Update RegNet

models/regnet.py

@@ -26,16 +26,16 @@ def forward(self, x):
 
 
 class Block(nn.Module):
-    def __init__(self, w_in, w_out, stride, num_groups, bottleneck_ratio, se_ratio):
+    def __init__(self, w_in, w_out, stride, group_width, bottleneck_ratio, se_ratio):
         super(Block, self).__init__()
         # 1x1
         w_b = int(round(w_out * bottleneck_ratio))
         self.conv1 = nn.Conv2d(w_in, w_b, kernel_size=1, bias=False)
         self.bn1 = nn.BatchNorm2d(w_b)
         # 3x3
-        groups = w_b // num_groups
+        num_groups = w_b // group_width
         self.conv2 = nn.Conv2d(w_b, w_b, kernel_size=3,
-                               stride=stride, padding=1, groups=groups, bias=False)
+                               stride=stride, padding=1, groups=num_groups, bias=False)
         self.bn2 = nn.BatchNorm2d(w_b)
         # se
         self.with_se = se_ratio > 0
@@ -83,15 +83,15 @@ def _make_layer(self, idx):
         depth = self.cfg['depths'][idx]
         width = self.cfg['widths'][idx]
         stride = self.cfg['strides'][idx]
-        num_groups = self.cfg['num_groups']
+        group_width = self.cfg['group_width']
         bottleneck_ratio = self.cfg['bottleneck_ratio']
         se_ratio = self.cfg['se_ratio']
 
         layers = []
         for i in range(depth):
             s = stride if i == 0 else 1
             layers.append(Block(self.in_planes, width,
-                                s, num_groups, bottleneck_ratio, se_ratio))
+                                s, group_width, bottleneck_ratio, se_ratio))
             self.in_planes = width
         return nn.Sequential(*layers)
 
@@ -112,7 +112,7 @@ def RegNetX_200MF():
         'depths': [1, 1, 4, 7],
         'widths': [24, 56, 152, 368],
         'strides': [1, 1, 2, 2],
-        'num_groups': 8,
+        'group_width': 8,
         'bottleneck_ratio': 1,
         'se_ratio': 0,
     }
@@ -124,7 +124,7 @@ def RegNetX_400MF():
         'depths': [1, 2, 7, 12],
         'widths': [32, 64, 160, 384],
         'strides': [1, 1, 2, 2],
-        'num_groups': 16,
+        'group_width': 16,
         'bottleneck_ratio': 1,
         'se_ratio': 0,
     }
@@ -136,15 +136,15 @@ def RegNetY_400MF():
         'depths': [1, 2, 7, 12],
         'widths': [32, 64, 160, 384],
         'strides': [1, 1, 2, 2],
-        'num_groups': 16,
+        'group_width': 16,
         'bottleneck_ratio': 1,
         'se_ratio': 0.25,
     }
     return RegNet(cfg)
 
 
 def test():
-    net = RegNetY_400MF()
+    net = RegNetX_200MF()
     print(net)
     x = torch.randn(2, 3, 32, 32)
     y = net(x)