Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
torch.Size([5, 3, 448, 448])
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image running toward the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many monkeys are in the image?'], responses:['δΈ‰']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is a person holding the dog 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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
question: ['Is the dog in the image running toward the camera?'], 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']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
tensor([1.3641e-04, 4.7823e-03, 7.6238e-02, 5.6081e-01, 1.8515e-01, 1.5805e-01,
4.7565e-03, 1.0079e-02], device='cuda:1', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([1.3641e-04, 4.7823e-03, 7.6238e-02, 5.6081e-01, 1.8515e-01, 1.5805e-01,
4.7565e-03, 1.0079e-02], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1351
tensor([1.0000e+00, 1.2017e-08, 1.3384e-10, 2.6027e-07, 9.9416e-11, 4.6715e-10,
1.2988e-10, 1.4543e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2017e-08, 1.3384e-10, 2.6027e-07, 9.9416e-11, 4.6715e-10,
1.2988e-10, 1.4543e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.3384e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3828e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.8831e-01, 1.1689e-02, 2.3052e-06, 1.5711e-10, 2.5863e-07, 2.2071e-08,
6.0819e-09, 4.6180e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.8831e-01, 1.1689e-02, 2.3052e-06, 1.5711e-10, 2.5863e-07, 2.2071e-08,
6.0819e-09, 4.6180e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.0819e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are there plants in vases in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['Are there plants in vases 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([1, 3, 448, 448]) knan debug pixel values shape
tensor([9.9883e-01, 5.3947e-09, 1.1695e-03, 7.2356e-09, 1.3635e-12, 1.7812e-11,
6.7409e-11, 2.6297e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9883e-01, 5.3947e-09, 1.1695e-03, 7.2356e-09, 1.3635e-12, 1.7812e-11,
6.7409e-11, 2.6297e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9988, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0012, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.9849e-09, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.3691e-09, 2.3127e-10, 2.7791e-08, 8.7819e-11, 1.1811e-10,
2.8411e-11, 1.1865e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.3691e-09, 2.3127e-10, 2.7791e-08, 8.7819e-11, 1.1811e-10,
2.8411e-11, 1.1865e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.3127e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.3127e-10, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:27:12,186] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.37 | optimizer_step: 0.34
[2024-10-24 10:27:12,186] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4452.54 | backward_microstep: 5697.51 | backward_inner_microstep: 4240.55 | backward_allreduce_microstep: 1456.87 | step_microstep: 7.81
[2024-10-24 10:27:12,187] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4452.55 | backward: 5697.50 | backward_inner: 4240.59 | backward_allreduce: 1456.83 | step: 7.82
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4753/4844 [19:45:55<19:19, 12.74s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image being held on a leash?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many open laptops can be seen in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is there at least one person in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many water buffaloes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the dog in the image being held on a leash?'], 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([1, 3, 448, 448]) knan debug pixel values shape