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compute_scores.sh
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# env = hdgan2
# iou env = pytorch
# name='BG10L1_birds'
# name='SEG10_birds'
# name='charCNNRNN_birds'
# name='SEG10_BG10L1_char_birds'
# name='SEG10_BG10L1_birds'
# name='SEG10_BG10L1_0KL_birds'
# name='SEG10_char_birds'
# name='BASELINE_birds'
# name='SEG10_0KL_birds'
epoch='500'
device=0
# ALL ALIGNED
# align='all'
# h5_file="$name""_G_epoch_""$epoch""_align_$align"".h5"
# CUDA_VISIBLE_DEVICES=${device} python evaluation/inception_score/inception_score.py \
# --checkpoint_dir evaluation/inception_score/inception_finetuned_models/birds_valid299/model.ckpt \
# --image_folder results \
# --h5_file ${h5_file} \
# --batch_size 10 \
# --num_classes 50
# CUDA_VISIBLE_DEVICES=${device} python evaluation/fid/fid_example.py \
# --image_folder results \
# --h5_file ${h5_file}
# CUDA_VISIBLE_DEVICES=${device} python evaluation/neudist/neudist.py \
# --dataset birds \
# --testing_path results/${h5_file} \
# --model_name neudist_birds \
# --load_from_epoch 595
# h5_file="$name""_G_epoch_""$epoch"".h5"
# python evaluation/ms_ssim/msssim_score.py \
# --image_folder results \
# --h5_file ${h5_file} \
# --evaluate_overall_score \
# --sample_per_cls 100
# # ALL ALIGNED AND RANDOM SHAPE NOISE
# align='all'
# h5_file="$name""_G_epoch_""$epoch""_align_""$align""_shape_noise"".h5"
# CUDA_VISIBLE_DEVICES=${device} python evaluation/inception_score/inception_score.py \
# --checkpoint_dir evaluation/inception_score/inception_finetuned_models/birds_valid299/model.ckpt \
# --image_folder results \
# --h5_file ${h5_file} \
# --batch_size 10 \
# --num_classes 50
# CUDA_VISIBLE_DEVICES=${device} python evaluation/fid/fid_example.py \
# --image_folder results \
# --h5_file ${h5_file}
# # ALL ALIGNED AND RANDOM BACKGROUND NOISE
# align='all'
# h5_file="$name""_G_epoch_""$epoch""_align_""$align""_background_noise"".h5"
# CUDA_VISIBLE_DEVICES=${device} python evaluation/inception_score/inception_score.py \
# --checkpoint_dir evaluation/inception_score/inception_finetuned_models/birds_valid299/model.ckpt \
# --image_folder results \
# --h5_file ${h5_file} \
# --batch_size 10 \
# --num_classes 50
# CUDA_VISIBLE_DEVICES=${device} python evaluation/fid/fid_example.py \
# --image_folder results \
# --h5_file ${h5_file}
# python evaluation/ms_ssim/msssim_score.py \
# --image_folder results \
# --h5_file ${h5_file} \
# --evaluate_overall_score \
# --sample_per_cls 200
# # ALL ALIGNED AND RANDOM BACKGROUND AND SHAPE NOISES
# align='all'
# h5_file="$name""_G_epoch_""$epoch""_align_""$align""_shape_noise_background_noise"".h5"
# CUDA_VISIBLE_DEVICES=${device} python evaluation/inception_score/inception_score.py \
# --checkpoint_dir evaluation/inception_score/inception_finetuned_models/birds_valid299/model.ckpt \
# --image_folder results \
# --h5_file ${h5_file} \
# --batch_size 10 \
# --num_classes 50
# CUDA_VISIBLE_DEVICES=${device} python evaluation/fid/fid_example.py \
# --image_folder results \
# --h5_file ${h5_file}
# SHAPE ALIGNED
# align='shape'
# h5_file="$name""_G_epoch_""$epoch""_align_$align"".h5"
# CUDA_VISIBLE_DEVICES=${device} python evaluation/inception_score/inception_score.py \
# --checkpoint_dir evaluation/inception_score/inception_finetuned_models/birds_valid299/model.ckpt \
# --image_folder results \
# --h5_file ${h5_file} \
# --batch_size 10 \
# --num_classes 50
# CUDA_VISIBLE_DEVICES=${device} python evaluation/fid/fid_example.py \
# --image_folder results \
# --h5_file ${h5_file}
# CUDA_VISIBLE_DEVICES=${device} python evaluation/neudist/neudist.py \
# --dataset birds \
# --testing_path results/${h5_file} \
# --model_name neudist_birds \
# --load_from_epoch 595
# BACKGROUND ALIGNED
# align='background'
# h5_file="$name""_G_epoch_""$epoch""_align_$align"".h5"
# CUDA_VISIBLE_DEVICES=${device} python evaluation/inception_score/inception_score.py \
# --checkpoint_dir evaluation/inception_score/inception_finetuned_models/birds_valid299/model.ckpt \
# --image_folder results \
# --h5_file ${h5_file} \
# --batch_size 10 \
# --num_classes 50
# CUDA_VISIBLE_DEVICES=${device} python evaluation/fid/fid_example.py \
# --image_folder results \
# --h5_file ${h5_file}
# CUDA_VISIBLE_DEVICES=${device} python evaluation/neudist/neudist.py \
# --dataset birds \
# --testing_path results/${h5_file} \
# --model_name neudist_birds \
# --load_from_epoch 595
# NOTHING ALIGNED
# align='none'
# h5_file="$name""_G_epoch_""$epoch""_align_$align"".h5"
# CUDA_VISIBLE_DEVICES=${device} python evaluation/inception_score/inception_score.py \
# --checkpoint_dir evaluation/inception_score/inception_finetuned_models/birds_valid299/model.ckpt \
# --image_folder results \
# --h5_file ${h5_file} \
# --batch_size 10 \
# --num_classes 50
# CUDA_VISIBLE_DEVICES=${device} python evaluation/fid/fid_example.py \
# --image_folder results \
# --h5_file ${h5_file}
# CUDA_VISIBLE_DEVICES=${device} python evaluation/neudist/neudist.py \
# --dataset birds \
# --testing_path results/${h5_file} \
# --model_name neudist_birds \
# --load_from_epoch 595
align='all'
name='SEG10_BG10L1_birds'
h5_file="$name""_G_epoch_""$epoch""_align_""$align""_background_noise"".h5"
python evaluation/ms_ssim/msssim_score.py \
--image_folder results \
--h5_file ${h5_file} \
--evaluate_overall_score \
--sample_per_cls 200
align='all' #tanto faz
name='BG10L1_birds'
h5_file="$name""_G_epoch_""$epoch""_align_""$align""_background_noise"".h5"
python evaluation/ms_ssim/msssim_score.py \
--image_folder results \
--h5_file ${h5_file} \
--evaluate_overall_score \
--sample_per_cls 200
align='all'
name='SEG10_birds'
h5_file="$name""_G_epoch_""$epoch""_align_$align"".h5"
python evaluation/ms_ssim/msssim_score.py \
--image_folder results \
--h5_file ${h5_file} \
--evaluate_overall_score \
--sample_per_cls 200
align='all'
name='SEG10_BG10L1_char_birds'
h5_file="$name""_G_epoch_""$epoch""_align_""$align""_background_noise"".h5"
python evaluation/ms_ssim/msssim_score.py \
--image_folder results \
--h5_file ${h5_file} \
--evaluate_overall_score \
--sample_per_cls 200
align='all'
name='SEG10_BG10L1_0KL_birds'
h5_file="$name""_G_epoch_""$epoch""_align_""$align""_background_noise"".h5"
python evaluation/ms_ssim/msssim_score.py \
--image_folder results \
--h5_file ${h5_file} \
--evaluate_overall_score \
--sample_per_cls 200
align='none'
name='BASELINE_birds'
h5_file="$name""_G_epoch_""$epoch"".h5"
python evaluation/ms_ssim/msssim_score.py \
--image_folder results \
--h5_file ${h5_file} \
--evaluate_overall_score \
--sample_per_cls 200