How to use a trained checkpoint to make inference on validation set and resume from checkpoint. about flipped-vqa HOT 5 CLOSED

torchrun --rdzv_endpoint 127.0.0.1:1234 --nproc_per_node 1 train.py --model 7B --max_seq_len 128 --batch_size 8 --epochs 5 --warmup_epochs 2 --bias 3.5 --tau 100. --max_feats 10 --dataset nextqa --blr 9e-2 --weight_decay 0.14 --output_dir ./checkpoint/nextqa --accum_iter 2 --vaq --qav --resume checkpoint/nextqa/checkpoint_best.pth

Traceback (most recent call last):
  File "train.py", line 163, in <module>
    main(args)
  File "train.py", line 121, in main
    misc.load_model(args=args, model_without_ddp=model_without_ddp, optimizer=optimizer, loss_scaler=loss_scaler)
  File "/home/admin-guest/Documents/multimodal-ml/iqui/Flipped-VQA/util/misc.py", line 302, in load_model
    model_without_ddp.load_state_dict(checkpoint['model'])
  File "/home/admin-guest/anaconda3/envs/flippedvqa_env/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1482, in load_state_dict
    raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Transformer:
	Missing key(s) in state_dict: "tok_embeddings.weight", "layers.0.attention.wq.weight", "layers.0.attention.wk.weight", "layers.0.attention.wv.weight", "layers.0.attention.wo.weight", "layers.0.feed_forward.w1.weight", "layers.0.feed_forward.w2.weight", "layers.0.feed_forward.w3.weight", "layers.0.attention_norm.weight", "layers.0.ffn_norm.weight", "layers.1.attention.wq.weight", "layers.1.attention.wk.weight", "layers.1.attention.wv.weight", "layers.1.attention.wo.weight", "layers.1.feed_forward.w1.weight", "layers.1.feed_forward.w2.weight"...