Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
1.16k
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
tensor([1.0000e+00, 7.5826e-10, 2.8987e-07, 7.2625e-11, 2.2449e-10, 8.1873e-08,
1.4871e-09, 6.1751e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.5826e-10, 2.8987e-07, 7.2625e-11, 2.2449e-10, 8.1873e-08,
1.4871e-09, 6.1751e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.5826e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 9.5852e-10, 5.2578e-07, 3.9032e-10, 3.2128e-09, 2.3214e-07,
4.1103e-09, 2.8281e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.5852e-10, 5.2578e-07, 3.9032e-10, 3.2128e-09, 2.3214e-07,
4.1103e-09, 2.8281e-07], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.5489e-01, 3.3316e-02, 5.7878e-06, 2.7766e-03, 5.6622e-03, 3.1181e-04,
2.1778e-03, 8.5864e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.5489e-01, 3.3316e-02, 5.7878e-06, 2.7766e-03, 5.6622e-03, 3.1181e-04,
2.1778e-03, 8.5864e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.5852e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many chimpanzees are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many chimpanzees 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many pandas are in the image?'], responses:['1']
[('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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.3288e-06, 1.8371e-08, 5.5159e-09, 1.2212e-09, 6.8498e-10,
2.2811e-09, 2.6612e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.3288e-06, 1.8371e-08, 5.5159e-09, 1.2212e-09, 6.8498e-10,
2.2811e-09, 2.6612e-10], 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(1.3571e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.7462e-10, 7.1639e-11, 1.5164e-10, 1.0342e-10, 1.8050e-08,
6.5503e-09, 2.5289e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.7462e-10, 7.1639e-11, 1.5164e-10, 1.0342e-10, 1.8050e-08,
6.5503e-09, 2.5289e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(6.5503e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:52:42,740] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-24 09:52:42,741] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3130.18 | backward_microstep: 14678.62 | backward_inner_microstep: 3031.91 | backward_allreduce_microstep: 11646.57 | step_microstep: 7.89
[2024-10-24 09:52:42,741] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3130.19 | backward: 14678.62 | backward_inner: 3031.98 | backward_allreduce: 11646.55 | step: 7.90
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4614/4844 [19:11:26<1:01:29, 16.04s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
ANSWER0=VQA(image=RIGHT,question='How many shades are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is there a bracelet made of pins in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many spotted hyenas are gathered together in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many chimneys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many shades are in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.3531e-07, 1.2797e-08, 4.7567e-08, 2.0116e-09, 1.5197e-08,
5.9856e-08, 8.3143e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 8.3531e-07, 1.2797e-08, 4.7567e-08, 2.0116e-09, 1.5197e-08,
5.9856e-08, 8.3143e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0559e-06, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)