update

models/yolov7/yolov7_basic.py

@@ -79,9 +79,6 @@ class Conv(nn.Module):
 
 # ELAN Block
 class ELANBlock(nn.Module):
-    """
-    ELAN BLock of YOLOv7's backbone
-    """
     def __init__(self, in_dim, out_dim, expand_ratio=0.5, act_type='silu', norm_type='BN', depthwise=False):
         super(ELANBlock, self).__init__()
         inter_dim = int(in_dim * expand_ratio)
@@ -101,18 +98,10 @@ class ELANBlock(nn.Module):
 
 
     def forward(self, x):
-        """
-        Input:
-            x: [B, C, H, W]
-        Output:
-            out: [B, 2C, H, W]
-        """
         x1 = self.cv1(x)
         x2 = self.cv2(x)
         x3 = self.cv3(x2)
         x4 = self.cv4(x3)
-
-        # [B, C, H, W] -> [B, 2C, H, W]
         out = self.out(torch.cat([x1, x2, x3, x4], dim=1))
 
         return out
@@ -131,17 +120,8 @@ class DownSample(nn.Module):
         )
 
     def forward(self, x):
-        """
-        Input:
-            x: [B, C, H, W]
-        Output:
-            out: [B, C, H//2, W//2]
-        """
-        # [B, C, H, W] -> [B, C//2, H//2, W//2]
         x1 = self.cv1(self.mp(x))
         x2 = self.cv2(x)
-
-        # [B, C, H//2, W//2]
         out = torch.cat([x1, x2], dim=1)
 
         return out
@@ -149,9 +129,6 @@ class DownSample(nn.Module):
 
 # ELAN Block for PaFPN
 class ELANBlockFPN(nn.Module):
-    """
-    ELAN BLock of YOLOv7's head
-    """
     def __init__(self, in_dim, out_dim, act_type='silu', norm_type='BN', depthwise=False):
         super(ELANBlockFPN, self).__init__()
         # Basic parameters
@@ -181,12 +158,6 @@ class ELANBlockFPN(nn.Module):
 
 
     def forward(self, x):
-        """
-        Input:
-            x: [B, C_in, H, W]
-        Output:
-            out: [B, C_out, H, W]
-        """
         x1 = self.cv1(x)
         x2 = self.cv2(x)
         inter_outs = [x1, x2]
@@ -194,8 +165,6 @@ class ELANBlockFPN(nn.Module):
             y1 = inter_outs[-1]
             y2 = m(y1)
             inter_outs.append(y2)
-
-        # [B, C_in, H, W] -> [B, C_out, H, W]
         out = self.out(torch.cat(inter_outs, dim=1))
 
         return out
@@ -214,17 +183,8 @@ class DownSampleFPN(nn.Module):
         )
 
     def forward(self, x):
-        """
-        Input:
-            x: [B, C, H, W]
-        Output:
-            out: [B, 2C, H//2, W//2]
-        """
-        # [B, C, H, W] -> [B, C//2, H//2, W//2]
         x1 = self.cv1(self.mp(x))
         x2 = self.cv2(x)
-
-        # [B, C, H//2, W//2]
         out = torch.cat([x1, x2], dim=1)
 
         return out