text stringlengths 0 1.16k |
|---|
tensor([1.0000e+00, 6.7445e-09, 5.6671e-11, 7.4569e-09, 5.8140e-11, 7.5077e-11, |
4.0519e-11, 3.5186e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.7445e-09, 5.6671e-11, 7.4569e-09, 5.8140e-11, 7.5077e-11, |
4.0519e-11, 3.5186e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(5.6671e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-5.6671e-11, device='cuda:2', grad_fn=<SubBackward0>)} |
[2024-10-24 09:50:39,792] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.37 | optimizer_step: 0.33 |
[2024-10-24 09:50:39,793] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4436.91 | backward_microstep: 9432.60 | backward_inner_microstep: 4188.11 | backward_allreduce_microstep: 5244.40 | step_microstep: 8.04 |
[2024-10-24 09:50:39,793] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4436.91 | backward: 9432.59 | backward_inner: 4188.13 | backward_allreduce: 5244.34 | step: 8.06 |
95%|ββββββββββ| 4606/4844 [19:09:23<59:23, 14.97s/it] Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many sting rays are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 5') |
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=LEFT,question='Is there a tall window near a book case in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is there a human hand near a laptop in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many animals 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([7, 3, 448, 448]) knan debug pixel values shape |
question: ['Is there a tall window near a book case in the image?'], responses:['no'] |
question: ['How many sting rays are in the image?'], responses:['50'] |
question: ['Is there a human hand near a laptop in the image?'], responses:['yes'] |
[('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']] |
[('50', 0.12746329354121594), ('51', 0.12494443111915052), ('60', 0.12471995183640609), ('55', 0.12470016949940634), ('54', 0.12460076157014638), ('52', 0.12454269500997545), ('44', 0.12453681395238846), ('48', 0.1244918834713108)] |
[['50', '51', '60', '55', '54', '52', '44', '48']] |
[('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([13, 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: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
tensor([1.0000e+00, 4.2052e-08, 8.7801e-09, 2.9649e-07, 1.3204e-09, 2.5852e-09, |
5.0912e-09, 7.6861e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 4.2052e-08, 8.7801e-09, 2.9649e-07, 1.3204e-09, 2.5852e-09, |
5.0912e-09, 7.6861e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.5709e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many elephants are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
question: ['How many elephants 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
tensor([1.0000e+00, 7.8407e-09, 1.7630e-09, 1.2476e-07, 1.4607e-09, 9.8332e-09, |
4.8472e-10, 6.6223e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.8407e-09, 1.7630e-09, 1.2476e-07, 1.4607e-09, 9.8332e-09, |
4.8472e-10, 6.6223e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.5559e-09, 5.1378e-07, 5.5911e-10, 8.6506e-10, 2.0523e-07, |
3.6895e-09, 5.0437e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.5559e-09, 5.1378e-07, 5.5911e-10, 8.6506e-10, 2.0523e-07, |
3.6895e-09, 5.0437e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.7630e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1745e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the dog inside?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.5559e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the image show an oblong bowl-shaped sink?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
tensor([0.9090, 0.0028, 0.0344, 0.0367, 0.0021, 0.0015, 0.0058, 0.0077], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
50 ************* |
['50', '51', '60', '55', '54', '52', '44', '48'] tensor([0.9090, 0.0028, 0.0344, 0.0367, 0.0021, 0.0015, 0.0058, 0.0077], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.