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Shen-Chenhui 2024-06-17 03:50:04 +00:00
parent 32d10d4796
commit 1573dbbc01
3 changed files with 50 additions and 35 deletions
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15
eval/sample.sh
View file

@ -260,10 +260,17 @@ function run_vbench() {
--resolution $VBENCH_RES --aspect-ratio $VBENCH_ASP_RATIO --num-sampling-steps ${NUM_SAMPLING_STEPS} --flow ${FLOW} \
--batch-size $VBENCH_BS --num-frames $NUM_FRAMES --start-index $1 --end-index $2
else
eval $CMD --ckpt-path $CKPT --save-dir ${OUTPUT}_vbench --prompt-as-path --num-sample 5 \
--prompt-path assets/texts/VBench/all_dimension.txt \
--resolution $VBENCH_RES --aspect-ratio $VBENCH_ASP_RATIO --num-sampling-steps ${NUM_SAMPLING_STEPS} --flow ${FLOW} --llm-refine ${LLM_REFINE} \
--batch-size $VBENCH_BS --num-frames $NUM_FRAMES --start-index $1 --end-index $2
if [ "${FLOW}" = "None" ]; then
eval $CMD --ckpt-path $CKPT --save-dir ${OUTPUT}_vbench --prompt-as-path --num-sample 5 \
--prompt-path assets/texts/VBench/all_dimension.txt \
--resolution $VBENCH_RES --aspect-ratio $VBENCH_ASP_RATIO --num-sampling-steps ${NUM_SAMPLING_STEPS} --llm-refine ${LLM_REFINE} \
--batch-size $VBENCH_BS --num-frames $NUM_FRAMES --start-index $1 --end-index $2
else
eval $CMD --ckpt-path $CKPT --save-dir ${OUTPUT}_vbench --prompt-as-path --num-sample 5 \
--prompt-path assets/texts/VBench/all_dimension.txt \
--resolution $VBENCH_RES --aspect-ratio $VBENCH_ASP_RATIO --num-sampling-steps ${NUM_SAMPLING_STEPS} --flow ${FLOW} --llm-refine ${LLM_REFINE} \
--batch-size $VBENCH_BS --num-frames $NUM_FRAMES --start-index $1 --end-index $2
fi
fi
fi
fi

65
eval/vbench/calc_vbench.py
View file

@ -1,37 +1,46 @@
import argparse
import os
from vbench import VBench
import torch
import time
import torch
from vbench import VBench
full_info_path = "eval/vbench/VBench_full_info.json"
dimensions = [
# Quality Score
"subject_consistency",
"background_consistency",
"motion_smoothness",
"dynamic_degree",
"aesthetic_quality",
"imaging_quality",
"temporal_flickering",
# Semantic Score
"object_class",
"multiple_objects",
"color",
"spatial_relationship",
"scene",
"temporal_style",
"overall_consistency",
"human_action",
"appearance_style",
# a: 10min
"subject_consistency", # 4min
"imaging_quality", # 6min
# b: 12min
"background_consistency", # 2min
"motion_smoothness", # 5min
"overall_consistency", # 2min
"human_action", # 3min
# c: 14min
"multiple_objects", # 14min
# d: 14min
"spatial_relationship", # 14min
# e: 12min
"object_class", # 12min
# f: 12min
"color", # 12min
# g: 10.5min
"aesthetic_quality", # 2.5min
"appearance_style", # 6min
"temporal_flickering", # 2min
# h: 9min
"scene", # 3min
"temporal_style", # 2min
"dynamic_degree", # 4min
]
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("video_folder", type=str) # samples/samples..._vbench/eval
parser.add_argument("model_ckpt", type=str)
parser.add_argument("--start", type=int, default=0) # start index of dimension to be evaluated
parser.add_argument("--end", type=int, default=-1) # start index of dimension to be evaluated
parser.add_argument("--start", type=int, default=0) # start index of dimension to be evaluated
parser.add_argument("--end", type=int, default=-1) # start index of dimension to be evaluated
args = parser.parse_args()
return args
@ -44,23 +53,23 @@ if __name__ == "__main__":
video_path = args.video_folder
kwargs = {}
kwargs['imaging_quality_preprocessing_mode'] = 'longer' # use VBench/evaluate.py default
kwargs["imaging_quality_preprocessing_mode"] = "longer" # use VBench/evaluate.py default
start_time = time.time()
# NOTE: important to use torch.device("cuda"), else will have issue with object_class third_party module
my_VBench = VBench(torch.device("cuda"), full_info_path, output_dir)
if args.end == -1: # adjust end accordingly
if args.end == -1: # adjust end accordingly
args.end = len(dimensions)
for dim in dimensions[args.start:args.end]:
for dim in dimensions[args.start : args.end]:
my_VBench.evaluate(
videos_path=video_path,
name=dim,
local=False,
read_frame=False,
dimension_list=[dim],
mode='vbench_standard',
**kwargs
mode="vbench_standard",
**kwargs,
)
print("Runtime: %s seconds " % (time.time() - start_time))
print("Runtime: %s seconds " % (time.time() - start_time))

5
eval/vbench/launch_calc.sh
View file

@ -7,11 +7,10 @@ mkdir -p $LOG_BASE
echo "Logging to $LOG_BASE"
GPUS=(0 1 2 3 4 5 6 7)
START_INDEX_LIST=(0 2 4 6 8 10 12 14)
END_INDEX_LIST=(2 4 6 8 10 12 14 16)
START_INDEX_LIST=(0 2 6 7 8 9 10 13)
END_INDEX_LIST=(2 6 7 8 9 10 13 16)
TASK_ID_LIST=(calc_vbench_a calc_vbench_b calc_vbench_c calc_vbench_d calc_vbench_e calc_vbench_f calc_vbench_g calc_vbench_h) # for log records only
for i in "${!GPUS[@]}"; do
CUDA_VISIBLE_DEVICES=${GPUS[i]} python eval/vbench/calc_vbench.py $VIDEO_DIR $CKPT_DIR --start ${START_INDEX_LIST[i]} --end ${END_INDEX_LIST[i]} > ${LOG_BASE}/${TASK_ID_LIST[i]}.log 2>&1 &
done