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Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many rays are swimming near the sand?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='What color is the spaniel on the grass?') |
ANSWER1=EVAL(expr='{ANSWER0} == "brown and white"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='Are there tinted lips in the image?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
question: ['What dish is the food being served in?'], responses:['plate'] |
[('plate', 0.12673014572769747), ('bowl', 0.1248969376466481), ('delivery', 0.12487111978025815), ('container', 0.12476829192613588), ('blending', 0.12471798969044892), ('doll', 0.12470041906034778), ('hazy', 0.12466699410259863), ('sliding', 0.12464810206586503)] |
[['plate', 'bowl', 'delivery', 'container', 'blending', 'doll', 'hazy', 'sliding']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
question: ['Are there tinted lips in the image?'], responses:['no'] |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
[('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']] |
tensor([8.9541e-01, 1.0329e-01, 6.9884e-05, 5.4082e-04, 5.5192e-05, 6.0846e-05, |
1.0973e-04, 4.6906e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
plate ************* |
['plate', 'bowl', 'delivery', 'container', 'blending', 'doll', 'hazy', 'sliding'] tensor([8.9541e-01, 1.0329e-01, 6.9884e-05, 5.4082e-04, 5.5192e-05, 6.0846e-05, |
1.0973e-04, 4.6906e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
question: ['How many rays are swimming near the sand?'], responses:['2'] |
question: ['What color is the spaniel on the grass?'], responses:['b'] |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)} |
[('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']] |
[('b', 0.1481217199537866), ('e', 0.12602255820943076), ('g', 0.12601628916448182), ('k', 0.1220280012774652), ('f', 0.12073193162045133), ('v', 0.11959582364650344), ('c', 0.11887450331522846), ('bib', 0.11860917281265244)] |
[['b', 'e', 'g', 'k', 'f', 'v', 'c', 'bib']] |
ANSWER0=VQA(image=RIGHT,question='Is the dog on the right facing right?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['Is the dog on the right facing right?'], 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([7, 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: 1864 |
tensor([1.0000e+00, 8.1987e-10, 4.5420e-07, 1.0342e-10, 7.0936e-10, 4.0462e-08, |
4.5415e-09, 8.3942e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.1987e-10, 4.5420e-07, 1.0342e-10, 7.0936e-10, 4.0462e-08, |
4.5415e-09, 8.3942e-07], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.1987e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Are there tinted lips in the image?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
torch.Size([13, 3, 448, 448]) |
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: 1861 |
tensor([7.8559e-02, 3.4273e-01, 3.8096e-04, 5.7229e-02, 7.7648e-04, 7.6490e-03, |
1.5996e-02, 4.9667e-01], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
bib ************* |
['b', 'e', 'g', 'k', 'f', 'v', 'c', 'bib'] tensor([9.9988e-01, 4.8322e-05, 2.1022e-07, 7.0308e-05, 9.0413e-08, 1.4275e-09, |
6.0229e-08, 1.4054e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.8559e-02, 3.4273e-01, 3.8096e-04, 5.7229e-02, 7.7648e-04, 7.6490e-03, |
1.5996e-02, 4.9667e-01], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([9.9988e-01, 4.8322e-05, 2.1022e-07, 7.0308e-05, 9.0413e-08, 1.4275e-09, |
6.0229e-08, 1.4054e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many phones are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(7.0308e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9999, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
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: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
question: ['How many phones 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']] |
tensor([1.0000e+00, 4.8219e-09, 3.6877e-11, 2.0588e-08, 3.1970e-10, 9.2902e-10, |
8.5160e-11, 6.9877e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.8219e-09, 3.6877e-11, 2.0588e-08, 3.1970e-10, 9.2902e-10, |
8.5160e-11, 6.9877e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
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