Split (1)

train · 714k rows

text stringlengths 0 1.16k
ANSWER0=VQA(image=LEFT,question='How many lotion bottles are visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
最后的概率分布为: {True: tensor(0.5494, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.4506, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many monkeys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many lotion bottles are visible in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['Are there any human beings in the image?'], responses:['no']
question: ['How many monkeys are in the image?'], responses:['3']
[('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']]
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([0.6498, 0.2123, 0.0238, 0.0432, 0.0050, 0.0432, 0.0168, 0.0060],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.6498, 0.2123, 0.0238, 0.0432, 0.0050, 0.0432, 0.0168, 0.0060],
device='cuda:1', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.7168, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2832, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([9.4905e-01, 5.0308e-02, 5.5284e-05, 8.4358e-05, 1.2744e-04, 1.4498e-04,
1.5783e-04, 6.7742e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.4905e-01, 5.0308e-02, 5.5284e-05, 8.4358e-05, 1.2744e-04, 1.4498e-04,
1.5783e-04, 6.7742e-05], device='cuda:0', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.9491, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0503, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0006, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([0.6451, 0.0873, 0.0600, 0.0207, 0.0029, 0.1736, 0.0086, 0.0017],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.6451, 0.0873, 0.0600, 0.0207, 0.0029, 0.1736, 0.0086, 0.0017],
device='cuda:3', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.9400, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0600, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-23 14:50:24,371] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| optimizer_allgather: 1.36 \| optimizer_gradients: 0.25 \| optimizer_step: 0.32
[2024-10-23 14:50:24,372] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward_microstep: 7037.81 \| backward_microstep: 6882.28 \| backward_inner_microstep: 6794.08 \| backward_allreduce_microstep: 88.02 \| step_microstep: 7.53
[2024-10-23 14:50:24,372] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward: 7037.83 \| backward: 6882.27 \| backward_inner: 6794.12 \| backward_allreduce: 87.99 \| step: 7.54

1%\| \| 36/4844 [09:08<20:30:41, 15.36s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is a slice being lifted off the pizza in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image outside?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Does one dog have a red collar?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the food being served in a white dish?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is a slice being lifted off the pizza in the image?'], 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is the dog in the image outside?'], responses:['yes']
question: ['Is the food being served in a white dish?'], 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']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([6.1108e-01, 3.8766e-01, 6.5883e-05, 1.6912e-04, 1.1902e-04, 3.2726e-04,
4.8089e-04, 9.5096e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.1108e-01, 3.8766e-01, 6.5883e-05, 1.6912e-04, 1.1902e-04, 3.2726e-04,
4.8089e-04, 9.5096e-05], device='cuda:1', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.3877, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6111, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>)}

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