diff --git a/MODEL_ZOO.md b/MODEL_ZOO.md
index ca84069e404a1d443e99f13844830fd28605d609..7e589e6521d81757f3a64cf36273ff73c4b7b75e 100644
--- a/MODEL_ZOO.md
+++ b/MODEL_ZOO.md
@@ -109,15 +109,15 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m
| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN | caffe | 1x | 6.7 | 0.468 | 9.4 | 35.8 | - |
-| R-50-FPN | pytorch | 1x | 6.9 | 0.496 | 9.1 | 35.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-3d3c2142.pth) |
-| R-50-FPN | pytorch | 2x | 6.9 | 0.496 | 9.1 | 36.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_2x_20181125-e0dbec97.pth) |
+| R-50-FPN | pytorch | 1x | 6.9 | 0.496 | 9.1 | 35.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth) |
+| R-50-FPN | pytorch | 2x | 6.9 | 0.496 | 9.1 | 36.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_2x_20181125-8b724df2.pth) |
| R-101-FPN | caffe | 1x | 9.2 | 0.614 | 8.2 | 37.8 | - |
-| R-101-FPN | pytorch | 1x | 9.6 | 0.643 | 8.1 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f738a02f.pth) |
-| R-101-FPN | pytorch | 2x | 9.6 | 0.643 | 8.1 | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_2x_20181129-f654534b.pth) |
-| X-101-32x4d-FPN | pytorch | 1x| 10.8 | 0.792 | 6.7 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_1x_20181218-c140fb82.pth)
-| X-101-32x4d-FPN | pytorch | 2x| 10.8 | 0.792 | 6.7 | 39.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_2x_20181218-605dcd0a.pth)
-| X-101-64x4d-FPN | pytorch | 1x| 14.6 | 1.128 | 5.3 | 40.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_1x_20181218-2f6f778b.pth)
-| X-101-64x4d-FPN | pytorch | 2x| 14.6 | 1.128 | 5.3 | 39.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_2x_20181218-2f598dc5.pth)
+| R-101-FPN | pytorch | 1x | 9.6 | 0.643 | 8.1 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f016f384.pth) |
+| R-101-FPN | pytorch | 2x | 9.6 | 0.643 | 8.1 | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_2x_20181129-72c14526.pth) |
+| X-101-32x4d-FPN | pytorch | 1x| 10.8 | 0.792 | 6.7 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_1x_20181218-c84d7dfc.pth)
+| X-101-32x4d-FPN | pytorch | 2x| 10.8 | 0.792 | 6.7 | 39.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_2x_20181218-8596452d.pth)
+| X-101-64x4d-FPN | pytorch | 1x| 14.6 | 1.128 | 5.3 | 40.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_1x_20181218-a0a22662.pth)
+| X-101-64x4d-FPN | pytorch | 2x| 14.6 | 1.128 | 5.3 | 39.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_2x_20181218-5e88d045.pth)
### Cascade R-CNN
diff --git a/mmdet/models/anchor_heads/retina_head.py b/mmdet/models/anchor_heads/retina_head.py
index e4b95c50534eb5f85bc13a8a46586158ab8fb48b..27701f46e9ead7865d294f75341054092efa19a2 100644
--- a/mmdet/models/anchor_heads/retina_head.py
+++ b/mmdet/models/anchor_heads/retina_head.py
@@ -4,7 +4,7 @@ from mmcv.cnn import normal_init
from .anchor_head import AnchorHead
from ..registry import HEADS
-from ..utils import bias_init_with_prob
+from ..utils import bias_init_with_prob, ConvModule
@HEADS.register_module
@@ -16,10 +16,14 @@ class RetinaHead(AnchorHead):
stacked_convs=4,
octave_base_scale=4,
scales_per_octave=3,
+ conv_cfg=None,
+ normalize=None,
**kwargs):
self.stacked_convs = stacked_convs
self.octave_base_scale = octave_base_scale
self.scales_per_octave = scales_per_octave
+ self.conv_cfg = conv_cfg
+ self.normalize = normalize
octave_scales = np.array(
[2**(i / scales_per_octave) for i in range(scales_per_octave)])
anchor_scales = octave_scales * octave_base_scale
@@ -38,9 +42,25 @@ class RetinaHead(AnchorHead):
for i in range(self.stacked_convs):
chn = self.in_channels if i == 0 else self.feat_channels
self.cls_convs.append(
- nn.Conv2d(chn, self.feat_channels, 3, stride=1, padding=1))
+ ConvModule(
+ chn,
+ self.feat_channels,
+ 3,
+ stride=1,
+ padding=1,
+ conv_cfg=self.conv_cfg,
+ normalize=self.normalize,
+ bias=self.normalize is None))
self.reg_convs.append(
- nn.Conv2d(chn, self.feat_channels, 3, stride=1, padding=1))
+ ConvModule(
+ chn,
+ self.feat_channels,
+ 3,
+ stride=1,
+ padding=1,
+ conv_cfg=self.conv_cfg,
+ normalize=self.normalize,
+ bias=self.normalize is None))
self.retina_cls = nn.Conv2d(
self.feat_channels,
self.num_anchors * self.cls_out_channels,
@@ -51,9 +71,9 @@ class RetinaHead(AnchorHead):
def init_weights(self):
for m in self.cls_convs:
- normal_init(m, std=0.01)
+ normal_init(m.conv, std=0.01)
for m in self.reg_convs:
- normal_init(m, std=0.01)
+ normal_init(m.conv, std=0.01)
bias_cls = bias_init_with_prob(0.01)
normal_init(self.retina_cls, std=0.01, bias=bias_cls)
normal_init(self.retina_reg, std=0.01)
@@ -62,9 +82,9 @@ class RetinaHead(AnchorHead):
cls_feat = x
reg_feat = x
for cls_conv in self.cls_convs:
- cls_feat = self.relu(cls_conv(cls_feat))
+ cls_feat = cls_conv(cls_feat)
for reg_conv in self.reg_convs:
- reg_feat = self.relu(reg_conv(reg_feat))
+ reg_feat = reg_conv(reg_feat)
cls_score = self.retina_cls(cls_feat)
bbox_pred = self.retina_reg(reg_feat)
return cls_score, bbox_pred
diff --git a/tools/upgrade_model_version.py b/tools/upgrade_model_version.py
new file mode 100644
index 0000000000000000000000000000000000000000..00bcdf44ab185a1908124eb759bd6cdad0c45066
--- /dev/null
+++ b/tools/upgrade_model_version.py
@@ -0,0 +1,42 @@
+import argparse
+import re
+from collections import OrderedDict
+
+import torch
+
+
+def convert(in_file, out_file):
+ """Convert keys in checkpoints.
+
+ There can be some breaking changes during the development of mmdetection,
+ and this tool is used for upgrading checkpoints trained with old versions
+ to the latest one.
+ """
+ checkpoint = torch.load(in_file)
+ in_state_dict = checkpoint.pop('state_dict')
+ out_state_dict = OrderedDict()
+ for key, val in in_state_dict.items():
+ # Use ConvModule instead of nn.Conv2d in RetinaNet
+ # cls_convs.0.weight -> cls_convs.0.conv.weight
+ m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key)
+ if m is not None:
+ param = m.groups()[1]
+ new_key = key.replace(param, 'conv.{}'.format(param))
+ out_state_dict[new_key] = val
+ continue
+
+ out_state_dict[key] = val
+ checkpoint['state_dict'] = out_state_dict
+ torch.save(checkpoint, out_file)
+
+
+def main():
+ parser = argparse.ArgumentParser(description='Upgrade model version')
+ parser.add_argument('in_file', help='input checkpoint file')
+ parser.add_argument('out_file', help='output checkpoint file')
+ args = parser.parse_args()
+ convert(args.in_file, args.out_file)
+
+
+if __name__ == '__main__':
+ main()