Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['How many pug dogs are in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
question: ['How many cheetahs are in the image?'], responses:['4']
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([9.9476e-01, 5.2330e-03, 4.9930e-06, 1.8703e-06, 6.4778e-08, 6.3975e-08,
9.3074e-08, 3.9800e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9476e-01, 5.2330e-03, 4.9930e-06, 1.8703e-06, 6.4778e-08, 6.3975e-08,
9.3074e-08, 3.9800e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9948, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0052, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the afghan hound looking at the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
question: ['Is the afghan hound looking at the camera?'], 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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 7.1889e-07, 2.5244e-07, 2.0095e-09, 7.4751e-11, 3.4740e-07,
1.2066e-10, 3.2288e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 7.1889e-07, 2.5244e-07, 2.0095e-09, 7.4751e-11, 3.4740e-07,
1.2066e-10, 3.2288e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.3242e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.1227e-08, 4.6912e-08, 1.9694e-07, 3.1422e-09, 4.6912e-08,
1.0777e-09, 3.8204e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.1227e-08, 4.6912e-08, 1.9694e-07, 3.1422e-09, 4.6912e-08,
1.0777e-09, 3.8204e-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(4.6912e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.1072e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.1942e-01, 8.8058e-01, 1.5151e-07, 3.1032e-10, 6.9698e-06, 3.5788e-08,
2.7907e-09, 3.7639e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
5 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([1.1942e-01, 8.8058e-01, 1.5151e-07, 3.1032e-10, 6.9698e-06, 3.5788e-08,
2.7907e-09, 3.7639e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.5788e-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>)}
tensor([1.0000e+00, 1.0664e-08, 1.1120e-08, 2.3406e-08, 2.0407e-10, 2.9289e-09,
6.6832e-10, 3.4226e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.0664e-08, 1.1120e-08, 2.3406e-08, 2.0407e-10, 2.9289e-09,
6.6832e-10, 3.4226e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.3406e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:23:17,516] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.28 | optimizer_step: 0.33
[2024-10-24 09:23:17,516] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5085.04 | backward_microstep: 6108.75 | backward_inner_microstep: 4830.54 | backward_allreduce_microstep: 1278.00 | step_microstep: 7.51
[2024-10-24 09:23:17,516] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5085.06 | backward: 6108.74 | backward_inner: 4830.57 | backward_allreduce: 1277.94 | step: 7.52
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4496/4844 [18:42:01<1:12:34, 12.51s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the serving dish have a pedastal?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
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
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is there a person standing near the entrance of the store?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Are all of the drummers wearing purple shirts?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the image feature a 'fairy garden' terrarium shaped like a fishbowl on its side?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the serving dish have a pedastal?'], 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([1, 3, 448, 448]) knan debug pixel values shape
question: ['Are all of the drummers wearing purple shirts?'], 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.3153e-07, 9.7625e-08, 1.7016e-11, 3.2217e-12, 1.0298e-09,
1.3275e-10, 1.5003e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.3153e-07, 9.7625e-08, 1.7016e-11, 3.2217e-12, 1.0298e-09,