Datasets:
TMarcus
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InternVL

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ANSWER0=VQA(image=RIGHT,question='Does the right net/drape have a cone on the top?')
FINAL_ANSWER=RESULT(var=ANSWER0)
tensor([9.9816e-01, 2.3835e-05, 1.8101e-03, 9.7181e-10, 1.6030e-09, 4.3628e-06,
3.0732e-07, 4.1241e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9816e-01, 2.3835e-05, 1.8101e-03, 9.7181e-10, 1.6030e-09, 4.3628e-06,
3.0732e-07, 4.1241e-08], device='cuda:2', grad_fn=<SelectBackward0>)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0018, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9982, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['Does the right net/drape have a cone on the top?'], responses:['no']
[('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']]
question: ['How many dogs are in the image?'], responses:['2']
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
[('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([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.8478e-09, 3.8830e-07, 1.4140e-09, 1.3473e-09, 3.7364e-07,
3.2694e-08, 4.9690e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.8478e-09, 3.8830e-07, 1.4140e-09, 1.3473e-09, 3.7364e-07,
3.2694e-08, 4.9690e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8478e-09, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:3', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 3.3789e-08, 2.0134e-09, 3.5764e-07, 1.9935e-10, 4.7821e-10,
6.6589e-10, 5.0446e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.3789e-08, 2.0134e-09, 3.5764e-07, 1.9935e-10, 4.7821e-10,
6.6589e-10, 5.0446e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.5764e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 7.8115e-07, 4.9163e-08, 1.1769e-08, 1.2023e-09, 4.0592e-09,
3.4315e-09, 6.9038e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 7.8115e-07, 4.9163e-08, 1.1769e-08, 1.2023e-09, 4.0592e-09,
3.4315e-09, 6.9038e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.1769e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:33:29,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.26 | optimizer_step: 0.30
[2024-10-24 10:33:29,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3820.94 | backward_microstep: 9905.29 | backward_inner_microstep: 3527.08 | backward_allreduce_microstep: 6378.14 | step_microstep: 7.77
[2024-10-24 10:33:29,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3820.95 | backward: 9905.28 | backward_inner: 3527.09 | backward_allreduce: 6378.13 | step: 7.78
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4778/4844 [19:52:12<16:04, 14.61s/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='How many seals are in the water?')
ANSWER1=EVAL(expr='{ANSWER0} > 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are umbrellas set up over a dining area?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a person in front of a store in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
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: ['Are umbrellas set up over a dining area?'], responses:['no']
question: ['How many cheetahs are in the image?'], responses:['1']
question: ['How many seals are in the water?'], responses:['5']
[('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']]
[('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']]
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: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['Is there a person in front of a store in the image?'], responses:['no']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
[('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']]
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
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
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
tensor([1.0000e+00, 4.1875e-10, 2.2615e-07, 7.0243e-12, 6.3857e-11, 7.0027e-09,
1.5436e-09, 2.7559e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.1875e-10, 2.2615e-07, 7.0243e-12, 6.3857e-11, 7.0027e-09,
1.5436e-09, 2.7559e-07], device='cuda:1', grad_fn=<SelectBackward0>)