Datasets:
TMarcus
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InternVL

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device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([0.5734, 0.0243, 0.3941, 0.0016, 0.0009, 0.0028, 0.0007, 0.0021],
device='cuda:1', grad_fn=<SelectBackward0>)
tensor([4.6510e-01, 3.2029e-01, 9.7683e-02, 5.9128e-02, 4.3264e-02, 5.7944e-03,
8.5184e-03, 2.2354e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.6510e-01, 3.2029e-01, 9.7683e-02, 5.9128e-02, 4.3264e-02, 5.7944e-03,
8.5184e-03, 2.2354e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many striped straws are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4651, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5349, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.5734, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.3941, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0325, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many zipper pouches are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many parrots with a red head 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])
question: ['How many striped straws are in the image?'], responses:['0']
question: ['How many zipper pouches are in the image?'], responses:['3']
[('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']]
question: ['How many parrots with a red head are in the image?'], responses:['0']
[('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']]
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: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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: 1864
tensor([8.5295e-01, 2.0951e-02, 1.2288e-01, 1.2449e-03, 7.3089e-05, 3.2374e-04,
8.1050e-05, 1.5003e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.5295e-01, 2.0951e-02, 1.2288e-01, 1.2449e-03, 7.3089e-05, 3.2374e-04,
8.1050e-05, 1.5003e-03], device='cuda:2', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8529, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1229, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0242, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is wine pouring into the glass?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([9.7477e-01, 3.5636e-03, 1.6831e-03, 5.4644e-04, 1.0826e-03, 1.0711e-03,
3.2983e-03, 1.3987e-02], device='cuda:0', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.7477e-01, 3.5636e-03, 1.6831e-03, 5.4644e-04, 1.0826e-03, 1.0711e-03,
3.2983e-03, 1.3987e-02], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0.9748, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0252, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many striped animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([0.3516, 0.2265, 0.0795, 0.0959, 0.0118, 0.1907, 0.0400, 0.0039],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.3516, 0.2265, 0.0795, 0.0959, 0.0118, 0.1907, 0.0400, 0.0039],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7298, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2702, 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 keys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([9.7104e-01, 2.7183e-03, 4.2739e-03, 9.8042e-04, 3.4307e-03, 7.4392e-04,
2.8465e-03, 1.3962e-02], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.7104e-01, 2.7183e-03, 4.2739e-03, 9.8042e-04, 3.4307e-03, 7.4392e-04,
2.8465e-03, 1.3962e-02], device='cuda:1', grad_fn=<SelectBackward0>)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0.9710, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0290, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['How many keys 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
question: ['How many pillows are in the image?'], responses:['2']
tensor([0.2850, 0.2130, 0.1292, 0.2735, 0.0636, 0.0179, 0.0171, 0.0006],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.2850, 0.2130, 0.1292, 0.2735, 0.0636, 0.0179, 0.0171, 0.0006],
device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7265, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.2735, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Is wine pouring into the glass?'], responses:['yes']
[('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']]
ANSWER0=VQA(image=LEFT,question='What color are the vases?')
ANSWER1=EVAL(expr='{ANSWER0} == "silver"')
FINAL_ANSWER=RESULT(var=ANSWER1)