style: 💄 Black format.

src/cellmap_models/pytorch/untrained_models/README.md

@@ -4,13 +4,13 @@
 
 ## Models
 
-***ResNet***: Parameterizable 2D and 3D ResNet models with a variable number of layers and channels. This model is based on the original ResNet architecture with the addition of a decoding path, which mirrors the encoder, after the bottleneck, to produce an image output.
+- **ResNet**: Parameterizable 2D and 3D ResNet models with a variable number of layers and channels. This model is based on the original ResNet architecture with the addition of a decoding path, which mirrors the encoder, after the bottleneck, to produce an image output.
 
-***UNet2D***: A simple 2D UNet model with a variable number of output channels.
+- **UNet2D**: A simple 2D UNet model with a variable number of output channels.
 
-***UNet3D***: A simple 3D UNet model with a variable number of output channels.
+- **UNet3D**: A simple 3D UNet model with a variable number of output channels.
 
-***ViTVNet***: A 3D VNet model with a Vision Transformer (ViT) encoder. This model is based on the original VNet architecture with the addition of a ViT encoder in place of the original convolutional encoder.
+- **ViTVNet**: A 3D VNet model with a Vision Transformer (ViT) encoder. This model is based on the original VNet architecture with the addition of a ViT encoder in place of the original convolutional encoder.
 
 ## Usage
 

src/cellmap_models/pytorch/untrained_models/unet_2D.py

@@ -6,6 +6,7 @@
 # Original source code from:
 # https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_model.py
 
+
 class UNet2D(nn.Module):
     def __init__(self, n_channels, n_classes, trilinear=False):
         super(UNet2D, self).__init__()
@@ -38,10 +39,12 @@ def forward(self, x):
         logits = self.outc(x)
         return logits
 
+
 """ Parts of the 2D U-Net model """
 # Original source code from:
 # https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_parts.py
 
+
 class DoubleConv(nn.Module):
     """(convolution => [BN] => ReLU) * 2"""
 

src/cellmap_models/pytorch/untrained_models/unet_3D.py

@@ -1,4 +1,3 @@
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -7,6 +6,7 @@
 # Original source code from:
 # https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_model.py
 
+
 class UNet3D(nn.Module):
     def __init__(self, n_channels, n_classes, trilinear=False):
         super(UNet3D, self).__init__()
@@ -39,11 +39,13 @@ def forward(self, x):
         logits = self.outc(x)
         return logits
 
+
 """ Parts of the U-Net model """
 # Adapted from:
 # https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_parts.py
 # By Emma Avetissian, @aemmav
 
+
 class DoubleConv(nn.Module):
     """(convolution => [BN] => ReLU) * 2"""