Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many rolls of paper towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Does the left image show glasses garnished with a thin watermelon slice?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many rolls of paper towels are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
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
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
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
tensor([9.9025e-01, 4.8333e-04, 3.3549e-03, 1.6539e-05, 3.3130e-09, 4.5857e-03,
1.0147e-07, 1.3139e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.9025e-01, 4.8333e-04, 3.3549e-03, 1.6539e-05, 3.3130e-09, 4.5857e-03,
1.0147e-07, 1.3139e-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(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many lions are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many wine bottles are in the image?'], responses:['five']
question: ['Does the left image show glasses garnished with a thin watermelon slice?'], 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 lions are in the image?'], responses:['2']
question: ['How many dogs are in the image?'], responses:['1']
[('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']]
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([2.4737e-08, 9.7239e-02, 1.5483e-01, 3.6298e-04, 7.4602e-01, 1.2729e-04,
3.0559e-04, 1.1197e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
feet *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([2.4737e-08, 9.7239e-02, 1.5483e-01, 3.6298e-04, 7.4602e-01, 1.2729e-04,
3.0559e-04, 1.1197e-03], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 7.6176e-09, 1.2461e-11, 9.4119e-09, 8.7765e-11, 1.0045e-09,
4.0384e-12, 1.3356e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.6176e-09, 1.2461e-11, 9.4119e-09, 8.7765e-11, 1.0045e-09,
4.0384e-12, 1.3356e-08], device='cuda:1', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.2461e-11, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.2461e-11, device='cuda:1', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['How many dogs are in the image?'], responses:['2']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
[('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([3, 3, 448, 448]) knan debug pixel values shape
tensor([9.9807e-01, 1.2727e-07, 3.8433e-09, 1.9267e-03, 2.4957e-10, 3.5193e-10,
7.6273e-10, 2.5270e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9807e-01, 1.2727e-07, 3.8433e-09, 1.9267e-03, 2.4957e-10, 3.5193e-10,
7.6273e-10, 2.5270e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0019, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9981, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.0817e-08, 5.4304e-09, 5.3687e-07, 9.2708e-11, 4.3756e-10,
5.6846e-10, 1.0829e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.0817e-08, 5.4304e-09, 5.3687e-07, 9.2708e-11, 4.3756e-10,
5.6846e-10, 1.0829e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(5.3687e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many rodents 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.7462e-10, 5.6671e-11, 8.7860e-11, 8.5909e-11, 3.1919e-09,