Split (1)

train · 714k rows

text stringlengths 0 1.16k
[('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
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
question: ['How many dog feet are visible in the image?'], responses:['0']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([8.8469e-01, 2.4584e-02, 1.2294e-02, 5.9834e-03, 7.6923e-03, 4.2009e-03,
6.0186e-02, 3.7302e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.8469e-01, 2.4584e-02, 1.2294e-02, 5.9834e-03, 7.6923e-03, 4.2009e-03,
6.0186e-02, 3.7302e-04], device='cuda:0', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.8847, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1153, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.3737e-01, 1.0413e-02, 4.4789e-03, 1.9857e-03, 2.8017e-03, 1.6330e-03,
4.1180e-02, 1.4132e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.3737e-01, 1.0413e-02, 4.4789e-03, 1.9857e-03, 2.8017e-03, 1.6330e-03,
4.1180e-02, 1.4132e-04], device='cuda:1', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.0215, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9785, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.7027e-01, 3.3674e-03, 4.9030e-03, 7.3583e-04, 9.0067e-04, 1.0718e-03,
1.0936e-02, 7.8179e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.7027e-01, 3.3674e-03, 4.9030e-03, 7.3583e-04, 9.0067e-04, 1.0718e-03,
1.0936e-02, 7.8179e-03], device='cuda:3', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0.9703, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0297, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-23 14:49:02,683] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| optimizer_allgather: 1.35 \| optimizer_gradients: 0.27 \| optimizer_step: 0.32
[2024-10-23 14:49:02,683] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward_microstep: 5083.67 \| backward_microstep: 8910.61 \| backward_inner_microstep: 4813.31 \| backward_allreduce_microstep: 4097.21 \| step_microstep: 7.69
[2024-10-23 14:49:02,683] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward: 5083.68 \| backward: 8910.60 \| backward_inner: 4813.33 \| backward_allreduce: 4097.18 \| step: 7.72

1%\| \| 31/4844 [07:46<19:45:24, 14.78s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many balls are next to the hole?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many women are wearing swimsuits in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many women are wearing swimsuits 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([1, 3, 448, 448]) knan debug pixel values shape
tensor([5.3331e-01, 8.3343e-02, 1.8596e-02, 3.5088e-01, 8.0884e-03, 2.4390e-03,
3.0355e-03, 2.9824e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([5.3331e-01, 8.3343e-02, 1.8596e-02, 3.5088e-01, 8.0884e-03, 2.4390e-03,
3.0355e-03, 2.9824e-04], device='cuda:2', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.5333, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4667, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many vases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many dogs are in the image?'], responses:['1']
torch.Size([13, 3, 448, 448])
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many balls are next to the hole?'], responses:['4']
question: ['How many dogs are in the image?'], responses:['3']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many vases are in the image?'], responses:['2']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
[('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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