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0161e74 | 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 | #!/bin/bash
#PJM -L rscgrp=b-batch
#PJM -L gpu=1
#PJM -L elapse=3:00:00
#PJM -j
#PJM -S
#PJM -o /home/hp250092/ku50001222/qian/aivc/lfj/transfer/logs/ccfm_eval_joint_%j.out
# Evaluate CCFM with joint-generation inference (LatentForcing style)
# using the checkpoint from two-stage ODE training.
source /home/hp250092/ku50001222/qian/aivc/lfj/stack_env/bin/activate
cd /home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256
echo "=========================================="
echo "Job ID: $PJM_JOBID"
echo "Start: $(date)"
echo "Node: $(hostname)"
echo "Eval mode: joint-generate (LatentForcing style)"
echo "=========================================="
CHECKPOINT="/home/hp250092/ku50001222/qian/aivc/lfj/transfer/code/CCFM/result/ccfm-fusion_differential_perceiver-norman-cascaded-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-latent_weight_1.0-choose_latent_p_0.4/iteration_110000/checkpoint.pt"
python scripts/run_cascaded.py \
--data-name norman \
--d-model 128 \
--nhead 8 \
--nlayers 4 \
--batch-size 128 \
--lr 5e-5 \
--steps 200000 \
--fusion-method differential_perceiver \
--perturbation-function crisper \
--noise-type Gaussian \
--infer-top-gene 1000 \
--n-top-genes 5000 \
--use-mmd-loss \
--gamma 0.5 \
--split-method additive \
--fold 1 \
--scgpt-dim 512 \
--bottleneck-dim 128 \
--latent-weight 1.0 \
--choose-latent-p 0.4 \
--dh-depth 2 \
--latent-steps 20 \
--expr-steps 20 \
--result-path ./result_joint_generate \
--checkpoint-path "$CHECKPOINT" \
--test-only
echo "=========================================="
echo "Finished: $(date)"
echo "=========================================="
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