Configuration parity

The files in configs/point2rbox_v3/ reproduce the official mmrotate Point2RBox-v3 configuration. tests/parity/test_L0_v3_config.py compares the flattened values with stored reference goldens.

Framework mappings

PyTorch/mmrotate	Jittor/JDet	Equivalent behavior
`mmdet.ResNet(out_indices=...)`	`Resnet50(return_stages=...)`	same feature stages
torchvision ResNet-50 initialization	`pretrained=True`	same pretrained source
`mmdet.FPN`	`FPN`	matching channels and levels
`mmdet.FocalLoss`	`MMDetFocalLoss`	mmdetection reduction semantics
`GWDLoss`	`GDLoss(loss_type='gwd')`	matching Gaussian distance
AdamW + `clip_grad`	JDet AdamW with `grad_clip`	global L2 clip at 35
LinearLR + MultiStepLR	`LinearWarmupMultiStepLR`	pointwise-equal LR sequence
`SetEpochInfoHook`	runner `model.set_epoch(epoch)`	epoch switches preserved
mmrotate qbox transforms	`P2RV2DOTADataset`	qbox/rbox and point labels
mmrotate resize/flip	`MMRotateResize` / `MMRotateRandomFlip`	coordinate and angle parity

Locked training values

12 epochs, evaluation at epoch 12, checkpoint every epoch.
AdamW learning rate 5e-5, gradient clip 35.
Linear warmup from factor 1/3 for 500 iterations; learning-rate milestones at epochs 8 and 11 with factor 0.1.
End-to-end batch size 2 and second-stage batch size 4 on one GPU.
End-to-end weight decay 0.05; second-stage weight decay 0.005.
Five FPN strides [8, 16, 32, 64, 128] for v3.
Self-supervision probabilities [0.68, 0.07, 0.25].
Epoch 6 switches for edge supervision, pseudo-label assignment and copy-paste routing. The upstream key spelling label_assign_pseudo_label_switch_eopch is intentionally preserved.
Validation points to the reference trainval split, matching the official diagnostic protocol.

SAM filtering configuration

configs/point2rbox_v3/_base_sam-dotav1-0.py preserves the complete class-specific filtering table from the reference. The L0 test compares every key, value and tuple/list type. Notable intentional values include:

classes 3, 8 and 10 use circularity weight -3 with circularity penalty 100;
prompt points outside a mask receive the reference hard center-alignment penalty;
the internal fallback filter table is kept separate from the config table, because the reference values differ and the configured training path always passes the explicit table.

Infrastructure-only adaptation

The validated Jittor setup uses num_workers=0. Jittor 1.3.8.5 can deadlock in the multiprocessing dataset ring buffer for this variable-instance workload. This changes loading concurrency only; sample definitions, transforms and training math remain unchanged.

Upstream MobileSAM cache behavior

The upstream builder enters evaluation mode before loading the TinyViT state dict, which can leave a cached attention-bias tensor derived from initialization values. The Jittor builder loads weights first and then refreshes the evaluation cache. This is the deterministic checkpoint-loading behavior documented in porting_notes.md.