Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
4.4261e-12, 4.3562e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.3774e-09, 2.2807e-11, 7.4896e-09, 1.3756e-10, 1.7730e-10,
4.4261e-12, 4.3562e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.2807e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.2807e-11, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:09:27,091] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.33 | optimizer_step: 0.32
[2024-10-24 10:09:27,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5251.71 | backward_microstep: 8702.55 | backward_inner_microstep: 5004.02 | backward_allreduce_microstep: 3698.41 | step_microstep: 7.68
[2024-10-24 10:09:27,092] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5251.71 | backward: 8702.54 | backward_inner: 5004.05 | backward_allreduce: 3698.39 | step: 7.69
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4680/4844 [19:28:10<41:06, 15.04s/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
ANSWER0=VQA(image=LEFT,question='Are the beads hanging from the safety pin mostly purple and blue?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many flute-like musical instruments are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many males are posing in graduation attire?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a wild boar lying on the ground in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a wild boar lying on the ground 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many males are posing in graduation attire?'], responses:['0']
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Are the beads hanging from the safety pin mostly purple and blue?'], responses:['no']
tensor([1.0000e+00, 1.8041e-09, 8.1178e-11, 7.4859e-09, 1.1627e-10, 6.7299e-11,
2.8149e-11, 6.1321e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.8041e-09, 8.1178e-11, 7.4859e-09, 1.1627e-10, 6.7299e-11,
2.8149e-11, 6.1321e-09], device='cuda:3', grad_fn=<SelectBackward0>)
question: ['How many flute-like musical instruments are in the image?'], responses:['5']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.1178e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.1178e-11, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
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']]
[('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([7, 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: 3401
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
question: ['How many animals 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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([1.0000e+00, 3.0447e-07, 1.9328e-08, 1.2152e-12, 4.7999e-07, 7.6033e-09,
3.9084e-08, 7.4828e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 3.0447e-07, 1.9328e-08, 1.2152e-12, 4.7999e-07, 7.6033e-09,
3.9084e-08, 7.4828e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5497e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the bird sitting in the grass?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
question: ['Is the bird sitting 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([1.0000e+00, 1.6894e-07, 4.5020e-09, 1.4761e-08, 4.3543e-10, 1.9172e-10,
7.9460e-10, 4.9413e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.6894e-07, 4.5020e-09, 1.4761e-08, 4.3543e-10, 1.9172e-10,
7.9460e-10, 4.9413e-11], 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(1.8967e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([9.9814e-01, 8.9293e-08, 1.1496e-05, 1.7477e-03, 4.0078e-10, 1.0212e-04,
2.5783e-08, 3.1647e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9814e-01, 8.9293e-08, 1.1496e-05, 1.7477e-03, 4.0078e-10, 1.0212e-04,
2.5783e-08, 3.1647e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.0467e-08, 1.7610e-07, 1.6848e-12, 1.7167e-12, 3.8120e-10,