Merge branch 'dev_benchmark' of https://github.com/Justice-Eternal/mm…

…rotate into dev_benchmark
open-mmlab · Oct 23, 2022 · f05a726 · f05a726
2 parents f1136f3 + 9576feb
commit f05a726
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diff --git a/configs/convnext/README.md b/configs/convnext/README.md
@@ -25,7 +25,7 @@ DOTA1.0
 - ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
 
 ```shell
-pip install mmcls>=0.22.0
+pip install "mmcls>=1.0.0rc0"
 ```
 
 - The performance may be unstable according to mmdetection's experience.

diff --git a/configs/convnext/rotated-retinanet-rbox-le90_convnext-tiny_fpn_kld-stable_adamw-1x_dota.py b/configs/convnext/rotated-retinanet-rbox-le90_convnext-tiny_fpn_kld-stable_adamw-1x_dota.py
@@ -2,7 +2,7 @@
     '../kld/rotated-retinanet-rbox-le90_r50_fpn_kld-stable_adamw-1x_dota.py'
 ]
 
-# please install mmcls>=0.22.0
+# please install mmcls>=1.0.0rc0
 # import mmcls.models to trigger register_module in mmcls
 custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
 checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth'  # noqa

diff --git a/configs/kld/r3det-oc_r50_fpn_kld-stable_1x_dota.py b/configs/kld/r3det-oc_r50_fpn_kld-stable_1x_dota.py
@@ -2,7 +2,7 @@
 
 angle_version = 'oc'
 model = dict(
-    bbox_head=dict(
+    bbox_head_init=dict(
         reg_decoded_bbox=True,
         loss_bbox=dict(
             _delete_=True,
@@ -12,26 +12,28 @@
             tau=1.0,
             sqrt=False,
             loss_weight=5.0)),
-    refine_heads=[
+    bbox_head_refine=[
         dict(
-            type='RotatedRetinaRefineHead',
+            type='R3RefineHead',
             num_classes=15,
             in_channels=256,
             stacked_convs=4,
             feat_channels=256,
-            assign_by_circumhbbox=None,
+            frm_cfg=dict(
+                type='FRM', feat_channels=256, strides=[8, 16, 32, 64, 128]),
             anchor_generator=dict(
-                type='PseudoAnchorGenerator', strides=[8, 16, 32, 64, 128]),
+                type='PseudoRotatedAnchorGenerator',
+                strides=[8, 16, 32, 64, 128]),
             bbox_coder=dict(
-                type='DeltaXYWHAOBBoxCoder',
-                angle_range=angle_version,
+                type='DeltaXYWHTRBBoxCoder',
+                angle_version=angle_version,
                 norm_factor=None,
                 edge_swap=False,
                 proj_xy=False,
                 target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
                 target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
             loss_cls=dict(
-                type='FocalLoss',
+                type='mmdet.FocalLoss',
                 use_sigmoid=True,
                 gamma=2.0,
                 alpha=0.25,

diff --git a/configs/kld/r3det-oc_r50_fpn_kld_1x_dota.py b/configs/kld/r3det-oc_r50_fpn_kld_1x_dota.py
@@ -2,7 +2,7 @@
 
 angle_version = 'oc'
 model = dict(
-    bbox_head=dict(
+    bbox_head_init=dict(
         reg_decoded_bbox=True,
         loss_bbox=dict(
             _delete_=True,
@@ -11,26 +11,28 @@
             fun='log1p',
             tau=1.0,
             loss_weight=1.0)),
-    refine_heads=[
+    bbox_head_refine=[
         dict(
-            type='RotatedRetinaRefineHead',
+            type='R3RefineHead',
             num_classes=15,
             in_channels=256,
             stacked_convs=4,
             feat_channels=256,
-            assign_by_circumhbbox=None,
+            frm_cfg=dict(
+                type='FRM', feat_channels=256, strides=[8, 16, 32, 64, 128]),
             anchor_generator=dict(
-                type='PseudoAnchorGenerator', strides=[8, 16, 32, 64, 128]),
+                type='PseudoRotatedAnchorGenerator',
+                strides=[8, 16, 32, 64, 128]),
             bbox_coder=dict(
-                type='DeltaXYWHAOBBoxCoder',
-                angle_range=angle_version,
+                type='DeltaXYWHTRBBoxCoder',
+                angle_version=angle_version,
                 norm_factor=None,
                 edge_swap=False,
                 proj_xy=False,
                 target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
                 target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
             loss_cls=dict(
-                type='FocalLoss',
+                type='mmdet.FocalLoss',
                 use_sigmoid=True,
                 gamma=2.0,
                 alpha=0.25,

diff --git a/configs/kld/r3det-tiny-oc_r50_fpn_kld_1x_dota.py b/configs/kld/r3det-tiny-oc_r50_fpn_kld_1x_dota.py
@@ -2,7 +2,7 @@
 
 angle_version = 'oc'
 model = dict(
-    bbox_head=dict(
+    bbox_head_init=dict(
         reg_decoded_bbox=True,
         loss_bbox=dict(
             _delete_=True,
@@ -11,26 +11,28 @@
             fun='log1p',
             tau=1.0,
             loss_weight=1.0)),
-    refine_heads=[
+    bbox_head_refine=[
         dict(
-            type='RotatedRetinaRefineHead',
+            type='R3RefineHead',
             num_classes=15,
             in_channels=256,
             stacked_convs=2,
             feat_channels=256,
-            assign_by_circumhbbox=None,
+            frm_cfg=dict(
+                type='FRM', feat_channels=256, strides=[8, 16, 32, 64, 128]),
             anchor_generator=dict(
-                type='PseudoAnchorGenerator', strides=[8, 16, 32, 64, 128]),
+                type='PseudoRotatedAnchorGenerator',
+                strides=[8, 16, 32, 64, 128]),
             bbox_coder=dict(
-                type='DeltaXYWHAOBBoxCoder',
-                angle_range=angle_version,
+                type='DeltaXYWHTRBBoxCoder',
+                angle_version=angle_version,
                 norm_factor=None,
                 edge_swap=False,
                 proj_xy=False,
                 target_means=(0.0, 0.0, 0.0, 0.0, 0.0),
                 target_stds=(1.0, 1.0, 1.0, 1.0, 1.0)),
             loss_cls=dict(
-                type='FocalLoss',
+                type='mmdet.FocalLoss',
                 use_sigmoid=True,
                 gamma=2.0,
                 alpha=0.25,

diff --git a/configs/kld/rotated-retinanet-hbox-oc_r50_fpn_kld-stable_1x_dota.py b/configs/kld/rotated-retinanet-hbox-oc_r50_fpn_kld-stable_1x_dota.py
@@ -1,4 +1,4 @@
-_base_ = '../rotated_retinanet/rotated_retinanet-hbox-oc_r50_fpn_1x_dota.py'
+_base_ = '../rotated_retinanet/rotated-retinanet-hbox-oc_r50_fpn_1x_dota.py'
 
 model = dict(
     bbox_head=dict(

diff --git a/configs/kld/rotated-retinanet-rbox-le90_r50_fpn_kld-stable_adamw-1x_dota.py b/configs/kld/rotated-retinanet-rbox-le90_r50_fpn_kld-stable_adamw-1x_dota.py
@@ -1,4 +1,4 @@
-_base_ = './rotated_retinanet-rbox-le90_r50_fpn_kld-stable_1x_dota.py'
+_base_ = './rotated-retinanet-rbox-le90_r50_fpn_kld-stable_1x_dota.py'
 
 optim_wrapper = dict(
     optimizer=dict(

diff --git a/configs/oriented_rcnn/oriented-rcnn-le90_r50_fpn_amp-1x_dota.py b/configs/oriented_rcnn/oriented-rcnn-le90_r50_fpn_amp-1x_dota.py
@@ -1,3 +1,3 @@
 _base_ = './oriented-rcnn-le90_r50_fpn_1x_dota.py'
 
-optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
+optim_wrapper = dict(type='AmpOptimWrapper')
diff --git a/configs/oriented_rcnn/oriented-rcnn-le90_swin-tiny_fpn_1x_dota.py b/configs/oriented_rcnn/oriented-rcnn-le90_swin-tiny_fpn_1x_dota.py
@@ -5,7 +5,7 @@
 model = dict(
     backbone=dict(
         _delete_=True,
-        type='SwinTransformer',
+        type='mmdet.SwinTransformer',
         embed_dims=96,
         depths=depths,
         num_heads=[3, 6, 12, 24],

diff --git a/configs/redet/README.md b/configs/redet/README.md
@@ -34,6 +34,7 @@ Notes:
 - `MS` means multiple scale image split.
 - `RR` means random rotation.
 - Please download pretrained weight of ReResNet from [ReDet](https://github.com/csuhan/ReDet), and put it on `work_dirs/pretrain`. BTW, it is normal for `missing keys in source state_dict: xxx.filter ` to appear in the log. Don't worry!
+- Please use distributed training, there are some bug when use `train.py`.
 
 ## Citation
 

diff --git a/configs/redet/redet-le90_re50_refpn_amp-1x_dota.py b/configs/redet/redet-le90_re50_refpn_amp-1x_dota.py
@@ -1,3 +1,3 @@
 _base_ = './redet-le90_re50_refpn_1x_dota.py'
 
-optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
+optim_wrapper = dict(type='AmpOptimWrapper')
diff --git a/configs/roi_trans/roi-trans-le90_r50_fpn_amp-1x_dota.py b/configs/roi_trans/roi-trans-le90_r50_fpn_amp-1x_dota.py
@@ -1,3 +1,3 @@
 _base_ = './roi-trans-le90_r50_fpn_1x_dota.py'
 
-optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
+optim_wrapper = dict(type='AmpOptimWrapper')
diff --git a/configs/roi_trans/roi-trans-le90_swin-tiny_fpn_1x_dota.py b/configs/roi_trans/roi-trans-le90_swin-tiny_fpn_1x_dota.py
@@ -6,7 +6,7 @@
 model = dict(
     backbone=dict(
         _delete_=True,
-        type='SwinTransformer',
+        type='mmet.SwinTransformer',
         embed_dims=96,
         depths=depths,
         num_heads=[3, 6, 12, 24],

diff --git a/configs/rotated_reppoints/metafile.yml b/configs/rotated_reppoints/metafile.yml
@@ -14,9 +14,9 @@ Collections:
   README: configs/cfa/README.md
 
 Models:
-  - Name: rotated_reppoints_r50_fpn_1x_dota_oc
+  - Name: rotated-reppoints-qbox_r50_fpn_1x_dota
     In Collection: rotated_reppoints
-    Config: configs/rotated_reppoints/rotated_reppoints_r50_fpn_1x_dota_oc.py
+    Config: configs/rotated_reppoints/rotated-reppoints-qbox_r50_fpn_1x_dota.py
     Metadata:
       Training Data: DOTAv1.0
     Results:

diff --git a/configs/rotated_retinanet/rotated-retinanet-rbox-le90_r50_fpn_amp-1x_dota.py b/configs/rotated_retinanet/rotated-retinanet-rbox-le90_r50_fpn_amp-1x_dota.py
@@ -1,3 +1,3 @@
 _base_ = ['./rotated-retinanet-rbox-le90_r50_fpn_1x_dota.py']
 
-optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
+optim_wrapper = dict(type='AmpOptimWrapper')
diff --git a/mmrotate/models/backbones/re_resnet.py b/mmrotate/models/backbones/re_resnet.py
@@ -490,7 +490,7 @@ def __init__(self,
                  zero_init_residual: bool = True,
                  init_cfg: OptMultiConfig = None) -> None:
         super().__init__(init_cfg=init_cfg)
-        self.in_type = build_enn_trivial_feature(3)
+        self.in_type = build_enn_trivial_feature(in_channels)
 
         if depth not in self.arch_settings:
             raise KeyError(f'invalid depth {depth} for resnet')

diff --git a/mmrotate/models/roi_heads/roi_extractors/rotate_single_level_roi_extractor.py b/mmrotate/models/roi_heads/roi_extractors/rotate_single_level_roi_extractor.py
@@ -97,6 +97,7 @@ def forward(self, feats, rois, roi_scale_factor=None):
         Returns:
             torch.Tensor: Scaled RoI features.
         """
+        rois = rois.type_as(feats[0])
         from mmrotate import digit_version, mmcv_version
         if isinstance(self.roi_layers[0], ops.RiRoIAlignRotated
                       ) or mmcv_version == digit_version('1.4.5'):

diff --git a/mmrotate/structures/bbox/rotated_boxes.py b/mmrotate/structures/bbox/rotated_boxes.py
@@ -413,10 +413,10 @@ def overlaps(boxes1: BaseBoxes,
         Returns:
             Tensor: shape (m, n) if ``is_aligned`` is False else shape (m,)
         """
-        from ..iou_calculators import rbox_overlaps
+        from mmrotate.structures.bbox import rbbox_overlaps
         boxes1 = boxes1.convert_to('rbox')
         boxes2 = boxes2.convert_to('rbox')
-        return rbox_overlaps(
+        return rbbox_overlaps(
             boxes1.tensor,
             boxes2.tensor,
             mode=mode,