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InternVL

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ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
question: ['How many pandas 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([9.1911e-01, 1.1631e-02, 6.6558e-02, 1.6159e-03, 4.4865e-05, 1.6473e-04,
9.2223e-06, 8.7104e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.1911e-01, 1.1631e-02, 6.6558e-02, 1.6159e-03, 4.4865e-05, 1.6473e-04,
9.2223e-06, 8.7104e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9191, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.0666, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0143, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([9.7389e-01, 4.2367e-03, 1.7661e-03, 6.2881e-04, 9.4510e-04, 6.5379e-04,
1.7837e-02, 4.5584e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7389e-01, 4.2367e-03, 1.7661e-03, 6.2881e-04, 9.4510e-04, 6.5379e-04,
1.7837e-02, 4.5584e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0178, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9822, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:53:22,568] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-23 14:53:22,568] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7091.05 | backward_microstep: 10607.38 | backward_inner_microstep: 6797.94 | backward_allreduce_microstep: 3809.33 | step_microstep: 7.89
[2024-10-23 14:53:22,568] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7091.05 | backward: 10607.37 | backward_inner: 6797.98 | backward_allreduce: 3809.32 | step: 7.91
1%| | 47/4844 [12:06<21:50:42, 16.39s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many people are wearing graduation caps in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
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=RIGHT,question='Is there a flying bird in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there any animal in the water?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the door of the bus open?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the door of the bus open?'], 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 flying bird 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
tensor([8.7525e-01, 1.1940e-02, 1.1128e-01, 7.9364e-04, 6.6813e-05, 3.2817e-04,
3.6558e-05, 3.0524e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.7525e-01, 1.1940e-02, 1.1128e-01, 7.9364e-04, 6.6813e-05, 3.2817e-04,
3.6558e-05, 3.0524e-04], device='cuda:2', grad_fn=<SelectBackward0>)
question: ['How many people are wearing graduation caps in the image?'], responses:['2']
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.8753, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1113, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0135, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Is there any animal in the water?'], responses:['no']
ANSWER0=VQA(image=RIGHT,question='Is there a barber pole in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
[('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']]
[('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: ['Is there a barber pole 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']]
torch.Size([1, 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: 3399
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([5.0745e-01, 4.9133e-01, 1.6556e-05, 1.8356e-04, 4.4839e-04, 1.5760e-04,
3.9204e-04, 2.5763e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.0745e-01, 4.9133e-01, 1.6556e-05, 1.8356e-04, 4.4839e-04, 1.5760e-04,
3.9204e-04, 2.5763e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4913, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.5074, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0012, device='cuda:2', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([9.2043e-01, 1.4966e-02, 6.2636e-02, 1.3146e-03, 7.4059e-05, 2.4780e-04,
3.5491e-05, 2.9625e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************