text stringlengths 0 1.16k |
|---|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4619e-01, 4.5281e-01, 2.2885e-05, 1.1825e-04, 1.0293e-04, 1.9667e-04, |
5.4530e-04, 1.1593e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4528, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5462, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([5.9345e-01, 2.6073e-02, 6.9225e-03, 2.1066e-03, 3.1685e-03, 1.8125e-03, |
3.6635e-01, 1.1572e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.9345e-01, 2.6073e-02, 6.9225e-03, 2.1066e-03, 3.1685e-03, 1.8125e-03, |
3.6635e-01, 1.1572e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9859, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0141, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([8.0686e-01, 1.9164e-01, 5.3511e-05, 1.2004e-04, 2.5605e-04, 7.4098e-04, |
2.5924e-04, 6.7192e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.0686e-01, 1.9164e-01, 5.3511e-05, 1.2004e-04, 2.5605e-04, 7.4098e-04, |
2.5924e-04, 6.7192e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([9.4588e-01, 9.2726e-03, 2.8270e-03, 9.3455e-04, 1.1763e-03, 8.2818e-04, |
3.9041e-02, 3.5994e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4588e-01, 9.2726e-03, 2.8270e-03, 9.3455e-04, 1.1763e-03, 8.2818e-04, |
3.9041e-02, 3.5994e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8069, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1916, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0015, device='cuda:2', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0390, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9610, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-23 14:49:38,602] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.34 |
[2024-10-23 14:49:38,602] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7091.68 | backward_microstep: 10698.85 | backward_inner_microstep: 6785.96 | backward_allreduce_microstep: 3912.81 | step_microstep: 7.57 |
[2024-10-23 14:49:38,602] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7091.68 | backward: 10698.84 | backward_inner: 6785.98 | backward_allreduce: 3912.77 | step: 7.59 |
1%| | 33/4844 [08:22<21:54:00, 16.39s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is a person pushing the dispenser?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Does the laptop on the right display the tiles from the operating system Windows?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Is the drummer wearing a blue and white shirt?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=LEFT,question='How many dogs are standing in the grass?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is a person pushing the dispenser?'], responses:['no'] |
[('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']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
question: ['Is the drummer wearing a blue and white shirt?'], 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([3, 3, 448, 448]) knan debug pixel values shape |
tensor([5.9191e-01, 4.0681e-01, 1.1451e-04, 1.5338e-04, 3.5030e-04, 2.5778e-05, |
4.2502e-04, 2.1154e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.9191e-01, 4.0681e-01, 1.1451e-04, 1.5338e-04, 3.5030e-04, 2.5778e-05, |
4.2502e-04, 2.1154e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4068, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5919, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the dog on a grassy surface?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([1, 3, 448, 448]) |
question: ['Is the dog on a grassy surface?'], 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([1, 3, 448, 448]) knan debug pixel values shape |
tensor([8.4275e-01, 1.7419e-02, 1.3758e-01, 1.1187e-03, 1.2392e-04, 3.9829e-04, |
2.7348e-05, 5.8352e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.4275e-01, 1.7419e-02, 1.3758e-01, 1.1187e-03, 1.2392e-04, 3.9829e-04, |
2.7348e-05, 5.8352e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8428, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.1376, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0197, device='cuda:3', grad_fn=<SubBackward0>)} |
tensor([8.7070e-01, 2.2576e-02, 1.0399e-01, 1.0545e-03, 1.1138e-04, 3.2085e-04, |
2.7273e-05, 1.2198e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.7070e-01, 2.2576e-02, 1.0399e-01, 1.0545e-03, 1.1138e-04, 3.2085e-04, |
2.7273e-05, 1.2198e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
question: ['Does the laptop on the right display the tiles from the operating system Windows?'], responses:['No'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8707, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.1040, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0253, device='cuda:1', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the right image show a large group of animals on a road?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
question: ['How many dogs are standing in the grass?'], responses:['2'] |
[('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']] |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
question: ['Does the right image show a large group of animals on a road?'], 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']] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.