Change the init_weight for shufflenet models

mmcls/models/backbones/shufflenet_v1.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import torch.utils.checkpoint as cp
 from mmcv.cnn import (ConvModule, build_activation_layer, constant_init,
-                      kaiming_init)
+                      normal_init)
 from mmcv.runner import load_checkpoint
 from torch.nn.modules.batchnorm import _BatchNorm
 
@@ -255,11 +255,17 @@ class ShuffleNetV1(BaseBackbone):
             logger = logging.getLogger()
             load_checkpoint(self, pretrained, strict=False, logger=logger)
         elif pretrained is None:
-            for m in self.modules():
+            for name, m in self.named_modules():
                 if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
+                    if 'conv1' in name:
+                        normal_init(m, mean=0, std=0.01)
+                    else:
+                        normal_init(m, mean=0, std=1.0 / m.weight.shape[1])
                 elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
+                    constant_init(m.weight, val=1, bias=0.0001)
+                    if isinstance(m, _BatchNorm):
+                        if m.running_mean is not None:
+                            nn.init.constant_(m.running_mean, 0)
         else:
             raise TypeError('pretrained must be a str or None. But received '
                             f'{type(pretrained)}')

mmcls/models/backbones/shufflenet_v2.py

@@ -3,7 +3,7 @@ import logging
 import torch
 import torch.nn as nn
 import torch.utils.checkpoint as cp
-from mmcv.cnn import ConvModule, constant_init, kaiming_init
+from mmcv.cnn import ConvModule, constant_init, normal_init
 from mmcv.runner import load_checkpoint
 from torch.nn.modules.batchnorm import _BatchNorm
 
@@ -260,11 +260,18 @@ class ShuffleNetV2(BaseBackbone):
             logger = logging.getLogger()
             load_checkpoint(self, pretrained, strict=False, logger=logger)
         elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
-                    constant_init(m, 1)
+            for name, m in self.named_modules():
+                for name, m in self.named_modules():
+                    if isinstance(m, nn.Conv2d):
+                        if 'conv1' in name:
+                            normal_init(m, mean=0, std=0.01)
+                        else:
+                            normal_init(m, mean=0, std=1.0 / m.weight.shape[1])
+                    elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+                        constant_init(m.weight, val=1, bias=0.0001)
+                        if isinstance(m, _BatchNorm):
+                            if m.running_mean is not None:
+                                nn.init.constant_(m.running_mean, 0)
         else:
             raise TypeError('pretrained must be a str or None. But received '
                             f'{type(pretrained)}')