Datasets:
TMarcus
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InternVL

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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([8.2734e-01, 5.9999e-02, 1.7190e-02, 8.7294e-02, 4.4839e-03, 2.0512e-03,
1.5491e-03, 8.8610e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.2734e-01, 5.9999e-02, 1.7190e-02, 8.7294e-02, 4.4839e-03, 2.0512e-03,
1.5491e-03, 8.8610e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9746, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0254, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many snow plows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many snow plows are in the image?'], responses:['1']
question: ['Is the door open?'], 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']]
[('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
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393
tensor([8.3682e-01, 2.8667e-02, 1.1226e-02, 3.1171e-03, 4.5357e-03, 2.2060e-03,
1.1325e-01, 1.7410e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.3682e-01, 2.8667e-02, 1.1226e-02, 3.1171e-03, 4.5357e-03, 2.2060e-03,
1.1325e-01, 1.7410e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8368, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1632, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([6.8946e-01, 1.5382e-02, 3.2242e-03, 4.9418e-04, 8.4095e-04, 3.4678e-04,
2.9023e-01, 1.9323e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.8946e-01, 1.5382e-02, 3.2242e-03, 4.9418e-04, 8.4095e-04, 3.4678e-04,
2.9023e-01, 1.9323e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.6895, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3105, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([8.9268e-01, 1.0662e-01, 4.6958e-05, 5.6249e-05, 5.4841e-05, 2.8645e-04,
1.5312e-04, 1.1066e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.9268e-01, 1.0662e-01, 4.6958e-05, 5.6249e-05, 5.4841e-05, 2.8645e-04,
1.5312e-04, 1.1066e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1066, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.8927, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0007, device='cuda:3', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
tensor([4.9945e-01, 4.9945e-01, 8.9198e-05, 1.4407e-04, 1.4321e-04, 6.8347e-05,
4.2747e-04, 2.2056e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.9945e-01, 4.9945e-01, 8.9198e-05, 1.4407e-04, 1.4321e-04, 6.8347e-05,
4.2747e-04, 2.2056e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4995, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.4995, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0011, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-23 14:52:20,342] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-23 14:52:20,343] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8437.10 | backward_microstep: 8110.62 | backward_inner_microstep: 8104.59 | backward_allreduce_microstep: 5.78 | step_microstep: 7.49
[2024-10-23 14:52:20,343] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8437.12 | backward: 8110.61 | backward_inner: 8104.72 | backward_allreduce: 5.73 | step: 7.50
1%| | 43/4844 [11:04<22:04:47, 16.56s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there a pug lying on its back in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a female wearing a pink bikini?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the collar on the dog clearly visible?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the collar on the dog clearly visible?'], 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 a female wearing a pink bikini?'], 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: ['Is there a pug lying on its back 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']]
tensor([6.5089e-01, 3.4840e-01, 4.9957e-05, 1.2028e-04, 4.2168e-05, 2.5100e-04,
2.1019e-04, 3.5907e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************