define the forward pass."""
         x = self.h0(x)
         x = self.h1(x)
         x = self.h2(x)
-        x = self.out(x)
-        return x
+        return self.out(x)
 
 
 model = SimpleDense()

docs/examples/graph/plot_custom_node_color.py

@@ -1,30 +1,33 @@
-"""
-Custom Color
+"""Custom Color
 =======================================
 
 Visualization of custom color
-"""
+"""  # noqa: D205
 
-import torch.nn as nn
-import visualtorch
-import matplotlib.pyplot as plt
 from collections import defaultdict
 
+import matplotlib.pyplot as plt
+import torch
+import visualtorch
+from torch import nn
+
 
 class SimpleDense(nn.Module):
-    def __init__(self):
-        super(SimpleDense, self).__init__()
+    """Simple Dense Model."""
+
+    def __init__(self) -> None:
+        super().__init__()
         self.h0 = nn.Linear(4, 8)
         self.h1 = nn.Linear(8, 8)
         self.h2 = nn.Linear(8, 4)
         self.out = nn.Linear(4, 2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Define the forward pass."""
         x = self.h0(x)
         x = self.h1(x)
         x = self.h2(x)
-        x = self.out(x)
-        return x
+        return self.out(x)
 
 
 model = SimpleDense()

docs/examples/graph/plot_custom_node_size.py

@@ -1,29 +1,31 @@
-"""
-Custom Node Size
+"""Custom Node Size
 =======================================
 
 Visualization of custom node size
-"""
+"""  # noqa: D205
 
-import torch.nn as nn
-import visualtorch
 import matplotlib.pyplot as plt
+import torch
+import visualtorch
+from torch import nn
 
 
 class SimpleDense(nn.Module):
-    def __init__(self):
-        super(SimpleDense, self).__init__()
+    """Simple Dense Model."""
+
+    def __init__(self) -> None:
+        super().__init__()
         self.h0 = nn.Linear(4, 8)
         self.h1 = nn.Linear(8, 8)
         self.h2 = nn.Linear(8, 4)
         self.out = nn.Linear(4, 2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Define the forward pass."""
         x = self.h0(x)
         x = self.h1(x)
         x = self.h2(x)
-        x = self.out(x)
-        return x
+        return self.out(x)
 
 
 model = SimpleDense()

docs/examples/graph/plot_custom_spacing_layers.py

@@ -1,29 +1,31 @@
-"""
-Custom Spacing for Layers
+"""Custom Spacing for Layers
 =======================================
 
 Visualization of custom spacing between layers
-"""
+"""  # noqa: D205
 
-import torch.nn as nn
-import visualtorch
 import matplotlib.pyplot as plt
+import torch
+import visualtorch
+from torch import nn
 
 
 class SimpleDense(nn.Module):
-    def __init__(self):
-        super(SimpleDense, self).__init__()
+    """Simple Dense Model."""
+
+    def __init__(self) -> None:
+        super().__init__()
         self.h0 = nn.Linear(4, 8)
         self.h1 = nn.Linear(8, 8)
         self.h2 = nn.Linear(8, 4)
         self.out = nn.Linear(4, 2)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Define the forward pass."""
         x = self.h0(x)
         x = self.h1(x)
         x = self.h2(x)
-        x = self.out(x)
-        return x
+        return self.out(x)
 
 
 model = SimpleDense()

docs/examples/layered/plot_2d_view.py

@@ -1,13 +1,12 @@
-"""
-2D View
+"""2D View
 =======================================
 
 Visualization of 2D view
-"""
+"""  # noqa: D205
 
-import visualtorch
-import torch.nn as nn
 import matplotlib.pyplot as plt
+import visualtorch
+from torch import nn
 
 # Example of a simple CNN model using nn.Sequential
 model = nn.Sequential(

docs/examples/layered/plot_basic_custom.py

@@ -1,41 +1,43 @@
-"""
-Basic Custom
+"""Basic Custom
 =======================================
 
 Visualization of basic custom model
-"""
+"""  # noqa: D205
 
-import torch.nn as nn
-import torch.nn.functional as F
-import visualtorch
 import matplotlib.pyplot as plt
+import torch
+import torch.nn.functional as func
+import visualtorch
+from torch import nn
 
 
 # Example of a simple CNN model
 class SimpleCNN(nn.Module):
-    def __init__(self):
+    """Simple CNN Model."""
+
+    def __init__(self) -> None:
         super().__init__()
         self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
         self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
         self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
         self.fc1 = nn.Linear(64 * 28 * 28, 128)
         self.fc2 = nn.Linear(128, 10)
 
-    def forward(self, x):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """Define the forward pass."""
         x = self.conv1(x)
-        x = F.relu(x)
-        x = F.max_pool2d(x, 2, 2)
+        x = func.relu(x)
+        x = func.max_pool2d(x, 2, 2)
         x = self.conv2(x)
-        x = F.relu(x)
-        x = F.max_pool2d(x, 2, 2)
+        x = func.relu(x)
+        x = func.max_pool2d(x, 2, 2)
         x = self.conv3(x)
-        x = F.relu(x)
-        x = F.max_pool2d(x, 2, 2)
+        x = func.relu(x)
+        x = func.max_pool2d(x, 2, 2)
         x = x.view(x.size(0), -1)
         x = self.fc1(x)
-        x = F.relu(x)
-        x = self.fc2(x)
-        return x
+        x = func.relu(x)
+        return self.fc2(x)
 
 
 # Create an instance of the SimpleCNN

docs/examples/layered/plot_basic_sequential.py

@@ -1,13 +1,12 @@
-"""
-Basic Sequential
+"""Basic Sequential
 =======================================
 
 Visualization of basic sequential model
-"""
+"""  # noqa: D205
 
-import visualtorch
-import torch.nn as nn
 import matplotlib.pyplot as plt
+import visualtorch
+from torch import nn
 
 # Example of a simple CNN model using nn.Sequential
 model = nn.Sequential(

docs/examples/layered/plot_custom_color.py

@@ -1,15 +1,15 @@
-"""
-Custom Color
+"""Custom Color
 =======================================
 
 Visualization of custom color
-"""
+"""  # noqa: D205
 
-import visualtorch
-import torch.nn as nn
-import matplotlib.pyplot as plt
 from collections import defaultdict
 
+import matplotlib.pyplot as plt
+import visualtorch
+from torch import nn
+
 # Example of a simple CNN model using nn.Sequential
 model = nn.Sequential(
     nn.Conv2d(3, 16, kernel_size=3, padding=1),

docs/examples/layered/plot_custom_opacity.py

@@ -1,13 +1,12 @@
-"""
-Custom Opacity
+"""Custom Opacity
 =======================================
 
 Change the color transparency
-"""
+"""  # noqa: D205
 
-import visualtorch
-import torch.nn as nn
 import matplotlib.pyplot as plt
+import visualtorch
+from torch import nn
 
 # Example of a simple CNN model using nn.Sequential
 model = nn.Sequential(

docs/examples/layered/plot_custom_orientation.py

@@ -1,13 +1,12 @@
-"""
-Custom Orientation
+"""Custom Orientation
 =======================================
 
 Visualization of custom orientation for 1 dim layers
-"""
+"""  # noqa: D205
 
-import visualtorch
-import torch.nn as nn
 import matplotlib.pyplot as plt
+import visualtorch
+from torch import nn
 
 # Example of a simple CNN model using nn.Sequential
 model = nn.Sequential(
@@ -29,7 +28,10 @@
 input_shape = (1, 3, 224, 224)
 
 img = visualtorch.layered_view(
-    model, input_shape=input_shape, one_dim_orientation="x", spacing=40
+    model,
+    input_shape=input_shape,
+    one_dim_orientation="x",
+    spacing=40,
 )
 
 plt.axis("off")

docs/examples/layered/plot_custom_shading.py

@@ -1,13 +1,12 @@
-"""
-Custom Shading
+"""Custom Shading
 =======================================
 
 Visualization of custom shading
-"""
+"""  # noqa: D205
 
-import visualtorch
-import torch.nn as nn
 import matplotlib.pyplot as plt
+import visualtorch
+from torch import nn
 
 # Example of a simple CNN model using nn.Sequential
 model = nn.Sequential(

docs/examples/layered/plot_custom_spacing.py

@@ -1,13 +1,12 @@
-"""
-Custom Spacing
+"""Custom Spacing
 =======================================
 
 Visualization of custom spacing
-"""
+"""  # noqa: D205
 
-import visualtorch
-import torch.nn as nn
 import matplotlib.pyplot as plt
+import visualtorch
+from torch import nn
 
 # Example of a simple CNN model using nn.Sequential
 model = nn.Sequential(