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InternVL

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1.3622e-09, 8.4741e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.5398e-10, 6.0037e-07, 2.4752e-12, 3.5059e-10, 7.0145e-09,
1.3622e-09, 8.4741e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.5398e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many glasses of wine are in the image?'], responses:['1']
[('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([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
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
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
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, 6.8503e-10, 1.4931e-10, 3.6381e-10, 1.8873e-10, 5.8304e-08,
1.0305e-08, 7.3955e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.8503e-10, 1.4931e-10, 3.6381e-10, 1.8873e-10, 5.8304e-08,
1.0305e-08, 7.3955e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0305e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:28:43,481] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.57 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-24 10:28:43,482] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8987.15 | backward_microstep: 8715.70 | backward_inner_microstep: 8710.64 | backward_allreduce_microstep: 4.98 | step_microstep: 7.70
[2024-10-24 10:28:43,482] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8987.15 | backward: 8715.69 | backward_inner: 8710.66 | backward_allreduce: 4.96 | step: 7.71
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4759/4844 [19:47:27<21:25, 15.13s/it]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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a silver colored dispenser in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is there a human in the ocean?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many bottles are visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many jellyfish are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many jellyfish are in the image?'], responses:['100']
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)]
[['100', '120', '88', '80', '60', '99', '90', '101']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
question: ['Is there a silver colored dispenser in the image?'], 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']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
tensor([9.3779e-01, 6.7074e-03, 1.3398e-04, 1.7493e-02, 2.4495e-02, 3.3614e-03,
7.2218e-03, 2.7949e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.3779e-01, 6.7074e-03, 1.3398e-04, 1.7493e-02, 2.4495e-02, 3.3614e-03,
7.2218e-03, 2.7949e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: question: ['Is there a human in the ocean?'], responses:['yes']
{True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many zebras are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many bottles are visible in the image?'], responses:['2']
torch.Size([7, 3, 448, 448])
[('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([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.7312e-09, 4.0588e-10, 2.6759e-09, 1.2944e-10, 1.6622e-11,
1.3293e-11, 4.7747e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.7312e-09, 4.0588e-10, 2.6759e-09, 1.2944e-10, 1.6622e-11,
1.3293e-11, 4.7747e-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(4.0588e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.0588e-10, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many wild dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many zebras are in the image?'], responses:['50']