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InternVL

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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 rolls of paper towel are in the package?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many coffee cups are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many televisions are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many old fashioned television sets are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many rolls of paper towel are in the package?'], 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
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
question: ['How many televisions are in the image?'], responses:['30']
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
[('30', 0.12740713819081037), ('29', 0.12530514884086683), ('40', 0.1249276424885007), ('28', 0.12486301766888525), ('31', 0.12483184010065636), ('32', 0.12430090544871905), ('26', 0.12425497754646514), ('35', 0.12410932971509633)]
[['30', '29', '40', '28', '31', '32', '26', '35']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
tensor([9.9866e-01, 1.3250e-03, 7.4014e-06, 9.5488e-07, 6.2684e-07, 1.1001e-06,
8.6978e-07, 3.6874e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9866e-01, 1.3250e-03, 7.4014e-06, 9.5488e-07, 6.2684e-07, 1.1001e-06,
8.6978e-07, 3.6874e-06], device='cuda:0', grad_fn=<SelectBackward0>)
question: ['How many old fashioned television sets are in the image?'], responses:['1']
question: ['How many coffee cups are in the image?'], responses:['3']
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there a human in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([3, 3, 448, 448])
[('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']]
[('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]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a human 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)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
tensor([0.4296, 0.0843, 0.1234, 0.0648, 0.0943, 0.0507, 0.0242, 0.1288],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
30 *************
['30', '29', '40', '28', '31', '32', '26', '35'] tensor([0.4296, 0.0843, 0.1234, 0.0648, 0.0943, 0.0507, 0.0242, 0.1288],
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(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
ANSWER0=VQA(image=RIGHT,question='How many persons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
tensor([1.0000e+00, 3.0758e-08, 8.1349e-10, 4.9629e-08, 1.6209e-09, 2.6055e-09,
1.2002e-09, 4.3281e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.0758e-08, 8.1349e-10, 4.9629e-08, 1.6209e-09, 2.6055e-09,
1.2002e-09, 4.3281e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(8.1349e-10, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1840e-07, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 4.8010e-10, 4.0186e-11, 4.2114e-11, 8.1178e-11, 4.7902e-09,
4.4069e-08, 8.8964e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.8010e-10, 4.0186e-11, 4.2114e-11, 8.1178e-11, 4.7902e-09,
4.4069e-08, 8.8964e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([9.9957e-01, 1.0675e-06, 6.2966e-09, 5.5135e-09, 3.8939e-11, 4.3055e-04,
5.9499e-10, 1.0070e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9957e-01, 1.0675e-06, 6.2966e-09, 5.5135e-09, 3.8939e-11, 4.3055e-04,
5.9499e-10, 1.0070e-09], device='cuda:3', grad_fn=<SelectBackward0>)