Split (1)

train · 714k rows

text stringlengths 0 1.16k
torch.Size([3, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['三']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many cheetahs 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
tensor([1.6150e-04, 1.4576e-03, 3.8122e-02, 5.6614e-01, 9.9224e-02, 8.5139e-02,
1.9662e-03, 2.0779e-01], device='cuda:2', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([1.6150e-04, 1.4576e-03, 3.8122e-02, 5.6614e-01, 9.9224e-02, 8.5139e-02,
1.9662e-03, 2.0779e-01], device='cuda:2', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9989e-01, 1.1751e-08, 1.0890e-04, 6.4691e-09, 8.2259e-12, 1.0923e-10,
1.1602e-10, 1.4494e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9989e-01, 1.1751e-08, 1.0890e-04, 6.4691e-09, 8.2259e-12, 1.0923e-10,
1.1602e-10, 1.4494e-08], device='cuda:3', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0001, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(6.0383e-08, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
tensor([9.9375e-01, 2.1739e-03, 1.9184e-03, 1.7844e-04, 2.8557e-07, 1.8022e-03,
6.2376e-07, 1.7844e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.9375e-01, 2.1739e-03, 1.9184e-03, 1.7844e-04, 2.8557e-07, 1.8022e-03,
6.2376e-07, 1.7844e-04], device='cuda:1', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 6.0836e-07, 4.5468e-08, 1.2238e-08, 5.5910e-10, 8.7275e-10,
1.6305e-09, 4.8277e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 6.0836e-07, 4.5468e-08, 1.2238e-08, 5.5910e-10, 8.7275e-10,
1.6305e-09, 4.8277e-11], device='cuda:0', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.6918e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:22:10,465] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| optimizer_allgather: 1.35 \| optimizer_gradients: 0.25 \| optimizer_step: 0.31
[2024-10-24 09:22:10,466] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward_microstep: 3796.86 \| backward_microstep: 3528.68 \| backward_inner_microstep: 3523.12 \| backward_allreduce_microstep: 5.50 \| step_microstep: 7.44
[2024-10-24 09:22:10,466] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward: 3796.86 \| backward: 3528.67 \| backward_inner: 3523.13 \| backward_allreduce: 5.47 \| step: 7.45

93%\|█████████▎\| 4491/4844 [18:40:54<1:10:33, 11.99s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
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=LEFT,question='How many wild boars are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are lying on the ground?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many seals are in the image?'], responses:['40']
question: ['How many dogs are lying on the ground?'], responses:['1']
[('40', 0.12638022987124733), ('39', 0.12509919407251455), ('42', 0.12494223232783619), ('41', 0.12482626048065008), ('45', 0.12479694604159434), ('38', 0.12473125094691345), ('47', 0.1246423477331973), ('32', 0.1245815385260468)]
[['40', '39', '42', '41', '45', '38', '47', '32']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many wild boars are in the image?'], responses:['5']
question: ['How many pandas are in the image?'], responses:['2']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
[('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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 1.2502e-09, 4.1950e-11, 7.6397e-12, 3.2671e-11, 1.6050e-09,
8.8516e-07, 6.2575e-12], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.2502e-09, 4.1950e-11, 7.6397e-12, 3.2671e-11, 1.6050e-09,
8.8516e-07, 6.2575e-12], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([0.8615, 0.0123, 0.0021, 0.0037, 0.0774, 0.0051, 0.0347, 0.0032],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
40 *************

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