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