Different results from paper

[lbx73737373]

Hi, thank you for your great job! I'm reproducing MSRVTT captioning results using the fine-tuned weights you provided in the repo(mPLUG2_ MSRVTT_Caption.pth downloaded from the link), but I cannot get the result reported in the paper, and there is a huge gap. What problem could it be? Thanks!

My results: {'Bleu_1': 0.2391483871053033, 'Bleu_2': 0.1397145198812077, 'Bleu_3': 0.08582614908051771, 'Bleu_4': 0.0554141450685924, 'CIDEr': 0.6409439525382706}

More information:

• using checkpoint mPLUG2_ MSRVTT_Caption.pth downloaded from the link
• using language_evaluation package from https://github.com/bckim92/language-evaluation
• using MSRVTT-test-1ka split, also called JSFUSION split, which is the same split in text-to-video-retrieval task

My eval logs:

| distributed init (rank 3): env://
| distributed init (rank 1): env://
| distributed init (rank 2): env://
| distributed init (rank 0): env://
Creating video caption datasets
Creating model
use_checkpoint:  True
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train_step_per_epoch: 11250
Selected optimization level O1:  Insert automatic casts around Pytorch functions and Tensor methods.

Defaults for this optimization level are:
enabled                : True
opt_level              : O1
cast_model_type        : None
patch_torch_functions  : True
keep_batchnorm_fp32    : None
master_weights         : None
loss_scale             : dynamic
Processing user overrides (additional kwargs that are not None)...
After processing overrides, optimization options are:
enabled                : True
opt_level              : O1
cast_model_type        : None
patch_torch_functions  : True
keep_batchnorm_fp32    : None
master_weights         : None
loss_scale             : dynamic
Warning:  multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback.  Original ImportError was: ModuleNotFoundError("No module named 'amp_C'")
load checkpoint from /home/lbx/MyHome/pretrained_model_weights/mPLUG-2/mPLUG2_MSRVTT_Caption.pth
<All keys matched successfully>
Warning:  apex was installed without --cpp_ext.  Falling back to Python flatten and unflatten.
Start training
[{'video_id': 'video9770', 'pred_caption': 'a boy is fixing a computer', 'gold_caption': 'a person is connecting something to system'}, {'video_id': 'video7026', 'pred_caption': 'a man is talking about a car', 'gold_caption': 'a man is giving a review on a vehicle'}, {'video_id': 'video9778', 'pred_caption': 'a boy is performing on the voice', 'gold_caption': 'a little boy singing in front of judges and crowd'}, {'video_id': 'video9772', 'pred_caption': 'a cartoon character is flying', 'gold_caption': 'some cartoon characters are moving around an area'}]
Generate Caption test result:  [ 0/63]  eta: 0:08:24    time: 8.0067  data: 5.3014  max mem: 16941
Generate Caption test result:  [50/63]  eta: 0:00:17    time: 1.1697  data: 0.0001  max mem: 17413
Generate Caption test result:  [62/63]  eta: 0:00:01    time: 1.1289  data: 0.0001  max mem: 17413
Generate Caption test result: Total time: 0:01:21 (1.2926 s / it)
result file saved to output/videocaption_msrvtt_4/result/caption_result_zeroshot.json
1000 {'Bleu_1': 0.2391483871053033, 'Bleu_2': 0.1397145198812077, 'Bleu_3': 0.08582614908051771, 'Bleu_4': 0.0554141450685924, 'CIDEr': 0.6409439525382706}
Training time 0:01:23