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run_imagenet_egc_train.sh
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run_imagenet_egc_train.sh
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#!/usr/bin/env bash
# install pkg
pip install blobfile
pip install tqdm
# execute
LOGDIR=$1
GPU=$2
NODE=$3
RANK=$4
MADDR=$5
OPENAI_LOGDIR=./openai_log/${LOGDIR} python3 -m torch.distributed.run \
--nproc_per_node=$GPU --nnodes=$NODE \
--node_rank=$RANK --master_addr=$MADDR --master_port=29500 \
scripts/image_train_ldm_clsonline_egc.py --attention_resolutions 32,16,8 \
--class_cond True --diffusion_steps 1000 --dropout 0.0 --image_size 32 \
--learn_sigma False --noise_schedule linear --linear_start 0.00085 --linear_end 0.012 \
--num_channels 384 --num_head_channels 64 --num_res_blocks 2 --resblock_updown False \
--use_new_attention_order True --use_fp16 False --use_scale_shift_norm False --pool sattn \
--lr 1e-4 --weight_decay 0.0 --batch_size 8 --val_batch_size 32 \
--val_data_dir ./data/imagenet256_features_val --data_dir ./data/imagenet256_features \
--cls_data_dir ./data/imagenet/train --ce_weight 0.005 --eval_interval 5000 --save_interval 10000 \
--label_smooth 0.2 --grad_clip 1.0 --channel_mult 1,2,4 --cls_batch_size 8 --microbatch 8 --in_channels 4 \
--sample_z False --autoencoder_path ./autoencoder_kl.pth --use_spatial_transformer True \
--context_dim 512 --transformer_depth 1