| # 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](porting_notes.md). |
|
|
