Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
tensor([7.7861e-01, 6.7168e-02, 4.2167e-06, 2.3705e-02, 3.9060e-02, 7.1527e-04,
8.5243e-02, 5.4982e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([7.7861e-01, 6.7168e-02, 4.2167e-06, 2.3705e-02, 3.9060e-02, 7.1527e-04,
8.5243e-02, 5.4982e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.4066e-08, 6.4121e-08, 5.1704e-08, 4.9726e-10, 1.7088e-09,
4.0516e-10, 1.1298e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4066e-08, 6.4121e-08, 5.1704e-08, 4.9726e-10, 1.7088e-09,
4.0516e-10, 1.1298e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.4121e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7430e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 3.8481e-09, 2.3588e-08, 2.7213e-09, 2.8115e-10, 2.6205e-10,
2.2132e-11, 1.5814e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.8481e-09, 2.3588e-08, 2.7213e-09, 2.8115e-10, 2.6205e-10,
2.2132e-11, 1.5814e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.3588e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.3588e-08, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many gorillas are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.3308e-09, 4.3096e-07, 2.9339e-12, 5.0238e-11, 1.6034e-09,
7.7750e-11, 4.6281e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.3308e-09, 4.3096e-07, 2.9339e-12, 5.0238e-11, 1.6034e-09,
7.7750e-11, 4.6281e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.3308e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.8328e-01, 1.1513e-03, 3.5462e-03, 8.8779e-05, 5.9818e-07, 9.6395e-03,
7.7574e-06, 2.2896e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.8328e-01, 1.1513e-03, 3.5462e-03, 8.8779e-05, 5.9818e-07, 9.6395e-03,
7.7574e-06, 2.2896e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:13:43,157] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.25 | optimizer_step: 0.31
[2024-10-24 10:13:43,157] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3815.02 | backward_microstep: 10015.08 | backward_inner_microstep: 3517.70 | backward_allreduce_microstep: 6497.31 | step_microstep: 7.53
[2024-10-24 10:13:43,157] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3815.02 | backward: 10015.07 | backward_inner: 3517.71 | backward_allreduce: 6497.29 | step: 7.54
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4697/4844 [19:32:26<35:12, 14.37s/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 EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the dog standing on grass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are the towels displayed in a basket-type container?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are there several people standing in a single line outside in the grass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are the towels displayed in a basket-type container?'], responses:['yes']
question: ['Are there several people standing in a single line outside in the grass?'], 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']]
[('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: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1869
question: ['Is the dog standing on grass?'], responses:['yes']
question: ['How many wolves are in the image?'], responses:['2']
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
[('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: 7, images per sample: 7.0, dynamic token length: 1867
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
tensor([1.0000e+00, 1.0165e-08, 1.9780e-10, 6.2448e-09, 5.4906e-11, 5.9392e-11,
8.6560e-12, 2.6822e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0165e-08, 1.9780e-10, 6.2448e-09, 5.4906e-11, 5.9392e-11,
8.6560e-12, 2.6822e-08], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 3.5851e-08, 1.0881e-10, 9.2533e-08, 3.9594e-09, 2.4856e-09,