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TMarcus
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InternVL

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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
tensor([6.0857e-01, 2.4023e-02, 3.6325e-01, 1.5800e-03, 1.9832e-04, 1.0182e-03,
1.3461e-04, 1.2227e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.0857e-01, 2.4023e-02, 3.6325e-01, 1.5800e-03, 1.9832e-04, 1.0182e-03,
1.3461e-04, 1.2227e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6086, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.3632, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0282, device='cuda:1', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
tensor([6.1107e-01, 3.8652e-01, 1.7828e-04, 3.3701e-04, 1.7418e-04, 1.9380e-04,
1.0785e-03, 4.4434e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.1107e-01, 3.8652e-01, 1.7828e-04, 3.3701e-04, 1.7418e-04, 1.9380e-04,
1.0785e-03, 4.4434e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.3865, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.6111, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0024, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([9.1045e-01, 3.7579e-02, 7.8760e-03, 3.9997e-02, 2.3283e-03, 8.8287e-04,
8.2996e-04, 5.2192e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.1045e-01, 3.7579e-02, 7.8760e-03, 3.9997e-02, 2.3283e-03, 8.8287e-04,
8.2996e-04, 5.2192e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many laptops are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9105, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0895, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the laptop facing right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many laptops 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['Is the laptop facing right?'], 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: 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: 1860
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([7.2076e-01, 1.1096e-01, 1.7016e-02, 1.4248e-01, 5.8800e-03, 1.3064e-03,
1.4864e-03, 1.1791e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.2076e-01, 1.1096e-01, 1.7016e-02, 1.4248e-01, 5.8800e-03, 1.3064e-03,
1.4864e-03, 1.1791e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.7208, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2792, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([5.3069e-01, 4.6833e-01, 2.7951e-05, 1.6387e-04, 1.0110e-04, 1.0117e-04,
5.6915e-04, 1.3590e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.3069e-01, 4.6833e-01, 2.7951e-05, 1.6387e-04, 1.0110e-04, 1.0117e-04,
5.6915e-04, 1.3590e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4683, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5307, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:49:56,527] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-23 14:49:56,527] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7083.80 | backward_microstep: 10819.39 | backward_inner_microstep: 6801.05 | backward_allreduce_microstep: 4018.27 | step_microstep: 7.84
[2024-10-23 14:49:56,527] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7083.82 | backward: 10819.38 | backward_inner: 6801.07 | backward_allreduce: 4018.26 | step: 7.85
1%| | 34/4844 [08:40<22:30:42, 16.85s/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
ANSWER0=VQA(image=LEFT,question='Is the dog wearing a collar?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='What is the material of the jewelry in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "safety pins"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many people are in the car?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many gorillas are in the image?'], responses:['2']
question: ['How many people are in the car?'], responses:['1']
[('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']]
[('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