Datasets:
TMarcus
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InternVL

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[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
question: ['Are the dogs heading to the right?'], 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
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([8.9712e-01, 1.2243e-02, 8.8827e-02, 9.8057e-04, 9.7514e-05, 2.4893e-04,
1.3973e-05, 4.7041e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9712e-01, 1.2243e-02, 8.8827e-02, 9.8057e-04, 9.7514e-05, 2.4893e-04,
1.3973e-05, 4.7041e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8971, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0888, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0141, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([5.1557e-01, 2.5098e-02, 4.5499e-01, 1.2688e-03, 1.6294e-04, 1.7467e-03,
1.3543e-04, 1.0274e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.1557e-01, 2.5098e-02, 4.5499e-01, 1.2688e-03, 1.6294e-04, 1.7467e-03,
1.3543e-04, 1.0274e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.5156, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.4550, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0294, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:42:11,198] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.35 | optimizer_step: 0.33
[2024-10-23 14:42:11,199] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6949.42 | backward_microstep: 10430.40 | backward_inner_microstep: 6672.39 | backward_allreduce_microstep: 3757.90 | step_microstep: 7.76
[2024-10-23 14:42:11,199] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6949.42 | backward: 10430.39 | backward_inner: 6672.42 | backward_allreduce: 3757.88 | step: 7.77
0%| | 3/4844 [00:55<23:45:00, 17.66s/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 people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([4, 3, 448, 448])
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the image contain a human child playing a saxophone?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is a person paddling a canoe diagonally to the left?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many bottles 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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many people 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: ['Does the image contain a human child playing a saxophone?'], 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([4, 3, 448, 448]) knan debug pixel values shape
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
tensor([0.3928, 0.2240, 0.0404, 0.1270, 0.0102, 0.1639, 0.0389, 0.0029],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.3928, 0.2240, 0.0404, 0.1270, 0.0102, 0.1639, 0.0389, 0.0029],
device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0404, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9596, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there at least one person in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['Is a person paddling a canoe diagonally to the left?'], responses:['no']
torch.Size([13, 3, 448, 448])
[('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']]
tensor([0.5151, 0.2587, 0.0185, 0.0840, 0.0055, 0.0953, 0.0213, 0.0016],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.5151, 0.2587, 0.0185, 0.0840, 0.0055, 0.0953, 0.0213, 0.0016],
device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8876, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1124, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dispensers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([5, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
tensor([8.3474e-01, 1.6437e-01, 1.0979e-04, 9.9948e-05, 3.3240e-04, 8.9658e-05,
7.4254e-05, 1.8366e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.3474e-01, 1.6437e-01, 1.0979e-04, 9.9948e-05, 3.3240e-04, 8.9658e-05,
7.4254e-05, 1.8366e-04], device='cuda:2', grad_fn=<SelectBackward0>)