text stringlengths 0 1.16k |
|---|
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
question: ['How many people are in the image?'], responses:['3'] |
[('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']] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
question: ['How many dogs are lying down?'], responses:['0'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
tensor([1.0000e+00, 4.0621e-09, 1.6212e-08, 1.2756e-09, 4.1143e-11, 1.6398e-10, |
1.2988e-11, 2.3967e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.0621e-09, 1.6212e-08, 1.2756e-09, 4.1143e-11, 1.6398e-10, |
1.2988e-11, 2.3967e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.6212e-08, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.6212e-08, device='cuda:2', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
question: ['How many animals 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([3, 3, 448, 448]) knan debug pixel values shape |
tensor([9.9999e-01, 2.3321e-06, 2.8012e-08, 6.5404e-06, 8.1520e-09, 5.0509e-10, |
3.2937e-09, 2.9077e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 2.3321e-06, 2.8012e-08, 6.5404e-06, 8.1520e-09, 5.0509e-10, |
3.2937e-09, 2.9077e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(8.9154e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([9.6993e-01, 2.5585e-03, 1.8244e-08, 1.6390e-07, 3.1953e-09, 2.7516e-02, |
2.0955e-08, 4.3843e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.6993e-01, 2.5585e-03, 1.8244e-08, 1.6390e-07, 3.1953e-09, 2.7516e-02, |
2.0955e-08, 4.3843e-09], device='cuda:1', grad_fn=<SelectBackward0>) |
torch.Size([7, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.8244e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many open binders are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([9.9998e-01, 3.8875e-07, 7.9288e-07, 1.1063e-09, 1.1194e-05, 2.5864e-08, |
9.5283e-07, 3.3553e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9998e-01, 3.8875e-07, 7.9288e-07, 1.1063e-09, 1.1194e-05, 2.5864e-08, |
9.5283e-07, 3.3553e-06], 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(1.6809e-05, device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([9.4633e-01, 2.3457e-03, 1.3497e-02, 2.0701e-03, 1.9065e-05, 2.8574e-02, |
4.6650e-03, 2.4971e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
11 ************* |
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.4633e-01, 2.3457e-03, 1.3497e-02, 2.0701e-03, 1.9065e-05, 2.8574e-02, |
4.6650e-03, 2.4971e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
question: ['How many hyenas are in the image?'], responses:['2'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', 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']] |
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 |
question: ['How many open binders are in the image?'], responses:['0'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
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([9.9662e-01, 3.3766e-03, 3.0487e-07, 1.6481e-06, 5.7163e-09, 1.5840e-10, |
2.2473e-09, 1.0985e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9662e-01, 3.3766e-03, 3.0487e-07, 1.6481e-06, 5.7163e-09, 1.5840e-10, |
2.2473e-09, 1.0985e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9966, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0034, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 3.4714e-07, 1.4521e-08, 9.9781e-12, 4.2686e-08, 2.9398e-09, |
7.3276e-08, 9.4393e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 3.4714e-07, 1.4521e-08, 9.9781e-12, 4.2686e-08, 2.9398e-09, |
7.3276e-08, 9.4393e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-24 10:19:04,894] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.38 | optimizer_step: 0.33 |
[2024-10-24 10:19:04,895] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5041.93 | backward_microstep: 8849.32 | backward_inner_microstep: 4822.06 | backward_allreduce_microstep: 4027.16 | step_microstep: 7.80 |
[2024-10-24 10:19:04,895] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5041.94 | backward: 8849.31 | backward_inner: 4822.09 | backward_allreduce: 4027.15 | step: 7.81 |
97%|ββββββββββ| 4719/4844 [19:37:48<28:49, 13.83s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.