use 'kaiming_uniform' to initialize resnet, disable gn after fc layer (#377)

* make pixel indexes 0-based for bounding box in pascal voc dataset

* replacing all instances of torch.distributed.deprecated with torch.distributed

* replacing all instances of torch.distributed.deprecated with torch.distributed

* add GroupNorm

* add GroupNorm -- sort out yaml files

* use torch.nn.GroupNorm instead, replace 'use_gn' with 'conv_block' and use 'BaseStem'&'Bottleneck' to simply codes

* modification on 'group_norm' and 'conv_with_kaiming_uniform' function

* modification on yaml files in configs/gn_baselines/ and reduce the amount of indentation and code duplication

* use 'kaiming_uniform' to initialize resnet, disable gn after fc layer, and add dilation into ResNetHead

maskrcnn_benchmark/modeling/backbone/resnet.py

@@ -244,6 +244,10 @@ class Bottleneck(nn.Module):
                 ),
                 norm_func(out_channels),
             )
+            for modules in [self.downsample,]:
+                for l in modules.modules():
+                    if isinstance(l, Conv2d):
+                        nn.init.kaiming_uniform_(l.weight, a=1)
 
         if dilation > 1:
             stride = 1 # reset to be 1
@@ -280,6 +284,9 @@ class Bottleneck(nn.Module):
         )
         self.bn3 = norm_func(out_channels)
 
+        for l in [self.conv1, self.conv2, self.conv3,]:
+            nn.init.kaiming_uniform_(l.weight, a=1)
+
     def forward(self, x):
         identity = x
 
@@ -314,6 +321,9 @@ class BaseStem(nn.Module):
         )
         self.bn1 = norm_func(out_channels)
 
+        for l in [self.conv1,]:
+            nn.init.kaiming_uniform_(l.weight, a=1)
+
     def forward(self, x):
         x = self.conv1(x)
         x = self.bn1(x)

maskrcnn_benchmark/modeling/make_layers.py

@@ -77,7 +77,7 @@ def make_conv3x3(
     return conv
 
 
-def make_fc(dim_in, hidden_dim, use_gn):
+def make_fc(dim_in, hidden_dim, use_gn=False):
     '''
         Caffe2 implementation uses XavierFill, which in fact
         corresponds to kaiming_uniform_ in PyTorch

maskrcnn_benchmark/modeling/roi_heads/box_head/roi_box_feature_extractors.py

@@ -33,6 +33,7 @@ class ResNet50Conv5ROIFeatureExtractor(nn.Module):
             stride_in_1x1=config.MODEL.RESNETS.STRIDE_IN_1X1,
             stride_init=None,
             res2_out_channels=config.MODEL.RESNETS.RES2_OUT_CHANNELS,
+            dilation=config.MODEL.RESNETS.RES5_DILATION
         )
 
         self.pooler = pooler
@@ -131,7 +132,7 @@ class FPNXconv1fcFeatureExtractor(nn.Module):
 
         input_size = conv_head_dim * resolution ** 2
         representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
-        self.fc6 = make_fc(input_size, representation_size, use_gn)
+        self.fc6 = make_fc(input_size, representation_size, use_gn=False)
 
     def forward(self, x, proposals):
         x = self.pooler(x, proposals)