Datasets:
TMarcus
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InternVL

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[('13', 0.12770862924411772), ('14', 0.12534395389083108), ('21', 0.12493249815266858), ('12', 0.12491814916612239), ('11', 0.12461120999761086), ('27', 0.12444592740053353), ('15', 0.12414436865504584), ('29', 0.1238952634930699)]
[['13', '14', '21', '12', '11', '27', '15', '29']]
[('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
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
question: ['Are the dogs dressed like cows?'], responses:['yes']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
question: ['How many animals are in the image?'], responses:['2']
[('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']]
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 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([7.3496e-01, 1.9587e-02, 2.3793e-04, 1.8959e-01, 5.1627e-02, 8.6177e-05,
3.9007e-03, 8.0690e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
13 *************
['13', '14', '21', '12', '11', '27', '15', '29'] tensor([7.3496e-01, 1.9587e-02, 2.3793e-04, 1.8959e-01, 5.1627e-02, 8.6177e-05,
3.9007e-03, 8.0690e-06], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are a pair of lips visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 1.5633e-08, 1.8872e-11, 3.8126e-08, 1.1416e-10, 1.4931e-10,
1.3016e-11, 3.6519e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.5633e-08, 1.8872e-11, 3.8126e-08, 1.1416e-10, 1.4931e-10,
1.3016e-11, 3.6519e-08], device='cuda:2', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.8872e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1919e-07, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Are a pair of lips visible 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']]
question: ['How many bottles are in the image?'], responses:['six']
[('7 eleven', 0.1258716720461554), ('dusk', 0.12512990238684168), ('blue', 0.12502287564185594), ('rose', 0.12495109740026594), ('peach', 0.12486403486105606), ('kitten', 0.12474151468778871), ('laundry', 0.12473504457146048), ('sunrise', 0.12468385840457588)]
[['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
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: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 1.4669e-08, 1.4307e-08, 1.0399e-08, 3.4756e-12, 5.4928e-11,
1.1638e-10, 1.8999e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4669e-08, 1.4307e-08, 1.0399e-08, 3.4756e-12, 5.4928e-11,
1.1638e-10, 1.8999e-08], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 5.1522e-08, 4.8678e-09, 3.0288e-08, 2.8556e-10, 1.2404e-09,
1.4164e-09, 1.2183e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.1522e-08, 4.8678e-09, 3.0288e-08, 2.8556e-10, 1.2404e-09,
1.4164e-09, 1.2183e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.4307e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.4307e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(8.9743e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='What color is the kick drum skin?')
ANSWER1=EVAL(expr='{ANSWER0} == "black"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
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([1.0000e+00, 2.3303e-09, 6.4121e-08, 1.0988e-08, 2.5792e-11, 2.7620e-11,
2.7034e-11, 2.0860e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.3303e-09, 6.4121e-08, 1.0988e-08, 2.5792e-11, 2.7620e-11,
2.7034e-11, 2.0860e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(6.4121e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.5088e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([2.0069e-04, 1.0613e-02, 9.3863e-04, 1.2679e-01, 2.9278e-01, 7.4878e-04,
3.0084e-01, 2.6709e-01], device='cuda:2', grad_fn=<SoftmaxBackward0>)
laundry *************
['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise'] tensor([2.0069e-04, 1.0613e-02, 9.3863e-04, 1.2679e-01, 2.9278e-01, 7.4878e-04,
3.0084e-01, 2.6709e-01], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {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>)}
question: ['How many seals are in the image?'], responses:['1']
[('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
question: ['What color is the kick drum skin?'], responses:['black']
[('black', 0.12706825260511387), ('white', 0.12527812565897103), ('dark', 0.1250491849195085), ('purple', 0.12486259083591467), ('orange', 0.12479002203010545), ('red', 0.12434049404478545), ('maroon', 0.12433890776852753), ('blue', 0.12427242213707339)]
[['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 4.5455e-10, 6.2241e-11, 9.0556e-11, 7.4493e-11, 6.4459e-09,
4.8401e-08, 1.3114e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)