Datasets:
TMarcus
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InternVL

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Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many water bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dung beetles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many laptop computers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many round mirrors are on the wall above the bathroom sink?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many water bottles are in the image?'], responses:['15']
question: ['How many laptop computers are in the image?'], responses:['1']
[('15', 0.12850265658859292), ('14', 0.12554598114685298), ('13', 0.12491622450863256), ('16', 0.12450938797787274), ('29', 0.12444750181633149), ('35', 0.12413627702798803), ('22', 0.12400388658176363), ('21', 0.12393808435196574)]
[['15', '14', '13', '16', '29', '35', '22', '21']]
[('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([3, 3, 448, 448]) knan debug pixel values shape
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many round mirrors are on the wall above the bathroom sink?'], 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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
tensor([1.0000e+00, 3.5817e-10, 2.0207e-11, 4.8469e-11, 4.2111e-11, 1.4513e-08,
4.8883e-07, 6.6647e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.5817e-10, 2.0207e-11, 4.8469e-11, 4.2111e-11, 1.4513e-08,
4.8883e-07, 6.6647e-11], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([0.5884, 0.0298, 0.0302, 0.0923, 0.0173, 0.0259, 0.1372, 0.0790],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
15 *************
['15', '14', '13', '16', '29', '35', '22', '21'] tensor([0.5884, 0.0298, 0.0302, 0.0923, 0.0173, 0.0259, 0.1372, 0.0790],
device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(5.0388e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many flutes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
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(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many laptops are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many dung beetles are in the image?'], responses:['1']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
[('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']]
question: ['How many laptops are in the image?'], responses:['six']
[('7 eleven', 0.1258716720461554), ('dusk', 0.12512990238684168), ('blue', 0.12502287564185594), ('rose', 0.12495109740026594), ('peach', 0.12486403486105606), ('kitten', 0.12474151468778871), ('laundry', 0.12473504457146048), ('sunrise', 0.12468385840457588)]
[['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['How many flutes are in the image?'], responses:['1']
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
[('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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
tensor([0.0009, 0.0013, 0.0009, 0.1381, 0.0994, 0.0006, 0.5366, 0.2222],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
laundry *************
['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise'] tensor([0.0009, 0.0013, 0.0009, 0.1381, 0.0994, 0.0006, 0.5366, 0.2222],
device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.2292e-09, 9.6604e-10, 1.2628e-10, 3.9957e-10, 9.5307e-09,
6.8256e-08, 7.1020e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.2292e-09, 9.6604e-10, 1.2628e-10, 3.9957e-10, 9.5307e-09,
6.8256e-08, 7.1020e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(6.8256e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are neon pink jellyfish shown in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([1.0000e+00, 1.4788e-09, 3.8880e-10, 2.3035e-10, 3.0997e-10, 1.5212e-08,
7.3074e-09, 1.8740e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4788e-09, 3.8880e-10, 2.3035e-10, 3.0997e-10, 1.5212e-08,
7.3074e-09, 1.8740e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(7.3074e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
question: ['Are neon pink jellyfish shown 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)]