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#!/bin/bash |
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DATASET_ROOT=/dataset/imageNet100_sicy/train/ #/raid/common/imagenet-raw/ |
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## train ViT-small for 300 epochs |
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OUTPUT_ROOT=./exps/vit_small_300ep |
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NPROC_PER_NODE=16 # GPU numbers |
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BATCH_SIZE_PER_GPU=64 |
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DEBUG=false # debug = true, then we only load subset of the whole training dataset |
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python -m torch.distributed.launch --nproc_per_node=$NPROC_PER_NODE main.py \ |
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--data_path $DATASET_ROOT \ |
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--output_dir $OUTPUT_ROOT \ |
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--arch vit_small \ |
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--instance_queue_size 65536 \ |
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--local_group_queue_size 65536 \ |
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--use_bn_in_head false \ |
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--instance_out_dim 256 \ |
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--instance_temp 0.2 \ |
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--local_group_out_dim 256 \ |
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--local_group_temp 0.2 \ |
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--local_group_knn_top_n 8 \ |
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--group_out_dim 65536 \ |
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--group_student_temp 0.1 \ |
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--group_warmup_teacher_temp 0.04 \ |
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--group_teacher_temp 0.07 \ |
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--group_warmup_teacher_temp_epochs 30 \ |
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--norm_last_layer false \ |
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--norm_before_pred true \ |
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--batch_size_per_gpu $BATCH_SIZE_PER_GPU \ |
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--epochs 300 \ |
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--warmup_epochs 10 \ |
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--clip_grad 3.0 \ |
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--lr 0.0008 \ |
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--min_lr 1e-06 \ |
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--patch_embed_lr_mult 0.2 \ |
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--drop_path_rate 0.1 \ |
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--weight_decay 0.04 \ |
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--weight_decay_end 0.1 \ |
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--freeze_last_layer 1 \ |
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--momentum_teacher 0.996 \ |
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--use_fp16 false \ |
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--local_crops_number 10 \ |
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--size_crops 96 \ |
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--global_crops_scale 0.25 1 \ |
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--local_crops_scale 0.05 0.25 \ |
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--timm_auto_augment_par rand-m9-mstd0.5-inc1 \ |
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--prob 0.5 \ |
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--use_prefetcher true \ |
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--debug $DEBUG |