File size: 5,213 Bytes
e37baa2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | # Per-model run commands β `foldsrunner_simplified_after_ablation.py`
Base config is already set in the file: `STRATEGIES=[2,3]`, 10 phases (`PHASE_EXECUTION_MODE="auto"`),
100% data, `BATCH_SIZE=48`, `NUM_WORKERS=12`, `STRATEGY_2_MAX_EPOCHS=100`, `STRATEGY_3_MAX_EPOCHS=120`,
`RUN_OPTUNA=False`, `STRATEGY2_CHECKPOINT_MODE="best"` (strategy 3 uses the strategy-2 base trained in
the same run β no checkpoint wiring needed).
**Validated at 128px:** DPT+ViT-tiny and UPerNet+Swin-tiny were dropped (weights locked to 224px) and
replaced with **PVTv2 + UPerNet** (b1 and b2), which run natively at 128px. Re-run
`python validate_models.py` to confirm all 6 pass on your box before launching.
---
## 1. SegFormer-B0 (transformer β SegFormer params, proj 192)
```bash
cd /workspace
sed -i -E \
-e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "mit_b0"/' \
-e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Segformer"/' \
-e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \
-e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
-e 's/^MODEL_NAME = .*/MODEL_NAME = "Segformer_B0"/' \
-e 's#"2:100": "[^"]*"#"2:100": "params/segformer_b0/best_params_strat2.json"#' \
-e 's#"3:100": "[^"]*"#"3:100": "params/segformer_b0/best_params_strat3.json"#' \
foldsrunner_simplified_after_ablation.py
python foldsrunner_simplified_after_ablation.py
```
## 2. U-Net + EfficientNet-B0 (CNN β U-Net params, proj 256)
```bash
cd /workspace
sed -i -E \
-e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "efficientnet-b0"/' \
-e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Unet"/' \
-e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \
-e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
-e 's/^MODEL_NAME = .*/MODEL_NAME = "Unet_EffB0"/' \
-e 's#"2:100": "[^"]*"#"2:100": "params/unet_effb0/best_params_strat2.json"#' \
-e 's#"3:100": "[^"]*"#"3:100": "params/unet_effb0/best_params_strat3.json"#' \
foldsrunner_simplified_after_ablation.py
python foldsrunner_simplified_after_ablation.py
```
## 3. DeepLabV3+ + ResNet34 (CNN β U-Net params, proj 256)
```bash
cd /workspace
sed -i -E \
-e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "resnet34"/' \
-e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "DeepLabV3Plus"/' \
-e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \
-e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
-e 's/^MODEL_NAME = .*/MODEL_NAME = "DeepLabV3Plus_ResNet34"/' \
-e 's#"2:100": "[^"]*"#"2:100": "params/deeplabv3plus_r34/best_params_strat2.json"#' \
-e 's#"3:100": "[^"]*"#"3:100": "params/deeplabv3plus_r34/best_params_strat3.json"#' \
foldsrunner_simplified_after_ablation.py
python foldsrunner_simplified_after_ablation.py
```
## 4. LinkNet + MobileNetV3-Large (CNN β U-Net params, proj 256)
```bash
cd /workspace
sed -i -E \
-e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "timm-mobilenetv3_large_100"/' \
-e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "Linknet"/' \
-e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 256/' \
-e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
-e 's/^MODEL_NAME = .*/MODEL_NAME = "Linknet_MobileNetV3"/' \
-e 's#"2:100": "[^"]*"#"2:100": "params/linknet_mbv3/best_params_strat2.json"#' \
-e 's#"3:100": "[^"]*"#"3:100": "params/linknet_mbv3/best_params_strat3.json"#' \
foldsrunner_simplified_after_ablation.py
python foldsrunner_simplified_after_ablation.py
```
## 5. UPerNet + PVTv2-b1 (transformer β SegFormer params, proj 192)
```bash
cd /workspace
sed -i -E \
-e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "tu-pvt_v2_b1"/' \
-e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "UPerNet"/' \
-e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \
-e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
-e 's/^MODEL_NAME = .*/MODEL_NAME = "UPerNet_PVTv2_b1"/' \
-e 's#"2:100": "[^"]*"#"2:100": "params/upernet_pvtv2_b1/best_params_strat2.json"#' \
-e 's#"3:100": "[^"]*"#"3:100": "params/upernet_pvtv2_b1/best_params_strat3.json"#' \
foldsrunner_simplified_after_ablation.py
python foldsrunner_simplified_after_ablation.py
```
## 6. UPerNet + PVTv2-b2 (transformer β SegFormer params, proj 192)
```bash
cd /workspace
sed -i -E \
-e 's/^SMP_ENCODER_NAME = .*/SMP_ENCODER_NAME = "tu-pvt_v2_b2"/' \
-e 's/^SMP_DECODER_TYPE = .*/SMP_DECODER_TYPE = "UPerNet"/' \
-e 's/^SMP_ENCODER_PROJ_DIM = .*/SMP_ENCODER_PROJ_DIM = 192/' \
-e 's/^SMP_ENCODER_DEPTH = .*/SMP_ENCODER_DEPTH = 5/' \
-e 's/^MODEL_NAME = .*/MODEL_NAME = "UPerNet_PVTv2_b2"/' \
-e 's#"2:100": "[^"]*"#"2:100": "params/upernet_pvtv2_b2/best_params_strat2.json"#' \
-e 's#"3:100": "[^"]*"#"3:100": "params/upernet_pvtv2_b2/best_params_strat3.json"#' \
foldsrunner_simplified_after_ablation.py
python foldsrunner_simplified_after_ablation.py
```
---
## After the runs β plots
```bash
cd /workspace
python plot_s2_vs_s3.py
```
One titled figure per model β `runs/_plots/<MODEL_NAME>__s2_vs_s3.png` (6 panels: BIoU band, BIoU
contour, inference time, total training time, time per epoch, time to best checkpoint β S2 vs S3,
mean Β± std across the 10 phases, Ξ annotated), plus `runs/_plots/summary_s2_vs_s3.csv`.
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