update readme and eval

train.sh

+#!/bin/bash
+export HIP_VISIBLE_DEVICES=0,1,2,3 # 自行修改为训练的卡号和数量
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export USE_MIOPEN_BATCHNORM=1
+
+DATA_PATH=/home/datasets
+name=painter_vit_large
+python -m torch.distributed.launch --nproc_per_node=4 \
+	--use_env main_train.py  \
+    --batch_size 2 \
+    --accum_iter 16  \
+    --model painter_vit_large_patch16_input896x448_win_dec64_8glb_sl1 \
+    --num_mask_patches 784 \
+    --max_mask_patches_per_block 392 \
+    --epochs 15 \
+    --warmup_epochs 1 \
+    --lr 1e-3 \
+    --clip_grad 3 \
+    --layer_decay 0.8 \
+    --drop_path 0.1 \
+    --input_size 896 448 \
+    --save_freq 1 \
+    --data_path $DATA_PATH/ \
+    --json_path  \
+    $DATA_PATH/nyu_depth_v2/nyuv2_sync_image_depth.json \
+    $DATA_PATH/ade20k/ade20k_training_image_semantic.json \
+    $DATA_PATH/coco/pano_ca_inst/coco_train_image_panoptic_inst.json \
+    $DATA_PATH/coco/pano_sem_seg/coco_train2017_image_panoptic_sem_seg.json \
+    $DATA_PATH/coco_pose/coco_pose_256x192_train.json \
+    $DATA_PATH/denoise/denoise_ssid_train.json \
+    $DATA_PATH/derain/derain_train.json \
+    $DATA_PATH/light_enhance/enhance_lol_train.json \
+    --val_json_path \
+    $DATA_PATH/nyu_depth_v2/nyuv2_test_image_depth.json \
+    $DATA_PATH/ade20k/ade20k_validation_image_semantic.json \
+    $DATA_PATH/coco/pano_ca_inst/coco_val_image_panoptic_inst.json \
+    $DATA_PATH/coco/pano_sem_seg/coco_val2017_image_panoptic_sem_seg.json \
+    $DATA_PATH/coco_pose/coco_pose_256x192_val.json \
+    $DATA_PATH/denoise/denoise_ssid_val.json \
+    $DATA_PATH/derain/derain_test_rain100h.json \
+    $DATA_PATH/light_enhance/enhance_lol_val.json \
+    --output_dir models/$name \
+    --log_dir models/$name/logs \
+    --finetune path/to/mae_pretrain_vit_large.pth \
+    # --log_wandb \
+

train_painter_vit_large.sh

 #!/bin/bash
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export USE_MIOPEN_BATCHNORM=1
 
-DATA_PATH=datasets
+DATA_PATH=/home/datasets
 name=painter_vit_large
 python -m torch.distributed.launch --nproc_per_node=8 \
 	--nnodes=${WORLD_SIZE} --node_rank=$RANK \

val.sh

+#!/bin/bash
+
+export HSA_FORCE_FINE_GRAIN_PCIE=1
+export USE_MIOPEN_BATCHNORM=1
+
+# LOL datasets
+python eval/lol/painter_inference_lol.py --ckpt_path pretrained_models/painter_vit_large.pth --data_dir toy_datasets
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