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luss919_pass.sh
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luss919_pass.sh
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CUDA='0,1,2,3,4,5,6,7'
N_GPU=8
BATCH=32
DATA=/data/imagenet
IMAGENETS=/data/ImageNetS/ImageNetS919
DUMP_PATH=./weights/pass919
DUMP_PATH_FINETUNE=${DUMP_PATH}/pixel_attention
DUMP_PATH_SEG=${DUMP_PATH}/pixel_finetuning
DIST_URL='tcp://localhost:10001'
QUEUE_LENGTH=3840
QUEUE_LENGTH_PIXELATT=3840
HIDDEN_DIM=2048
NUM_PROTOTYPE=3000
ARCH=resnet50
NUM_CLASSES=919
EPOCH=100
EPOCH_PIXELATT=20
EPOCH_SEG=20
FREEZE_PROTOTYPES=5005
FREEZE_PROTOTYPES_PIXELATT=0
mkdir -p ${DUMP_PATH_FINETUNE}
mkdir -p ${DUMP_PATH_SEG}
CUDA_VISIBLE_DEVICES=${CUDA} python -m torch.distributed.launch --nproc_per_node=${N_GPU} main_pretrain.py \
--arch ${ARCH} \
--data_path ${DATA}/train \
--dump_path ${DUMP_PATH} \
--nmb_crops 2 \
--size_crops 224 \
--min_scale_crops 0.08 \
--max_scale_crops 1.0 \
--crops_for_assign 0 1 \
--temperature 0.1 \
--epsilon 0.05 \
--sinkhorn_iterations 3 \
--feat_dim 128 \
--hidden_mlp ${HIDDEN_DIM} \
--nmb_prototypes ${NUM_PROTOTYPE} \
--queue_length ${QUEUE_LENGTH} \
--epoch_queue_starts 15 \
--epochs ${EPOCH} \
--batch_size ${BATCH} \
--base_lr 0.6 \
--final_lr 0.0006 \
--freeze_prototypes_niters ${FREEZE_PROTOTYPES} \
--wd 0.000001 \
--warmup_epochs 0 \
--use_fp16 true \
--sync_bn pytorch \
--workers 10 \
--dist_url ${DIST_URL} \
--seed 10010 \
--shallow 3 \
--weights 1 1
CUDA_VISIBLE_DEVICES=${CUDA} python -m torch.distributed.launch --nproc_per_node=${N_GPU} main_pixel_attention.py \
--arch ${ARCH} \
--data_path ${IMAGENETS}/train \
--dump_path ${DUMP_PATH_FINETUNE} \
--nmb_crops 2 \
--size_crops 224 \
--min_scale_crops 0.08 \
--max_scale_crops 1. \
--crops_for_assign 0 1 \
--temperature 0.1 \
--epsilon 0.05 \
--sinkhorn_iterations 3 \
--feat_dim 128 \
--hidden_mlp ${HIDDEN_DIM} \
--nmb_prototypes ${NUM_PROTOTYPE} \
--queue_length ${QUEUE_LENGTH_PIXELATT} \
--epoch_queue_starts 0 \
--epochs ${EPOCH_PIXELATT} \
--batch_size ${BATCH} \
--base_lr 0.6 \
--final_lr 0.0006 \
--freeze_prototypes_niters ${FREEZE_PROTOTYPES_PIXELATT} \
--wd 0.000001 \
--warmup_epochs 0 \
--use_fp16 true \
--sync_bn pytorch \
--workers 10 \
--dist_url ${DIST_URL} \
--seed 10010 \
--pretrained ${DUMP_PATH}/checkpoint.pth.tar
CUDA_VISIBLE_DEVICES=${CUDA} python cluster.py -a ${ARCH} \
--pretrained ${DUMP_PATH_FINETUNE}/checkpoint.pth.tar \
--data_path ${IMAGENETS} \
--dump_path ${DUMP_PATH_FINETUNE} \
-c ${NUM_CLASSES}
## Evaluating the pseudo labels on the validation set.
CUDA_VISIBLE_DEVICES=${CUDA} python inference_pixel_attention.py -a ${ARCH} \
--pretrained ${DUMP_PATH_FINETUNE}/checkpoint.pth.tar \
--data_path ${IMAGENETS} \
--dump_path ${DUMP_PATH_FINETUNE} \
-c ${NUM_CLASSES} \
--mode validation \
--dist_url ${DIST_URL} \
--test \
--centroid ${DUMP_PATH_FINETUNE}/cluster/centroids.npy
CUDA_VISIBLE_DEVICES=${CUDA} python evaluator.py \
--predict_path ${DUMP_PATH_FINETUNE} \
--data_path ${IMAGENETS} \
-c ${NUM_CLASSES} \
--mode validation \
--curve \
--min 0 \
--max 80
CUDA_VISIBLE_DEVICES=${CUDA} python inference_pixel_attention.py -a ${ARCH} \
--pretrained ${DUMP_PATH_FINETUNE}/checkpoint.pth.tar \
--data_path ${IMAGENETS} \
--dump_path ${DUMP_PATH_FINETUNE} \
-c ${NUM_CLASSES} \
--mode train \
--centroid ${DUMP_PATH_FINETUNE}/cluster/centroids.npy \
--dist_url ${DIST_URL} \
-t 0.37
CUDA_VISIBLE_DEVICES=${CUDA} python -m torch.distributed.launch --nproc_per_node=${N_GPU} main_pixel_finetuning.py \
--arch ${ARCH} \
--data_path ${DATA}/train \
--dump_path ${DUMP_PATH_SEG} \
--epochs ${EPOCH_SEG} \
--batch_size ${BATCH} \
--base_lr 0.6 \
--final_lr 0.0006 \
--wd 0.000001 \
--warmup_epochs 0 \
--use_fp16 true \
--sync_bn pytorch \
--workers 8 \
--dist_url ${DIST_URL} \
--num_classes ${NUM_CLASSES} \
--pseudo_path ${DUMP_PATH_FINETUNE}/train \
--pretrained ${DUMP_PATH}/checkpoint.pth.tar
CUDA_VISIBLE_DEVICES=${CUDA} python inference.py -a ${ARCH} \
--pretrained ${DUMP_PATH_SEG}/checkpoint.pth.tar \
--data_path ${IMAGENETS} \
--dump_path ${DUMP_PATH_SEG} \
-c ${NUM_CLASSES} \
--dist_url ${DIST_URL} \
--mode validation \
--match_file ${DUMP_PATH_SEG}/validation/match.json
CUDA_VISIBLE_DEVICES=${CUDA} python evaluator.py \
--predict_path ${DUMP_PATH_SEG} \
--data_path ${IMAGENETS} \
-c ${NUM_CLASSES} \
--mode validation
bash distance_matching/distance_matching.sh ${ARCH} \
${DUMP_PATH}/checkpoint.pth.tar \
${DUMP_PATH_SEG}/distance_matching \
${IMAGENETS} \
${NUM_CLASSES} \
validation