Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many frames are on the wall in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many wine bottles are in the image?'], responses:['five']
question: ['Do the bottles in the image have caps?'], responses:['yes']
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many power poles are in the image?'], responses:['0']
question: ['How many frames are on the wall in the image?'], responses:['111']
[('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']]
[('106', 0.12556070940736277), ('120', 0.12533922270280565), ('101', 0.1252441884519632), ('56', 0.12490260878466017), ('52', 0.12476067988206749), ('193', 0.1247440897055595), ('59', 0.12474156001416575), ('75', 0.12470694105141557)]
[['106', '120', '101', '56', '52', '193', '59', '75']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([9.1962e-09, 3.0762e-01, 1.2068e-01, 2.2130e-04, 5.7042e-01, 1.0369e-04,
3.2971e-04, 6.2689e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
feet *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([9.1962e-09, 3.0762e-01, 1.2068e-01, 2.2130e-04, 5.7042e-01, 1.0369e-04,
3.2971e-04, 6.2689e-04], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.5733e-08, 2.2661e-06, 5.5915e-09, 2.5064e-09, 1.3552e-09,
4.7437e-10, 1.7706e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.5733e-08, 2.2661e-06, 5.5915e-09, 2.5064e-09, 1.3552e-09,
4.7437e-10, 1.7706e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pug dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(2.2661e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1128e-09, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many warthogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
question: ['How many warthogs 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
question: ['How many pug dogs are in the image?'], responses:['ไธ‰']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([1.0000e+00, 1.2564e-06, 2.4748e-08, 7.8820e-10, 1.1241e-11, 5.5435e-07,
3.4733e-11, 4.8415e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 1.2564e-06, 2.4748e-08, 7.8820e-10, 1.1241e-11, 5.5435e-07,
3.4733e-11, 4.8415e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(2.4748e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([9.9999e-01, 2.3799e-06, 6.3805e-07, 1.2400e-09, 5.3638e-08, 5.3774e-08,
1.9796e-06, 1.1226e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 2.3799e-06, 6.3805e-07, 1.2400e-09, 5.3638e-08, 5.3774e-08,
1.9796e-06, 1.1226e-06], device='cuda:2', grad_fn=<SelectBackward0>)
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(6.1989e-06, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many sets of pads are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([0.0122, 0.9526, 0.0087, 0.0014, 0.0026, 0.0065, 0.0098, 0.0061],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
120 *************
['106', '120', '101', '56', '52', '193', '59', '75'] tensor([0.0122, 0.9526, 0.0087, 0.0014, 0.0026, 0.0065, 0.0098, 0.0061],
device='cuda:0', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {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='Is the dog wearing a collar?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
question: ['Is the dog wearing a collar?'], 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
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: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([1.0000e+00, 2.9019e-08, 5.6450e-11, 2.9311e-08, 1.2373e-10, 2.4235e-10,
3.0871e-11, 1.6674e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************