# python test_per_sample.py \
#         name=cifar_c_cnn_gru_alpha_0.0 \
#         problem=cifar_c \
#         problem/model=dt_net_2d_custom \
#         problem.model.model=cnn_gru \
#         problem.model.num_class=10 \
#         problem.hyp.epochs=150 \
#         problem.hyp.alpha=0.0 \
#         problem.hyp.beta=0.5 \
#         problem.hyp.test_batch_size=16 \
#         problem.model.test_iterations.high=100 \
#         problem.hyp.noise_problem='Gaussian Noise' \
#         problem.model.model_path="/home/tranhieu/workdir/research/deep-thinking/outputs/cifar_c_cnn_gru_alpha_0.0/training-2024-12-16 12:21:10.526568" \

# python test_per_sample.py \
#         name=cifar_c_cnn_gru_gn_width_128 \
#         problem=cifar_c \
#         problem/model=dt_net_2d_custom \
#         problem.model.model=cnn_gru_gn \
#         problem.model.num_class=10 \
#         problem.hyp.epochs=150 \
#         problem.hyp.shuffle=False \
#         problem.hyp.alpha=0.0 \
#         problem.hyp.beta=0.5 \
#         problem.hyp.test_batch_size=16 \
#         problem.model.test_iterations.high=100 \
#         problem.hyp.noise_problem='Gaussian Noise' \
#         problem.test_data=easy \
#         problem.model.model_path="/home/fis/workspace_AI/AI-RnD/hieutb2/deep-thinking/outputs/cifar_c_cnn_gru_gn_width_128/training-2025-04-21 23:50:27.992504" \


# python test_per_sample.py \
#         name=cifar100_c_cnn_gru_gn_width_256 \
#         problem=cifar_c \
#         problem/model=dt_net_2d_custom \
#         problem.model.model=cnn_gru_gn \
#         problem.model.num_class=10 \
#         problem.hyp.epochs=150 \
#         problem.hyp.shuffle=False \
#         problem.hyp.alpha=0.0 \
#         problem.hyp.beta=0.5 \
#         problem.hyp.test_batch_size=16 \
#         problem.model.test_iterations.high=100 \
#         problem.hyp.noise_problem='Gaussian Noise' \
#         problem.test_data=easy \
#         problem.model.model_path="/home/fis/workspace_AI/AI-RnD/hieutb2/deep-thinking/outputs/cifar_c_cnn_gru_gn_width_128/training-2025-04-21 23:50:27.992504" \



python test_per_sample.py \
        name=cifar_c_cnn_ligru_width_128 \
        problem=cifar_c \
        problem/model=dt_net_2d_custom \
        problem.model.model=cnn_ligru \
        problem.model.num_class=10 \
        problem.hyp.epochs=150 \
        problem.hyp.alpha=0.0 \
        problem.hyp.beta=0.5 \
        problem.hyp.shuffle=False \
        problem.hyp.test_batch_size=16 \
        problem.model.test_iterations.high=100 \
        problem.hyp.noise_problem='Glass Blur' \
        problem.test_data=hard \
        problem.model.model_path="/mnt/storage/aird/AI-RnD/hieutb2/deep-thinking/outputs/cifar_c_cnn_ligru_width_128/training-2025-04-30 12:52:43.507763"


# Augmentation problem hyp
# 'Total Noise'
# 'Gaussian Noise'
# 'Shot Noise'
# 'Impulse Noise'
# 'Defocus Blur'
# 'Glass Blur'
# 'Motion Blur'
# 'Zoom Blur'
# 'Snow'
# 'Frost'
# 'Fog'
# 'Brightness'
# 'Contrast'
# 'Elastic'
# 'Pixelate'
# 'JPEG'
# 'Speckle Noise'
# 'Gaussian Blur'
# 'Spatter'
# 'Saturate'