Datasets:
TMarcus
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InternVL

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[('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']]
question: ['How many bottles are visible in the image?'], responses:['ε››']
[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)]
[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.2624e-08, 2.0527e-10, 3.3344e-07, 1.4127e-09, 1.4166e-09,
7.6835e-09, 4.3990e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.2624e-08, 2.0527e-10, 3.3344e-07, 1.4127e-09, 1.4166e-09,
7.6835e-09, 4.3990e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.0527e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7663e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
question: ['How many wolves 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 3.3231e-10, 6.5740e-11, 1.2017e-10, 4.1787e-11, 5.1599e-09,
3.0942e-09, 6.8687e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.3231e-10, 6.5740e-11, 1.2017e-10, 4.1787e-11, 5.1599e-09,
3.0942e-09, 6.8687e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(8.8827e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([1.0000e+00, 3.0462e-09, 4.4507e-07, 4.7729e-10, 2.5938e-09, 6.2389e-08,
6.9252e-09, 1.5271e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.0462e-09, 4.4507e-07, 4.7729e-10, 2.5938e-09, 6.2389e-08,
6.9252e-09, 1.5271e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.0462e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([8.8384e-01, 4.3564e-03, 9.3221e-02, 4.0374e-08, 1.4309e-02, 4.0970e-03,
8.1649e-06, 1.6933e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([8.8384e-01, 4.3564e-03, 9.3221e-02, 4.0374e-08, 1.4309e-02, 4.0970e-03,
8.1649e-06, 1.6933e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many parrots 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']]
tensor([5.1747e-04, 1.6077e-02, 1.7386e-06, 2.2841e-01, 7.4502e-02, 7.3578e-03,
6.7092e-01, 2.2134e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
vegetable *************
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([5.1747e-04, 1.6077e-02, 1.7386e-06, 2.2841e-01, 7.4502e-02, 7.3578e-03,
6.7092e-01, 2.2134e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.3824e-07, 2.4097e-09, 2.7265e-06, 8.0716e-10, 8.7091e-11,
4.5633e-10, 3.8642e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.3824e-07, 2.4097e-09, 2.7265e-06, 8.0716e-10, 8.7091e-11,
4.5633e-10, 3.8642e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.7265e-06, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:48:54,385] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.38 | optimizer_step: 0.33
[2024-10-24 10:48:54,385] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5142.41 | backward_microstep: 8542.88 | backward_inner_microstep: 4963.19 | backward_allreduce_microstep: 3579.59 | step_microstep: 7.84
[2024-10-24 10:48:54,386] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5142.43 | backward: 8542.87 | backward_inner: 4963.22 | backward_allreduce: 3579.53 | step: 7.85
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‰| 4841/4844 [20:07:38<00:42, 14.12s/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 VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a pattern of bold diagonal lines near a red-orange train car?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a person sitting on the ground in front of a store?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a dark wooded floor in the image?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a dark wooded floor 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)]