Update README.md

README.md

@@ -103,12 +103,17 @@ The core idea of NCRF is taking a grid of patches as input, e.g. 3x3, using CNN
 ```python
 def forward(self, x):
     """
-        x here is assumed to be a 5-D tensor with shape of
+    Args:
+        x: 5D tensor with shape of
         [batch_size, grid_size, 3, crop_size, crop_size],
         where grid_size is the number of patches within a grid (e.g. 9 for
         a 3x3 grid); crop_size is 224 by default for ResNet input;
+    Returns:
+        logits, 2D tensor with shape of [batch_size, grid_size], the logit
+        of each patch within the grid being tumor
     """
     batch_size, grid_size, _, crop_size = x.shape[0:4]
+    # flatten grid_size dimension and combine it into batch dimension
     x = x.view(-1, 3, crop_size, crop_size)
 
     x = self.conv1(x)
@@ -122,9 +127,11 @@ def forward(self, x):
     x = self.layer4(x)
 
     x = self.avgpool(x)
+    # feats means features, i.e. patch embeddings from ResNet
     feats = x.view(x.size(0), -1)
     logits = self.fc(feats)
 
+    # restore grid_size dimension for CRF
     feats = feats.view((batch_size, grid_size, -1))
     logits = logits.view((batch_size, grid_size, -1))