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ANSWER0=VQA(image=LEFT,question='How many mountain goats are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
question: ['How many mountain goats 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
tensor([1.0000e+00, 3.7266e-06, 1.1611e-07, 3.5061e-09, 6.0690e-10, 4.8861e-10, |
8.2953e-10, 5.8883e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.7266e-06, 1.1611e-07, 3.5061e-09, 6.0690e-10, 4.8861e-10, |
8.2953e-10, 5.8883e-11], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.8482e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 7.8976e-09, 1.1270e-10, 3.6627e-08, 1.2181e-10, 3.5262e-10, |
9.3196e-11, 3.9337e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.8976e-09, 1.1270e-10, 3.6627e-08, 1.2181e-10, 3.5262e-10, |
9.3196e-11, 3.9337e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([9.9496e-01, 4.9047e-03, 4.4284e-07, 4.7610e-06, 3.6846e-09, 1.3071e-04, |
6.8588e-07, 5.6301e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9496e-01, 4.9047e-03, 4.4284e-07, 4.7610e-06, 3.6846e-09, 1.3071e-04, |
6.8588e-07, 5.6301e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.1270e-10, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1910e-07, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9950, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0050, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dispensers are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
tensor([9.9992e-01, 1.6700e-05, 3.4319e-08, 6.6051e-05, 3.0459e-09, 1.2185e-10, |
1.2501e-09, 1.0023e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9992e-01, 1.6700e-05, 3.4319e-08, 6.6051e-05, 3.0459e-09, 1.2185e-10, |
1.2501e-09, 1.0023e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
torch.Size([7, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.2790e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many black animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['How many dispensers 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([1, 3, 448, 448]) knan debug pixel values shape |
question: ['How many black animals 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([3, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 3.0467e-08, 2.1886e-07, 1.1807e-07, 8.2045e-10, 3.3892e-08, |
1.7611e-09, 7.9679e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.0467e-08, 2.1886e-07, 1.1807e-07, 8.2045e-10, 3.3892e-08, |
1.7611e-09, 7.9679e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.1807e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['How many dogs are in the image?'], responses:['five'] |
[('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']] |
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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
tensor([8.5079e-01, 1.5276e-04, 2.8424e-04, 1.3089e-01, 2.3563e-04, 5.6201e-05, |
1.7584e-02, 1.5688e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
7 ************* |
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([8.5079e-01, 1.5276e-04, 2.8424e-04, 1.3089e-01, 2.3563e-04, 5.6201e-05, |
1.7584e-02, 1.5688e-06], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', 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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
tensor([2.5344e-08, 7.2887e-01, 3.8141e-02, 2.3121e-04, 2.3245e-01, 7.3507e-05, |
1.8586e-04, 4.6850e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
babies ************* |
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([2.5344e-08, 7.2887e-01, 3.8141e-02, 2.3121e-04, 2.3245e-01, 7.3507e-05, |
1.8586e-04, 4.6850e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {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>)} |
[2024-10-24 09:43:49,003] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.22 | optimizer_step: 0.31 |
[2024-10-24 09:43:49,003] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7027.63 | backward_microstep: 6734.89 | backward_inner_microstep: 6729.63 | backward_allreduce_microstep: 5.20 | step_microstep: 7.45 |
[2024-10-24 09:43:49,003] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7027.64 | backward: 6734.88 | backward_inner: 6729.64 | backward_allreduce: 5.19 | step: 7.46 |
95%|ββββββββββ| 4580/4844 [19:02:32<1:05:45, 14.95s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
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