Make genmobilenet weight init switchable, fix fan_out in google style linear init

models/genmobilenet.py

@@ -289,9 +289,11 @@ class _BlockBuilder:
         return blocks
 
 
-def _initialize_weight(m):
+def _initialize_weight_goog(m):
+    # weight init as per Tensorflow Official impl
+    # https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py
     if isinstance(m, nn.Conv2d):
-        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels  # fan-out
         m.weight.data.normal_(0, math.sqrt(2.0 / n))
         if m.bias is not None:
             m.bias.data.zero_()
@@ -299,12 +301,22 @@ def _initialize_weight(m):
         m.weight.data.fill_(1.0)
         m.bias.data.zero_()
     elif isinstance(m, nn.Linear):
-        n = m.weight.size(1)
+        n = m.weight.size(0)  # fan-out
         init_range = 1.0 / math.sqrt(n)
         m.weight.data.uniform_(-init_range, init_range)
         m.bias.data.zero_()
 
 
+def _initialize_weight_default(m):
+    if isinstance(m, nn.Conv2d):
+        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+    elif isinstance(m, nn.BatchNorm2d):
+        m.weight.data.fill_(1.0)
+        m.bias.data.zero_()
+    elif isinstance(m, nn.Linear):
+        nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='linear')
+
+
 class DepthwiseSeparableConv(nn.Module):
     def __init__(self, in_chs, out_chs, kernel_size,
                  stride=1, act_fn=F.relu, noskip=False, pw_act=False,
@@ -485,7 +497,8 @@ class GenMobileNet(nn.Module):
     def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=32, num_features=1280,
                  depth_multiplier=1.0, depth_divisor=8, min_depth=None,
                  bn_momentum=_BN_MOMENTUM_PT_DEFAULT, bn_eps=_BN_EPS_PT_DEFAULT,
-                 drop_rate=0., act_fn=F.relu, global_pool='avg', skip_head_conv=False):
+                 drop_rate=0., act_fn=F.relu, global_pool='avg', skip_head_conv=False,
+                 weight_init='goog'):
         super(GenMobileNet, self).__init__()
         self.num_classes = num_classes
         self.depth_multiplier = depth_multiplier
@@ -515,7 +528,10 @@ class GenMobileNet(nn.Module):
         self.classifier = nn.Linear(self.num_features, self.num_classes)
 
         for m in self.modules():
-            _initialize_weight(m)
+            if weight_init == 'goog':
+                _initialize_weight_goog(m)
+            else:
+                _initialize_weight_default(m)
 
     def get_classifier(self):
         return self.classifier