Hugging Face's logo

sathiiii
/
SACFlow

Model card Files
xet
 Community
SACFlow / docs /EXPERIMENTS.md
sathiiii's picture
sathiiii
Add documentation
1734875 verified
preview code
|
Raw
History Blame Contribute Delete
3.71 kB

SACFlow-FM experiment guide

This guide lists the recommended first set of experiments.

0. Environment 

conda env create -f environment.yml
conda activate sacflow
pip install -e .

For AMD MI210/ROCm, install the PyTorch ROCm wheel that matches the cluster ROCm version. Example for ROCm 6.2:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2
python -c "import torch; print(torch.__version__, torch.cuda.is_available(), torch.cuda.device_count())"

On ROCm, PyTorch still exposes AMD GPUs through the torch.cuda API. Use torchrun exactly as with CUDA GPU clusters.

1. Prepare dataset manifests

The code uses JSON manifests rather than hard-coded dataset layouts.

MMWHS CT -> MR 

python tools/create_dataset_json.py \
  --name mmwhs_ct2mr \
  --source-images /data/MMWHS/CT/images \
  --source-labels /data/MMWHS/CT/labels \
  --target-images /data/MMWHS/MR/images \
  --target-labels /data/MMWHS/MR/labels \
  --output data_json/mmwhs_ct2mr.json \
  --val-fraction 0.2 \
  --test-fraction 0.2 \
  --seed 1337

MMWHS MR -> CT

Swap source and target folders and write data_json/mmwhs_mr2ct.json.

CrossMoDA ceT1 -> hrT2 

python tools/create_dataset_json.py \
  --name crossmoda_t1_to_t2 \
  --source-images /data/CrossMoDA/ceT1/images \
  --source-labels /data/CrossMoDA/ceT1/labels \
  --target-images /data/CrossMoDA/hrT2/images \
  --target-labels /data/CrossMoDA/hrT2/labels_optional_for_eval \
  --output data_json/crossmoda_t1_to_t2.json

If target labels are not available, keep target-labels omitted and use a separate held-out validation set only when you have labels.

2. Main MMWHS experiment sequence

A. Source training 

bash scripts/mmwhs/train_source_ct2mr.sh

B. Export compact source memory 

bash scripts/mmwhs/export_memory_ct.sh

C. Source-only target evaluation 

bash scripts/mmwhs/eval_source_ct2mr.sh

D. Oracle target supervised upper bound 

bash scripts/mmwhs/train_oracle_mr.sh

E. Internal SFDA baselines 

bash scripts/mmwhs/run_selftrain_ct2mr.sh
bash scripts/mmwhs/run_peft_ct2mr.sh
bash scripts/mmwhs/run_proto_align_ct2mr.sh

F. SACFlow-FM and key ablations 

bash scripts/mmwhs/run_sacflow_ct2mr.sh
bash scripts/mmwhs/run_sacflow_linear_ct2mr.sh
bash scripts/mmwhs/run_sacflow_whole_feature_ct2mr.sh
bash scripts/mmwhs/run_sacflow_random_subspace_ct2mr.sh

3. External baselines

For CCRC, ProtoContra, A3-DualUD, HEAL, and RSA, prefer official repositories and run them on the exact same manifest split. Do not compare reported numbers in the main table unless the dataset split, backbone, label set, and metric are exactly identical.

Recommended first external baselines:

  1. CCRC on MMWHS.
  2. ProtoContra or A3-DualUD on MMWHS.
  3. RSA on CrossMoDA.

4. W&B

Set:

export WANDB_PROJECT=SACFlow-FM
export WANDB_ENTITY=<your_entity_or_leave_empty>
wandb login

For offline logging:

export WANDB_MODE=offline

5. DDP on 8 AMD GPUs

Each script uses torchrun. Example:

torchrun --standalone --nnodes=1 --nproc_per_node=8 tools/train.py --config configs/experiments/mmwhs_sacflow_ct2mr.yaml

6. Metrics to monitor

Training logs include:

  • total loss and per-loss components;
  • CE, Dice, entropy, consistency;
  • SACFlow FM loss;
  • path-state validity weight;
  • domain-progress score;
  • task KL/logit drift;
  • boundary loss;
  • velocity magnitude;
  • nullspace leakage;
  • pseudo-label confidence and class distribution;
  • Dice and HD95/ASSD during validation;
  • accepted-state rate;
  • ETA and iteration speed.