changed to absolute imports and added docs (#881)

pl_examples/full_examples/semantic_segmentation/models/unet/model.py

@@ -2,30 +2,33 @@
 import torch.nn as nn
 import torch.nn.functional as F
 
-from parts import DoubleConv, Down, Up
+from models.unet.parts import DoubleConv, Down, Up
 
 
 class UNet(nn.Module):
     '''
     Architecture based on U-Net: Convolutional Networks for Biomedical Image Segmentation
     Link - https://arxiv.org/abs/1505.04597
+
+    Parameters:
+    num_classes (int) - Number of output classes required (default 19 for KITTI dataset)
+    bilinear (bool) - Whether to use bilinear interpolation or transposed
+    convolutions for upsampling.
     '''
     def __init__(self, num_classes=19, bilinear=False):
         super().__init__()
-        self.bilinear = bilinear
-        self.num_classes = num_classes
         self.layer1 = DoubleConv(3, 64)
         self.layer2 = Down(64, 128)
         self.layer3 = Down(128, 256)
         self.layer4 = Down(256, 512)
         self.layer5 = Down(512, 1024)
 
-        self.layer6 = Up(1024, 512, bilinear=self.bilinear)
-        self.layer7 = Up(512, 256, bilinear=self.bilinear)
-        self.layer8 = Up(256, 128, bilinear=self.bilinear)
-        self.layer9 = Up(128, 64, bilinear=self.bilinear)
+        self.layer6 = Up(1024, 512, bilinear=bilinear)
+        self.layer7 = Up(512, 256, bilinear=bilinear)
+        self.layer8 = Up(256, 128, bilinear=bilinear)
+        self.layer9 = Up(128, 64, bilinear=bilinear)
 
-        self.layer10 = nn.Conv2d(64, self.num_classes, kernel_size=1)
+        self.layer10 = nn.Conv2d(64, num_classes, kernel_size=1)
 
     def forward(self, x):
         x1 = self.layer1(x)