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FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['Does the image have at least one striped straw?'], responses:['no'] |
question: ['Does the train have any round windows?'], responses:['no'] |
question: ['Does the image have a wine glass next to the bottle?'], responses:['yes'] |
question: ['How many shoes are in the image?'], responses:['19'] |
[('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']] |
[('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']] |
[('19', 0.12947391708430636), ('17', 0.12533682461313317), ('21', 0.12528221370298373), ('18', 0.12507008909943373), ('23', 0.12421253737136882), ('20', 0.12396361828614631), ('22', 0.12381053774619734), ('15', 0.12285026209643048)] |
[['19', '17', '21', '18', '23', '20', '22', '15']] |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([0.3220, 0.2307, 0.0475, 0.0738, 0.0808, 0.1284, 0.0560, 0.0609], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
19 ************* |
['19', '17', '21', '18', '23', '20', '22', '15'] tensor([0.3220, 0.2307, 0.0475, 0.0738, 0.0808, 0.1284, 0.0560, 0.0609], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([1.0000e+00, 1.8189e-09, 5.1405e-07, 1.5591e-08, 8.7953e-09, 6.4171e-07, |
4.2292e-08, 5.9877e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.8189e-09, 5.1405e-07, 1.5591e-08, 8.7953e-09, 6.4171e-07, |
4.2292e-08, 5.9877e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.2673e-08, 6.6256e-11, 3.0594e-08, 2.0384e-10, 1.6657e-10, |
4.1058e-11, 7.6466e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2673e-08, 6.6256e-11, 3.0594e-08, 2.0384e-10, 1.6657e-10, |
4.1058e-11, 7.6466e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 7.7344e-08, 2.4061e-07, 8.5074e-11, 5.6558e-11, 1.9709e-09, |
2.9780e-10, 2.9708e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.7344e-08, 2.4061e-07, 8.5074e-11, 5.6558e-11, 1.9709e-09, |
2.9780e-10, 2.9708e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.8189e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9073e-06, device='cuda:2', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.6256e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.6256e-11, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many birds are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(7.7344e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.3644e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many folded towels are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many balloons are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many folded towels are in the image?'], responses:['3'] |
[('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([5, 3, 448, 448]) knan debug pixel values shape |
question: ['How many balloons are in the image?'], responses:['7'] |
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)] |
[['7', '8', '11', '5', '9', '10', '6', '12']] |
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: 1861 |
question: ['How many birds are in the image?'], responses:['7'] |
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)] |
[['7', '8', '11', '5', '9', '10', '6', '12']] |
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 |
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 |
tensor([9.9988e-01, 1.2339e-04, 1.9141e-07, 1.6371e-07, 4.6707e-10, 9.7200e-07, |
1.1228e-08, 2.4333e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9988e-01, 1.2339e-04, 1.9141e-07, 1.6371e-07, 4.6707e-10, 9.7200e-07, |
1.1228e-08, 2.4333e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9999, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0001, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
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([9.8445e-01, 4.1094e-04, 1.5854e-03, 1.6506e-03, 1.4899e-03, 9.1335e-05, |
1.0275e-02, 4.3000e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.8445e-01, 4.1094e-04, 1.5854e-03, 1.6506e-03, 1.4899e-03, 9.1335e-05, |
1.0275e-02, 4.3000e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {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>)} |
tensor([9.8579e-01, 3.7464e-04, 1.0285e-02, 4.2446e-04, 1.5773e-03, 4.2452e-04, |
1.0843e-03, 4.2042e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.8579e-01, 3.7464e-04, 1.0285e-02, 4.2446e-04, 1.5773e-03, 4.2452e-04, |
1.0843e-03, 4.2042e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {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>)} |
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