text stringlengths 0 1.16k |
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.5520e-01, 2.4783e-02, 2.1637e-01, 2.1064e-03, 1.2114e-04, 4.9499e-04, |
2.0160e-04, 7.2251e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.5520e-01, 2.4783e-02, 2.1637e-01, 2.1064e-03, 1.2114e-04, 4.9499e-04, |
2.0160e-04, 7.2251e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
torch.Size([1, 3, 448, 448]) |
tensor([5.4589e-01, 4.5256e-01, 5.3730e-05, 9.3794e-05, 1.4841e-04, 8.9485e-04, |
3.2581e-04, 4.0809e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4589e-01, 4.5256e-01, 5.3730e-05, 9.3794e-05, 1.4841e-04, 8.9485e-04, |
3.2581e-04, 4.0809e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4589e-01, 4.5256e-01, 5.3730e-05, 9.3794e-05, 1.4841e-04, 8.9485e-04, |
3.2581e-04, 4.0809e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.2537, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7155, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0307, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Does at least one dog have its mouth open?') |
ANSWER1=VQA(image=RIGHT,question='Does at least one dog have its mouth open?') |
ANSWER2=EVAL(expr='{ANSWER0} or {ANSWER1}') |
FINAL_ANSWER=RESULT(var=ANSWER2) |
torch.Size([1, 3, 448, 448]) |
tensor([6.0722e-01, 3.9205e-01, 5.5576e-05, 1.0152e-04, 1.2925e-04, 1.0877e-04, |
2.6938e-04, 7.1675e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.0722e-01, 3.9205e-01, 5.5576e-05, 1.0152e-04, 1.2925e-04, 1.0877e-04, |
2.6938e-04, 7.1675e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.0722e-01, 3.9205e-01, 5.5576e-05, 1.0152e-04, 1.2925e-04, 1.0877e-04, |
2.6938e-04, 7.1675e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.5703, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4283, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0015, device='cuda:2', grad_fn=<DivBackward0>)} |
question: ['Is there a sofa/chair near the tall window?'], responses:['no'] |
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
question: ['Does at least one dog have its mouth open?'], responses:['yes'] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
[('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([1, 3, 448, 448]) knan debug pixel values shape |
[2024-10-22 17:11:27,815] torch.distributed.run: [WARNING] |
[2024-10-22 17:11:27,815] torch.distributed.run: [WARNING] ***************************************** |
[2024-10-22 17:11:27,815] 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 17:11:27,815] torch.distributed.run: [WARNING] ***************************************** |
[2024-10-22 17:11:29,538] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:11:29,544] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:11:29,557] [INFO] [real_accelerator.py:133:get_accelerator] Setting ds_accelerator to cuda (auto detect) |
[2024-10-22 17:11:29,561] [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 17:11:32,801] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented |
[2024-10-22 17:11:32,801] [INFO] [comm.py:616:init_distributed] cdb=None |
[2024-10-22 17:11:32,801] [INFO] [comm.py:643:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl |
[2024-10-22 17:11:33,002] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented |
[2024-10-22 17:11:33,002] [INFO] [comm.py:616:init_distributed] cdb=None |
[2024-10-22 17:11:33,014] [WARNING] [comm.py:152:init_deepspeed_backend] NCCL backend in DeepSpeed not yet implemented |
[2024-10-22 17:11:33,014] [INFO] [comm.py:616:init_distributed] cdb=None |
10/22/2024 17:11:33 - WARNING - __main__ - Process rank: 0, device: cuda:0, n_gpu: 1distributed training: True, 16-bits training: False |
10/22/2024 17:11:33 - 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, |
fsdp=[], |
fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_grad_ckpt': False}, |
fsdp_min_num_params=0, |
fsdp_transformer_layer_cls_to_wrap=None, |
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