Datasets:
TMarcus
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InternVL

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6.3488e-09, 4.8773e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.2500e-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>)}
ANSWER0=VQA(image=LEFT,question='Are there tinted lips in the image?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 1.0364e-09, 1.5065e-07, 3.8048e-11, 4.8477e-10, 4.3964e-09,
4.3487e-10, 3.7418e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.0364e-09, 1.5065e-07, 3.8048e-11, 4.8477e-10, 4.3964e-09,
4.3487e-10, 3.7418e-07], device='cuda:1', grad_fn=<SelectBackward0>)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0364e-09, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there a towel in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Are there tinted lips 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']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
tensor([1.0000e+00, 1.4307e-08, 2.9959e-07, 2.1555e-10, 1.0847e-09, 3.7195e-08,
9.6553e-09, 2.7927e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.4307e-08, 2.9959e-07, 2.1555e-10, 1.0847e-09, 3.7195e-08,
9.6553e-09, 2.7927e-07], 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(1.4307e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([0.4337, 0.0008, 0.5232, 0.0006, 0.0062, 0.0032, 0.0062, 0.0260],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
11 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([0.4337, 0.0008, 0.5232, 0.0006, 0.0062, 0.0032, 0.0062, 0.0260],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0008, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9992, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Is there a towel 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.1824e-08, 1.6144e-10, 3.0951e-08, 1.3624e-10, 5.4077e-11,
7.0576e-11, 1.1259e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.1824e-08, 1.6144e-10, 3.0951e-08, 1.3624e-10, 5.4077e-11,
7.0576e-11, 1.1259e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.6144e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.6144e-10, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:26:05,081] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.37 | optimizer_step: 0.32
[2024-10-24 10:26:05,081] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3177.68 | backward_microstep: 6747.40 | backward_inner_microstep: 2967.45 | backward_allreduce_microstep: 3779.88 | step_microstep: 8.10
[2024-10-24 10:26:05,081] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3177.68 | backward: 6747.39 | backward_inner: 2967.46 | backward_allreduce: 3779.87 | step: 8.12
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4748/4844 [19:44:48<19:50, 12.40s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT 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 person standing among several dogs?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many birds are on the beach in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many balloons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 7')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is there a frame mounted to the wall?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many balloons are in the image?'], responses:['7']
question: ['Is there a frame mounted to the wall?'], responses:['no']
[('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']]
[('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([3, 3, 448, 448]) knan debug pixel values shape
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 3.2937e-09, 7.0387e-07, 2.3860e-10, 6.4972e-10, 9.0359e-08,
2.4975e-09, 4.6644e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.2937e-09, 7.0387e-07, 2.3860e-10, 6.4972e-10, 9.0359e-08,
2.4975e-09, 4.6644e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([9.9474e-01, 6.6382e-04, 6.2337e-04, 4.8069e-05, 8.0043e-04, 1.4807e-04,
2.9740e-03, 2.1118e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.9474e-01, 6.6382e-04, 6.2337e-04, 4.8069e-05, 8.0043e-04, 1.4807e-04,
2.9740e-03, 2.1118e-06], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.2937e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are the animals in the snow?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)