Datasets:
TMarcus
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InternVL

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torch.Size([7, 3, 448, 448])
question: ['How many green and yellow balloons 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([1, 3, 448, 448]) knan debug pixel values shape
tensor([6.4422e-01, 1.2657e-01, 3.4073e-02, 1.7417e-01, 1.2229e-02, 3.8306e-03,
4.6113e-03, 2.9396e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.4422e-01, 1.2657e-01, 3.4073e-02, 1.7417e-01, 1.2229e-02, 3.8306e-03,
4.6113e-03, 2.9396e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the animal in the image just above the seafloor?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many humans are holding cell phones in the image?'], responses:['3']
torch.Size([7, 3, 448, 448])
question: ['How many power poles are in the image?'], responses:['4']
[('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']]
question: ['Are there triangular pennants on display in the image?'], responses:['no']
[('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']]
[('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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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: ['Is the animal in the image just above the seafloor?'], 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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
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
tensor([5.3021e-01, 2.5100e-02, 7.9754e-02, 3.8490e-03, 8.8601e-04, 3.5748e-01,
2.1931e-03, 5.2064e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([5.3021e-01, 2.5100e-02, 7.9754e-02, 3.8490e-03, 8.8601e-04, 3.5748e-01,
2.1931e-03, 5.2064e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.9675, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0325, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([0.1519, 0.1546, 0.1532, 0.1114, 0.1261, 0.0828, 0.1348, 0.0852],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
5 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([0.1519, 0.1546, 0.1532, 0.1114, 0.1261, 0.0828, 0.1348, 0.0852],
device='cuda:3', grad_fn=<SelectBackward0>)
tensor([5.9198e-01, 4.0686e-01, 2.8195e-05, 1.1306e-04, 2.4586e-04, 4.0059e-04,
3.2724e-04, 4.2242e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.9198e-01, 4.0686e-01, 2.8195e-05, 1.1306e-04, 2.4586e-04, 4.0059e-04,
3.2724e-04, 4.2242e-05], device='cuda:1', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.3227, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.6773, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many smart phones are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4069, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5920, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0012, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many sleeping dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many smart phones are in the image?'], responses:['3']
question: ['How many sleeping dogs are in the image?'], responses:['1']
[('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']]
[('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
tensor([5.7692e-01, 4.2209e-01, 5.0002e-05, 1.4375e-04, 1.8894e-04, 3.0997e-04,
2.8084e-04, 2.5733e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.7692e-01, 4.2209e-01, 5.0002e-05, 1.4375e-04, 1.8894e-04, 3.0997e-04,
2.8084e-04, 2.5733e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4221, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5769, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many animals are in the image?'], responses:['2']
ANSWER0=VQA(image=LEFT,question='How many oxen are yolked to the cart in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([9.1543e-01, 1.0173e-02, 3.6247e-03, 1.2522e-03, 1.8810e-03, 1.2450e-03,
6.6314e-02, 7.8641e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.1543e-01, 1.0173e-02, 3.6247e-03, 1.2522e-03, 1.8810e-03, 1.2450e-03,
6.6314e-02, 7.8641e-05], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([0.4846, 0.4123, 0.0091, 0.0648, 0.0013, 0.0200, 0.0069, 0.0010],