Datasets:
TMarcus
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InternVL

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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
tensor([1.0000e+00, 6.7583e-09, 1.9143e-07, 1.2379e-10, 1.6630e-10, 3.8460e-09,
6.3444e-10, 9.8444e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.7583e-09, 1.9143e-07, 1.2379e-10, 1.6630e-10, 3.8460e-09,
6.3444e-10, 9.8444e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.7583e-09, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:2', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
tensor([6.5135e-01, 3.4864e-01, 9.3804e-08, 3.2652e-07, 5.9126e-07, 9.0771e-08,
1.4467e-07, 4.8131e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.5135e-01, 3.4864e-01, 9.3804e-08, 3.2652e-07, 5.9126e-07, 9.0771e-08,
1.4467e-07, 4.8131e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3486, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.6514, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there a dark blue bottle in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([9.9937e-01, 1.7787e-08, 5.8248e-05, 5.5263e-04, 3.2072e-10, 1.4723e-05,
4.0715e-08, 4.4702e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9937e-01, 1.7787e-08, 5.8248e-05, 5.5263e-04, 3.2072e-10, 1.4723e-05,
4.0715e-08, 4.4702e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([13, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many dogs 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a dark blue bottle in the image?'], 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']]
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: 3399
tensor([1.0000e+00, 2.1724e-10, 4.2115e-11, 1.7870e-10, 7.3913e-11, 1.4303e-08,
4.3635e-09, 2.0865e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.1724e-10, 4.2115e-11, 1.7870e-10, 7.3913e-11, 1.4303e-08,
4.3635e-09, 2.0865e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.9387e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([1.0000e+00, 1.0572e-07, 2.5492e-07, 2.9569e-12, 3.1473e-11, 6.8907e-10,
9.6569e-11, 2.2259e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.0572e-07, 2.5492e-07, 2.9569e-12, 3.1473e-11, 6.8907e-10,
9.6569e-11, 2.2259e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0572e-07, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:46:40,185] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-24 09:46:40,185] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9016.56 | backward_microstep: 8712.72 | backward_inner_microstep: 8707.71 | backward_allreduce_microstep: 4.92 | step_microstep: 7.40
[2024-10-24 09:46:40,185] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9016.56 | backward: 8712.71 | backward_inner: 8707.74 | backward_allreduce: 4.91 | step: 7.41
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4591/4844 [19:05:23<1:09:42, 16.53s/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 VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there a dog standing near a fence in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
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 white dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many hanging lights 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])
torch.Size([13, 3, 448, 448])
question: ['How many water bottles are in the image?'], responses:['7']
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([0.7389, 0.0013, 0.0064, 0.1727, 0.0060, 0.0012, 0.0727, 0.0008],
device='cuda:3', grad_fn=<SoftmaxBackward0>)