Datasets:
TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(7.4113e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many soap dispensers are in the image?'], responses:['δΈ‰']
[('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([5, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.7275e-10, 1.6137e-07, 2.7191e-11, 8.5508e-11, 2.5132e-08,
7.5015e-09, 3.5674e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.7275e-10, 1.6137e-07, 2.7191e-11, 8.5508e-11, 2.5132e-08,
7.5015e-09, 3.5674e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(8.7275e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([2.0581e-04, 4.6027e-04, 3.2148e-02, 6.5256e-01, 1.1794e-01, 1.7631e-01,
8.1281e-03, 1.2247e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([2.0581e-04, 4.6027e-04, 3.2148e-02, 6.5256e-01, 1.1794e-01, 1.7631e-01,
8.1281e-03, 1.2247e-02], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:25:31,020] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 10:25:31,020] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3125.65 | backward_microstep: 9619.39 | backward_inner_microstep: 3001.17 | backward_allreduce_microstep: 6618.08 | step_microstep: 7.40
[2024-10-24 10:25:31,021] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3125.65 | backward: 9619.38 | backward_inner: 3001.21 | backward_allreduce: 6618.07 | step: 7.41
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4745/4844 [19:44:14<24:02, 14.57s/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=LEFT,question='Is the dog sitting on a wooden surface?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
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 pelicans perch on wood posts in the water?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many flute illustrations are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([11, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the dog sitting on a wooden surface?'], responses:['yes']
question: ['How many dogs are in the image?'], responses:['37']
[('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']]
[('37', 0.12602601760154822), ('38', 0.12520142583637134), ('39', 0.12518201874785773), ('36', 0.12516664760231044), ('47', 0.12478763564484581), ('42', 0.12462790950563608), ('41', 0.12453088059191597), ('46', 0.12447746446951438)]
[['37', '38', '39', '36', '47', '42', '41', '46']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.3106e-09, 2.4806e-11, 1.8415e-08, 2.6202e-10, 4.9727e-10,
4.1376e-11, 1.4161e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.3106e-09, 2.4806e-11, 1.8415e-08, 2.6202e-10, 4.9727e-10,
4.1376e-11, 1.4161e-08], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([0.5156, 0.0347, 0.2599, 0.0224, 0.0806, 0.0047, 0.0620, 0.0198],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
37 *************
['37', '38', '39', '36', '47', '42', '41', '46'] tensor([0.5156, 0.0347, 0.2599, 0.0224, 0.0806, 0.0047, 0.0620, 0.0198],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.4806e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.4806e-11, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many llamas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many llamas are in the image?'], responses:['4']
[('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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many flute illustrations 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']]
tensor([9.9961e-01, 3.3547e-04, 5.4680e-05, 1.5397e-10, 9.0806e-09, 2.0063e-08,
2.9472e-09, 3.4516e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9961e-01, 3.3547e-04, 5.4680e-05, 1.5397e-10, 9.0806e-09, 2.0063e-08,
2.9472e-09, 3.4516e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.4680e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many pelicans perch on wood posts in the water?'], 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([11, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['How many dogs are in the image?'], responses:['35']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
[('35', 0.1266245324595504), ('36', 0.12492216702890122), ('37', 0.12490354225462116), ('55', 0.12481909164720137), ('34', 0.12478368073004217), ('42', 0.12474218613469536), ('39', 0.12461919022571973), ('41', 0.1245856095192685)]
[['35', '36', '37', '55', '34', '42', '39', '41']]