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configs/lsknet/lsk_s_ema_fpn_1x_dota_le90.py

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+_base_ = [
+    '../_base_/datasets/dotav1.py', '../_base_/schedules/schedule_1x.py',
+    '../_base_/default_runtime.py'
+]
+
+angle_version = 'le90'
+gpu_number = 8
+# fp16 = dict(loss_scale='dynamic')
+model = dict(
+    type='OrientedRCNN',
+    backbone=dict(
+        type='LSKNet',
+        embed_dims=[64, 128, 320, 512],
+        drop_rate=0.1,
+        drop_path_rate=0.1,
+        depths=[2,2,4,2],
+        init_cfg=dict(type='Pretrained', checkpoint="/data/pretrained/lsk_s_backbone.pth.tar"),
+        norm_cfg=dict(type='SyncBN', requires_grad=True)),
+    neck=dict(
+        type='FPN',
+        in_channels=[64, 128, 320, 512],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='OrientedRPNHead',
+        in_channels=256,
+        feat_channels=256,
+        version=angle_version,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='MidpointOffsetCoder',
+            angle_range=angle_version,
+            target_means=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            target_stds=[1.0, 1.0, 1.0, 1.0, 0.5, 0.5]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(
+            type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
+    roi_head=dict(
+        type='OrientedStandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='RotatedSingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlignRotated',
+                out_size=7,
+                sample_num=2,
+                clockwise=True),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='RotatedShared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=15,
+            bbox_coder=dict(
+                type='DeltaXYWHAOBBoxCoder',
+                angle_range=angle_version,
+                norm_factor=None,
+                edge_swap=True,
+                proj_xy=True,
+                target_means=(.0, .0, .0, .0, .0),
+                target_stds=(0.1, 0.1, 0.2, 0.2, 0.1)),
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                gpu_assign_thr=800,
+                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_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.8),
+            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,
+                match_low_quality=False,
+                iou_calculator=dict(type='RBboxOverlaps2D'),
+                gpu_assign_thr=800,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RRandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.8),
+            min_bbox_size=0),
+        rcnn=dict(
+            nms_pre=2000,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(iou_thr=0.1),
+            max_per_img=2000)))
+
+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),
+    dict(type='RResize', img_scale=(1024, 1024)),
+    dict(
+        type='RRandomFlip',
+        flip_ratio=[0.25, 0.25, 0.25],
+        direction=['horizontal', 'vertical', 'diagonal'],
+        version=angle_version),
+    dict(
+        type='PolyRandomRotate',
+        rotate_ratio=0.5,
+        angles_range=180,
+        auto_bound=False,
+        rect_classes=[9, 11],
+        version=angle_version),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline, version=angle_version),
+    val=dict(version=angle_version),
+    test=dict(version=angle_version))
+
+custom_hooks=[dict(
+        type='ExpMomentumEMAHook',
+        total_iter = 8541*12,
+        priority=49)
+    ]
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0002, #/8*gpu_number,
+    betas=(0.9, 0.999),
+    weight_decay=0.05)