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InternVL

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3.1110e-10, 3.3184e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.7145e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many warthogs are in the image?'], responses:['four']
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)]
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']]
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
question: ['How many animals 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([1.0000e+00, 3.2535e-09, 5.2634e-09, 4.9244e-09, 1.3457e-11, 2.8215e-12,
4.1395e-10, 2.0880e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.2535e-09, 5.2634e-09, 4.9244e-09, 1.3457e-11, 2.8215e-12,
4.1395e-10, 2.0880e-09], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 9.7362e-10, 2.9009e-07, 1.8795e-12, 5.4381e-13, 1.3292e-09,
1.2661e-10, 2.3760e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.7362e-10, 2.9009e-07, 1.8795e-12, 5.4381e-13, 1.3292e-09,
1.2661e-10, 2.3760e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(5.2634e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-5.2634e-09, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.7362e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
tensor([2.0609e-14, 9.9990e-01, 6.5868e-07, 3.8693e-05, 5.8915e-05, 6.5517e-07,
2.4632e-07, 3.0390e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
4 *************
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([2.0609e-14, 9.9990e-01, 6.5868e-07, 3.8693e-05, 5.8915e-05, 6.5517e-07,
2.4632e-07, 3.0390e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.6093e-06, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 6.0836e-07, 4.6912e-08, 1.0030e-06, 1.7630e-09, 1.8190e-09,
7.4225e-09, 3.0332e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 6.0836e-07, 4.6912e-08, 1.0030e-06, 1.7630e-09, 1.8190e-09,
7.4225e-09, 3.0332e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.6693e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:25:20,028] [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 09:25:20,029] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5767.84 | backward_microstep: 5666.50 | backward_inner_microstep: 5470.75 | backward_allreduce_microstep: 195.60 | step_microstep: 7.56
[2024-10-24 09:25:20,029] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5767.85 | backward: 5666.49 | backward_inner: 5470.84 | backward_allreduce: 195.55 | step: 7.57
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4505/4844 [18:44:03<1:13:58, 13.09s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many cabinets are on the bottom of the hutch?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a green colored plastic soda bottle in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are all the shades in the image partially open?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} % 2 == 0')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a green colored plastic soda bottle in the image?'], responses:['yes']
question: ['How many animals are in the image?'], responses:['2']
[('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']]
[('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([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: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many cabinets are on the bottom of the hutch?'], responses:['4']
question: ['Are all the shades in the image partially open?'], responses:['yes']
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']]
[('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']]
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860