diff --git a/configs/cascade_mask_rcnn_r50_c4_1x.py b/configs/cascade_mask_rcnn_r50_c4_1x.py
index d60a165c10341289234453e49f1fbc753e26db40..a31e5187184fa9e2e9e4761d16e7fa556bbf074a 100644
--- a/configs/cascade_mask_rcnn_r50_c4_1x.py
+++ b/configs/cascade_mask_rcnn_r50_c4_1x.py
@@ -1,4 +1,5 @@
# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
@@ -11,7 +12,7 @@ model = dict(
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True,
style='caffe'),
shared_head=dict(
@@ -21,7 +22,7 @@ model = dict(
stride=2,
dilation=1,
style='caffe',
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True),
rpn_head=dict(
type='RPNHead',
diff --git a/configs/cascade_rcnn_r50_c4_1x.py b/configs/cascade_rcnn_r50_c4_1x.py
index d2ea3d0457a1a2813677861dd4f315ff528830e0..8224f2f1e0f96a1a2ff6c2dff99562ee1bf3a299 100644
--- a/configs/cascade_rcnn_r50_c4_1x.py
+++ b/configs/cascade_rcnn_r50_c4_1x.py
@@ -1,4 +1,5 @@
# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
@@ -11,7 +12,7 @@ model = dict(
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True,
style='caffe'),
shared_head=dict(
@@ -21,7 +22,7 @@ model = dict(
stride=2,
dilation=1,
style='caffe',
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True),
rpn_head=dict(
type='RPNHead',
diff --git a/configs/fast_rcnn_r50_c4_1x.py b/configs/fast_rcnn_r50_c4_1x.py
index 052e5780cf8e9b632adba3034054a052dec3c30a..a204d13d25e29a90670f1d9572974c2a846187ce 100644
--- a/configs/fast_rcnn_r50_c4_1x.py
+++ b/configs/fast_rcnn_r50_c4_1x.py
@@ -1,4 +1,5 @@
# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
@@ -10,7 +11,7 @@ model = dict(
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True,
style='caffe'),
shared_head=dict(
@@ -20,7 +21,7 @@ model = dict(
stride=2,
dilation=1,
style='caffe',
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True),
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
diff --git a/configs/faster_rcnn_r50_c4_1x.py b/configs/faster_rcnn_r50_c4_1x.py
index 01183203bfbebf3a69d8c4788dbe48d0f9136ab3..61ae19ddde4f150644bdd71c13edff08dc6de0e1 100644
--- a/configs/faster_rcnn_r50_c4_1x.py
+++ b/configs/faster_rcnn_r50_c4_1x.py
@@ -1,4 +1,5 @@
# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnet50_caffe',
@@ -10,7 +11,7 @@ model = dict(
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True,
style='caffe'),
shared_head=dict(
@@ -20,7 +21,7 @@ model = dict(
stride=2,
dilation=1,
style='caffe',
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True),
rpn_head=dict(
type='RPNHead',
diff --git a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py
index 067703db1473c30562e58547e082bf26a8153660..ef635839bb026a747154622285a67cef6a37e600 100644
--- a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py
+++ b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws_1x.py
@@ -1,6 +1,6 @@
# model settings
conv_cfg = dict(type='ConvWS')
-normalize = dict(type='GN', num_groups=32, requires_grad=True)
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
@@ -12,14 +12,14 @@ model = dict(
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
rpn_head=dict(
type='RPNHead',
in_channels=256,
@@ -48,7 +48,7 @@ model = dict(
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
conv_cfg=conv_cfg,
- normalize=normalize))
+ norm_cfg=norm_cfg))
# model training and testing settings
train_cfg = dict(
rpn=dict(
diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py
index 12a0c2b7aa006c42df631959cb55ff14bc7f88fd..5a79bfd6c4ac4e21d74ebc12c3aaf1b5194775da 100644
--- a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py
+++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_20_23_24e.py
@@ -1,6 +1,6 @@
# model settings
conv_cfg = dict(type='ConvWS')
-normalize = dict(type='GN', num_groups=32, requires_grad=True)
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
@@ -12,14 +12,14 @@ model = dict(
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
rpn_head=dict(
type='RPNHead',
in_channels=256,
@@ -48,7 +48,7 @@ model = dict(
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
@@ -61,7 +61,7 @@ model = dict(
conv_out_channels=256,
num_classes=81,
conv_cfg=conv_cfg,
- normalize=normalize))
+ norm_cfg=norm_cfg))
# model training and testing settings
train_cfg = dict(
rpn=dict(
diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py
index 7ae7740e27c687e0368aeddb92b89d24ea314f64..7294aee69286503898d9747d724d36d975380a05 100644
--- a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py
+++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws_2x.py
@@ -1,6 +1,6 @@
# model settings
conv_cfg = dict(type='ConvWS')
-normalize = dict(type='GN', num_groups=32, requires_grad=True)
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnet50_gn_ws',
@@ -12,14 +12,14 @@ model = dict(
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
rpn_head=dict(
type='RPNHead',
in_channels=256,
@@ -48,7 +48,7 @@ model = dict(
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
@@ -61,7 +61,7 @@ model = dict(
conv_out_channels=256,
num_classes=81,
conv_cfg=conv_cfg,
- normalize=normalize))
+ norm_cfg=norm_cfg))
# model training and testing settings
train_cfg = dict(
rpn=dict(
diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py
index 1d5fb980c951a24d11f6fc4a6375365f2680b4a6..4ed83b251be8b6fcb0ad0eb55043e913b5c9b4c8 100644
--- a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py
+++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws_2x.py
@@ -1,6 +1,6 @@
# model settings
conv_cfg = dict(type='ConvWS')
-normalize = dict(type='GN', num_groups=32, requires_grad=True)
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws',
@@ -14,14 +14,14 @@ model = dict(
frozen_stages=1,
style='pytorch',
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
rpn_head=dict(
type='RPNHead',
in_channels=256,
@@ -50,7 +50,7 @@ model = dict(
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
conv_cfg=conv_cfg,
- normalize=normalize),
+ norm_cfg=norm_cfg),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
@@ -63,7 +63,7 @@ model = dict(
conv_out_channels=256,
num_classes=81,
conv_cfg=conv_cfg,
- normalize=normalize))
+ norm_cfg=norm_cfg))
# model training and testing settings
train_cfg = dict(
rpn=dict(
diff --git a/configs/gn/mask_rcnn_r101_fpn_gn_2x.py b/configs/gn/mask_rcnn_r101_fpn_gn_2x.py
index 97b0c95c7d1b494009a188d309c90a9342b3eae9..3f61dc402a534b59010b1b5c0acfd1fe3c557605 100644
--- a/configs/gn/mask_rcnn_r101_fpn_gn_2x.py
+++ b/configs/gn/mask_rcnn_r101_fpn_gn_2x.py
@@ -1,5 +1,5 @@
# model settings
-normalize = dict(type='GN', num_groups=32, requires_grad=True)
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
@@ -11,13 +11,13 @@ model = dict(
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
- normalize=normalize),
+ norm_cfg=norm_cfg),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
- normalize=normalize),
+ norm_cfg=norm_cfg),
rpn_head=dict(
type='RPNHead',
in_channels=256,
@@ -45,7 +45,7 @@ model = dict(
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
- normalize=normalize),
+ norm_cfg=norm_cfg),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
@@ -57,7 +57,7 @@ model = dict(
in_channels=256,
conv_out_channels=256,
num_classes=81,
- normalize=normalize))
+ norm_cfg=norm_cfg))
# model training and testing settings
train_cfg = dict(
diff --git a/configs/gn/mask_rcnn_r50_fpn_gn_2x.py b/configs/gn/mask_rcnn_r50_fpn_gn_2x.py
index edbd7d8349c72d4f9ce98780a7ac9f891be9a868..165c4aa06fc018a6a4fdf489351090e8c1bbf04a 100644
--- a/configs/gn/mask_rcnn_r50_fpn_gn_2x.py
+++ b/configs/gn/mask_rcnn_r50_fpn_gn_2x.py
@@ -1,5 +1,5 @@
# model settings
-normalize = dict(type='GN', num_groups=32, requires_grad=True)
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
@@ -11,13 +11,13 @@ model = dict(
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
- normalize=normalize),
+ norm_cfg=norm_cfg),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
- normalize=normalize),
+ norm_cfg=norm_cfg),
rpn_head=dict(
type='RPNHead',
in_channels=256,
@@ -45,7 +45,7 @@ model = dict(
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
- normalize=normalize),
+ norm_cfg=norm_cfg),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
@@ -57,7 +57,7 @@ model = dict(
in_channels=256,
conv_out_channels=256,
num_classes=81,
- normalize=normalize))
+ norm_cfg=norm_cfg))
# model training and testing settings
train_cfg = dict(
diff --git a/configs/gn/mask_rcnn_r50_fpn_gn_contrib_2x.py b/configs/gn/mask_rcnn_r50_fpn_gn_contrib_2x.py
index 5c63992f0f932547e844278f90c4e873c3a4b3f9..00760fb031eb6a034b285ec2e8abe18b0f5527cc 100644
--- a/configs/gn/mask_rcnn_r50_fpn_gn_contrib_2x.py
+++ b/configs/gn/mask_rcnn_r50_fpn_gn_contrib_2x.py
@@ -1,5 +1,5 @@
# model settings
-normalize = dict(type='GN', num_groups=32, requires_grad=True)
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='MaskRCNN',
@@ -11,13 +11,13 @@ model = dict(
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
- normalize=normalize),
+ norm_cfg=norm_cfg),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
- normalize=normalize),
+ norm_cfg=norm_cfg),
rpn_head=dict(
type='RPNHead',
in_channels=256,
@@ -45,7 +45,7 @@ model = dict(
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2],
reg_class_agnostic=False,
- normalize=normalize),
+ norm_cfg=norm_cfg),
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
@@ -57,7 +57,7 @@ model = dict(
in_channels=256,
conv_out_channels=256,
num_classes=81,
- normalize=normalize))
+ norm_cfg=norm_cfg))
# model training and testing settings
train_cfg = dict(
diff --git a/configs/mask_rcnn_r50_c4_1x.py b/configs/mask_rcnn_r50_c4_1x.py
index 63884a4ed6746eb2e625787229ec607eeec86d26..e7584be88e6eb234ac2f25656de6b8a6918b999b 100644
--- a/configs/mask_rcnn_r50_c4_1x.py
+++ b/configs/mask_rcnn_r50_c4_1x.py
@@ -1,4 +1,5 @@
# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://resnet50_caffe',
@@ -10,7 +11,7 @@ model = dict(
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True,
style='caffe'),
shared_head=dict(
@@ -20,7 +21,7 @@ model = dict(
stride=2,
dilation=1,
style='caffe',
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=norm_cfg,
norm_eval=True),
rpn_head=dict(
type='RPNHead',
diff --git a/configs/rpn_r50_c4_1x.py b/configs/rpn_r50_c4_1x.py
index bc3d2a8f1f64b90f211bde8952ce7e631c4bf55e..ac9ad836956aa3968a84654285a02eb15f8832a2 100644
--- a/configs/rpn_r50_c4_1x.py
+++ b/configs/rpn_r50_c4_1x.py
@@ -10,7 +10,7 @@ model = dict(
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
- normalize=dict(type='BN', requires_grad=False),
+ norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe'),
neck=None,
diff --git a/mmdet/models/anchor_heads/retina_head.py b/mmdet/models/anchor_heads/retina_head.py
index 27701f46e9ead7865d294f75341054092efa19a2..815e8f0f91601588e31b9ce16a821c4e6aa57873 100644
--- a/mmdet/models/anchor_heads/retina_head.py
+++ b/mmdet/models/anchor_heads/retina_head.py
@@ -17,13 +17,13 @@ class RetinaHead(AnchorHead):
octave_base_scale=4,
scales_per_octave=3,
conv_cfg=None,
- normalize=None,
+ norm_cfg=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
+ self.norm_cfg = norm_cfg
octave_scales = np.array(
[2**(i / scales_per_octave) for i in range(scales_per_octave)])
anchor_scales = octave_scales * octave_base_scale
@@ -49,8 +49,7 @@ class RetinaHead(AnchorHead):
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
- bias=self.normalize is None))
+ norm_cfg=self.norm_cfg))
self.reg_convs.append(
ConvModule(
chn,
@@ -59,8 +58,7 @@ class RetinaHead(AnchorHead):
stride=1,
padding=1,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
- bias=self.normalize is None))
+ norm_cfg=self.norm_cfg))
self.retina_cls = nn.Conv2d(
self.feat_channels,
self.num_anchors * self.cls_out_channels,
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index c9daeef68c81213c2809d0c985e024a2953f2cc3..037fbb14d0bb7d9d46069dba7685194c51b6d713 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -23,13 +23,13 @@ class BasicBlock(nn.Module):
style='pytorch',
with_cp=False,
conv_cfg=None,
- normalize=dict(type='BN'),
+ norm_cfg=dict(type='BN'),
dcn=None):
super(BasicBlock, self).__init__()
assert dcn is None, "Not implemented yet."
- self.norm1_name, norm1 = build_norm_layer(normalize, planes, postfix=1)
- self.norm2_name, norm2 = build_norm_layer(normalize, planes, postfix=2)
+ self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
+ self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.conv1 = build_conv_layer(
conv_cfg,
@@ -95,7 +95,7 @@ class Bottleneck(nn.Module):
style='pytorch',
with_cp=False,
conv_cfg=None,
- normalize=dict(type='BN'),
+ norm_cfg=dict(type='BN'),
dcn=None):
"""Bottleneck block for ResNet.
If style is "pytorch", the stride-two layer is the 3x3 conv layer,
@@ -106,21 +106,26 @@ class Bottleneck(nn.Module):
assert dcn is None or isinstance(dcn, dict)
self.inplanes = inplanes
self.planes = planes
+ self.stride = stride
+ self.dilation = dilation
+ self.downsample = downsample
+ self.style = style
+ self.with_cp = with_cp
self.conv_cfg = conv_cfg
- self.normalize = normalize
+ self.norm_cfg = norm_cfg
self.dcn = dcn
self.with_dcn = dcn is not None
- if style == 'pytorch':
+ if self.style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = stride
else:
self.conv1_stride = stride
self.conv2_stride = 1
- self.norm1_name, norm1 = build_norm_layer(normalize, planes, postfix=1)
- self.norm2_name, norm2 = build_norm_layer(normalize, planes, postfix=2)
+ self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
+ self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
- normalize, planes * self.expansion, postfix=3)
+ norm_cfg, planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
conv_cfg,
@@ -180,11 +185,6 @@ class Bottleneck(nn.Module):
self.add_module(self.norm3_name, norm3)
self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
- self.dilation = dilation
- self.with_cp = with_cp
- self.normalize = normalize
@property
def norm1(self):
@@ -249,7 +249,7 @@ def make_res_layer(block,
style='pytorch',
with_cp=False,
conv_cfg=None,
- normalize=dict(type='BN'),
+ norm_cfg=dict(type='BN'),
dcn=None):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
@@ -261,7 +261,7 @@ def make_res_layer(block,
kernel_size=1,
stride=stride,
bias=False),
- build_norm_layer(normalize, planes * block.expansion)[1],
+ build_norm_layer(norm_cfg, planes * block.expansion)[1],
)
layers = []
@@ -275,7 +275,7 @@ def make_res_layer(block,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
- normalize=normalize,
+ norm_cfg=norm_cfg,
dcn=dcn))
inplanes = planes * block.expansion
for i in range(1, blocks):
@@ -288,7 +288,7 @@ def make_res_layer(block,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
- normalize=normalize,
+ norm_cfg=norm_cfg,
dcn=dcn))
return nn.Sequential(*layers)
@@ -309,7 +309,7 @@ class ResNet(nn.Module):
the first 1x1 conv layer.
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
-1 means not freezing any parameters.
- normalize (dict): dictionary to construct and config norm layer.
+ norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
@@ -336,7 +336,7 @@ class ResNet(nn.Module):
style='pytorch',
frozen_stages=-1,
conv_cfg=None,
- normalize=dict(type='BN', requires_grad=True),
+ norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
dcn=None,
stage_with_dcn=(False, False, False, False),
@@ -356,7 +356,7 @@ class ResNet(nn.Module):
self.style = style
self.frozen_stages = frozen_stages
self.conv_cfg = conv_cfg
- self.normalize = normalize
+ self.norm_cfg = norm_cfg
self.with_cp = with_cp
self.norm_eval = norm_eval
self.dcn = dcn
@@ -386,7 +386,7 @@ class ResNet(nn.Module):
style=self.style,
with_cp=with_cp,
conv_cfg=conv_cfg,
- normalize=normalize,
+ norm_cfg=norm_cfg,
dcn=dcn)
self.inplanes = planes * self.block.expansion
layer_name = 'layer{}'.format(i + 1)
@@ -411,8 +411,7 @@ class ResNet(nn.Module):
stride=2,
padding=3,
bias=False)
- self.norm1_name, norm1 = build_norm_layer(
- self.normalize, 64, postfix=1)
+ self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
self.add_module(self.norm1_name, norm1)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py
index 3cdb1cb26e49bf011b6cc7a47f3a4ef02427b325..c869a02ebc93e0a40d66ef4c13d6aab7c0a5bb4f 100644
--- a/mmdet/models/backbones/resnext.py
+++ b/mmdet/models/backbones/resnext.py
@@ -24,11 +24,11 @@ class Bottleneck(_Bottleneck):
width = math.floor(self.planes * (base_width / 64)) * groups
self.norm1_name, norm1 = build_norm_layer(
- self.normalize, width, postfix=1)
+ self.norm_cfg, width, postfix=1)
self.norm2_name, norm2 = build_norm_layer(
- self.normalize, width, postfix=2)
+ self.norm_cfg, width, postfix=2)
self.norm3_name, norm3 = build_norm_layer(
- self.normalize, self.planes * self.expansion, postfix=3)
+ self.norm_cfg, self.planes * self.expansion, postfix=3)
self.conv1 = build_conv_layer(
self.conv_cfg,
@@ -102,7 +102,7 @@ def make_res_layer(block,
style='pytorch',
with_cp=False,
conv_cfg=None,
- normalize=dict(type='BN'),
+ norm_cfg=dict(type='BN'),
dcn=None):
downsample = None
if stride != 1 or inplanes != planes * block.expansion:
@@ -114,7 +114,7 @@ def make_res_layer(block,
kernel_size=1,
stride=stride,
bias=False),
- build_norm_layer(normalize, planes * block.expansion)[1],
+ build_norm_layer(norm_cfg, planes * block.expansion)[1],
)
layers = []
@@ -130,7 +130,7 @@ def make_res_layer(block,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
- normalize=normalize,
+ norm_cfg=norm_cfg,
dcn=dcn))
inplanes = planes * block.expansion
for i in range(1, blocks):
@@ -145,7 +145,7 @@ def make_res_layer(block,
style=style,
with_cp=with_cp,
conv_cfg=conv_cfg,
- normalize=normalize,
+ norm_cfg=norm_cfg,
dcn=dcn))
return nn.Sequential(*layers)
@@ -168,7 +168,7 @@ class ResNeXt(ResNet):
the first 1x1 conv layer.
frozen_stages (int): Stages to be frozen (all param fixed). -1 means
not freezing any parameters.
- normalize (dict): dictionary to construct and config norm layer.
+ norm_cfg (dict): dictionary to construct and config norm layer.
norm_eval (bool): Whether to set norm layers to eval mode, namely,
freeze running stats (mean and var). Note: Effect on Batch Norm
and its variants only.
@@ -208,7 +208,7 @@ class ResNeXt(ResNet):
style=self.style,
with_cp=self.with_cp,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
+ norm_cfg=self.norm_cfg,
dcn=dcn)
self.inplanes = planes * self.block.expansion
layer_name = 'layer{}'.format(i + 1)
diff --git a/mmdet/models/bbox_heads/convfc_bbox_head.py b/mmdet/models/bbox_heads/convfc_bbox_head.py
index af2a5e34907b9a2c7ba865f7916fd592b5b0d54b..c0e715415f8b8eb5f519dd25e22d9df757d299c8 100644
--- a/mmdet/models/bbox_heads/convfc_bbox_head.py
+++ b/mmdet/models/bbox_heads/convfc_bbox_head.py
@@ -25,7 +25,7 @@ class ConvFCBBoxHead(BBoxHead):
conv_out_channels=256,
fc_out_channels=1024,
conv_cfg=None,
- normalize=None,
+ norm_cfg=None,
*args,
**kwargs):
super(ConvFCBBoxHead, self).__init__(*args, **kwargs)
@@ -46,8 +46,7 @@ class ConvFCBBoxHead(BBoxHead):
self.conv_out_channels = conv_out_channels
self.fc_out_channels = fc_out_channels
self.conv_cfg = conv_cfg
- self.normalize = normalize
- self.with_bias = normalize is None
+ self.norm_cfg = norm_cfg
# add shared convs and fcs
self.shared_convs, self.shared_fcs, last_layer_dim = \
@@ -104,8 +103,7 @@ class ConvFCBBoxHead(BBoxHead):
3,
padding=1,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
- bias=self.with_bias))
+ norm_cfg=self.norm_cfg))
last_layer_dim = self.conv_out_channels
# add branch specific fc layers
branch_fcs = nn.ModuleList()
diff --git a/mmdet/models/mask_heads/fcn_mask_head.py b/mmdet/models/mask_heads/fcn_mask_head.py
index 614497a8e288bc8afe8d5d96c67d704e620f0e91..9274e7c8cea60d3c3d5a606b5e9f3207aeacb7a5 100644
--- a/mmdet/models/mask_heads/fcn_mask_head.py
+++ b/mmdet/models/mask_heads/fcn_mask_head.py
@@ -23,7 +23,7 @@ class FCNMaskHead(nn.Module):
num_classes=81,
class_agnostic=False,
conv_cfg=None,
- normalize=None):
+ norm_cfg=None):
super(FCNMaskHead, self).__init__()
if upsample_method not in [None, 'deconv', 'nearest', 'bilinear']:
raise ValueError(
@@ -39,8 +39,7 @@ class FCNMaskHead(nn.Module):
self.num_classes = num_classes
self.class_agnostic = class_agnostic
self.conv_cfg = conv_cfg
- self.normalize = normalize
- self.with_bias = normalize is None
+ self.norm_cfg = norm_cfg
self.convs = nn.ModuleList()
for i in range(self.num_convs):
@@ -54,8 +53,7 @@ class FCNMaskHead(nn.Module):
self.conv_kernel_size,
padding=padding,
conv_cfg=conv_cfg,
- normalize=normalize,
- bias=self.with_bias))
+ norm_cfg=norm_cfg))
upsample_in_channels = (self.conv_out_channels
if self.num_convs > 0 else in_channels)
if self.upsample_method is None:
diff --git a/mmdet/models/mask_heads/fused_semantic_head.py b/mmdet/models/mask_heads/fused_semantic_head.py
index f24adf357eb0d8a18ab215066a33fb8f264343dd..12b8302ab11507234e3a78146f77a0ba67f4e242 100644
--- a/mmdet/models/mask_heads/fused_semantic_head.py
+++ b/mmdet/models/mask_heads/fused_semantic_head.py
@@ -31,7 +31,7 @@ class FusedSemanticHead(nn.Module):
ignore_label=255,
loss_weight=0.2,
conv_cfg=None,
- normalize=None):
+ norm_cfg=None):
super(FusedSemanticHead, self).__init__()
self.num_ins = num_ins
self.fusion_level = fusion_level
@@ -42,8 +42,7 @@ class FusedSemanticHead(nn.Module):
self.ignore_label = ignore_label
self.loss_weight = loss_weight
self.conv_cfg = conv_cfg
- self.normalize = normalize
- self.with_bias = normalize is None
+ self.norm_cfg = norm_cfg
self.lateral_convs = nn.ModuleList()
for i in range(self.num_ins):
@@ -53,8 +52,7 @@ class FusedSemanticHead(nn.Module):
self.in_channels,
1,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
- bias=self.with_bias,
+ norm_cfg=self.norm_cfg,
inplace=False))
self.convs = nn.ModuleList()
@@ -67,15 +65,13 @@ class FusedSemanticHead(nn.Module):
3,
padding=1,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
- bias=self.with_bias))
+ norm_cfg=self.norm_cfg))
self.conv_embedding = ConvModule(
conv_out_channels,
conv_out_channels,
1,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
- bias=self.with_bias)
+ norm_cfg=self.norm_cfg)
self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1)
self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label)
diff --git a/mmdet/models/mask_heads/htc_mask_head.py b/mmdet/models/mask_heads/htc_mask_head.py
index 21f3130494c3e1ed145c0d42d52565f837d4e18f..9ba3ed72298a36ed63a185f581de889b72324f8d 100644
--- a/mmdet/models/mask_heads/htc_mask_head.py
+++ b/mmdet/models/mask_heads/htc_mask_head.py
@@ -13,8 +13,7 @@ class HTCMaskHead(FCNMaskHead):
self.conv_out_channels,
1,
conv_cfg=self.conv_cfg,
- normalize=self.normalize,
- bias=self.with_bias)
+ norm_cfg=self.norm_cfg)
def init_weights(self):
super(HTCMaskHead, self).init_weights()
diff --git a/mmdet/models/necks/fpn.py b/mmdet/models/necks/fpn.py
index 3e49fc4943994dd110f3f63c98b550475c228b44..7b33b69de7de8432c7b1e40ec737b1cdc63a28e8 100644
--- a/mmdet/models/necks/fpn.py
+++ b/mmdet/models/necks/fpn.py
@@ -18,7 +18,7 @@ class FPN(nn.Module):
add_extra_convs=False,
extra_convs_on_inputs=True,
conv_cfg=None,
- normalize=None,
+ norm_cfg=None,
activation=None):
super(FPN, self).__init__()
assert isinstance(in_channels, list)
@@ -27,7 +27,6 @@ class FPN(nn.Module):
self.num_ins = len(in_channels)
self.num_outs = num_outs
self.activation = activation
- self.with_bias = normalize is None
if end_level == -1:
self.backbone_end_level = self.num_ins
@@ -51,8 +50,7 @@ class FPN(nn.Module):
out_channels,
1,
conv_cfg=conv_cfg,
- normalize=normalize,
- bias=self.with_bias,
+ norm_cfg=norm_cfg,
activation=self.activation,
inplace=False)
fpn_conv = ConvModule(
@@ -61,8 +59,7 @@ class FPN(nn.Module):
3,
padding=1,
conv_cfg=conv_cfg,
- normalize=normalize,
- bias=self.with_bias,
+ norm_cfg=norm_cfg,
activation=self.activation,
inplace=False)
@@ -83,8 +80,8 @@ class FPN(nn.Module):
3,
stride=2,
padding=1,
- normalize=normalize,
- bias=self.with_bias,
+ conv_cfg=conv_cfg,
+ norm_cfg=norm_cfg,
activation=self.activation,
inplace=False)
self.fpn_convs.append(extra_fpn_conv)
diff --git a/mmdet/models/shared_heads/res_layer.py b/mmdet/models/shared_heads/res_layer.py
index ea306e54559c42d768693357caddd649d9fa1980..743c2eefe60cf79008284fae218a4e41c8630521 100644
--- a/mmdet/models/shared_heads/res_layer.py
+++ b/mmdet/models/shared_heads/res_layer.py
@@ -17,13 +17,13 @@ class ResLayer(nn.Module):
stride=2,
dilation=1,
style='pytorch',
- normalize=dict(type='BN', requires_grad=True),
+ norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
with_cp=False,
dcn=None):
super(ResLayer, self).__init__()
self.norm_eval = norm_eval
- self.normalize = normalize
+ self.norm_cfg = norm_cfg
self.stage = stage
block, stage_blocks = ResNet.arch_settings[depth]
stage_block = stage_blocks[stage]
@@ -39,7 +39,7 @@ class ResLayer(nn.Module):
dilation=dilation,
style=style,
with_cp=with_cp,
- normalize=self.normalize,
+ norm_cfg=self.norm_cfg,
dcn=dcn)
self.add_module('layer{}'.format(stage + 1), res_layer)
diff --git a/mmdet/models/utils/conv_module.py b/mmdet/models/utils/conv_module.py
index b3bf9c77ca0b72223d6ac817d8e4122be6a811fa..329b2352767949eab9c1e3d5adda52e1f23558e5 100644
--- a/mmdet/models/utils/conv_module.py
+++ b/mmdet/models/utils/conv_module.py
@@ -42,6 +42,27 @@ def build_conv_layer(cfg, *args, **kwargs):
class ConvModule(nn.Module):
+ """Conv-Norm-Activation block.
+
+ Args:
+ in_channels (int): Same as nn.Conv2d.
+ out_channels (int): Same as nn.Conv2d.
+ kernel_size (int or tuple[int]): Same as nn.Conv2d.
+ stride (int or tuple[int]): Same as nn.Conv2d.
+ padding (int or tuple[int]): Same as nn.Conv2d.
+ dilation (int or tuple[int]): Same as nn.Conv2d.
+ groups (int): Same as nn.Conv2d.
+ bias (bool or str): If specified as `auto`, it will be decided by the
+ norm_cfg. Bias will be set as True if norm_cfg is None, otherwise
+ False.
+ conv_cfg (dict): Config dict for convolution layer.
+ norm_cfg (dict): Config dict for normalization layer.
+ activation (str or None): Activation type, "ReLU" by default.
+ inplace (bool): Whether to use inplace mode for activation.
+ activate_last (bool): Whether to apply the activation layer in the
+ last. (Do not use this flag since the behavior and api may be
+ changed in the future.)
+ """
def __init__(self,
in_channels,
@@ -51,35 +72,42 @@ class ConvModule(nn.Module):
padding=0,
dilation=1,
groups=1,
- bias=True,
+ bias='auto',
conv_cfg=None,
- normalize=None,
+ norm_cfg=None,
activation='relu',
inplace=True,
activate_last=True):
super(ConvModule, self).__init__()
assert conv_cfg is None or isinstance(conv_cfg, dict)
- assert normalize is None or isinstance(normalize, dict)
- self.with_norm = normalize is not None
- self.with_activatation = activation is not None
- self.with_bias = bias
+ assert norm_cfg is None or isinstance(norm_cfg, dict)
+ self.conv_cfg = conv_cfg
+ self.norm_cfg = norm_cfg
self.activation = activation
+ self.inplace = inplace
self.activate_last = activate_last
+ self.with_norm = norm_cfg is not None
+ self.with_activatation = activation is not None
+ # if the conv layer is before a norm layer, bias is unnecessary.
+ if bias == 'auto':
+ bias = False if self.with_norm else True
+ self.with_bias = bias
+
if self.with_norm and self.with_bias:
warnings.warn('ConvModule has norm and bias at the same time')
- self.conv = build_conv_layer(
- conv_cfg,
- in_channels,
- out_channels,
- kernel_size,
- stride,
- padding,
- dilation,
- groups,
- bias=bias)
-
+ # build convolution layer
+ self.conv = build_conv_layer(conv_cfg,
+ in_channels,
+ out_channels,
+ kernel_size,
+ stride=stride,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias)
+ # export the attributes of self.conv to a higher level for convenience
self.in_channels = self.conv.in_channels
self.out_channels = self.conv.out_channels
self.kernel_size = self.conv.kernel_size
@@ -90,17 +118,21 @@ class ConvModule(nn.Module):
self.output_padding = self.conv.output_padding
self.groups = self.conv.groups
+ # build normalization layers
if self.with_norm:
norm_channels = out_channels if self.activate_last else in_channels
- self.norm_name, norm = build_norm_layer(normalize, norm_channels)
+ self.norm_name, norm = build_norm_layer(norm_cfg, norm_channels)
self.add_module(self.norm_name, norm)
+ # build activation layer
if self.with_activatation:
- assert activation in ['relu'], 'Only ReLU supported.'
+ if self.activation not in ['relu']:
+ raise ValueError('{} is currently not supported.'.format(
+ self.activation))
if self.activation == 'relu':
self.activate = nn.ReLU(inplace=inplace)
- # Default using msra init
+ # Use msra init by default
self.init_weights()
@property
@@ -121,6 +153,7 @@ class ConvModule(nn.Module):
if activate and self.with_activatation:
x = self.activate(x)
else:
+ # WARN: this may be removed or modified
if norm and self.with_norm:
x = self.norm(x)
if activate and self.with_activatation: