add o2_rtdetr dotav1.5

wokaikaixinxin · wokaikaixinxin · commit 01c560c346ec · 2025-10-12T20:51:04.000+08:00
diff --git a/projects/rotated_rtdetr/configs/dotav15_rr.py b/projects/rotated_rtdetr/configs/dotav15_rr.py
@@ -0,0 +1,101 @@
+# dataset settings
+dataset_type = 'DOTAv15Dataset'
+data_root = 'data/split_ss_dota1.5/'
+
+backend_args = None
+
+train_pipeline = [
+    dict(type='mmdet.LoadImageFromFile', backend_args=backend_args),
+    dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
+    dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
+    dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
+    dict(
+        type='mmdet.RandomFlip',
+        prob=0.75,
+        direction=['horizontal', 'vertical', 'diagonal']),
+    dict(
+        type='RandomRotate',
+        prob=0.5,
+        angle_range=180,
+        rect_obj_labels=[9, 11]),
+    dict(
+        type='mmdet.Pad', size=(1024, 1024),
+        pad_val=dict(img=(114, 114, 114))),
+    dict(type='mmdet.PackDetInputs')
+]
+val_pipeline = [
+    dict(type='mmdet.LoadImageFromFile', backend_args=backend_args),
+    dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
+    # avoid bboxes being resized
+    dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
+    dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
+    dict(
+        type='mmdet.Pad', size=(1024, 1024),
+        pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+test_pipeline = [
+    dict(type='mmdet.LoadImageFromFile', backend_args=backend_args),
+    dict(type='mmdet.Resize', scale=(1024, 1024), keep_ratio=True),
+    dict(
+        type='mmdet.Pad', size=(1024, 1024),
+        pad_val=dict(img=(114, 114, 114))),
+    dict(
+        type='mmdet.PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor'))
+]
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    batch_sampler=None,
+    pin_memory=False,
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='trainval/annfiles/',
+        data_prefix=dict(img_path='trainval/images/'),
+        filter_cfg=dict(filter_empty_gt=True),
+        pipeline=train_pipeline))
+val_dataloader = dict(
+    batch_size=4,
+    num_workers=4,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='trainval/annfiles/',
+        data_prefix=dict(img_path='trainval/images/'),
+        test_mode=True,
+        pipeline=val_pipeline))
+# test_dataloader = val_dataloader
+
+val_evaluator = dict(type='DOTAMetric', metric='mAP')
+# test_evaluator = val_evaluator
+
+# inference on test dataset and format the output results
+# for submission. Note: the test set has no annotation.
+test_dataloader = dict(
+    batch_size=4,
+    num_workers=4,
+    persistent_workers=False,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img_path='test/images/'),
+        test_mode=True,
+        pipeline=test_pipeline))
+test_evaluator = dict(
+    type='DOTAMetric',
+    format_only=True,
+    merge_patches=True,
+    outfile_prefix='./work_dirs/Task1')
diff --git a/projects/rotated_rtdetr/configs/o2_rtdetr_r101vd_2xb4_72e_dotav15.py b/projects/rotated_rtdetr/configs/o2_rtdetr_r101vd_2xb4_72e_dotav15.py
@@ -0,0 +1,22 @@
+from mmengine.config import read_base
+with read_base():
+    from .o2_rtdetr_r50vd_2xb4_72e_dotav15 import *
+
+pretrained = ('https://www.modelscope.cn/models/wokaikaixinxin/ai4rs/resolve/'
+              'master/rtdetr/resnet101vd_ssld_pretrained_64ed664a.pth')  # noqa
+
+model.update(
+    backbone=dict(
+        depth=101, init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=dict(out_channels=384),
+    encoder=dict(
+        in_channels=[384, 384, 384],
+        fpn_cfg=dict(in_channels=[384, 384, 384]),
+        layer_cfg=dict(
+            self_attn_cfg=dict(embed_dims=384),
+            ffn_cfg=dict(embed_dims=384, feedforward_channels=2048))))
+
+# optimizer
+optim_wrapper.update(
+    paramwise_cfg=dict(
+        custom_keys={'backbone': dict(lr_mult=0.01)}, norm_decay_mult=1))
diff --git a/projects/rotated_rtdetr/configs/o2_rtdetr_r18vd_2xb4_72e_dotav15.py b/projects/rotated_rtdetr/configs/o2_rtdetr_r18vd_2xb4_72e_dotav15.py
@@ -0,0 +1,41 @@
+from mmengine.config import read_base
+with read_base():
+    from .o2_rtdetr_r50vd_2xb4_72e_dotav15 import *
+
+pretrained = ('https://www.modelscope.cn/models/wokaikaixinxin/ai4rs/resolve/'
+              'master/rtdetr/resnet18vd_pretrained_55f5a0d6.pth')  # noqa
+
+model.update(
+    backbone=dict(
+        depth=18,
+        frozen_stages=-1,
+        norm_cfg=dict(requires_grad=True),
+        norm_eval=False,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=dict(in_channels=[128, 256, 512]),
+    encoder=dict(fpn_cfg=dict(expansion=0.5)),
+    decoder=dict(num_layers=3))
+
+# set all norm layers in backbone to lr_mult=0.1 and decay_mult=0.0
+# set all other layers in backbone to lr_mult=0.1
+num_blocks_list = (2, 2, 2, 2)  # r18
+downsample_norm_idx_list = (2, 3, 3, 3)  # r18
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+custom_keys = {'backbone': dict(lr_mult=0.1)}
+custom_keys.update({
+    f'backbone.layer{stage_id + 1}.{block_id}.bn': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(num_blocks_list)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.layer{stage_id + 1}.{block_id}.downsample.{downsample_norm_idx - 1}':  # noqa
+    backbone_norm_multi
+    for stage_id, (num_blocks, downsample_norm_idx) in enumerate(
+        zip(num_blocks_list, downsample_norm_idx_list))
+    for block_id in range(num_blocks)
+})
+
+# optimizer
+optim_wrapper.paramwise_cfg.pop('custom_keys')
+optim_wrapper.update(
+    paramwise_cfg=dict(custom_keys=dict(**custom_keys)))
diff --git a/projects/rotated_rtdetr/configs/o2_rtdetr_r34vd_2xb4_72e_dotav15.py b/projects/rotated_rtdetr/configs/o2_rtdetr_r34vd_2xb4_72e_dotav15.py
@@ -0,0 +1,41 @@
+from mmengine.config import read_base
+with read_base():
+    from .o2_rtdetr_r50vd_2xb4_72e_dotav15 import *
+
+pretrained = ('https://www.modelscope.cn/models/wokaikaixinxin/ai4rs/resolve/'
+              'master/rtdetr/resnet34vd_pretrained_f6a72dc5.pth')  # noqa
+
+model.update(
+    backbone=dict(
+        depth=34,
+        frozen_stages=-1,
+        norm_cfg=dict(requires_grad=True),
+        norm_eval=False,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=dict(in_channels=[128, 256, 512]),
+    encoder=dict(fpn_cfg=dict(expansion=0.5)),
+    decoder=dict(num_layers=4))
+
+# set all norm layers in backbone to lr_mult=0.1 and decay_mult=0.0
+# set all other layers in backbone to lr_mult=0.1
+num_blocks_list = (3, 4, 6, 3)  # r34
+downsample_norm_idx_list = (2, 3, 3, 3)  # r34
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+custom_keys = {'backbone': dict(lr_mult=0.1)}
+custom_keys.update({
+    f'backbone.layer{stage_id + 1}.{block_id}.bn': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(num_blocks_list)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.layer{stage_id + 1}.{block_id}.downsample.{downsample_norm_idx - 1}':  # noqa
+    backbone_norm_multi
+    for stage_id, (num_blocks, downsample_norm_idx) in enumerate(
+        zip(num_blocks_list, downsample_norm_idx_list))
+    for block_id in range(num_blocks)
+})
+
+# optimizer
+optim_wrapper.paramwise_cfg.pop('custom_keys')
+optim_wrapper.update(
+    paramwise_cfg=dict(custom_keys=dict(**custom_keys)))
diff --git a/projects/rotated_rtdetr/configs/o2_rtdetr_r50vd_2xb4_72e_dotav15.py b/projects/rotated_rtdetr/configs/o2_rtdetr_r50vd_2xb4_72e_dotav15.py
@@ -0,0 +1,180 @@
+from torch.optim.adamw import AdamW
+from mmengine.config import read_base
+from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
+from mmengine.optim.optimizer import OptimWrapper
+from mmengine.optim.scheduler.lr_scheduler import LinearLR
+from mmengine.hooks.ema_hook import EMAHook
+from mmdet.models.data_preprocessors import DetDataPreprocessor
+from mmdet.models.necks import ChannelMapper
+from mmdet.models.losses import L1Loss
+from mmdet.models.task_modules import FocalLossCost, HungarianAssigner
+from mmdet.models.layers.ema import ExpMomentumEMA
+from ai4rs.models.losses import GDLoss
+from projects.rotated_dino.rotated_dino.match_cost import ChamferCost, GDCost
+from projects.rotated_rtdetr.rotated_rtdetr import (RotatedRTDETR, RTDETRFPN, ResNetV1dPaddle,
+                                                    RotatedRTDETRHead, RTDETRVarifocalLoss)
+
+with read_base():
+    from .dotav15_rr import *
+    from .default_runtime import *
+
+pretrained = ('https://www.modelscope.cn/models/wokaikaixinxin/ai4rs/resolve/'
+              'master/rtdetr/resnet50vd_ssld_v2_pretrained_d037e232.pth')  # noqa
+
+angle_cfg = dict(
+    width_longer=True,
+    start_angle=0,
+)
+angle_factor=3.1415926535897932384626433832795
+
+
+model = dict(
+    type=RotatedRTDETR,
+    num_queries=300,  # num_matching_queries, 900 for DINO
+    with_box_refine=True,
+    as_two_stage=True,
+    data_preprocessor=dict(
+        type=DetDataPreprocessor,
+        mean=[103.53, 116.28, 123.675],
+        std=[57.375, 57.12, 58.395],
+        bgr_to_rgb=False,
+        boxtype2tensor=False,
+        batch_augments=None),
+    backbone=dict(
+        type=ResNetV1dPaddle,  # ResNet for DINO
+        depth=50,
+        num_stages=4,
+        out_indices=(1, 2, 3),
+        frozen_stages=0,  # -1 for DINO
+        norm_cfg=dict(type='BN', requires_grad=False),  # BN for DINO
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=dict(
+        type=ChannelMapper,
+        in_channels=[512, 1024, 2048],
+        kernel_size=1,
+        out_channels=256,
+        act_cfg=None,
+        norm_cfg=dict(type='BN', requires_grad=True),  # GN for DINO
+        num_outs=3,  # 4 for DINO
+        init_cfg=dict(
+            type='Kaiming',
+            layer='Conv2d',
+            a=5**0.5,
+            distribution='uniform',
+            mode='fan_in',
+            nonlinearity='leaky_relu')),
+    encoder=dict(
+        use_encoder_idx=[-1],
+        num_encoder_layers=1,
+        in_channels=[256, 256, 256],
+        fpn_cfg=dict(
+            type=RTDETRFPN,
+            in_channels=[256, 256, 256],
+            out_channels=256,
+            expansion=1.0,
+            norm_cfg=dict(type='BN', requires_grad=True)),
+        layer_cfg=dict(
+            self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
+            ffn_cfg=dict(
+                embed_dims=256,
+                feedforward_channels=1024,  # 2048 for DINO
+                ffn_drop=0.0,
+                act_cfg=dict(type='GELU')))),  # ReLU for DINO
+    decoder=dict(
+        num_layers=6,
+        return_intermediate=True,
+        angle_factor=angle_factor,
+        layer_cfg=dict(
+            self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
+            cross_attn_cfg=dict(
+                embed_dims=256,
+                num_levels=3,  # 4 for DINO
+                dropout=0.0),
+            ffn_cfg=dict(
+                embed_dims=256,
+                feedforward_channels=1024,  # 2048 for DINO
+                ffn_drop=0.0)),
+        post_norm_cfg=None),
+    bbox_head=dict(
+        type=RotatedRTDETRHead,
+        num_classes=16,
+        angle_cfg=angle_cfg,
+        angle_factor=angle_factor,
+        sync_cls_avg_factor=True,
+        loss_cls=dict(
+            type=RTDETRVarifocalLoss,
+            varifocal_loss_iou_type='hbox_iou', # hbox_iou, rbox_iou, prob_iou
+            use_sigmoid=True,
+            alpha=0.75,
+            gamma=2.0,
+            iou_weighted=True,
+            loss_weight=1.0),
+        loss_bbox=dict(type=L1Loss, loss_weight=5.0),
+        loss_iou=dict(
+            type=GDLoss,
+            loss_type='kld',
+            fun='log1p',
+            tau=1,
+            sqrt=False,
+            loss_weight=2.0)),
+    dn_cfg=dict(  # TODO: Move to model.train_cfg ?
+        label_noise_scale=0.5,
+        box_noise_scale=1.0,
+        angle_cfg=angle_cfg,
+        angle_factor=angle_factor,
+        noise_mode='only_xyxy',  # 'only_xyxy', 'only_angle', 'only_xywh', 'all_xyxya'
+        group_cfg=dict(dynamic=True,
+                       num_groups=None,
+                       num_dn_queries=100)),  # TODO: half num_dn_queries
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type=HungarianAssigner,
+            match_costs=[
+                dict(type=FocalLossCost, weight=2.0),
+                dict(type=ChamferCost, weight=5.0, box_format='xywha'),
+                dict(
+                    type=GDCost,
+                    loss_type='kld',
+                    fun='log1p',
+                    tau=1,
+                    sqrt=False,
+                    weight=2.0)])),
+    test_cfg=dict(max_per_img=300))
+
+
+# optimizer
+optim_wrapper = dict(
+    type=OptimWrapper,
+    optimizer=dict(type=AdamW, lr=0.0001, weight_decay=0.0001),
+    clip_grad=dict(max_norm=0.1, norm_type=2),
+    paramwise_cfg=dict(
+        custom_keys={'backbone': dict(lr_mult=0.1)},
+        norm_decay_mult=0,
+        bypass_duplicate=True))
+
+# learning policy
+max_epochs = 72
+train_cfg = dict(
+    type=EpochBasedTrainLoop, max_epochs=max_epochs, val_interval=6)
+val_cfg = dict(type=ValLoop)
+test_cfg = dict(type=TestLoop)
+param_scheduler = [
+    dict(
+        type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=2000)
+]
+custom_hooks = [
+    dict(
+        type=EMAHook,
+        ema_type=ExpMomentumEMA,
+        momentum=0.0001,
+        update_buffers=True,
+        priority=49)
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (2 GPUs) x (4 samples per GPU)
+auto_scale_lr = dict(enable=False, base_batch_size=8)
