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TMarcus
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InternVL

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tensor([0.6360, 0.0713, 0.0317, 0.0175, 0.0025, 0.2323, 0.0075, 0.0012],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.6360, 0.0713, 0.0317, 0.0175, 0.0025, 0.2323, 0.0075, 0.0012],
device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.2323, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7677, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.2410e-01, 3.8142e-02, 8.5102e-03, 2.4624e-02, 2.5955e-03, 9.5434e-04,
1.0159e-03, 6.3012e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.2410e-01, 3.8142e-02, 8.5102e-03, 2.4624e-02, 2.5955e-03, 9.5434e-04,
1.0159e-03, 6.3012e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9241, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0759, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:52:03,773] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.36 | optimizer_step: 0.32
[2024-10-23 14:52:03,773] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7052.11 | backward_microstep: 10886.06 | backward_inner_microstep: 6776.70 | backward_allreduce_microstep: 4109.22 | step_microstep: 7.86
[2024-10-23 14:52:03,773] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7052.11 | backward: 10886.05 | backward_inner: 6776.78 | backward_allreduce: 4109.20 | step: 7.87
1%| | 42/4844 [10:47<22:04:36, 16.55s/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 VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many sledding dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many goats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the animal in the image on the right standing on all fours?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Does the left image contain a woman carrying groceries?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([11, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many goats 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']]
question: ['Does the left image contain a woman carrying groceries?'], 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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is the animal in the image on the right standing on all fours?'], 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 sledding dogs 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([11, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891
tensor([8.4460e-01, 6.1166e-02, 9.9789e-03, 7.8560e-02, 3.5612e-03, 9.2884e-04,
1.1205e-03, 7.9854e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.4460e-01, 6.1166e-02, 9.9789e-03, 7.8560e-02, 3.5612e-03, 9.2884e-04,
1.1205e-03, 7.9854e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9214, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0786, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([7.8637e-01, 2.2370e-02, 1.8746e-01, 1.7818e-03, 1.6371e-04, 5.8415e-04,
6.0547e-05, 1.2111e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.8637e-01, 2.2370e-02, 1.8746e-01, 1.7818e-03, 1.6371e-04, 5.8415e-04,
6.0547e-05, 1.2111e-03], device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.7864, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1875, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0262, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dingo laying on the grass?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892
question: ['How many boats are in the image?'], responses:['1']
tensor([6.2577e-01, 3.7350e-01, 8.2363e-06, 9.4431e-05, 1.0218e-04, 3.0361e-04,
2.0517e-04, 1.3343e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.2577e-01, 3.7350e-01, 8.2363e-06, 9.4431e-05, 1.0218e-04, 3.0361e-04,
2.0517e-04, 1.3343e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.3735, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.6258, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Is the dingo laying on the grass?'], responses:['no']
[('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']]
ANSWER0=VQA(image=RIGHT,question='Is the door open?')
FINAL_ANSWER=RESULT(var=ANSWER0)
[('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)]