Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
1.16k
question: ['How many windows are on the left wall?'], responses:['2']
[WARNING|tokenization_utils_base.py:2697] 2024-10-22 17:18:42,571 >> Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
[('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([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is liquid being poured into a cup?'], responses:['no']
[('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([5, 3, 448, 448]) knan debug pixel values shape
[('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']]
tensor([5.6711e-01, 1.3724e-02, 4.1491e-01, 1.9970e-03, 4.0551e-04, 9.5994e-04,
9.0195e-05, 8.0902e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6711e-01, 1.3724e-02, 4.1491e-01, 1.9970e-03, 4.0551e-04, 9.5994e-04,
9.0195e-05, 8.0902e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4149, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5671, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0180, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the pair of shoes on the left of the single shoe?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['Is the pair of shoes on the left of the single shoe?'], responses:['no']
[('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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([4.9933e-01, 4.9933e-01, 2.5817e-05, 2.6338e-04, 3.0606e-04, 3.4874e-04,
3.9058e-04, 1.0902e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.9933e-01, 4.9933e-01, 2.5817e-05, 2.6338e-04, 3.0606e-04, 3.4874e-04,
3.9058e-04, 1.0902e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4993, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.4993, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([7.3011e-01, 2.6859e-01, 6.2645e-05, 2.6047e-04, 1.1166e-04, 1.5239e-04,
5.7891e-04, 1.3085e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.3011e-01, 2.6859e-01, 6.2645e-05, 2.6047e-04, 1.1166e-04, 1.5239e-04,
5.7891e-04, 1.3085e-04], device='cuda:2', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.2686, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.7301, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many velcro closures are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many velcro closures are in the image?'], responses:['4']
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([6.8600e-01, 3.0140e-02, 8.1122e-03, 1.9267e-03, 3.4890e-03, 1.4992e-03,
2.6864e-01, 1.9551e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.8600e-01, 3.0140e-02, 8.1122e-03, 1.9267e-03, 3.4890e-03, 1.4992e-03,
2.6864e-01, 1.9551e-04], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.3140, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.6860, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many boats are in the image?'], responses:['3']
ANSWER0=VQA(image=RIGHT,question='Is there a human holding a dog in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([0.1978, 0.1359, 0.1745, 0.0878, 0.1359, 0.0775, 0.1540, 0.0366],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.1978, 0.1359, 0.1745, 0.0878, 0.1359, 0.0775, 0.1540, 0.0366],
device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.1540, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8460, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([3.0321e-01, 2.6758e-01, 1.4322e-01, 1.6229e-01, 8.6870e-02, 1.4631e-02,
2.1964e-02, 2.3281e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] ANSWER0=VQA(image=RIGHT,question='What color is the car?')
ANSWER1=EVAL(expr='{ANSWER0} == "light blue"')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([3.0321e-01, 2.6758e-01, 1.4322e-01, 1.6229e-01, 8.6870e-02, 1.4631e-02,
2.1964e-02, 2.3281e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.2676, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.7324, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many shoes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is there a human holding a 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
question: ['How many shoes 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([7, 3, 448, 448]) knan debug pixel values shape