mmbench-test-en.log

+ CHECKPOINT=work_dirs/InternVL2-2B
+ DATASET=mmbench-test-en
++ pwd
+ CHECKPOINT=/mnt/petrelfs/wangweiyun/workspace_zyc/VLM-Dev/work_dirs/InternVL2-2B
++ pwd
+ export PYTHONPATH=/mnt/petrelfs/wangweiyun/workspace_zyc/VLM-Dev:/mnt/petrelfs/wangweiyun/workspace_wwy/pkgs/petrel-oss-sdk-2.3.14:/mnt/petrelfs/share_data/wangweiyun/share_pkgs/petrel-oss-sdk-2.3.12:
+ PYTHONPATH=/mnt/petrelfs/wangweiyun/workspace_zyc/VLM-Dev:/mnt/petrelfs/wangweiyun/workspace_wwy/pkgs/petrel-oss-sdk-2.3.14:/mnt/petrelfs/share_data/wangweiyun/share_pkgs/petrel-oss-sdk-2.3.12:
+ echo 'CHECKPOINT: /mnt/petrelfs/wangweiyun/workspace_zyc/VLM-Dev/work_dirs/InternVL2-2B'
CHECKPOINT: /mnt/petrelfs/wangweiyun/workspace_zyc/VLM-Dev/work_dirs/InternVL2-2B
+ MASTER_PORT=63669
+ PORT=63665
+ GPUS=8
+ GPUS_PER_NODE=8
+ NODES=1
+ export MASTER_PORT=63669
+ MASTER_PORT=63669
+ export PORT=63665
+ PORT=63665
+ ARGS=("$@")
+ [[ 5 -gt 0 ]]
+ case "$1" in
+ shift
+ [[ 4 -gt 0 ]]
+ case "$1" in
+ shift
+ [[ 3 -gt 0 ]]
+ case "$1" in
+ shift
+ [[ 2 -gt 0 ]]
+ case "$1" in
+ shift
+ [[ 1 -gt 0 ]]
+ case "$1" in
+ shift
+ [[ 0 -gt 0 ]]
+ echo 'GPUS: 8'
GPUS: 8
+ [[ /mnt/petrelfs/wangweiyun/workspace_zyc/VLM-Dev/work_dirs/InternVL2-2B == */ ]]
+ '[' mmbench-test-en == mme ']'
+ '[' mmbench-test-en == caption ']'
+ '[' mmbench-test-en == caption-coco ']'
+ '[' mmbench-test-en == caption-flickr30k ']'
+ '[' mmbench-test-en == caption-nocaps ']'
+ '[' mmbench-test-en == vqa ']'
+ '[' mmbench-test-en == vqa-okvqa-val ']'
+ '[' mmbench-test-en == vqa-textvqa-val ']'
+ '[' mmbench-test-en == vqa-textvqa-val-ocr ']'
+ '[' mmbench-test-en == vqa-vizwiz-val ']'
+ '[' mmbench-test-en == vqa-vizwiz-test ']'
+ '[' mmbench-test-en == vqa-vqav2-testdev ']'
+ '[' mmbench-test-en == vqa-ai2d-test ']'
+ '[' mmbench-test-en == vqa-vqav2-val ']'
+ '[' mmbench-test-en == vqa-gqa-testdev ']'
+ '[' mmbench-test-en == vqa-docvqa-val ']'
+ '[' mmbench-test-en == vqa-docvqa-test ']'
+ '[' mmbench-test-en == vqa-chartqa-test ']'
+ '[' mmbench-test-en == vqa-infovqa-val ']'
+ '[' mmbench-test-en == vqa-infovqa-test ']'
+ '[' mmbench-test-en == vqa-chartqa-test-human ']'
+ '[' mmbench-test-en == vqa-chartqa-test-augmented ']'
+ '[' mmbench-test-en == vqa-ocrvqa-val ']'
+ '[' mmbench-test-en == vqa-ocrvqa-test ']'
+ '[' mmbench-test-en == refcoco ']'
+ '[' mmbench-test-en == refcoco-val ']'
+ '[' mmbench-test-en == llava-bench ']'
+ '[' mmbench-test-en == pope ']'
+ '[' mmbench-test-en == tiny_lvlm ']'
+ '[' mmbench-test-en == mmvet ']'
+ '[' mmbench-test-en == cmmmu ']'
+ '[' mmbench-test-en == mmbench-dev-en ']'
+ '[' mmbench-test-en == mmbench-dev-cn ']'
+ '[' mmbench-test-en == mmbench-test-en ']'
+ torchrun --nnodes=1 --node_rank=0 --master_addr=127.0.0.1 --nproc_per_node=8 --master_port=63669 eval/mmbench/evaluate_mmbench.py --checkpoint /mnt/petrelfs/wangweiyun/workspace_zyc/VLM-Dev/work_dirs/InternVL2-2B --datasets mmbench_test_en_20231003 --dynamic --max-num 6
[2024-08-07 21:58:59,972] torch.distributed.run: [WARNING] 
[2024-08-07 21:58:59,972] torch.distributed.run: [WARNING] *****************************************
[2024-08-07 21:58:59,972] torch.distributed.run: [WARNING] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
[2024-08-07 21:58:59,972] torch.distributed.run: [WARNING] *****************************************
datasets: ['mmbench_test_en_20231003']
datasets: ['mmbench_test_en_20231003']
datasets:datasets:  ['mmbench_test_en_20231003']['mmbench_test_en_20231003']

datasets: ['mmbench_test_en_20231003']
datasets: ['mmbench_test_en_20231003']
datasets: ['mmbench_test_en_20231003']
datasets: ['mmbench_test_en_20231003']
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6
[test] total_params: 2.205754368B, use num_beams: 1
[test] image_size: 448
[test] template: internlm2-chat
[test] dynamic_image_size: True
[test] use_thumbnail: True
[test] max_num: 6

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0it [00:00, ?it/s]/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(
/mnt/petrelfs/wangweiyun/miniconda3/envs/internvl/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:392: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.
  warnings.warn(

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788it [01:31,  7.29it/s]
773it [01:31,  9.08it/s]
802it [01:31,  7.34it/s]
776it [01:31, 11.99it/s]
820it [01:31, 11.27it/s]
801it [01:31,  7.59it/s]
774it [01:31,  9.21it/s]
783it [01:31,  9.40it/s]
790it [01:31,  8.06it/s]
781it [01:31,  8.80it/s]
803it [01:31,  7.33it/s]
776it [01:31,  9.86it/s]
778it [01:31, 11.21it/s]
803it [01:31,  8.45it/s]
791it [01:31,  7.85it/s]
804it [01:31,  7.32it/s]
822it [01:31,  9.68it/s]
785it [01:31,  9.28it/s]
783it [01:31,  9.49it/s]
777it [01:31,  9.35it/s]
804it [01:31,  8.12it/s]
792it [01:32,  7.70it/s]
805it [01:32,  7.32it/s]
780it [01:32, 10.76it/s]
784it [01:32,  9.45it/s]
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778it [01:32,  9.22it/s]
805it [01:32,  7.88it/s]
793it [01:32,  7.59it/s]
785it [01:32,  9.12it/s]
782it [01:32, 11.86it/s]
806it [01:32,  7.33it/s]
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779it [01:32,  8.56it/s]
807it [01:32,  9.93it/s]
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808it [01:32,  9.77it/s]
794it [01:32,  7.51it/s]
780it [01:32,  8.88it/s]
825it [01:32,  8.51it/s]
788it [01:32,  8.11it/s]
787it [01:32,  9.83it/s]
810it [01:32, 11.56it/s]
795it [01:32,  7.44it/s]
782it [01:32, 10.95it/s]
826it [01:32,  8.24it/s]
809it [01:32,  9.83it/s]
789it [01:32,  7.88it/s]
786it [01:32, 11.74it/s]
789it [01:32, 10.28it/s]
812it [01:32, 12.87it/s]
784it [01:32, 12.38it/s]
796it [01:32,  7.39it/s]
827it [01:32,  8.03it/s]
810it [01:32,  9.15it/s]
791it [01:32,  9.30it/s]
788it [01:32, 11.99it/s]
814it [01:32, 13.79it/s]
786it [01:32, 13.31it/s]
797it [01:32,  7.36it/s]
828it [01:32,  7.86it/s]
811it [01:32,  8.62it/s]
792it [01:32,  8.72it/s]
791it [01:32,  9.43it/s]
816it [01:32, 14.48it/s]
788it [01:32, 13.47it/s]
798it [01:32,  7.33it/s]
829it [01:32,  7.72it/s]
790it [01:32, 11.23it/s]
813it [01:32, 10.47it/s]
793it [01:32,  8.29it/s]
792it [01:32,  8.90it/s]
818it [01:32, 14.95it/s]
799it [01:32,  7.31it/s]
792it [01:32, 12.22it/s]
830it [01:32,  7.62it/s]
794it [01:33,  8.00it/s]
790it [01:33, 12.06it/s]
794it [01:33,  9.80it/s]
815it [01:33,  9.96it/s]
831it [01:33,  7.54it/s]
795it [01:33,  7.80it/s]
792it [01:33, 12.98it/s]
820it [01:33, 11.24it/s]
801it [01:33,  8.04it/s]
794it [01:33, 11.08it/s]
832it [01:33,  7.49it/s]
796it [01:33,  9.78it/s]
797it [01:33,  9.30it/s]
802it [01:33,  7.84it/s]
817it [01:33,  8.85it/s]
794it [01:33, 11.83it/s]
833it [01:33,  7.44it/s]
822it [01:33, 10.56it/s]
796it [01:33, 10.66it/s]
833it [01:33,  8.91it/s]

798it [01:33,  9.98it/s]
798it [01:33,  8.72it/s]
803it [01:33,  7.69it/s]
818it [01:33,  8.48it/s]
796it [01:33, 11.19it/s]
799it [01:33,  8.30it/s]
804it [01:33,  7.57it/s]
798it [01:33, 10.17it/s]
819it [01:33,  8.18it/s]
800it [01:33, 10.32it/s]
824it [01:33,  9.29it/s]
798it [01:33, 12.26it/s]
800it [01:33,  8.01it/s]
805it [01:33,  7.48it/s]
820it [01:33,  7.95it/s]
802it [01:33, 10.35it/s]
800it [01:33,  9.64it/s]
802it [01:33,  9.77it/s]
806it [01:33,  7.41it/s]
821it [01:33,  7.76it/s]
800it [01:33, 11.42it/s]
826it [01:33,  8.58it/s]
801it [01:33,  9.09it/s]
803it [01:34,  9.04it/s]
807it [01:34,  7.37it/s]
822it [01:34,  7.63it/s]
802it [01:34, 10.90it/s]
804it [01:34,  9.08it/s]
802it [01:34,  8.62it/s]
828it [01:34,  8.99it/s]
804it [01:34,  8.53it/s]
808it [01:34,  7.34it/s]
823it [01:34,  7.53it/s]
805it [01:34,  8.61it/s]
803it [01:34,  8.24it/s]
829it [01:34,  8.64it/s]
809it [01:34,  7.33it/s]
804it [01:34, 10.30it/s]
806it [01:34,  8.85it/s]
825it [01:34,  8.41it/s]
806it [01:34,  8.80it/s]
810it [01:34,  7.31it/s]
805it [01:34,  8.77it/s]
831it [01:34,  9.07it/s]
808it [01:34, 10.19it/s]
826it [01:34,  8.06it/s]
811it [01:34,  7.31it/s]
808it [01:34,  9.19it/s]
806it [01:34,  9.15it/s]
832it [01:34,  8.64it/s]
806it [01:34,  8.36it/s]
810it [01:34, 11.62it/s]
828it [01:34,  9.42it/s]
812it [01:34,  7.31it/s]
807it [01:34,  8.76it/s]
833it [01:34,  8.30it/s]
810it [01:34,  9.20it/s]
833it [01:34,  8.79it/s]

808it [01:34,  8.86it/s]
812it [01:34, 11.03it/s]
830it [01:34, 10.34it/s]
813it [01:34,  7.31it/s]
808it [01:34,  8.41it/s]
814it [01:34, 12.19it/s]
814it [01:34,  7.32it/s]
812it [01:35,  9.42it/s]
810it [01:35,  9.18it/s]
810it [01:35,  9.55it/s]
832it [01:35,  9.01it/s]
815it [01:35,  7.32it/s]
813it [01:35,  8.92it/s]
811it [01:35,  9.49it/s]
816it [01:35, 11.42it/s]
812it [01:35,  9.38it/s]
833it [01:35,  8.61it/s]
816it [01:35,  7.33it/s]
813it [01:35, 11.11it/s]
814it [01:35,  8.51it/s]
818it [01:35, 10.96it/s]
814it [01:35, 10.74it/s]
834it [01:35,  8.27it/s]
815it [01:35,  8.19it/s]
818it [01:35,  9.11it/s]
834it [01:35,  8.74it/s]

820it [01:35, 12.07it/s]
815it [01:35,  9.82it/s]
816it [01:35,  7.94it/s]
816it [01:35, 10.41it/s]
820it [01:35, 10.24it/s]
822it [01:35, 12.93it/s]
817it [01:35,  7.75it/s]
817it [01:35,  9.58it/s]
822it [01:35,  9.85it/s]
824it [01:35, 11.79it/s]
818it [01:35,  7.60it/s]
818it [01:35,  9.14it/s]
818it [01:35,  9.02it/s]
823it [01:35,  9.17it/s]
819it [01:35,  7.50it/s]
826it [01:35, 12.10it/s]
820it [01:35, 10.43it/s]
819it [01:36,  8.56it/s]
825it [01:36, 10.20it/s]
821it [01:36,  9.71it/s]
822it [01:36, 10.45it/s]
828it [01:36, 11.36it/s]
821it [01:36,  9.00it/s]
822it [01:36,  8.94it/s]
827it [01:36, 10.93it/s]
830it [01:36, 12.36it/s]
822it [01:36,  8.56it/s]
824it [01:36,  9.89it/s]
824it [01:36,  8.81it/s]
823it [01:36,  8.22it/s]
829it [01:36,  9.40it/s]
832it [01:36, 11.19it/s]
826it [01:36, 10.11it/s]
825it [01:36,  8.41it/s]
833it [01:36,  8.62it/s]

830it [01:36,  8.91it/s]
825it [01:36,  8.81it/s]
832it [01:36,  9.93it/s]
827it [01:36,  8.70it/s]
828it [01:36,  9.54it/s]
826it [01:36,  8.41it/s]
829it [01:36,  9.32it/s]
829it [01:36,  9.99it/s]
827it [01:36,  8.11it/s]
834it [01:37, 10.00it/s]
834it [01:37,  8.59it/s]

831it [01:37,  9.45it/s]
831it [01:37,  9.89it/s]
829it [01:37,  8.49it/s]
832it [01:37,  8.91it/s]
832it [01:37,  9.25it/s]
830it [01:37,  8.19it/s]
833it [01:37,  8.46it/s]
833it [01:37,  8.75it/s]
833it [01:37,  8.55it/s]

833it [01:37,  8.54it/s]

832it [01:37,  8.55it/s]
833it [01:37,  8.24it/s]
833it [01:37,  8.52it/s]
Evaluating mmbench_test_en_20231003 ...
Results saved to results/mmbench_test_en_20231003_240807220052.xlsx
+ '[' mmbench-test-en == mmbench-test-cn ']'
+ '[' mmbench-test-en == ccbench-dev ']'
+ '[' mmbench-test-en == scienceqa ']'
+ '[' mmbench-test-en == mmmu-dev ']'
+ '[' mmbench-test-en == mmmu-val ']'
+ '[' mmbench-test-en == mmmu-test ']'
+ '[' mmbench-test-en == mmmu-dev-cot ']'
+ '[' mmbench-test-en == mmmu-val-cot ']'
+ '[' mmbench-test-en == mmmu-test-cot ']'
+ '[' mmbench-test-en == mmvp ']'
+ '[' mmbench-test-en == mathvista-testmini ']'
+ '[' mmbench-test-en == mathvista-test ']'
+ '[' mmbench-test-en == seed ']'
+ '[' mmbench-test-en == mvbench ']'