text
stringlengths 0
1.16k
|
|---|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.7914e-01, 1.9911e-02, 1.9700e-01, 1.7878e-03, 1.0029e-04, 5.1458e-04,
|
3.7752e-05, 1.5059e-03], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7791, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1970, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0239, device='cuda:3', grad_fn=<DivBackward0>)}
|
question: ['How many kids are holding pillows 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']]
|
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} > 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([3, 3, 448, 448])
|
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
|
question: ['How many animals are in the image?'], responses:['1']
|
tensor([8.1682e-01, 1.8225e-01, 2.7560e-05, 8.3327e-05, 1.4315e-04, 3.6947e-04,
|
2.6904e-04, 2.9660e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.1682e-01, 1.8225e-01, 2.7560e-05, 8.3327e-05, 1.4315e-04, 3.6947e-04,
|
2.6904e-04, 2.9660e-05], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1823, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8168, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:0', grad_fn=<DivBackward0>)}
|
[('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']]
|
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([3, 3, 448, 448])
|
tensor([0.3966, 0.1003, 0.0396, 0.4339, 0.0168, 0.0058, 0.0063, 0.0006],
|
device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.3966, 0.1003, 0.0396, 0.4339, 0.0168, 0.0058, 0.0063, 0.0006],
|
device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3966, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6034, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
|
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
|
question: ['How many hyenas 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']]
|
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
tensor([9.7378e-01, 4.3707e-03, 1.8219e-03, 6.7012e-04, 8.6058e-04, 6.0760e-04,
|
1.7836e-02, 5.3070e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7378e-01, 4.3707e-03, 1.8219e-03, 6.7012e-04, 8.6058e-04, 6.0760e-04,
|
1.7836e-02, 5.3070e-05], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0262, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9738, 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 zebras are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
|
tensor([8.4332e-01, 2.6274e-02, 9.9711e-03, 4.5535e-03, 5.5048e-03, 2.9246e-03,
|
1.0722e-01, 2.3536e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.4332e-01, 2.6274e-02, 9.9711e-03, 4.5535e-03, 5.5048e-03, 2.9246e-03,
|
1.0722e-01, 2.3536e-04], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1072, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8928, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
|
question: ['How many zebras 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']]
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
tensor([5.6221e-01, 2.3293e-02, 4.1132e-01, 1.5315e-03, 1.2447e-04, 6.0886e-04,
|
1.4363e-04, 7.6975e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6221e-01, 2.3293e-02, 4.1132e-01, 1.5315e-03, 1.2447e-04, 6.0886e-04,
|
1.4363e-04, 7.6975e-04], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5622, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.4113, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0265, device='cuda:2', grad_fn=<SubBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='Is the dog facing left?')
|
FINAL_ANSWER=RESULT(var=ANSWER0)
|
torch.Size([13, 3, 448, 448])
|
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.92 GiB. GPU 2 has a total capacty of 44.34 GiB of which 972.94 MiB is free. Including non-PyTorch memory, this process has 43.38 GiB memory in use. Of the allocated memory 40.77 GiB is allocated by PyTorch, and 1.97 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
|
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
|
ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
|
tensor([9.6361e-01, 6.4921e-03, 2.3884e-03, 1.0255e-03, 1.3185e-03, 9.8591e-04,
|
2.4124e-02, 5.7483e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6361e-01, 6.4921e-03, 2.3884e-03, 1.0255e-03, 1.3185e-03, 9.8591e-04,
|
2.4124e-02, 5.7483e-05], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9636, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0364, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
|
[2024-10-22 17:22:11,020] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.32 | optimizer_step: 0.33
|
[2024-10-22 17:22:11,020] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10812.84 | backward_microstep: 14712.44 | backward_inner_microstep: 10220.94 | backward_allreduce_microstep: 4490.95 | step_microstep: 7.87
|
[2024-10-22 17:22:11,021] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10812.86 | backward: 14712.43 | backward_inner: 10221.40 | backward_allreduce: 4490.93 | step: 7.89
|
0%| | 9/2424 [03:43<16:22:28, 24.41s/it]Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=LEFT,question='Is there at least one dog standing on all fours in the image?')
|
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
|
Registering VQA_lavis step
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.