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TMarcus
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InternVL

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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([8.7892e-01, 3.6274e-02, 1.1058e-02, 6.7772e-02, 3.2700e-03, 1.4501e-03,
1.1660e-03, 8.9528e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.7892e-01, 3.6274e-02, 1.1058e-02, 6.7772e-02, 3.2700e-03, 1.4501e-03,
1.1660e-03, 8.9528e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0678, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9322, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([0.7914, 0.0871, 0.0351, 0.0064, 0.0084, 0.0246, 0.0211, 0.0259],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
black *************
['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue'] tensor([0.7914, 0.0871, 0.0351, 0.0064, 0.0084, 0.0246, 0.0211, 0.0259],
device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:44:31,533] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.28 | optimizer_step: 0.31
[2024-10-23 14:44:31,534] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6991.22 | backward_microstep: 10798.24 | backward_inner_microstep: 6766.52 | backward_allreduce_microstep: 4031.66 | step_microstep: 7.88
[2024-10-23 14:44:31,534] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6991.24 | backward: 10798.23 | backward_inner: 6766.53 | backward_allreduce: 4031.65 | step: 7.89
0%| | 12/4844 [03:15<21:48:04, 16.24s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many locks are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image show a hound standing on thick green grass?')
ANSWER1=RESULT(var=ANSWER0)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many dogs are visible on grassy ground?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is there a chalkboard in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a chalkboard 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['Does the image show a hound standing on thick green grass?'], 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: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([9.1874e-01, 1.9948e-02, 5.8733e-02, 1.4298e-03, 6.6042e-05, 2.6145e-04,
3.7745e-05, 7.8167e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.1874e-01, 1.9948e-02, 5.8733e-02, 1.4298e-03, 6.6042e-05, 2.6145e-04,
3.7745e-05, 7.8167e-04], device='cuda:2', grad_fn=<SelectBackward0>)
question: ['How many locks are in the image?'], responses:['3']
question: ['How many dogs are visible on grassy ground?'], responses:['2']
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9187, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0587, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0225, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Are the boats in the water?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
[('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']]
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
question: ['Are the boats in the water?'], 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: 1865
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([7.6521e-01, 2.6094e-02, 2.0595e-01, 1.1879e-03, 1.9192e-04, 6.3233e-04,
7.5718e-05, 6.5571e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.6521e-01, 2.6094e-02, 2.0595e-01, 1.1879e-03, 1.9192e-04, 6.3233e-04,
7.5718e-05, 6.5571e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.7652, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.2060, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0288, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
tensor([8.8008e-01, 1.1898e-01, 6.3163e-05, 6.3749e-05, 7.5981e-05, 3.4108e-04,
3.1500e-04, 7.6418e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.8008e-01, 1.1898e-01, 6.3163e-05, 6.3749e-05, 7.5981e-05, 3.4108e-04,
3.1500e-04, 7.6418e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1190, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.8801, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0009, device='cuda:0', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many cats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)