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torch.Size([7, 3, 448, 448])
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.3908, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6092, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([8.7410e-01, 2.8098e-02, 1.3693e-02, 5.0354e-03, 7.3332e-03, 3.9046e-03,
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6.7449e-02, 3.8651e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7410e-01, 2.8098e-02, 1.3693e-02, 5.0354e-03, 7.3332e-03, 3.9046e-03,
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6.7449e-02, 3.8651e-04], device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1259, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8741, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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question: ['How many dogs are in the image?'], responses:['1']
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[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many animals are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([5.8707e-01, 6.1017e-02, 2.3157e-02, 5.5646e-03, 8.7936e-03, 4.1958e-03,
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3.1005e-01, 1.5254e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.8707e-01, 6.1017e-02, 2.3157e-02, 5.5646e-03, 8.7936e-03, 4.1958e-03,
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3.1005e-01, 1.5254e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5871, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4129, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the mouth of the dog open?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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tensor([9.5269e-01, 8.7737e-03, 4.4111e-03, 2.1458e-03, 2.9370e-03, 1.8641e-03,
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2.7028e-02, 1.5042e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5269e-01, 8.7737e-03, 4.4111e-03, 2.1458e-03, 2.9370e-03, 1.8641e-03,
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2.7028e-02, 1.5042e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9527, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0473, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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question: ['Is the mouth of the dog open?'], responses:['yes']
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[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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[2024-10-22 17:16:55,835] torch.distributed.run: [WARNING]
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[2024-10-22 17:16:55,835] torch.distributed.run: [WARNING] *****************************************
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[2024-10-22 17:16:55,835] 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.
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[2024-10-22 17:16:55,835] torch.distributed.run: [WARNING] *****************************************
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[2024-10-22 17:16:57,548] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
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[2024-10-22 17:16:57,551] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
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[2024-10-22 17:16:57,556] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
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[2024-10-22 17:16:57,576] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect)
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petrel_client is not installed. If you read data locally instead of from ceph, ignore it.
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petrel_client is not installed. If you read data locally instead of from ceph, ignore it.
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petrel_client is not installed. If you read data locally instead of from ceph, ignore it.
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petrel_client is not installed. If you read data locally instead of from ceph, ignore it.
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Replace train sampler!!
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petrel_client is not installed. Using PIL to load images.
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Replace train sampler!!
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petrel_client is not installed. Using PIL to load images.
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Replace train sampler!!
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petrel_client is not installed. Using PIL to load images.
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Replace train sampler!!
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petrel_client is not installed. Using PIL to load images.
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[2024-10-22 17:17:00,747] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented
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[2024-10-22 17:17:00,747] [INFO] [comm.py:616:init_distributed] cdb=None
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[2024-10-22 17:17:00,747] [INFO] [comm.py:643:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
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10/22/2024 17:17:01 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False
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10/22/2024 17:17:01 - INFO - __main__ - Training/evaluation parameters TrainingArguments(
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_n_gpu=1,
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adafactor=False,
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adam_beta1=0.9,
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adam_beta2=0.999,
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adam_epsilon=1e-08,
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auto_find_batch_size=False,
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bf16=True,
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bf16_full_eval=False,
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data_seed=None,
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dataloader_drop_last=False,
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dataloader_num_workers=4,
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dataloader_persistent_workers=False,
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dataloader_pin_memory=True,
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ddp_backend=None,
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ddp_broadcast_buffers=None,
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ddp_bucket_cap_mb=None,
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ddp_find_unused_parameters=None,
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ddp_timeout=1800,
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debug=[],
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deepspeed=zero_stage1_config.json,
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disable_tqdm=False,
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dispatch_batches=None,
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do_eval=False,
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do_predict=False,
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do_train=True,
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eval_accumulation_steps=None,
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eval_delay=0,
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eval_steps=None,
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evaluation_strategy=no,
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fp16=False,
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fp16_backend=auto,
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fp16_full_eval=False,
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fp16_opt_level=O1,
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