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InternVL

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[2024-10-24 10:16:58,312] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7062.13 | backward_microstep: 6873.81 | backward_inner_microstep: 6815.56 | backward_allreduce_microstep: 58.19 | step_microstep: 7.58
[2024-10-24 10:16:58,312] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7062.14 | backward: 6873.80 | backward_inner: 6815.57 | backward_allreduce: 58.18 | step: 7.59
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4710/4844 [19:35:42<32:26, 14.53s/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 step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a vehicle parked in the area near the sales?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many lipsticks are standing up with their caps off?')
ANSWER1=EVAL(expr='{ANSWER0} % 2 == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the image feature a profile orientation of the phone?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is there at least one person standing on the shore?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many lipsticks are standing up with their caps off?'], responses:['5']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
question: ['Does the image feature a profile orientation of the phone?'], responses:['no']
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
[('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([9.9975e-01, 1.1537e-07, 9.4663e-05, 1.4662e-04, 1.1857e-09, 6.2757e-06,
9.1834e-07, 1.5315e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9975e-01, 1.1537e-07, 9.4663e-05, 1.4662e-04, 1.1857e-09, 6.2757e-06,
9.1834e-07, 1.5315e-06], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9998, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0002, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the lipstick come in a red case?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['Is there a vehicle parked in the area near the sales?'], responses:['no']
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448])
[('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 at least one person standing on the shore?'], 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
tensor([9.9478e-01, 5.2200e-03, 2.5314e-07, 1.4539e-09, 2.3007e-10, 4.8808e-10,
1.5905e-09, 2.3089e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9478e-01, 5.2200e-03, 2.5314e-07, 1.4539e-09, 2.3007e-10, 4.8808e-10,
1.5905e-09, 2.3089e-08], device='cuda:3', grad_fn=<SelectBackward0>)
question: ['Does the lipstick come in a red case?'], responses:['no']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0052, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9948, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the bed canopy closed?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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([13, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 6.4995e-09, 5.4252e-07, 3.6315e-09, 1.7330e-07, 7.9086e-07,
3.6762e-08, 4.9395e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.4995e-09, 5.4252e-07, 3.6315e-09, 1.7330e-07, 7.9086e-07,
3.6762e-08, 4.9395e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.4995e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.0266e-06, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are some of the crab\'s legs red?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 4.5231e-09, 1.4699e-10, 3.0788e-09, 1.1270e-10, 5.0800e-11,
4.4562e-11, 9.1005e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.5231e-09, 1.4699e-10, 3.0788e-09, 1.1270e-10, 5.0800e-11,
4.4562e-11, 9.1005e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.4699e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.4699e-10, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
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