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job_dncnn.sh
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/
job_dncnn.sh
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#!/bin/bash
#SBATCH --gres=gpu:1
#SBATCH --mem=40G
#SBATCH --output=logs/job_output_%j.out
#SBATCH --error=logs/job_error_%j.out
#SBATCH --job-name=dncnn_nf
hostname
whoami
### Training
## Train DnCNN with Gaussian noise
python train_dncnn_noiseflow.py \
--model DnCNN_Gauss \
--train_data '/home/abdo/Downloads/SIDD_Medium_Raw/Data' \
--save_every 20 \
--max_epoch 2000 \
--num_gpus 1
## Train DnCNN with signal-dependent noise using camera noise level functions
#python train_dncnn_noiseflow.py \
# --model DnCNN_CamNLF \
# --train_data '/home/abdo/Downloads/SIDD_Medium_Raw/Data' \
# --save_every 20 \
# --max_epoch 2000 \
# --num_gpus 1
## Train DnCNN with noise generated with Noise Flow
#python train_dncnn_noiseflow.py \
# --model DnCNN_NF \
# --train_data '/home/abdo/Downloads/SIDD_Medium_Raw/Data' \
# --save_every 20 \
# --max_epoch 2000 \
# --num_gpus 1
## Train DnCNN with real noise
#python train_dncnn_noiseflow.py \
# --model DnCNN_Real \
# --train_data '/home/abdo/Downloads/SIDD_Medium_Raw/Data' \
# --save_every 20 \
# --max_epoch 2000 \
# --num_gpus 1
### Testing
#python test_dncnn_noiseflow.py \
# --model_name 'DnCNN_Gauss' \
# --save_result \
# --min_epc 1 \
# --max_epc 2000 \
# --epc_step 20
#python test_dncnn_noiseflow.py \
# --model_name 'DnCNN_CamNLF' \
# --save_result \
# --min_epc 1 \
# --max_epc 2000 \
# --epc_step 20
#python test_dncnn_noiseflow.py \
# --model_name 'DnCNN_NF' \
# --save_result \
# --min_epc 1 \
# --max_epc 2000 \
# --epc_step 20
#python test_dncnn_noiseflow.py \
# --model_name 'DnCNN_Real' \
# --save_result \
# --min_epc 1 \
# --max_epc 2000 \
# --epc_step 20