Comments (8)
ArrowLuo
commented on August 25, 2024
Hi @dawnlh, would you provide your log.txt here? I can not locate the problem through the command.
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dawnlh
commented on August 25, 2024
Hi @dawnlh, would you provide your log.txt here? I can not locate the problem through the command.
Thanks a lot! Here is the log file:
2021-05-25 11:15:57,643:INFO: Effective parameters:
2021-05-25 11:15:57,644:INFO: <<< batch_size: 256
2021-05-25 11:15:57,644:INFO: <<< batch_size_val: 32
2021-05-25 11:15:57,644:INFO: <<< bert_model: bert-base-uncased
2021-05-25 11:15:57,644:INFO: <<< cache_dir:
2021-05-25 11:15:57,644:INFO: <<< coef_lr: 0.1
2021-05-25 11:15:57,644:INFO: <<< cross_model: cross-base
2021-05-25 11:15:57,644:INFO: <<< cross_num_hidden_layers: 2
2021-05-25 11:15:57,644:INFO: <<< data_path: data/msrvtt/MSRVTT_data.json
2021-05-25 11:15:57,644:INFO: <<< datatype: msrvtt
2021-05-25 11:15:57,644:INFO: <<< decoder_model: decoder-base
2021-05-25 11:15:57,644:INFO: <<< decoder_num_hidden_layers: 3
2021-05-25 11:15:57,644:INFO: <<< do_eval: True
2021-05-25 11:15:57,644:INFO: <<< do_lower_case: True
2021-05-25 11:15:57,644:INFO: <<< do_pretrain: False
2021-05-25 11:15:57,644:INFO: <<< do_train: False
2021-05-25 11:15:57,644:INFO: <<< epochs: 20
2021-05-25 11:15:57,644:INFO: <<< feature_framerate: 1
2021-05-25 11:15:57,644:INFO: <<< features_path: data/msrvtt/msrvtt_videos_features.pickle
2021-05-25 11:15:57,644:INFO: <<< fp16: False
2021-05-25 11:15:57,644:INFO: <<< fp16_opt_level: O1
2021-05-25 11:15:57,644:INFO: <<< gradient_accumulation_steps: 1
2021-05-25 11:15:57,644:INFO: <<< hard_negative_rate: 0.5
2021-05-25 11:15:57,644:INFO: <<< init_model: weight/univl.pretrained.bin
2021-05-25 11:15:57,644:INFO: <<< local_rank: 0
2021-05-25 11:15:57,644:INFO: <<< lr: 0.0001
2021-05-25 11:15:57,644:INFO: <<< lr_decay: 0.9
2021-05-25 11:15:57,644:INFO: <<< margin: 0.1
2021-05-25 11:15:57,644:INFO: <<< max_frames: 100
2021-05-25 11:15:57,644:INFO: <<< max_words: 20
2021-05-25 11:15:57,644:INFO: <<< min_time: 5.0
2021-05-25 11:15:57,645:INFO: <<< n_display: 100
2021-05-25 11:15:57,645:INFO: <<< n_gpu: 1
2021-05-25 11:15:57,645:INFO: <<< n_pair: 1
2021-05-25 11:15:57,645:INFO: <<< negative_weighting: 1
2021-05-25 11:15:57,645:INFO: <<< num_thread_reader: 4
2021-05-25 11:15:57,645:INFO: <<< output_dir: ckpts/ckpt_msrvtt_caption
2021-05-25 11:15:57,645:INFO: <<< sampled_use_mil: False
2021-05-25 11:15:57,645:INFO: <<< seed: 42
2021-05-25 11:15:57,645:INFO: <<< stage_two: True
2021-05-25 11:15:57,645:INFO: <<< task_type: caption
2021-05-25 11:15:57,645:INFO: <<< text_num_hidden_layers: 12
2021-05-25 11:15:57,645:INFO: <<< train_csv: data/youcookii_singlef_train.csv
2021-05-25 11:15:57,645:INFO: <<< use_mil: False
2021-05-25 11:15:57,645:INFO: <<< val_csv: data/msrvtt/MSRVTT_JSFUSION_test.csv
2021-05-25 11:15:57,645:INFO: <<< video_dim: 1024
2021-05-25 11:15:57,645:INFO: <<< visual_model: visual-base
2021-05-25 11:15:57,645:INFO: <<< visual_num_hidden_layers: 6
2021-05-25 11:15:57,645:INFO: <<< warmup_proportion: 0.1
2021-05-25 11:15:57,645:INFO: <<< world_size: 1
2021-05-25 11:15:57,646:INFO: device: cuda:0 n_gpu: 1
2021-05-25 11:15:57,646:INFO: loading vocabulary file /data2/zzh/project/SCI_caption/UniVL/modules/bert-base-uncased/vocab.txt
2021-05-25 11:15:58,017:INFO: loading archive file /data2/zzh/project/SCI_caption/UniVL/modules/bert-base-uncased
2021-05-25 11:15:58,018:INFO: Model config {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 30522
}
2021-05-25 11:15:58,018:INFO: loading archive file /data2/zzh/project/SCI_caption/UniVL/modules/visual-base
2021-05-25 11:15:58,018:INFO: Model config {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 1,
"type_vocab_size": 2,
"vocab_size": 1024
}
2021-05-25 11:15:58,018:INFO: Weight doesn't exsits. /data2/zzh/project/SCI_caption/UniVL/modules/visual-base/visual_pytorch_model.bin
2021-05-25 11:15:58,018:INFO: loading archive file /data2/zzh/project/SCI_caption/UniVL/modules/cross-base
2021-05-25 11:15:58,018:INFO: Model config {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 1024,
"num_attention_heads": 12,
"num_hidden_layers": 2,
"type_vocab_size": 2,
"vocab_size": 768
}
2021-05-25 11:15:58,018:INFO: Weight doesn't exsits. /data2/zzh/project/SCI_caption/UniVL/modules/cross-base/cross_pytorch_model.bin
2021-05-25 11:15:58,018:INFO: loading archive file /data2/zzh/project/SCI_caption/UniVL/modules/decoder-base
2021-05-25 11:15:58,019:INFO: Model config {
"attention_probs_dropout_prob": 0.1,
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_target_embeddings": 512,
"num_attention_heads": 12,
"num_decoder_layers": 1,
"num_hidden_layers": 12,
"type_vocab_size": 2,
"vocab_size": 30522
}
2021-05-25 11:15:58,019:INFO: Weight doesn't exsits. /data2/zzh/project/SCI_caption/UniVL/modules/decoder-base/decoder_pytorch_model.bin
2021-05-25 11:15:58,019:WARNING: Stage-One:False, Stage-Two:True
2021-05-25 11:15:58,019:WARNING: Set bert_config.num_hidden_layers: 12.
2021-05-25 11:15:59,122:WARNING: Set visual_config.num_hidden_layers: 6.
2021-05-25 11:15:59,591:WARNING: Set cross_config.num_hidden_layers: 2.
2021-05-25 11:15:59,763:WARNING: Set decoder_config.num_decoder_layers: 3.
2021-05-25 11:16:02,843:INFO: --------------------
2021-05-25 11:16:02,843:INFO: Weights from pretrained model not used in UniVL:
cls.predictions.bias
cls.predictions.transform.dense.weight
cls.predictions.transform.dense.bias
cls.predictions.transform.LayerNorm.weight
cls.predictions.transform.LayerNorm.bias
cls.predictions.decoder.weight
cls_visual.predictions.weight
cls_visual.predictions.bias
cls_visual.predictions.transform.dense.weight
cls_visual.predictions.transform.dense.bias
cls_visual.predictions.transform.LayerNorm.weight
cls_visual.predictions.transform.LayerNorm.bias
similarity_pooler.dense.weight
similarity_pooler.dense.bias
2021-05-25 11:16:10,136:INFO: ***** Running test *****
2021-05-25 11:16:10,136:INFO: Num examples = 2990
2021-05-25 11:16:10,136:INFO: Batch size = 32
2021-05-25 11:16:10,136:INFO: Num steps = 94
2021-05-25 11:23:31,867:INFO: >>> BLEU_1: 0.1410, BLEU_2: 0.0450, BLEU_3: 0.0142, BLEU_4: 0.0052
2021-05-25 11:23:31,877:INFO: >>> METEOR: 0.0684, ROUGE_L: 0.1229, CIDEr: 0.0045
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ArrowLuo
commented on August 25, 2024
Hi @dawnlh, I suppose that you evaluate the pretrained weight (zero-shot) directly instead of finetuning. You should finetune with --do_train at first.
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dawnlh
commented on August 25, 2024
Hi @dawnlh, I suppose that you evaluate the pretrained weight (zero-shot) directly instead of finetuning. You should finetune with
--do_trainat first.
Yes, I evaluated the pretrained weight (zero-shot) directly. I tried to finetune the model, but failed due to limited GPU memory ๏ผeven setting batch_size to 1) . Can you give an estimation about how much GPU memory is needed to finetune the model? Or is it convenient for you to share the weights for captioning task (no transcript) ?
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ArrowLuo
commented on August 25, 2024
Hi @dawnlh. We finetuned the model with 4 Tesla V100 GPUs. I am so sorry that we can not provide the finetuned weights.
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dawnlh
commented on August 25, 2024
Okay, thanks anyway~ I'll try to figure out the GPU limitation problem. Another question is that if you can provide some instructions or codes on making use of finetuned model to deal with video captioning tasks for self-captured videos? I mean the input video processing (how to extract the same feature as the training set to serve as the model input) and output visualization.
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ArrowLuo
commented on August 25, 2024
More information about the feature extractor can be found at README. The caption results are saved in --output_dir.
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dawnlh
commented on August 25, 2024
More information about the feature extractor can be found at README. The caption results are saved in
--output_dir.
Got it! Thank you a lot for your patient replying.
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Related Issues (20)
- How to fine-tune with additional layers before UniVL? HOT 2
- Run Without Distributed HOT 3
- TypeError: bad operand type for unary -: 'list' HOT 6
- How to run captioning task on my own video datasets? HOT 1
- Pre-training acceleration using multi-machine distributed training HOT 1
- Can you share your HowTo100M.csv file? HOT 3
- This repo is missing important files HOT 1
- Unable to run video captioning code HOT 3
- where to get transcript to generate youcookii_data.pickle HOT 2
- end-to-end video file captioning process HOT 3
- feature & data shape HOT 6
- How can I create my video feature pickle HOT 4
- video only test for youcook HOT 2
- How to only input text feature or video feature HOT 2
- Is there a code for Finetune on CMU-MOSI here? HOT 1
- Issues about Freezing some additional layers instead of meanP in CLIP4Clip HOT 2
- Error message (torch.distributed.elastic.multiprocessing.errors.ChildFailedError:)
- Estimate of zero-shot performance HOT 1
- Zero score (every output is None) on evaluation captioning with pretrained model HOT 1
- Non-Configurable GPU Count via Arguments
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