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TMarcus
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InternVL

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[['5', '8', '4', '6', '3', '7', '11', '9']]
torch.Size([3, 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: 3404
tensor([0.1818, 0.1344, 0.1176, 0.1799, 0.0648, 0.1622, 0.0589, 0.1004],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.1818, 0.1344, 0.1176, 0.1799, 0.0648, 0.1622, 0.0589, 0.1004],
device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0648, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9352, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3405
question: ['Is the animal in the image on all fours?'], 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: 13, images per sample: 13.0, dynamic token length: 3405
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([6.6678e-01, 1.8633e-02, 3.1156e-01, 1.6885e-03, 1.5570e-04, 5.8873e-04,
1.2538e-04, 4.6262e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.6678e-01, 1.8633e-02, 3.1156e-01, 1.6885e-03, 1.5570e-04, 5.8873e-04,
1.2538e-04, 4.6262e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6668, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3116, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0217, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the left image feature a barn style door made of weathered-look horizontal wood boards?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Does the left image feature a barn style door made of weathered-look horizontal wood boards?'], 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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1871
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1871
tensor([0.8144, 0.0431, 0.0210, 0.0144, 0.0154, 0.0096, 0.0806, 0.0015],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([0.8144, 0.0431, 0.0210, 0.0144, 0.0154, 0.0096, 0.0806, 0.0015],
device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1856, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8144, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1871
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1871
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1872
tensor([7.0184e-01, 2.2294e-02, 2.7268e-01, 9.9192e-04, 2.0705e-04, 6.9527e-04,
5.8169e-05, 1.2312e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.0184e-01, 2.2294e-02, 2.7268e-01, 9.9192e-04, 2.0705e-04, 6.9527e-04,
5.8169e-05, 1.2312e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.7018, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2727, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0255, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([6.0770e-01, 3.9052e-01, 9.8209e-05, 2.0057e-04, 2.7657e-04, 3.2352e-04,
6.8434e-04, 1.9957e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.0770e-01, 3.9052e-01, 9.8209e-05, 2.0057e-04, 2.7657e-04, 3.2352e-04,
6.8434e-04, 1.9957e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.3905, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.6077, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0018, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-23 14:42:52,414] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-23 14:42:52,414] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7050.17 | backward_microstep: 6773.78 | backward_inner_microstep: 6767.81 | backward_allreduce_microstep: 5.85 | step_microstep: 7.66
[2024-10-23 14:42:52,415] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7050.18 | backward: 6773.77 | backward_inner: 6767.86 | backward_allreduce: 5.73 | step: 7.67
0%| | 6/4844 [01:36<19:49:23, 14.75s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there an unworn knee pad to the right of a model's legs?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the sleepwear feature a Disney Princess theme on the front?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many sled dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many white dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the sleepwear feature a Disney Princess theme on the front?'], 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: ['Is there an unworn knee pad to the right of a model'], 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']]
tensor([5.7085e-01, 4.2716e-01, 4.3167e-04, 1.2963e-04, 2.9889e-04, 5.7084e-04,
2.1611e-04, 3.4355e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.7085e-01, 4.2716e-01, 4.3167e-04, 1.2963e-04, 2.9889e-04, 5.7084e-04,
2.1611e-04, 3.4355e-04], device='cuda:1', grad_fn=<SelectBackward0>)