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TMarcus
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InternVL

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[2024-10-24 09:54:33,594] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.47 | optimizer_gradients: 0.30 | optimizer_step: 0.32
[2024-10-24 09:54:33,594] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2487.69 | backward_microstep: 10085.07 | backward_inner_microstep: 2185.66 | backward_allreduce_microstep: 7899.31 | step_microstep: 7.75
[2024-10-24 09:54:33,594] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2487.70 | backward: 10085.06 | backward_inner: 2185.68 | backward_allreduce: 7899.27 | step: 7.77
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4622/4844 [19:13:17<50:28, 13.64s/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='Are there plants in vases in the image?')
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')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many water bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many round mounds of dough are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many water bottles are in the image?'], responses:['1']
question: ['Are there plants in vases in the image?'], responses:['no']
question: ['How many round mounds of dough are in the image?'], responses:['4']
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
[('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']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
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: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
question: ['How many animals are in the image?'], responses:['1']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
tensor([1.0000e+00, 5.7348e-10, 1.8874e-10, 4.7916e-10, 4.3025e-10, 1.6155e-08,
7.6581e-09, 2.6401e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.7348e-10, 1.8874e-10, 4.7916e-10, 4.3025e-10, 1.6155e-08,
7.6581e-09, 2.6401e-10], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.9984e-01, 1.5844e-04, 3.8045e-07, 9.8464e-12, 1.8060e-10, 4.1875e-09,
2.8250e-09, 2.0128e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9984e-01, 1.5844e-04, 3.8045e-07, 9.8464e-12, 1.8060e-10, 4.1875e-09,
2.8250e-09, 2.0128e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([9.9940e-01, 3.8057e-04, 2.1655e-04, 2.3100e-10, 2.9373e-08, 9.0220e-09,
1.2853e-08, 8.5871e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9940e-01, 3.8057e-04, 2.1655e-04, 2.3100e-10, 2.9373e-08, 9.0220e-09,
1.2853e-08, 8.5871e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.5749e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
{True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.1875e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0002, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9998, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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 vultures are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Does the bottle in the left image have a wooden look?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Does the bottle in the left image have a wooden look?'], 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many dogs 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
tensor([1.0000e+00, 5.0046e-09, 5.2005e-11, 1.7066e-08, 1.4465e-10, 2.9694e-10,
3.5013e-12, 6.8739e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.0046e-09, 5.2005e-11, 1.7066e-08, 1.4465e-10, 2.9694e-10,
3.5013e-12, 6.8739e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(5.2005e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-5.2005e-11, device='cuda:2', grad_fn=<SubBackward0>)}
question: ['How many vultures are in the image?'], responses:['11']