diff --git a/mmdet/core/bbox/assigners/center_region_assigner.py b/mmdet/core/bbox/assigners/center_region_assigner.py
index 1dc205e0defc0fcb5484347784e410aa41ba6685..91f8c014188b45d7c35819dcc34109461e6744a5 100644
--- a/mmdet/core/bbox/assigners/center_region_assigner.py
+++ b/mmdet/core/bbox/assigners/center_region_assigner.py
@@ -68,7 +68,7 @@ def bboxes_area(bboxes):
return areas
-@BBOX_ASSIGNERS.register_module
+@BBOX_ASSIGNERS.register_module()
class CenterRegionAssigner(BaseAssigner):
"""Assign pixels at the center region of a bbox as positive.
diff --git a/mmdet/core/bbox/coder/tblr_bbox_coder.py b/mmdet/core/bbox/coder/tblr_bbox_coder.py
index 330a31702e87d67c8bd13f1942485bedd5912dbf..16ee7f77e18e094b282f70e23bda5a26e758ca23 100644
--- a/mmdet/core/bbox/coder/tblr_bbox_coder.py
+++ b/mmdet/core/bbox/coder/tblr_bbox_coder.py
@@ -4,7 +4,7 @@ from ..builder import BBOX_CODERS
from .base_bbox_coder import BaseBBoxCoder
-@BBOX_CODERS.register_module
+@BBOX_CODERS.register_module()
class TBLRBBoxCoder(BaseBBoxCoder):
"""TBLR BBox coder
diff --git a/mmdet/models/anchor_heads/atss_head.py b/mmdet/models/anchor_heads/atss_head.py
index ba0f1f2f20d0865edd94354b2ebaea92920d731d..8af8ce0427d571e07de2193c30fa5442452bd6d5 100644
--- a/mmdet/models/anchor_heads/atss_head.py
+++ b/mmdet/models/anchor_heads/atss_head.py
@@ -1,12 +1,11 @@
import torch
import torch.distributed as dist
import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
force_fp32, images_to_levels, multi_apply,
multiclass_nms, unmap)
-from mmdet.ops import ConvModule, Scale
from ..builder import HEADS, build_loss
from .anchor_head import AnchorHead
diff --git a/mmdet/models/anchor_heads/fcos_head.py b/mmdet/models/anchor_heads/fcos_head.py
index ffa0fd5c42bfed89b4a45c45a9ea9d9b7bb8e9c2..5445aab720294ef156d2aaec221b97d62eef07b1 100644
--- a/mmdet/models/anchor_heads/fcos_head.py
+++ b/mmdet/models/anchor_heads/fcos_head.py
@@ -1,9 +1,8 @@
import torch
import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
from mmdet.core import distance2bbox, force_fp32, multi_apply, multiclass_nms
-from mmdet.ops import ConvModule, Scale
from ..builder import HEADS, build_loss
INF = 1e8
diff --git a/mmdet/models/anchor_heads/fovea_head.py b/mmdet/models/anchor_heads/fovea_head.py
index 73a88fed71b04fa6886b0cc289dd8b34c20aab61..2f80cb8e34dfb0df3438648cc4aff146f689c0f0 100644
--- a/mmdet/models/anchor_heads/fovea_head.py
+++ b/mmdet/models/anchor_heads/fovea_head.py
@@ -1,9 +1,9 @@
import torch
import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmdet.core import multi_apply, multiclass_nms
-from mmdet.ops import ConvModule, DeformConv
+from mmdet.ops import DeformConv
from ..builder import HEADS, build_loss
INF = 1e8
diff --git a/mmdet/models/anchor_heads/fsaf_head.py b/mmdet/models/anchor_heads/fsaf_head.py
index f3577fb7d412c2c0734d94f75f69518b4fddc355..7da2d8ec8207113c6ad27c13538e55c10ec80f6f 100644
--- a/mmdet/models/anchor_heads/fsaf_head.py
+++ b/mmdet/models/anchor_heads/fsaf_head.py
@@ -9,7 +9,7 @@ from ..losses.utils import weight_reduce_loss
from .retina_head import RetinaHead
-@HEADS.register_module
+@HEADS.register_module()
class FSAFHead(RetinaHead):
"""FSAF anchor-free head used in [1].
diff --git a/mmdet/models/anchor_heads/ga_retina_head.py b/mmdet/models/anchor_heads/ga_retina_head.py
index db2639f97289a81a78c8b6d08ebf8eb5c250c7b7..3545172e083cebbfa5afb07489810779eeffe876 100644
--- a/mmdet/models/anchor_heads/ga_retina_head.py
+++ b/mmdet/models/anchor_heads/ga_retina_head.py
@@ -1,7 +1,7 @@
import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-from mmdet.ops import ConvModule, MaskedConv2d
+from mmdet.ops import MaskedConv2d
from ..builder import HEADS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
diff --git a/mmdet/models/anchor_heads/reppoints_head.py b/mmdet/models/anchor_heads/reppoints_head.py
index 7b18f022499a23e49d133bdf43af3f585090a93d..2a95da431a262038fa5e493a6650dfdb76998750 100644
--- a/mmdet/models/anchor_heads/reppoints_head.py
+++ b/mmdet/models/anchor_heads/reppoints_head.py
@@ -1,11 +1,11 @@
import numpy as np
import torch
import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmdet.core import (PointGenerator, build_assigner, build_sampler,
images_to_levels, multi_apply, multiclass_nms, unmap)
-from mmdet.ops import ConvModule, DeformConv
+from mmdet.ops import DeformConv
from ..builder import HEADS, build_loss
diff --git a/mmdet/models/anchor_heads/retina_head.py b/mmdet/models/anchor_heads/retina_head.py
index 55ec4dbb31aa7d54f1b481d4794ec8789a6754b3..871a92d204aa4d7f334d01d058705d792039c783 100644
--- a/mmdet/models/anchor_heads/retina_head.py
+++ b/mmdet/models/anchor_heads/retina_head.py
@@ -1,7 +1,6 @@
import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-from mmdet.ops import ConvModule
from ..builder import HEADS
from .anchor_head import AnchorHead
diff --git a/mmdet/models/anchor_heads/retina_sepbn_head.py b/mmdet/models/anchor_heads/retina_sepbn_head.py
index 4e93caf185e547d5c679934d7011d71909d31f4c..1ec5e980d6d770f0e0937675408ff584396c94fc 100644
--- a/mmdet/models/anchor_heads/retina_sepbn_head.py
+++ b/mmdet/models/anchor_heads/retina_sepbn_head.py
@@ -1,7 +1,6 @@
import torch.nn as nn
-from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
-from mmdet.ops import ConvModule
from ..builder import HEADS
from .anchor_head import AnchorHead
diff --git a/mmdet/models/backbones/hrnet.py b/mmdet/models/backbones/hrnet.py
index 06efe578e060a0866c2d115084976b6b31142097..da488019c238b98cd1a7d3bc084ef7e135ff1904 100644
--- a/mmdet/models/backbones/hrnet.py
+++ b/mmdet/models/backbones/hrnet.py
@@ -1,9 +1,9 @@
import torch.nn as nn
-from mmcv.cnn import constant_init, kaiming_init
+from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
+ kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
-from mmdet.ops import build_conv_layer, build_norm_layer
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
from .resnet import BasicBlock, Bottleneck
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index ad3941edb35fa1b4ee0a2f92984a3f800fb5c5a1..32cf180a12ff9317d9bcf12b31461c0dc9cd4564 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -1,10 +1,11 @@
import torch.nn as nn
import torch.utils.checkpoint as cp
-from mmcv.cnn import constant_init, kaiming_init
+from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
+ kaiming_init)
from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
-from mmdet.ops import build_conv_layer, build_norm_layer, build_plugin_layer
+from mmdet.ops import build_plugin_layer
from mmdet.utils import get_root_logger
from ..builder import BACKBONES
from ..utils import ResLayer
diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py
index a882081d12d9234bcb5e35d6897eb21613df7a57..dc8c7eba325e128f37a24c2365ec67383e0b15c9 100644
--- a/mmdet/models/backbones/resnext.py
+++ b/mmdet/models/backbones/resnext.py
@@ -1,6 +1,7 @@
import math
-from mmdet.ops import build_conv_layer, build_norm_layer
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
from ..builder import BACKBONES
from ..utils import ResLayer
from .resnet import Bottleneck as _Bottleneck
diff --git a/mmdet/models/bbox_heads/convfc_bbox_head.py b/mmdet/models/bbox_heads/convfc_bbox_head.py
index 244092ab06353368778e3a422136bf1f56bdccbf..d57be85526da5db435cc987da6d0f58211ee53d7 100644
--- a/mmdet/models/bbox_heads/convfc_bbox_head.py
+++ b/mmdet/models/bbox_heads/convfc_bbox_head.py
@@ -1,6 +1,6 @@
import torch.nn as nn
+from mmcv.cnn import ConvModule
-from mmdet.ops import ConvModule
from ..builder import HEADS
from .bbox_head import BBoxHead
diff --git a/mmdet/models/bbox_heads/double_bbox_head.py b/mmdet/models/bbox_heads/double_bbox_head.py
index 3289922d224fb3b9e47bb3aa296401378c85bd12..9a3a1165707c304f0852770f2847bcdf48b544aa 100644
--- a/mmdet/models/bbox_heads/double_bbox_head.py
+++ b/mmdet/models/bbox_heads/double_bbox_head.py
@@ -1,7 +1,6 @@
import torch.nn as nn
-from mmcv.cnn.weight_init import normal_init, xavier_init
+from mmcv.cnn import ConvModule, normal_init, xavier_init
-from mmdet.ops import ConvModule
from ..backbones.resnet import Bottleneck
from ..builder import HEADS
from .bbox_head import BBoxHead
diff --git a/mmdet/models/detectors/fsaf.py b/mmdet/models/detectors/fsaf.py
index 40b185217ebc1f6fdc7e88cf91b077de717a9cb4..b315794e22b52b9475e23d12b8f6d85221184c15 100644
--- a/mmdet/models/detectors/fsaf.py
+++ b/mmdet/models/detectors/fsaf.py
@@ -2,7 +2,7 @@ from ..builder import DETECTORS
from .single_stage import SingleStageDetector
-@DETECTORS.register_module
+@DETECTORS.register_module()
class FSAF(SingleStageDetector):
def __init__(self,
diff --git a/mmdet/models/mask_heads/fcn_mask_head.py b/mmdet/models/mask_heads/fcn_mask_head.py
index d8bb7cf1d9ad005c14a694ec8bf494635948aca4..0e808490e4aec90321934a6e4dd6c5231f71b3f8 100644
--- a/mmdet/models/mask_heads/fcn_mask_head.py
+++ b/mmdet/models/mask_heads/fcn_mask_head.py
@@ -3,10 +3,11 @@ import pycocotools.mask as mask_util
import torch
import torch.nn as nn
import torch.nn.functional as F
+from mmcv.cnn import ConvModule, build_upsample_layer
from torch.nn.modules.utils import _pair
from mmdet.core import auto_fp16, force_fp32, mask_target
-from mmdet.ops import Conv2d, ConvModule, build_upsample_layer
+from mmdet.ops import Conv2d
from mmdet.ops.carafe import CARAFEPack
from ..builder import HEADS, build_loss
diff --git a/mmdet/models/mask_heads/fused_semantic_head.py b/mmdet/models/mask_heads/fused_semantic_head.py
index dd671ad4096ff19b5ee84bf8ab6be34227012586..99e775a38bcfcffc9545fce9fe9bb16fa9ae91ae 100644
--- a/mmdet/models/mask_heads/fused_semantic_head.py
+++ b/mmdet/models/mask_heads/fused_semantic_head.py
@@ -1,9 +1,8 @@
import torch.nn as nn
import torch.nn.functional as F
-from mmcv.cnn import kaiming_init
+from mmcv.cnn import ConvModule, kaiming_init
from mmdet.core import auto_fp16, force_fp32
-from mmdet.ops import ConvModule
from ..builder import HEADS
diff --git a/mmdet/models/mask_heads/grid_head.py b/mmdet/models/mask_heads/grid_head.py
index 8678f105380a16e07becdccdc41749e6ad7ef9cf..34855597542b0fa27b8a736abc2e82ea6e945930 100644
--- a/mmdet/models/mask_heads/grid_head.py
+++ b/mmdet/models/mask_heads/grid_head.py
@@ -2,9 +2,8 @@ import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
-from mmcv.cnn import kaiming_init, normal_init
+from mmcv.cnn import ConvModule, kaiming_init, normal_init
-from mmdet.ops import ConvModule
from ..builder import HEADS, build_loss
diff --git a/mmdet/models/mask_heads/htc_mask_head.py b/mmdet/models/mask_heads/htc_mask_head.py
index 91472f3bef72d1bc6eda8c09a246a6f39c2e6de8..a8b2812b01b49ffa248f498daf82eeecd5724271 100644
--- a/mmdet/models/mask_heads/htc_mask_head.py
+++ b/mmdet/models/mask_heads/htc_mask_head.py
@@ -1,4 +1,5 @@
-from mmdet.ops import ConvModule
+from mmcv.cnn import ConvModule
+
from ..builder import HEADS
from .fcn_mask_head import FCNMaskHead
diff --git a/mmdet/models/necks/bfp.py b/mmdet/models/necks/bfp.py
index b128b76fdf645d1d677b09fcc606c2ed866b831d..ed4a2c3ef567cb0a2e40e1d55198ba3be9dd5282 100644
--- a/mmdet/models/necks/bfp.py
+++ b/mmdet/models/necks/bfp.py
@@ -1,8 +1,8 @@
import torch.nn as nn
import torch.nn.functional as F
-from mmcv.cnn import xavier_init
+from mmcv.cnn import ConvModule, xavier_init
-from mmdet.ops import ConvModule, NonLocal2D
+from mmdet.ops import NonLocal2D
from ..builder import NECKS
diff --git a/mmdet/models/necks/fpn.py b/mmdet/models/necks/fpn.py
index f5c45d0b66a293bd2202d478322317aff37b5a96..da508652885bfc0d9a7b677b3cfa1599bb3adad8 100644
--- a/mmdet/models/necks/fpn.py
+++ b/mmdet/models/necks/fpn.py
@@ -1,9 +1,8 @@
import torch.nn as nn
import torch.nn.functional as F
-from mmcv.cnn import xavier_init
+from mmcv.cnn import ConvModule, xavier_init
from mmdet.core import auto_fp16
-from mmdet.ops import ConvModule
from ..builder import NECKS
diff --git a/mmdet/models/necks/fpn_carafe.py b/mmdet/models/necks/fpn_carafe.py
index 488c4a8299402204b3735e4ea84c2cdc06c6af01..3180bd1de2cb624bf0649716ddd676028912dbd6 100644
--- a/mmdet/models/necks/fpn_carafe.py
+++ b/mmdet/models/necks/fpn_carafe.py
@@ -1,7 +1,6 @@
import torch.nn as nn
-from mmcv.cnn import xavier_init
+from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init
-from mmdet.ops import ConvModule, build_upsample_layer
from mmdet.ops.carafe import CARAFEPack
from ..builder import NECKS
diff --git a/mmdet/models/necks/hrfpn.py b/mmdet/models/necks/hrfpn.py
index c9af7c720f1b1db9376a1c08e69a53fd20106bf2..efc60076857975346b1393e45f80eaaedb80c443 100644
--- a/mmdet/models/necks/hrfpn.py
+++ b/mmdet/models/necks/hrfpn.py
@@ -1,10 +1,9 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
-from mmcv.cnn.weight_init import caffe2_xavier_init
+from mmcv.cnn import ConvModule, caffe2_xavier_init
from torch.utils.checkpoint import checkpoint
-from mmdet.ops import ConvModule
from ..builder import NECKS
diff --git a/mmdet/models/necks/nas_fpn.py b/mmdet/models/necks/nas_fpn.py
index b4cf12d7cc7fafb33f03f474c3a2955d5505fe2c..16bf01e9ebdc6520fa9cc3dec0bb6e14ff748ca4 100644
--- a/mmdet/models/necks/nas_fpn.py
+++ b/mmdet/models/necks/nas_fpn.py
@@ -1,8 +1,7 @@
import torch.nn as nn
import torch.nn.functional as F
-from mmcv.cnn import caffe2_xavier_init
+from mmcv.cnn import ConvModule, caffe2_xavier_init
-from mmdet.ops import ConvModule
from ..builder import NECKS
diff --git a/mmdet/models/necks/pafpn.py b/mmdet/models/necks/pafpn.py
index 01164553b52d6c4a55fb7e6d32f0ba4f6d703341..de14e36598a6be6f7a5dc349a81923bfe6a541b3 100644
--- a/mmdet/models/necks/pafpn.py
+++ b/mmdet/models/necks/pafpn.py
@@ -1,8 +1,8 @@
import torch.nn as nn
import torch.nn.functional as F
+from mmcv.cnn import ConvModule
from mmdet.core import auto_fp16
-from mmdet.ops import ConvModule
from ..builder import NECKS
from .fpn import FPN
diff --git a/mmdet/models/utils/res_layer.py b/mmdet/models/utils/res_layer.py
index 77c0419f494f1200cbee5d2d4b6f8830889ee8ff..53d6154078323bd4db82a84abbb174fd749c1123 100644
--- a/mmdet/models/utils/res_layer.py
+++ b/mmdet/models/utils/res_layer.py
@@ -1,7 +1,6 @@
+from mmcv.cnn import build_conv_layer, build_norm_layer
from torch import nn as nn
-from mmdet.ops import build_conv_layer, build_norm_layer
-
class ResLayer(nn.Sequential):
"""ResLayer to build ResNet style backbone.
diff --git a/mmdet/ops/__init__.py b/mmdet/ops/__init__.py
index 7a248b3b902f067cd0a769511952f8b9c1dfe39c..32983ccd02ca1742bcebf95113fd93a21921ae21 100644
--- a/mmdet/ops/__init__.py
+++ b/mmdet/ops/__init__.py
@@ -1,6 +1,4 @@
from .context_block import ContextBlock
-from .conv import build_conv_layer
-from .conv_module import ConvModule
from .conv_ws import ConvWS2d, conv_ws_2d
from .dcn import (DeformConv, DeformConvPack, DeformRoIPooling,
DeformRoIPoolingPack, ModulatedDeformConv,
@@ -10,13 +8,10 @@ from .generalized_attention import GeneralizedAttention
from .masked_conv import MaskedConv2d
from .nms import batched_nms, nms, soft_nms
from .non_local import NonLocal2D
-from .norm import build_norm_layer
from .plugin import build_plugin_layer
from .roi_align import RoIAlign, roi_align
from .roi_pool import RoIPool, roi_pool
-from .scale import Scale
from .sigmoid_focal_loss import SigmoidFocalLoss, sigmoid_focal_loss
-from .upsample import build_upsample_layer
from .utils import get_compiler_version, get_compiling_cuda_version
from .wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d
@@ -27,8 +22,7 @@ __all__ = [
'ModulatedDeformConvPack', 'deform_conv', 'modulated_deform_conv',
'deform_roi_pooling', 'SigmoidFocalLoss', 'sigmoid_focal_loss',
'MaskedConv2d', 'ContextBlock', 'GeneralizedAttention', 'NonLocal2D',
- 'get_compiler_version', 'get_compiling_cuda_version', 'build_conv_layer',
- 'ConvModule', 'ConvWS2d', 'conv_ws_2d', 'build_norm_layer', 'Scale',
- 'build_upsample_layer', 'build_plugin_layer', 'batched_nms', 'Conv2d',
+ 'get_compiler_version', 'get_compiling_cuda_version', 'ConvWS2d',
+ 'conv_ws_2d', 'build_plugin_layer', 'batched_nms', 'Conv2d',
'ConvTranspose2d', 'MaxPool2d', 'Linear'
]
diff --git a/mmdet/ops/activation.py b/mmdet/ops/activation.py
deleted file mode 100644
index 53b72b2ad79b8abfd9b73b0231440d5ab062e986..0000000000000000000000000000000000000000
--- a/mmdet/ops/activation.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import torch.nn as nn
-
-activation_cfg = {
- # layer_abbreviation: module
- 'ReLU': nn.ReLU,
- 'LeakyReLU': nn.LeakyReLU,
- 'PReLU': nn.PReLU,
- 'RReLU': nn.RReLU,
- 'ReLU6': nn.ReLU6,
- 'SELU': nn.SELU,
- 'CELU': nn.CELU
-}
-
-
-def build_activation_layer(cfg):
- """ Build activation layer
-
- Args:
- cfg (dict): cfg should contain:
- type (str): Identify activation layer type.
- layer args: args needed to instantiate a activation layer.
-
- Returns:
- layer (nn.Module): Created activation layer
- """
- assert isinstance(cfg, dict) and 'type' in cfg
- cfg_ = cfg.copy()
-
- layer_type = cfg_.pop('type')
- if layer_type not in activation_cfg:
- raise KeyError(f'Unrecognized activation type {layer_type}')
- else:
- activation = activation_cfg[layer_type]
- if activation is None:
- raise NotImplementedError
-
- layer = activation(**cfg_)
- return layer
diff --git a/mmdet/ops/carafe/carafe.py b/mmdet/ops/carafe/carafe.py
index d8a15c3e047ca7f1a40398e8ad74fd60e64b0142..73c14dfc36dce518576609796fdea848bab04046 100644
--- a/mmdet/ops/carafe/carafe.py
+++ b/mmdet/ops/carafe/carafe.py
@@ -1,7 +1,7 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
-from mmcv.cnn import normal_init, xavier_init
+from mmcv.cnn import UPSAMPLE_LAYERS, normal_init, xavier_init
from torch.autograd import Function
from torch.nn.modules.module import Module
@@ -157,6 +157,7 @@ class CARAFE(Module):
self.group_size, self.scale_factor)
+@UPSAMPLE_LAYERS.register_module(name='carafe')
class CARAFEPack(nn.Module):
""" A unified package of CARAFE upsampler that contains:
1) channel compressor 2) content encoder 3) CARAFE op
diff --git a/mmdet/ops/conv.py b/mmdet/ops/conv.py
deleted file mode 100644
index 8316001dfc26c580b99010d2ff3bdc0fae443e23..0000000000000000000000000000000000000000
--- a/mmdet/ops/conv.py
+++ /dev/null
@@ -1,39 +0,0 @@
-from .conv_ws import ConvWS2d
-from .dcn import DeformConvPack, ModulatedDeformConvPack
-from .wrappers import Conv2d
-
-conv_cfg = {
- 'Conv': Conv2d,
- 'ConvWS': ConvWS2d,
- 'DCN': DeformConvPack,
- 'DCNv2': ModulatedDeformConvPack,
- # TODO: octave conv
-}
-
-
-def build_conv_layer(cfg, *args, **kwargs):
- """ Build convolution layer
-
- Args:
- cfg (None or dict): cfg should contain:
- type (str): identify conv layer type.
- layer args: args needed to instantiate a conv layer.
-
- Returns:
- layer (nn.Module): created conv layer
- """
- if cfg is None:
- cfg_ = dict(type='Conv')
- else:
- assert isinstance(cfg, dict) and 'type' in cfg
- cfg_ = cfg.copy()
-
- layer_type = cfg_.pop('type')
- if layer_type not in conv_cfg:
- raise KeyError(f'Unrecognized norm type {layer_type}')
- else:
- conv_layer = conv_cfg[layer_type]
-
- layer = conv_layer(*args, **kwargs, **cfg_)
-
- return layer
diff --git a/mmdet/ops/conv_module.py b/mmdet/ops/conv_module.py
deleted file mode 100644
index 975e00f69ab562c0516b19d192ed2c2abe7a918b..0000000000000000000000000000000000000000
--- a/mmdet/ops/conv_module.py
+++ /dev/null
@@ -1,132 +0,0 @@
-import warnings
-
-import torch.nn as nn
-from mmcv.cnn import constant_init, kaiming_init
-
-from .activation import build_activation_layer
-from .conv import build_conv_layer
-from .norm import build_norm_layer
-
-
-class ConvModule(nn.Module):
- """A conv block that contains conv/norm/activation layers.
-
- 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.
- act_cfg (dict): Config dict for activation layer, "relu" by default.
- inplace (bool): Whether to use inplace mode for activation.
- order (tuple[str]): The order of conv/norm/activation layers. It is a
- sequence of "conv", "norm" and "act". Examples are
- ("conv", "norm", "act") and ("act", "conv", "norm").
- """
-
- def __init__(self,
- in_channels,
- out_channels,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias='auto',
- conv_cfg=None,
- norm_cfg=None,
- act_cfg=dict(type='ReLU'),
- inplace=True,
- order=('conv', 'norm', 'act')):
- super(ConvModule, self).__init__()
- assert conv_cfg is None or isinstance(conv_cfg, dict)
- assert norm_cfg is None or isinstance(norm_cfg, dict)
- assert act_cfg is None or isinstance(act_cfg, dict)
- self.conv_cfg = conv_cfg
- self.norm_cfg = norm_cfg
- self.act_cfg = act_cfg
- self.inplace = inplace
- self.order = order
- assert isinstance(self.order, tuple) and len(self.order) == 3
- assert set(order) == set(['conv', 'norm', 'act'])
-
- self.with_norm = norm_cfg is not None
- self.with_activation = act_cfg 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')
-
- # 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
- self.stride = self.conv.stride
- self.padding = self.conv.padding
- self.dilation = self.conv.dilation
- self.transposed = self.conv.transposed
- self.output_padding = self.conv.output_padding
- self.groups = self.conv.groups
-
- # build normalization layers
- if self.with_norm:
- # norm layer is after conv layer
- if order.index('norm') > order.index('conv'):
- norm_channels = out_channels
- else:
- norm_channels = in_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_activation:
- act_cfg_ = act_cfg.copy()
- act_cfg_.setdefault('inplace', inplace)
- self.activate = build_activation_layer(act_cfg_)
-
- # Use msra init by default
- self.init_weights()
-
- @property
- def norm(self):
- return getattr(self, self.norm_name)
-
- def init_weights(self):
- if self.with_activation and self.act_cfg['type'] == 'LeakyReLU':
- nonlinearity = 'leaky_relu'
- else:
- nonlinearity = 'relu'
- kaiming_init(self.conv, nonlinearity=nonlinearity)
- if self.with_norm:
- constant_init(self.norm, 1, bias=0)
-
- def forward(self, x, activate=True, norm=True):
- for layer in self.order:
- if layer == 'conv':
- x = self.conv(x)
- elif layer == 'norm' and norm and self.with_norm:
- x = self.norm(x)
- elif layer == 'act' and activate and self.with_activation:
- x = self.activate(x)
- return x
diff --git a/mmdet/ops/conv_ws.py b/mmdet/ops/conv_ws.py
index 5ccd735fde047e254e22a3c27fad78290143bc82..7704683ffc777297b94cb2e08ff6cba36233c95f 100644
--- a/mmdet/ops/conv_ws.py
+++ b/mmdet/ops/conv_ws.py
@@ -1,5 +1,6 @@
import torch.nn as nn
import torch.nn.functional as F
+from mmcv.cnn import CONV_LAYERS
def conv_ws_2d(input,
@@ -18,6 +19,7 @@ def conv_ws_2d(input,
return F.conv2d(input, weight, bias, stride, padding, dilation, groups)
+@CONV_LAYERS.register_module('ConvWS')
class ConvWS2d(nn.Conv2d):
def __init__(self,
diff --git a/mmdet/ops/dcn/deform_conv.py b/mmdet/ops/dcn/deform_conv.py
index 42245d5c210fc9ab2586420abf277959ace7bf39..7d08ceca7503b5da720f70ec10cfdd5913ee3f8f 100644
--- a/mmdet/ops/dcn/deform_conv.py
+++ b/mmdet/ops/dcn/deform_conv.py
@@ -3,6 +3,7 @@ import math
import torch
import torch.nn as nn
import torch.nn.functional as F
+from mmcv.cnn import CONV_LAYERS
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair, _single
@@ -252,6 +253,7 @@ class DeformConv(nn.Module):
return out
+@CONV_LAYERS.register_module('DCN')
class DeformConvPack(DeformConv):
"""A Deformable Conv Encapsulation that acts as normal Conv layers.
@@ -371,6 +373,7 @@ class ModulatedDeformConv(nn.Module):
self.groups, self.deformable_groups)
+@CONV_LAYERS.register_module('DCNv2')
class ModulatedDeformConvPack(ModulatedDeformConv):
"""A ModulatedDeformable Conv Encapsulation that acts as normal Conv layers.
diff --git a/mmdet/ops/non_local.py b/mmdet/ops/non_local.py
index 3ac4bf9f529ef33a50c2ff1642c213f9f2a42531..3630eb2f73a1116a0cf727c01c281c9df95c61ff 100644
--- a/mmdet/ops/non_local.py
+++ b/mmdet/ops/non_local.py
@@ -1,8 +1,6 @@
import torch
import torch.nn as nn
-from mmcv.cnn import constant_init, normal_init
-
-from .conv_module import ConvModule
+from mmcv.cnn import ConvModule, constant_init, normal_init
class NonLocal2D(nn.Module):
diff --git a/mmdet/ops/norm.py b/mmdet/ops/norm.py
deleted file mode 100644
index 99fc93116e89b60e30d69d73ff2fdea450ecfa2b..0000000000000000000000000000000000000000
--- a/mmdet/ops/norm.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import torch.nn as nn
-
-norm_cfg = {
- # format: layer_type: (abbreviation, module)
- 'BN': ('bn', nn.BatchNorm2d),
- 'SyncBN': ('bn', nn.SyncBatchNorm),
- 'GN': ('gn', nn.GroupNorm),
- # and potentially 'SN'
-}
-
-
-def build_norm_layer(cfg, num_features, postfix=''):
- """ Build normalization layer
-
- Args:
- cfg (dict): cfg should contain:
- type (str): identify norm layer type.
- layer args: args needed to instantiate a norm layer.
- requires_grad (bool): [optional] whether stop gradient updates
- num_features (int): number of channels from input.
- postfix (int, str): appended into norm abbreviation to
- create named layer.
-
- Returns:
- name (str): abbreviation + postfix
- layer (nn.Module): created norm layer
- """
- assert isinstance(cfg, dict) and 'type' in cfg
- cfg_ = cfg.copy()
-
- layer_type = cfg_.pop('type')
- if layer_type not in norm_cfg:
- raise KeyError(f'Unrecognized norm type {layer_type}')
- else:
- abbr, norm_layer = norm_cfg[layer_type]
- if norm_layer is None:
- raise NotImplementedError
-
- assert isinstance(postfix, (int, str))
- name = abbr + str(postfix)
-
- requires_grad = cfg_.pop('requires_grad', True)
- cfg_.setdefault('eps', 1e-5)
- if layer_type != 'GN':
- layer = norm_layer(num_features, **cfg_)
- if layer_type == 'SyncBN':
- layer._specify_ddp_gpu_num(1)
- else:
- assert 'num_groups' in cfg_
- layer = norm_layer(num_channels=num_features, **cfg_)
-
- for param in layer.parameters():
- param.requires_grad = requires_grad
-
- return name, layer
diff --git a/mmdet/ops/scale.py b/mmdet/ops/scale.py
deleted file mode 100644
index 2461af8a6fb23d911d7aac4e81bdfee36a31cadb..0000000000000000000000000000000000000000
--- a/mmdet/ops/scale.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-class Scale(nn.Module):
- """
- A learnable scale parameter
- """
-
- def __init__(self, scale=1.0):
- super(Scale, self).__init__()
- self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float))
-
- def forward(self, x):
- return x * self.scale
diff --git a/mmdet/ops/upsample.py b/mmdet/ops/upsample.py
deleted file mode 100644
index 2e405a037ebaa8eb6f4ea4dbcaa07ae639ad73c4..0000000000000000000000000000000000000000
--- a/mmdet/ops/upsample.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import xavier_init
-
-from .carafe import CARAFEPack
-from .wrappers import ConvTranspose2d
-
-
-class PixelShufflePack(nn.Module):
- """ Pixel Shuffle upsample layer
-
- Args:
- in_channels (int): Number of input channels
- out_channels (int): Number of output channels
- scale_factor (int): Upsample ratio
- upsample_kernel (int): Kernel size of Conv layer to expand the channels
-
- Returns:
- upsampled feature map
- """
-
- def __init__(self, in_channels, out_channels, scale_factor,
- upsample_kernel):
- super(PixelShufflePack, self).__init__()
- self.in_channels = in_channels
- self.out_channels = out_channels
- self.scale_factor = scale_factor
- self.upsample_kernel = upsample_kernel
- self.upsample_conv = nn.Conv2d(
- self.in_channels,
- self.out_channels * scale_factor * scale_factor,
- self.upsample_kernel,
- padding=(self.upsample_kernel - 1) // 2)
- self.init_weights()
-
- def init_weights(self):
- xavier_init(self.upsample_conv, distribution='uniform')
-
- def forward(self, x):
- x = self.upsample_conv(x)
- x = F.pixel_shuffle(x, self.scale_factor)
- return x
-
-
-upsample_cfg = {
- # layer_abbreviation: module
- 'nearest': nn.Upsample,
- 'bilinear': nn.Upsample,
- 'deconv': ConvTranspose2d,
- 'pixel_shuffle': PixelShufflePack,
- 'carafe': CARAFEPack
-}
-
-
-def build_upsample_layer(cfg):
- """ Build upsample layer
-
- Args:
- cfg (dict): cfg should contain:
- type (str): Identify upsample layer type.
- upsample ratio (int): Upsample ratio
- layer args: args needed to instantiate a upsample layer.
-
- Returns:
- layer (nn.Module): Created upsample layer
- """
- assert isinstance(cfg, dict) and 'type' in cfg
- cfg_ = cfg.copy()
-
- layer_type = cfg_.pop('type')
- if layer_type not in upsample_cfg:
- raise KeyError(f'Unrecognized upsample type {layer_type}')
- else:
- upsample = upsample_cfg[layer_type]
- if upsample is None:
- raise NotImplementedError
-
- layer = upsample(**cfg_)
- return layer
diff --git a/mmdet/ops/wrappers.py b/mmdet/ops/wrappers.py
index a5f9fb9f8c87f3278c08b921c170744390f9cbce..8177fee8db558923113c03a8a7bfe61f1cfe4549 100644
--- a/mmdet/ops/wrappers.py
+++ b/mmdet/ops/wrappers.py
@@ -9,6 +9,7 @@ import math
import torch
import torch.nn as nn
+from mmcv.cnn import CONV_LAYERS
from torch.nn.modules.utils import _pair
@@ -25,6 +26,7 @@ class NewEmptyTensorOp(torch.autograd.Function):
return NewEmptyTensorOp.apply(grad, shape), None
+@CONV_LAYERS.register_module('Conv', force=True)
class Conv2d(nn.Conv2d):
def forward(self, x):
diff --git a/requirements/runtime.txt b/requirements/runtime.txt
index f6d104e33ae64efdab4ea69a8715cce4ac2254c7..13194bca0e83e3d32e663684267bc032503deaff 100644
--- a/requirements/runtime.txt
+++ b/requirements/runtime.txt
@@ -1,5 +1,5 @@
matplotlib
-mmcv>=0.4.4
+mmcv>=0.5.0
numpy
# need older pillow until torchvision is fixed
Pillow<=6.2.2
diff --git a/setup.py b/setup.py
index 390d08a1afe1633a569b5e48a29061df582a61b4..29b5e1e2f155bb7f74b78195c20191889646b801 100755
--- a/setup.py
+++ b/setup.py
@@ -15,8 +15,8 @@ def readme():
return content
-MAJOR = 1
-MINOR = 1
+MAJOR = 2
+MINOR = 0
PATCH = 0
SUFFIX = ''
if PATCH != '':