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#!/bin/bash
set -x
models=(
# "/mnt/petrelfs/weixilin/cache/Qwen2-VL-t_scale2_change_freq-128frames-16card_8k-context-330k-llava-video"
# "/mnt/petrelfs/weixilin/cache/Qwen2-VL-m_rope-128frames-16card_8k-context-330k-llava-video"
"/mnt/petrelfs/weixilin/cache/Qwen2-VL-vanilla_rope-128frames-16card_8k-context-330k-llava-video"
"/mnt/petrelfs/weixilin/cache/Qwen2-VL-time_rope-128frames-16card_8k-context-330k-llava-video"
# "/mnt/petrelfs/weixilin/cache/Qwen2-VL-t_only-128frames-16card_8k-context-330k-llava-video"
)
rope_types=(
# "t_scale2_change_freq"
"vanilla_rope"
"time_rope"
# "t_only"
)
base_port=6015
for i in "${!models[@]}"; do
model=${models[$i]}
rope_type=${rope_types[$i]}
port=$((base_port + i))
echo "evaluating model: $model"
echo "using rope_type: $rope_type"
echo "port: $port"
accelerate launch --num_processes 8 --config_file vision_niah_d/easy_context/accelerate_configs/deepspeed_inference.yaml \
--main_process_port "$port" vision_niah_d/eval_vision_niah.py \
--model "$model" \
--needle_dataset vision_niah_d/needle_datasets/dataset.json \
--needle_embedding_dir vision_niah_d/video_needle_haystack/data/needle_qwen2_embeddings_144tokens_dataset \
--haystack_dir vision_niah_d/video_needle_haystack/data/haystack_qwen2_embeddings_6000frames \
--prompt_template qwen2 \
--max_frame_num 3000 \
--min_frame_num 100 \
--frame_interval 200 \
--output_path vision_niah_d/niah_output/ \
--rope_type "$rope_type" \
--image_tokens 144 \
--depth_interval 0.2
echo "model $model evaluation has done."
echo "------------------------------------"
done
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