diff --git a/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
index c27fff20cd53d578e716f26d477d62ed25362bcd..40a2d97d06fcf27e5ea929490f0a67f381b0f42d 100644
--- a/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -11,7 +11,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False, deformable_groups=1, fallback_on_stride=False),
+ type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py
index 2a4740b392bcd07cc8cd9c2204d0734811fc44ec..9d4402a852d440809c0aadc551e7c74edbe59bdf 100644
--- a/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -11,7 +11,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False, deformable_groups=1, fallback_on_stride=False),
+ type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py
index 11c7dd35e6aee4503de4e4bacb8013434dad8c6f..83967ff92efc512ea7281796117fd884cdc1a7ca 100644
--- a/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -10,7 +10,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False, deformable_groups=1, fallback_on_stride=False),
+ type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py b/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py
index 9156b0d8ba6f509c593b6ce4613c2566cb958a93..06aa6eeaf77703482125a7790c56650ad73fb49e 100644
--- a/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py
+++ b/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py
@@ -12,8 +12,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False,
- groups=32,
+ type='DCN',
deformable_groups=1,
fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
diff --git a/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py b/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py
index 4e91bb0b05b03eabad02acb6365349323125bfe2..e469223ca9035d62c9646cf03063f20d9d581efc 100644
--- a/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py
+++ b/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py
@@ -10,7 +10,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=True, deformable_groups=4, fallback_on_stride=False),
+ type='DCNv2', deformable_groups=4, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py b/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py
index 484b4aff10782debbace72a218bf538f9785fefd..2e79fa85dc6a304fd962b753adfa585e208afcee 100644
--- a/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py
@@ -10,7 +10,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=True, deformable_groups=1, fallback_on_stride=False),
+ type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py b/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
index c3de699a1c4aff0551830aa4e034edddeb15fd84..8ed0af0de89760f0bff6d5bf2df15419035cdd6f 100644
--- a/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
+++ b/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py
@@ -10,7 +10,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False, deformable_groups=1, fallback_on_stride=False),
+ type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
neck=dict(
type='FPN',
diff --git a/configs/dcn/mask_rcnn_mdconv_c3-c5_r50_fpn_1x.py b/configs/dcn/mask_rcnn_mdconv_c3-c5_r50_fpn_1x.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4902567ba9412e3eb5f7cf1364b04aa0d23f861
--- /dev/null
+++ b/configs/dcn/mask_rcnn_mdconv_c3-c5_r50_fpn_1x.py
@@ -0,0 +1,190 @@
+# model settings
+model = dict(
+ type='MaskRCNN',
+ pretrained='torchvision://resnet50',
+ backbone=dict(
+ type='ResNet',
+ depth=50,
+ num_stages=4,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=1,
+ style='pytorch',
+ dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
+ stage_with_dcn=(False, True, True, True)),
+ neck=dict(
+ type='FPN',
+ in_channels=[256, 512, 1024, 2048],
+ out_channels=256,
+ num_outs=5),
+ rpn_head=dict(
+ type='RPNHead',
+ in_channels=256,
+ feat_channels=256,
+ anchor_scales=[8],
+ anchor_ratios=[0.5, 1.0, 2.0],
+ anchor_strides=[4, 8, 16, 32, 64],
+ target_means=[.0, .0, .0, .0],
+ target_stds=[1.0, 1.0, 1.0, 1.0],
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+ loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+ bbox_roi_extractor=dict(
+ type='SingleRoIExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32]),
+ bbox_head=dict(
+ type='SharedFCBBoxHead',
+ num_fcs=2,
+ in_channels=256,
+ fc_out_channels=1024,
+ roi_feat_size=7,
+ num_classes=81,
+ target_means=[0., 0., 0., 0.],
+ target_stds=[0.1, 0.1, 0.2, 0.2],
+ reg_class_agnostic=False,
+ loss_cls=dict(
+ type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+ loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+ mask_roi_extractor=dict(
+ type='SingleRoIExtractor',
+ roi_layer=dict(type='RoIAlign', out_size=14, sample_num=2),
+ out_channels=256,
+ featmap_strides=[4, 8, 16, 32]),
+ mask_head=dict(
+ type='FCNMaskHead',
+ num_convs=4,
+ in_channels=256,
+ conv_out_channels=256,
+ num_classes=81,
+ loss_mask=dict(
+ type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))
+# model training and testing settings
+train_cfg = dict(
+ rpn=dict(
+ assigner=dict(
+ type='MaxIoUAssigner',
+ pos_iou_thr=0.7,
+ neg_iou_thr=0.3,
+ min_pos_iou=0.3,
+ ignore_iof_thr=-1),
+ sampler=dict(
+ type='RandomSampler',
+ num=256,
+ pos_fraction=0.5,
+ neg_pos_ub=-1,
+ add_gt_as_proposals=False),
+ allowed_border=0,
+ pos_weight=-1,
+ debug=False),
+ rpn_proposal=dict(
+ nms_across_levels=False,
+ nms_pre=2000,
+ nms_post=2000,
+ max_num=2000,
+ nms_thr=0.7,
+ min_bbox_size=0),
+ rcnn=dict(
+ assigner=dict(
+ type='MaxIoUAssigner',
+ pos_iou_thr=0.5,
+ neg_iou_thr=0.5,
+ min_pos_iou=0.5,
+ ignore_iof_thr=-1),
+ sampler=dict(
+ type='RandomSampler',
+ num=512,
+ pos_fraction=0.25,
+ neg_pos_ub=-1,
+ add_gt_as_proposals=True),
+ mask_size=28,
+ pos_weight=-1,
+ debug=False))
+test_cfg = dict(
+ rpn=dict(
+ nms_across_levels=False,
+ nms_pre=1000,
+ nms_post=1000,
+ max_num=1000,
+ nms_thr=0.7,
+ min_bbox_size=0),
+ rcnn=dict(
+ score_thr=0.05,
+ nms=dict(type='nms', iou_thr=0.5),
+ max_per_img=100,
+ mask_thr_binary=0.5))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+ dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+ dict(type='RandomFlip', flip_ratio=0.5),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(type='Pad', size_divisor=32),
+ dict(type='DefaultFormatBundle'),
+ dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(
+ type='MultiScaleFlipAug',
+ img_scale=(1333, 800),
+ flip=False,
+ transforms=[
+ dict(type='Resize', keep_ratio=True),
+ dict(type='RandomFlip'),
+ dict(type='Normalize', **img_norm_cfg),
+ dict(type='Pad', size_divisor=32),
+ dict(type='ImageToTensor', keys=['img']),
+ dict(type='Collect', keys=['img']),
+ ])
+]
+data = dict(
+ imgs_per_gpu=2,
+ workers_per_gpu=2,
+ train=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_train2017.json',
+ img_prefix=data_root + 'train2017/',
+ pipeline=train_pipeline),
+ val=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ pipeline=test_pipeline),
+ test=dict(
+ type=dataset_type,
+ ann_file=data_root + 'annotations/instances_val2017.json',
+ img_prefix=data_root + 'val2017/',
+ pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+ policy='step',
+ warmup='linear',
+ warmup_iters=500,
+ warmup_ratio=1.0 / 3,
+ step=[8, 11])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+ interval=50,
+ hooks=[
+ dict(type='TextLoggerHook'),
+ # dict(type='TensorboardLoggerHook')
+ ])
+# yapf:enable
+# runtime settings
+total_epochs = 12
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/mask_rcnn_dconv_c3-c5_r50_fpn_1x'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py
index d264b9bf4ad1f18c533ea7fe35b4ef27117a95e8..7d0843e560062a609483141218d3373ca99c7dbf 100644
--- a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py
+++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x.py
@@ -13,7 +13,7 @@ model = dict(
spatial_range=-1, num_heads=8, attention_type='0010', kv_stride=2),
stage_with_gen_attention=[[], [], [0, 1, 2, 3, 4, 5], [0, 1, 2]],
dcn=dict(
- modulated=False, deformable_groups=1, fallback_on_stride=False),
+ type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
),
neck=dict(
diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py
index f39edee846eaeb5e886f06b88141da7d96e17ed0..f603e21d67b93a8ce6ed64ffa5690e2701dd129d 100644
--- a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py
+++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py
@@ -13,7 +13,7 @@ model = dict(
spatial_range=-1, num_heads=8, attention_type='1111', kv_stride=2),
stage_with_gen_attention=[[], [], [0, 1, 2, 3, 4, 5], [0, 1, 2]],
dcn=dict(
- modulated=False, deformable_groups=1, fallback_on_stride=False),
+ type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
),
neck=dict(
diff --git a/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py b/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py
index 275b7fbb590d0272651f247723a0484ee87642e7..b9139b3723c65df737a27fe610d5bb1c26882d9f 100644
--- a/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py
+++ b/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py
@@ -15,7 +15,7 @@ model = dict(
frozen_stages=1,
style='pytorch',
dcn=dict(
- modulated=False,
+ type='DCN',
groups=64,
deformable_groups=1,
fallback_on_stride=False),
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
index 3343c5c504e39b72dcd118bbcc513148e39a7e5b..d2f3b7c517eae3c0c6efeb9d7e4ab65389c840aa 100644
--- a/mmdet/models/backbones/resnet.py
+++ b/mmdet/models/backbones/resnet.py
@@ -5,7 +5,7 @@ from mmcv.runner import load_checkpoint
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.plugins import GeneralizedAttention
-from mmdet.ops import ContextBlock, DeformConv, ModulatedDeformConv
+from mmdet.ops import ContextBlock
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
@@ -143,10 +143,8 @@ class Bottleneck(nn.Module):
bias=False)
self.add_module(self.norm1_name, norm1)
fallback_on_stride = False
- self.with_modulated_dcn = False
if self.with_dcn:
- fallback_on_stride = dcn.get('fallback_on_stride', False)
- self.with_modulated_dcn = dcn.get('modulated', False)
+ fallback_on_stride = dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
conv_cfg,
@@ -158,30 +156,17 @@ class Bottleneck(nn.Module):
dilation=dilation,
bias=False)
else:
- assert conv_cfg is None, 'conv_cfg must be None for DCN'
- self.deformable_groups = dcn.get('deformable_groups', 1)
- if not self.with_modulated_dcn:
- conv_op = DeformConv
- offset_channels = 18
- else:
- conv_op = ModulatedDeformConv
- offset_channels = 27
- self.conv2_offset = nn.Conv2d(
- planes,
- self.deformable_groups * offset_channels,
- kernel_size=3,
- stride=self.conv2_stride,
- padding=dilation,
- dilation=dilation)
- self.conv2 = conv_op(
+ assert self.conv_cfg is None, 'conv_cfg cannot be None for DCN'
+ self.conv2 = build_conv_layer(
+ dcn,
planes,
planes,
kernel_size=3,
stride=self.conv2_stride,
padding=dilation,
dilation=dilation,
- deformable_groups=self.deformable_groups,
bias=False)
+
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
conv_cfg,
@@ -224,17 +209,7 @@ class Bottleneck(nn.Module):
out = self.norm1(out)
out = self.relu(out)
- if not self.with_dcn:
- out = self.conv2(out)
- elif self.with_modulated_dcn:
- offset_mask = self.conv2_offset(out)
- offset = offset_mask[:, :18 * self.deformable_groups, :, :]
- mask = offset_mask[:, -9 * self.deformable_groups:, :, :]
- mask = mask.sigmoid()
- out = self.conv2(out, offset, mask)
- else:
- offset = self.conv2_offset(out)
- out = self.conv2(out, offset)
+ out = self.conv2(out)
out = self.norm2(out)
out = self.relu(out)
diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py
index 38afee28b6f99307e2497203b93b7dba317648b6..0c184abb6ad6bae6f73b8a9636b26ba746f9acb2 100644
--- a/mmdet/models/backbones/resnext.py
+++ b/mmdet/models/backbones/resnext.py
@@ -2,7 +2,6 @@ import math
import torch.nn as nn
-from mmdet.ops import DeformConv, ModulatedDeformConv
from ..registry import BACKBONES
from ..utils import build_conv_layer, build_norm_layer
from .resnet import Bottleneck as _Bottleneck
@@ -41,8 +40,7 @@ class Bottleneck(_Bottleneck):
fallback_on_stride = False
self.with_modulated_dcn = False
if self.with_dcn:
- fallback_on_stride = self.dcn.get('fallback_on_stride', False)
- self.with_modulated_dcn = self.dcn.get('modulated', False)
+ fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
if not self.with_dcn or fallback_on_stride:
self.conv2 = build_conv_layer(
self.conv_cfg,
@@ -56,22 +54,8 @@ class Bottleneck(_Bottleneck):
bias=False)
else:
assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
- groups = self.dcn.get('groups', 1)
- deformable_groups = self.dcn.get('deformable_groups', 1)
- if not self.with_modulated_dcn:
- conv_op = DeformConv
- offset_channels = 18
- else:
- conv_op = ModulatedDeformConv
- offset_channels = 27
- self.conv2_offset = nn.Conv2d(
- width,
- deformable_groups * offset_channels,
- kernel_size=3,
- stride=self.conv2_stride,
- padding=self.dilation,
- dilation=self.dilation)
- self.conv2 = conv_op(
+ self.conv2 = build_conv_layer(
+ self.dcn,
width,
width,
kernel_size=3,
@@ -79,8 +63,8 @@ class Bottleneck(_Bottleneck):
padding=self.dilation,
dilation=self.dilation,
groups=groups,
- deformable_groups=deformable_groups,
bias=False)
+
self.add_module(self.norm2_name, norm2)
self.conv3 = build_conv_layer(
self.conv_cfg,
diff --git a/mmdet/models/utils/conv_module.py b/mmdet/models/utils/conv_module.py
index f2bddc445b5eb5217695b734cf8a6d4006915033..a1771f8cdcd9a08e932543da2cdc99f2c36997cc 100644
--- a/mmdet/models/utils/conv_module.py
+++ b/mmdet/models/utils/conv_module.py
@@ -3,12 +3,15 @@ import warnings
import torch.nn as nn
from mmcv.cnn import constant_init, kaiming_init
+from mmdet.ops import DeformConvPack, ModulatedDeformConvPack
from .conv_ws import ConvWS2d
from .norm import build_norm_layer
conv_cfg = {
'Conv': nn.Conv2d,
'ConvWS': ConvWS2d,
+ 'DCN': DeformConvPack,
+ 'DCNv2': ModulatedDeformConvPack,
# TODO: octave conv
}
diff --git a/mmdet/ops/dcn/deform_conv.py b/mmdet/ops/dcn/deform_conv.py
index 7f6841a585e504c8a480d5ac134807ae63edcd5d..24e5b08cbfd2a9581fae59fe56ed419ea9441e31 100644
--- a/mmdet/ops/dcn/deform_conv.py
+++ b/mmdet/ops/dcn/deform_conv.py
@@ -4,7 +4,7 @@ import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
-from torch.nn.modules.utils import _pair
+from torch.nn.modules.utils import _pair, _single
from . import deform_conv_cuda
@@ -24,7 +24,7 @@ class DeformConvFunction(Function):
im2col_step=64):
if input is not None and input.dim() != 4:
raise ValueError(
- "Expected 4D tensor as input, got {}D tensor instead.".format(
+ 'Expected 4D tensor as input, got {}D tensor instead.'.format(
input.dim()))
ctx.stride = _pair(stride)
ctx.padding = _pair(padding)
@@ -105,7 +105,7 @@ class DeformConvFunction(Function):
output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, )
if not all(map(lambda s: s > 0, output_size)):
raise ValueError(
- "convolution input is too small (output would be {})".format(
+ 'convolution input is too small (output would be {})'.format(
'x'.join(map(str, output_size))))
return output_size
@@ -217,6 +217,9 @@ class DeformConv(nn.Module):
self.dilation = _pair(dilation)
self.groups = groups
self.deformable_groups = deformable_groups
+ # enable compatibility with nn.Conv2d
+ self.transposed = False
+ self.output_padding = _single(0)
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels // self.groups,
@@ -237,6 +240,22 @@ class DeformConv(nn.Module):
class DeformConvPack(DeformConv):
+ """A Deformable Conv Encapsulation that acts as normal Conv 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.
+ """
+
+ _version = 2
def __init__(self, *args, **kwargs):
super(DeformConvPack, self).__init__(*args, **kwargs)
@@ -260,6 +279,33 @@ class DeformConvPack(DeformConv):
return deform_conv(x, offset, self.weight, self.stride, self.padding,
self.dilation, self.groups, self.deformable_groups)
+ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+ missing_keys, unexpected_keys, error_msgs):
+ version = local_metadata.get('version', None)
+
+ if version is None or version < 2:
+ # the key is different in early versions
+ # In version < 2, DeformConvPack loads previous benchmark models.
+ if (prefix + 'conv_offset.weight' not in state_dict
+ and prefix[:-1] + '_offset.weight' in state_dict):
+ state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
+ prefix[:-1] + '_offset.weight')
+ if (prefix + 'conv_offset.bias' not in state_dict
+ and prefix[:-1] + '_offset.bias' in state_dict):
+ state_dict[prefix +
+ 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
+ '_offset.bias')
+
+ if version is not None and version > 1:
+ from mmdet.apis import get_root_logger
+ logger = get_root_logger()
+ logger.info('DeformConvPack {} is upgraded to version 2.'.format(
+ prefix.rstrip('.')))
+
+ super()._load_from_state_dict(state_dict, prefix, local_metadata,
+ strict, missing_keys, unexpected_keys,
+ error_msgs)
+
class ModulatedDeformConv(nn.Module):
@@ -283,6 +329,9 @@ class ModulatedDeformConv(nn.Module):
self.groups = groups
self.deformable_groups = deformable_groups
self.with_bias = bias
+ # enable compatibility with nn.Conv2d
+ self.transposed = False
+ self.output_padding = _single(0)
self.weight = nn.Parameter(
torch.Tensor(out_channels, in_channels // groups,
@@ -309,11 +358,27 @@ class ModulatedDeformConv(nn.Module):
class ModulatedDeformConvPack(ModulatedDeformConv):
+ """A ModulatedDeformable Conv Encapsulation that acts as normal Conv 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.
+ """
+
+ _version = 2
def __init__(self, *args, **kwargs):
super(ModulatedDeformConvPack, self).__init__(*args, **kwargs)
- self.conv_offset_mask = nn.Conv2d(
+ self.conv_offset = nn.Conv2d(
self.in_channels,
self.deformable_groups * 3 * self.kernel_size[0] *
self.kernel_size[1],
@@ -324,14 +389,43 @@ class ModulatedDeformConvPack(ModulatedDeformConv):
self.init_offset()
def init_offset(self):
- self.conv_offset_mask.weight.data.zero_()
- self.conv_offset_mask.bias.data.zero_()
+ self.conv_offset.weight.data.zero_()
+ self.conv_offset.bias.data.zero_()
def forward(self, x):
- out = self.conv_offset_mask(x)
+ out = self.conv_offset(x)
o1, o2, mask = torch.chunk(out, 3, dim=1)
offset = torch.cat((o1, o2), dim=1)
mask = torch.sigmoid(mask)
return modulated_deform_conv(x, offset, mask, self.weight, self.bias,
self.stride, self.padding, self.dilation,
self.groups, self.deformable_groups)
+
+ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+ missing_keys, unexpected_keys, error_msgs):
+ version = local_metadata.get('version', None)
+
+ if version is None or version < 2:
+ # the key is different in early versions
+ # In version < 2, ModulatedDeformConvPack
+ # loads previous benchmark models.
+ if (prefix + 'conv_offset.weight' not in state_dict
+ and prefix[:-1] + '_offset.weight' in state_dict):
+ state_dict[prefix + 'conv_offset.weight'] = state_dict.pop(
+ prefix[:-1] + '_offset.weight')
+ if (prefix + 'conv_offset.bias' not in state_dict
+ and prefix[:-1] + '_offset.bias' in state_dict):
+ state_dict[prefix +
+ 'conv_offset.bias'] = state_dict.pop(prefix[:-1] +
+ '_offset.bias')
+
+ if version is not None and version > 1:
+ from mmdet.apis import get_root_logger
+ logger = get_root_logger()
+ logger.info(
+ 'ModulatedDeformConvPack {} is upgraded to version 2.'.format(
+ prefix.rstrip('.')))
+
+ super()._load_from_state_dict(state_dict, prefix, local_metadata,
+ strict, missing_keys, unexpected_keys,
+ error_msgs)
diff --git a/requirements.txt b/requirements.txt
index 5cacde1c99f1c0db65011225a8524491e223775c..01cfcdedb6fb4037b9306e389a8469aac799a8fb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,7 @@
albumentations>=0.3.2
imagecorruptions
matplotlib
-mmcv>=0.2.15
+mmcv>=0.2.16
numpy
pycocotools
six