text stringlengths 0 1.16k |
|---|
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9969e-01, 3.0942e-04, 8.6571e-08, 7.3918e-08, 8.7714e-11, 8.5569e-08, |
3.5349e-10, 3.5464e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9997, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0003, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-24 10:25:55,132] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.25 | optimizer_step: 0.31 |
[2024-10-24 10:25:55,132] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7032.93 | backward_microstep: 6782.98 | backward_inner_microstep: 6776.63 | backward_allreduce_microstep: 6.21 | step_microstep: 7.46 |
[2024-10-24 10:25:55,132] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7032.95 | backward: 6782.97 | backward_inner: 6776.69 | backward_allreduce: 6.02 | step: 7.47 |
98%|ββββββββββ| 4747/4844 [19:44:38<21:44, 13.45s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many animals are eating in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many birds are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
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 |
torch.Size([3, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Does the structure in the image appear to have been hewn from the mountain?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Does the image in the right television display a person?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many birds are in the image?'], responses:['1'] |
question: ['Does the structure in the image appear to have been hewn from the mountain?'], responses:['yes'] |
[('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']] |
[('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 |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 848 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
question: ['How many animals are eating in the image?'], responses:['1'] |
question: ['Does the image in the right television display a person?'], responses:['no'] |
[('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: 3, images per sample: 3.0, dynamic token length: 846 |
[('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']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 846 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 846 |
tensor([1.0000e+00, 2.2590e-10, 5.7562e-11, 1.9321e-10, 7.0342e-11, 1.1668e-08, |
3.5614e-09, 3.2401e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.2590e-10, 5.7562e-11, 1.9321e-10, 7.0342e-11, 1.1668e-08, |
3.5614e-09, 3.2401e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.2539e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 5.2129e-09, 1.4307e-08, 4.9003e-09, 3.2773e-11, 7.3760e-12, |
2.3006e-11, 3.9196e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.2129e-09, 1.4307e-08, 4.9003e-09, 3.2773e-11, 7.3760e-12, |
2.3006e-11, 3.9196e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.4307e-08, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.4307e-08, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many rings are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 8') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
question: ['How many pandas 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([3, 3, 448, 448]) knan debug pixel values shape |
question: ['How many rings 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([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348 |
tensor([1.0203e-05, 5.9844e-01, 1.2344e-02, 7.6130e-04, 3.8801e-01, 2.8898e-04, |
8.0257e-05, 6.5079e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
babies ************* |
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([1.0203e-05, 5.9844e-01, 1.2344e-02, 7.6130e-04, 3.8801e-01, 2.8898e-04, |
8.0257e-05, 6.5079e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 6.1405e-10, 1.5526e-10, 2.0249e-10, 1.6652e-10, 1.4525e-08, |
6.3488e-09, 4.8773e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.1405e-10, 1.5526e-10, 2.0249e-10, 1.6652e-10, 1.4525e-08, |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.