Datasets:
TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1644, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.8347, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0009, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
question: ['How many dispensers 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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3404
question: ['Is there at least one person 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']]
question: ['How many 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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
tensor([8.4886e-01, 3.0892e-02, 1.0028e-02, 2.3758e-03, 4.3099e-03, 2.1127e-03,
1.0129e-01, 1.3715e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.4886e-01, 3.0892e-02, 1.0028e-02, 2.3758e-03, 4.3099e-03, 2.1127e-03,
1.0129e-01, 1.3715e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1511, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8489, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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([8.9109e-01, 3.3488e-02, 2.3746e-02, 4.2573e-03, 6.7339e-04, 4.4361e-02,
1.6673e-03, 7.1422e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.9109e-01, 3.3488e-02, 2.3746e-02, 4.2573e-03, 6.7339e-04, 4.4361e-02,
1.6673e-03, 7.1422e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8911, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1089, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([5.7683e-01, 4.2202e-01, 7.9357e-05, 1.2073e-04, 2.9483e-04, 2.3299e-04,
3.6959e-04, 5.2879e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.7683e-01, 4.2202e-01, 7.9357e-05, 1.2073e-04, 2.9483e-04, 2.3299e-04,
3.6959e-04, 5.2879e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4220, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5768, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there water in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
question: ['Is there water 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
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
tensor([9.1888e-01, 8.0291e-02, 6.4983e-05, 1.4110e-04, 2.7153e-04, 5.8700e-05,
2.2102e-04, 6.6964e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.1888e-01, 8.0291e-02, 6.4983e-05, 1.4110e-04, 2.7153e-04, 5.8700e-05,
2.2102e-04, 6.6964e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0803, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9189, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>)}
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: 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([8.8032e-01, 1.2691e-02, 1.0505e-01, 1.1962e-03, 8.3918e-05, 2.5204e-04,
1.7539e-05, 3.9071e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.8032e-01, 1.2691e-02, 1.0505e-01, 1.1962e-03, 8.3918e-05, 2.5204e-04,
1.7539e-05, 3.9071e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8803, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.1051, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0146, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-23 14:42:24,950] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-23 14:42:24,950] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7023.43 | backward_microstep: 6706.86 | backward_inner_microstep: 6701.02 | backward_allreduce_microstep: 5.73 | step_microstep: 7.46
[2024-10-23 14:42:24,951] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7023.45 | backward: 6706.85 | backward_inner: 6701.06 | backward_allreduce: 5.67 | step: 7.47
0%| | 4/4844 [01:08<21:40:11, 16.12s/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
ANSWER0=VQA(image=LEFT,question='How many chimneys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is a person holding up the crab?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many drawers are on the cabinet?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])