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InternVL

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device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0213, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9787, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many arches are in the image?'], responses:['6']
[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
[['6', '8', '12', '5', '4', '1', '11', '3']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
tensor([0.4136, 0.1257, 0.0800, 0.0515, 0.0122, 0.2688, 0.0408, 0.0074],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.4136, 0.1257, 0.0800, 0.0515, 0.0122, 0.2688, 0.0408, 0.0074],
device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4136, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5864, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
torch.Size([7, 3, 448, 448])
tensor([0.5061, 0.0807, 0.0185, 0.1029, 0.1195, 0.0409, 0.0943, 0.0369],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([0.5061, 0.0807, 0.0185, 0.1029, 0.1195, 0.0409, 0.0943, 0.0369],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a red canoe in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
question: ['How many seals 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
question: ['Is there a red canoe 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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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: 3397
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: 3397
tensor([7.9829e-01, 9.5342e-02, 2.7304e-02, 6.5522e-02, 8.3307e-03, 2.4610e-03,
2.6175e-03, 1.3369e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.9829e-01, 9.5342e-02, 2.7304e-02, 6.5522e-02, 8.3307e-03, 2.4610e-03,
2.6175e-03, 1.3369e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7983, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.2017, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([0.2257, 0.1834, 0.0811, 0.2103, 0.1212, 0.0121, 0.0981, 0.0683],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
6 *************
['6', '8', '12', '5', '4', '1', '11', '3'] tensor([0.2257, 0.1834, 0.0811, 0.2103, 0.1212, 0.0121, 0.0981, 0.0683],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.2015, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7985, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([6.7114e-01, 2.5040e-02, 3.0106e-01, 1.3228e-03, 1.4861e-04, 4.8784e-04,
1.0772e-04, 6.9917e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.7114e-01, 2.5040e-02, 3.0106e-01, 1.3228e-03, 1.4861e-04, 4.8784e-04,
1.0772e-04, 6.9917e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6711, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.3011, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0278, device='cuda:3', grad_fn=<SubBackward0>)}
[2024-10-23 14:46:26,665] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.23 | optimizer_step: 0.30
[2024-10-23 14:46:26,665] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7066.71 | backward_microstep: 6912.06 | backward_inner_microstep: 6788.04 | backward_allreduce_microstep: 123.94 | step_microstep: 7.49
[2024-10-23 14:46:26,665] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7066.73 | backward: 6912.05 | backward_inner: 6788.07 | backward_allreduce: 123.91 | step: 7.50
0%| | 20/4844 [05:10<20:19:27, 15.17s/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='How many mostly black dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many elephants are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a blue seating area near the books in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='How many horned animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
question: ['Is there a blue seating area near the books in the image?'], responses:['no']
question: ['How many horned animals are in the image?'], responses:['1']
[('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']]
[('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']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many mostly black dogs are in the image?'], responses:['1']
question: ['How many elephants are in the image?'], responses:['6']