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InternVL

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8.3836e-04, 6.5450e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.2139e-01, 2.6138e-02, 5.6520e-03, 4.3089e-02, 2.0153e-03, 8.1261e-04,
8.3836e-04, 6.5450e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9645, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0355, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([3.8213e-01, 3.5897e-01, 8.5268e-02, 1.4058e-01, 2.3679e-02, 3.8651e-03,
5.2615e-03, 2.5058e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([3.8213e-01, 3.5897e-01, 8.5268e-02, 1.4058e-01, 2.3679e-02, 3.8651e-03,
5.2615e-03, 2.5058e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1183, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8817, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are there humans in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
torch.Size([1, 3, 448, 448])
question: ['Are there humans 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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([9.6844e-01, 3.1130e-02, 5.1777e-05, 6.2972e-05, 9.3809e-05, 5.0720e-05,
9.2704e-05, 7.8456e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.6844e-01, 3.1130e-02, 5.1777e-05, 6.2972e-05, 9.3809e-05, 5.0720e-05,
9.2704e-05, 7.8456e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0311, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9684, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0004, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([8.8026e-01, 1.1900e-01, 5.3963e-05, 7.2236e-05, 4.8586e-05, 2.3567e-04,
2.3475e-04, 8.8109e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.8026e-01, 1.1900e-01, 5.3963e-05, 7.2236e-05, 4.8586e-05, 2.3567e-04,
2.3475e-04, 8.8109e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1190, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8803, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:3', grad_fn=<DivBackward0>)}
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: 3398
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([5.9156e-01, 4.0657e-01, 5.3046e-05, 2.1778e-04, 6.2833e-04, 6.3164e-04,
2.9728e-04, 3.7568e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.9156e-01, 4.0657e-01, 5.3046e-05, 2.1778e-04, 6.2833e-04, 6.3164e-04,
2.9728e-04, 3.7568e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4066, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5916, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0019, device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-23 14:45:11,737] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.21 | optimizer_step: 0.30
[2024-10-23 14:45:11,737] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6341.91 | backward_microstep: 6159.72 | backward_inner_microstep: 6154.48 | backward_allreduce_microstep: 5.18 | step_microstep: 7.18
[2024-10-23 14:45:11,737] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6341.92 | backward: 6159.71 | backward_inner: 6154.49 | backward_allreduce: 5.17 | step: 7.19
0%| | 15/4844 [03:55<19:08:13, 14.27s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL stepRegistering EVAL step
Registering RESULT step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many televisions are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many golf balls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is part of a round metal tray visible between at least two slices of pizza?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the drain in the bottom of the basin visible?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([1, 3, 448, 448])
torch.Size([11, 3, 448, 448])
question: ['How many televisions are in the image?'], responses:['1']
question: ['Is the drain in the bottom of the basin visible?'], responses:['yes']
[('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']]
[('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
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['Is part of a round metal tray visible between at least two slices of pizza?'], 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
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847
tensor([9.5199e-01, 8.4963e-03, 3.1249e-03, 1.0431e-03, 1.5206e-03, 1.1658e-03,
3.2572e-02, 9.0821e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5199e-01, 8.4963e-03, 3.1249e-03, 1.0431e-03, 1.5206e-03, 1.1658e-03,
3.2572e-02, 9.0821e-05], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([5.3939e-01, 1.1107e-02, 4.4714e-01, 8.9555e-04, 1.5977e-04, 5.9243e-04,
8.8957e-05, 6.2371e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.3939e-01, 1.1107e-02, 4.4714e-01, 8.9555e-04, 1.5977e-04, 5.9243e-04,
8.8957e-05, 6.2371e-04], device='cuda:1', grad_fn=<SelectBackward0>)