Datasets:
TMarcus
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InternVL

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Registering EVAL step
Registering RESULT step
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 wine bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 7')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many cats are lying down?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many dogs are sitting in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are there mountains visible behind the sleds?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([11, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are there mountains visible behind the sleds?'], responses:['no']
question: ['How many cats are lying down?'], responses:['1']
[('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']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many wine bottles are in the image?'], responses:['five']
[('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']]
question: ['How many dogs are sitting in the image?'], responses:['5']
torch.Size([11, 3, 448, 448]) knan debug pixel values shape
[('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']]
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
tensor([1.0000e+00, 1.5080e-09, 3.6899e-07, 4.4830e-11, 6.0918e-10, 4.6559e-09,
1.8482e-09, 4.4941e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.5080e-09, 3.6899e-07, 4.4830e-11, 6.0918e-10, 4.6559e-09,
1.8482e-09, 4.4941e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([9.9957e-01, 1.0141e-08, 3.2854e-10, 9.9988e-12, 3.4358e-11, 6.3458e-09,
4.3056e-04, 3.9691e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9957e-01, 1.0141e-08, 3.2854e-10, 9.9988e-12, 3.4358e-11, 6.3458e-09,
4.3056e-04, 3.9691e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.5080e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0004, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9996, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many short-legged dogs are standing outdoors?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2885
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2886
question: ['How many dogs are in the image?'], responses:['3']
tensor([1.2815e-07, 2.7220e-01, 2.3495e-01, 3.4810e-04, 4.9075e-01, 7.7793e-05,
4.2151e-04, 1.2498e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
feet *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([1.2815e-07, 2.7220e-01, 2.3495e-01, 3.4810e-04, 4.9075e-01, 7.7793e-05,
4.2151e-04, 1.2498e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many short-legged dogs are standing outdoors?'], responses:['0']
ANSWER0=VQA(image=LEFT,question='How many yaks are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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])
[('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([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.9700e-01, 3.5304e-08, 7.9440e-05, 2.8004e-03, 1.4458e-09, 1.2304e-04,
1.9195e-08, 4.1275e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9700e-01, 3.5304e-08, 7.9440e-05, 2.8004e-03, 1.4458e-09, 1.2304e-04,
1.9195e-08, 4.1275e-08], device='cuda:1', grad_fn=<SelectBackward0>)
question: ['How many yaks are in the image?'], responses:['7']
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many golf balls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]