Datasets:
TMarcus
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InternVL

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Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many inflated sails are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many containers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the image have a ferret peeking out of the snow?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many cream colored Chow Chow puppies are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many inflated sails are in the image?'], responses:['3']
question: ['How many containers are in the image?'], responses:['0']
question: ['Does the image have a ferret peeking out of the snow?'], responses:['yes']
question: ['How many cream colored Chow Chow puppies 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']]
[('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']]
[('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']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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: 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: 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: 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: 1866
tensor([9.9867e-01, 3.4617e-07, 1.6704e-08, 1.2908e-08, 1.2682e-09, 1.3250e-03,
6.3040e-08, 1.1846e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9867e-01, 3.4617e-07, 1.6704e-08, 1.2908e-08, 1.2682e-09, 1.3250e-03,
6.3040e-08, 1.1846e-08], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([9.9998e-01, 5.0914e-07, 2.4242e-07, 1.1085e-09, 1.7923e-06, 7.6030e-08,
4.1900e-07, 1.4946e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9998e-01, 5.0914e-07, 2.4242e-07, 1.1085e-09, 1.7923e-06, 7.6030e-08,
4.1900e-07, 1.4946e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9987, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0013, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.8001e-05, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.5614e-09, 1.0024e-10, 2.3234e-08, 4.4454e-10, 1.7185e-10,
4.1180e-11, 1.5086e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.5614e-09, 1.0024e-10, 2.3234e-08, 4.4454e-10, 1.7185e-10,
4.1180e-11, 1.5086e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many vultures are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([9.9979e-01, 6.6783e-07, 1.2480e-06, 2.6798e-09, 6.7266e-11, 2.0331e-04,
6.3795e-10, 1.2012e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9979e-01, 6.6783e-07, 1.2480e-06, 2.6798e-09, 6.7266e-11, 2.0331e-04,
6.3795e-10, 1.2012e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='Does the image show a stalk of broccoli inside of a cup?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0024e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.0024e-10, device='cuda:3', grad_fn=<SubBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: torch.Size([7, 3, 448, 448])
{True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many monkeys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many cheetahs 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: ['Does the image show a stalk of broccoli inside of a cup?'], 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']]
question: ['How many cheetahs 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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
question: ['How many vultures are in the image?'], responses:['4']
question: ['How many monkeys are in the image?'], responses:['3']
[('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: 1863