diff --git a/configs/swin/swin_base_patch4_window7_mstrain_480-800_adamw_3x_coco.py b/configs/swin/swin_base_patch4_window7_mstrain_480-800_adamw_3x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..716085df42b439ffeec553628d61042d2bd797c5
--- /dev/null
+++ b/configs/swin/swin_base_patch4_window7_mstrain_480-800_adamw_3x_coco.py
@@ -0,0 +1,91 @@
+_base_ = [
+ '../_base_/datasets/coco_instance.py',
+ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+
+model = dict(
+ type='NoneDetector',
+ pretrained=None,
+ backbone=dict(
+ type='SwinTransformer',
+ embed_dim=128,
+ depths=[2, 2, 18, 2],
+ num_heads=[4, 8, 16, 32],
+ window_size=7,
+ mlp_ratio=4.,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.,
+ attn_drop_rate=0.,
+ drop_path_rate=0.3,
+ ape=False,
+ patch_norm=True,
+ out_indices=(0, 1, 2, 3),
+ use_checkpoint=False),
+
+ # model training and testing settings
+ train_cfg=None,
+ test_cfg=None)
+
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR / Sparse RCNN
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+ dict(type='RandomFlip', flip_ratio=0.5),
+ dict(type='AutoAugment',
+ policies=[
+ [
+ dict(type='Resize',
+ img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+ (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+ (736, 1333), (768, 1333), (800, 1333)],
+ multiscale_mode='value',
+ keep_ratio=True)
+ ],
+ [
+ dict(type='Resize',
+ img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+ multiscale_mode='value',
+ keep_ratio=True),
+ dict(type='RandomCrop',
+ crop_type='absolute_range',
+ crop_size=(384, 600),
+ allow_negative_crop=True),
+ dict(type='Resize',
+ img_scale=[(480, 1333), (512, 1333), (544, 1333),
+ (576, 1333), (608, 1333), (640, 1333),
+ (672, 1333), (704, 1333), (736, 1333),
+ (768, 1333), (800, 1333)],
+ multiscale_mode='value',
+ override=True,
+ keep_ratio=True)
+ ]
+ ]),
+ 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']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
+ paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
+ 'relative_position_bias_table': dict(decay_mult=0.),
+ 'norm': dict(decay_mult=0.)}))
+lr_config = dict(step=[27, 33])
+runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
+
+# do not use mmdet version fp16
+fp16 = None
+optimizer_config = dict(
+ type="DistOptimizerHook",
+ update_interval=1,
+ grad_clip=None,
+ coalesce=True,
+ bucket_size_mb=-1,
+ use_fp16=True,
+)
diff --git a/configs/swin/swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py b/configs/swin/swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3d9e53360bdf58f490679bb230526bcd5d1915b
--- /dev/null
+++ b/configs/swin/swin_small_patch4_window7_mstrain_480-800_adamw_3x_coco.py
@@ -0,0 +1,90 @@
+_base_ = [
+ '../_base_/datasets/coco_instance.py',
+ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+ type='NoneDetector',
+ pretrained=None,
+ backbone=dict(
+ type='SwinTransformer',
+ embed_dim=96,
+ depths=[2, 2, 18, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ mlp_ratio=4.,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.,
+ attn_drop_rate=0.,
+ drop_path_rate=0.2,
+ ape=False,
+ patch_norm=True,
+ out_indices=(0, 1, 2, 3),
+ use_checkpoint=False),
+
+ # model training and testing settings
+ train_cfg=None,
+ test_cfg=None)
+
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR / Sparse RCNN
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+ dict(type='RandomFlip', flip_ratio=0.5),
+ dict(type='AutoAugment',
+ policies=[
+ [
+ dict(type='Resize',
+ img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+ (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+ (736, 1333), (768, 1333), (800, 1333)],
+ multiscale_mode='value',
+ keep_ratio=True)
+ ],
+ [
+ dict(type='Resize',
+ img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+ multiscale_mode='value',
+ keep_ratio=True),
+ dict(type='RandomCrop',
+ crop_type='absolute_range',
+ crop_size=(384, 600),
+ allow_negative_crop=True),
+ dict(type='Resize',
+ img_scale=[(480, 1333), (512, 1333), (544, 1333),
+ (576, 1333), (608, 1333), (640, 1333),
+ (672, 1333), (704, 1333), (736, 1333),
+ (768, 1333), (800, 1333)],
+ multiscale_mode='value',
+ override=True,
+ keep_ratio=True)
+ ]
+ ]),
+ 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']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
+ paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
+ 'relative_position_bias_table': dict(decay_mult=0.),
+ 'norm': dict(decay_mult=0.)}))
+lr_config = dict(step=[27, 33])
+runner = dict(type='EpochBasedRunnerAmp', max_epochs=36)
+
+# do not use mmdet version fp16
+fp16 = None
+optimizer_config = dict(
+ type="DistOptimizerHook",
+ update_interval=1,
+ grad_clip=None,
+ coalesce=True,
+ bucket_size_mb=-1,
+ use_fp16=True,
+)
diff --git a/configs/swin/swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py b/configs/swin/swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py
new file mode 100644
index 0000000000000000000000000000000000000000..36f5b229c0657b9461b039275d1c84fd2127883b
--- /dev/null
+++ b/configs/swin/swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py
@@ -0,0 +1,91 @@
+_base_ = [
+ '../_base_/datasets/coco_instance.py',
+ '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+ type='NoneDetector',
+ pretrained=None,
+ backbone=dict(
+ type='SwinTransformer',
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ mlp_ratio=4.,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.,
+ attn_drop_rate=0.,
+ drop_path_rate=0.1,
+ ape=False,
+ patch_norm=True,
+ out_indices=(0, 1, 2, 3),
+ use_checkpoint=False),
+
+ # model training and testing settings
+ train_cfg=None,
+ test_cfg=None)
+
+img_norm_cfg = dict(
+ mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR / Sparse RCNN
+train_pipeline = [
+ dict(type='LoadImageFromFile'),
+ dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+ dict(type='RandomFlip', flip_ratio=0.5),
+ dict(type='AutoAugment',
+ policies=[
+ [
+ dict(type='Resize',
+ img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+ (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+ (736, 1333), (768, 1333), (800, 1333)],
+ multiscale_mode='value',
+ keep_ratio=True)
+ ],
+ [
+ dict(type='Resize',
+ img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+ multiscale_mode='value',
+ keep_ratio=True),
+ dict(type='RandomCrop',
+ crop_type='absolute_range',
+ crop_size=(384, 600),
+ allow_negative_crop=True),
+ dict(type='Resize',
+ img_scale=[(480, 1333), (512, 1333), (544, 1333),
+ (576, 1333), (608, 1333), (640, 1333),
+ (672, 1333), (704, 1333), (736, 1333),
+ (768, 1333), (800, 1333)],
+ multiscale_mode='value',
+ override=True,
+ keep_ratio=True)
+ ]
+ ]),
+ 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']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.05,
+ paramwise_cfg=dict(custom_keys={'absolute_pos_embed': dict(decay_mult=0.),
+ 'relative_position_bias_table': dict(decay_mult=0.),
+ 'norm': dict(decay_mult=0.)}))
+lr_config = dict(step=[8, 11])
+#runner = dict(type='EpochBasedRunnerAmp', max_epochs=12)
+runner = dict(type='EpochBasedRunner', max_epochs=12)
+
+# do not use mmdet version fp16
+# fp16 = None
+# optimizer_config = dict(
+# type="DistOptimizerHook",
+# update_interval=1,
+# grad_clip=None,
+# coalesce=True,
+# bucket_size_mb=-1,
+# use_fp16=True,
+# )
diff --git a/mmdet/models/detectors/__init__.py b/mmdet/models/detectors/__init__.py
index 04011130435cf9fdfadeb821919046b1bddab7d4..8a9635217ef795fd6dae7b6d7724c23a27b0f8a0 100644
--- a/mmdet/models/detectors/__init__.py
+++ b/mmdet/models/detectors/__init__.py
@@ -28,6 +28,7 @@ from .two_stage import TwoStageDetector
from .vfnet import VFNet
from .yolact import YOLACT
from .yolo import YOLOV3
+from .none import NoneDetector
__all__ = [
'ATSS', 'BaseDetector', 'SingleStageDetector',
@@ -36,5 +37,5 @@ __all__ = [
'RetinaNet', 'FCOS', 'GridRCNN', 'MaskScoringRCNN', 'RepPointsDetector',
'FOVEA', 'FSAF', 'NASFCOS', 'PointRend', 'GFL', 'CornerNet', 'PAA',
'YOLOV3', 'YOLACT', 'VFNet', 'DETR', 'TridentFasterRCNN', 'SparseRCNN',
- 'SCNet'
+ 'SCNet', 'NoneDetector'
]
diff --git a/mmdet/models/detectors/none.py b/mmdet/models/detectors/none.py
new file mode 100644
index 0000000000000000000000000000000000000000..05a3e4316c4dc82ce8d4d6b14118bda15108162c
--- /dev/null
+++ b/mmdet/models/detectors/none.py
@@ -0,0 +1,149 @@
+import torch
+import torch.nn as nn
+
+# from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from .base import BaseDetector
+
+
+@DETECTORS.register_module()
+class NoneDetector(BaseDetector):
+ """class for none detectors.
+
+ None detectors only extract backbone features.
+ """
+
+ def __init__(self,
+ backbone,
+ neck=None,
+ train_cfg=None,
+ test_cfg=None,
+ pretrained=None):
+ super(NoneDetector, self).__init__()
+ self.backbone = build_backbone(backbone)
+
+ if neck is not None:
+ self.neck = build_neck(neck)
+
+ self.train_cfg = train_cfg
+ self.test_cfg = test_cfg
+
+ self.init_weights(pretrained=pretrained)
+
+ @property
+ def with_rpn(self):
+ """bool: whether the detector has RPN"""
+ return hasattr(self, 'rpn_head') and self.rpn_head is not None
+
+ @property
+ def with_roi_head(self):
+ """bool: whether the detector has a RoI head"""
+ return hasattr(self, 'roi_head') and self.roi_head is not None
+
+ def init_weights(self, pretrained=None):
+ """Initialize the weights in detector.
+
+ Args:
+ pretrained (str, optional): Path to pre-trained weights.
+ Defaults to None.
+ """
+ super(NoneDetector, self).init_weights(pretrained)
+ self.backbone.init_weights(pretrained=pretrained)
+ if self.with_neck:
+ if isinstance(self.neck, nn.Sequential):
+ for m in self.neck:
+ m.init_weights()
+ else:
+ self.neck.init_weights()
+ if self.with_rpn:
+ self.rpn_head.init_weights()
+ if self.with_roi_head:
+ self.roi_head.init_weights(pretrained)
+
+ def extract_feat(self, img):
+ """Directly extract features from the backbone+neck."""
+ x = self.backbone(img)
+ if self.with_neck:
+ x = self.neck(x)
+ return x
+
+ def forward_dummy(self, img):
+ """Used for computing network flops.
+
+ See `mmdetection/tools/analysis_tools/get_flops.py`
+ """
+ # backbone
+ x = self.extract_feat(img)
+ return x
+
+ def forward_train(self,
+ img,
+ img_metas,
+ gt_bboxes,
+ gt_labels,
+ gt_bboxes_ignore=None,
+ gt_masks=None,
+ proposals=None,
+ **kwargs):
+ """
+ Args:
+ img (Tensor): of shape (N, C, H, W) encoding input images.
+ Typically these should be mean centered and std scaled.
+
+ img_metas (list[dict]): list of image info dict where each dict
+ has: 'img_shape', 'scale_factor', 'flip', and may also contain
+ 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+ For details on the values of these keys see
+ `mmdet/datasets/pipelines/formatting.py:Collect`.
+
+ gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+ shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+
+ gt_labels (list[Tensor]): class indices corresponding to each box
+
+ gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+ boxes can be ignored when computing the loss.
+
+ gt_masks (None | Tensor) : true segmentation masks for each box
+ used if the architecture supports a segmentation task.
+
+ proposals : override rpn proposals with custom proposals. Use when
+ `with_rpn` is False.
+
+ Returns:
+ dict[str, Tensor]: a dictionary of loss components
+ """
+ x = self.extract_feat(img)
+
+ losses = dict()
+
+ return losses
+
+ async def async_simple_test(self,
+ img,
+ img_meta,
+ proposals=None,
+ rescale=False):
+ """Async test without augmentation."""
+ x = self.extract_feat(img)
+ return x
+
+ def simple_test(self, img, img_metas, proposals=None, rescale=False):
+ """Test without augmentation."""
+ x = self.extract_feat(img)
+
+ # get origin input shape to onnx dynamic input shape
+ if torch.onnx.is_in_onnx_export():
+ img_shape = torch._shape_as_tensor(img)[2:]
+ img_metas[0]['img_shape_for_onnx'] = img_shape
+
+ return x
+
+ def aug_test(self, imgs, img_metas, rescale=False):
+ """Test with augmentations.
+
+ If rescale is False, then returned bboxes and masks will fit the scale
+ of imgs[0].
+ """
+ x = self.extract_feats(imgs)
+ return x