text
stringlengths 0
1.16k
|
|---|
question: ['What color is the purse in the image?'], responses:['white']
|
[('white', 0.12741698904857263), ('black', 0.12562195821587463), ('purple', 0.12482758531934457), ('orange', 0.12467593918870701), ('maroon', 0.12456097552653009), ('color', 0.12448461429606533), ('brown', 0.12421598902969112), ('dark', 0.12419594937521464)]
|
[['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark']]
|
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
|
tensor([0.5853, 0.0655, 0.0331, 0.0174, 0.0426, 0.0039, 0.2427, 0.0095],
|
device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
white *************
|
['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark'] tensor([0.5853, 0.0655, 0.0331, 0.0174, 0.0426, 0.0039, 0.2427, 0.0095],
|
device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
|
question: ['What color is the keyboard?'], responses:['black']
|
[('black', 0.12706825260511387), ('white', 0.12527812565897103), ('dark', 0.1250491849195085), ('purple', 0.12486259083591467), ('orange', 0.12479002203010545), ('red', 0.12434049404478545), ('maroon', 0.12433890776852753), ('blue', 0.12427242213707339)]
|
[['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue']]
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
tensor([0.8472, 0.0290, 0.0309, 0.0049, 0.0008, 0.0839, 0.0024, 0.0009],
|
device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
3 *************
|
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.8472, 0.0290, 0.0309, 0.0049, 0.0008, 0.0839, 0.0024, 0.0009],
|
device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8472, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1528, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many seals are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
question: ['How many seals are in the image?'], responses:['1']
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
[('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
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
tensor([0.2743, 0.2215, 0.2214, 0.0611, 0.1007, 0.0732, 0.0463, 0.0015],
|
device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.2743, 0.2215, 0.2214, 0.0611, 0.1007, 0.0732, 0.0463, 0.0015],
|
device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6646, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3354, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many baboons are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
question: ['How many baboons 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']]
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
|
tensor([4.3334e-01, 1.0954e-01, 2.5216e-02, 3.1070e-03, 6.3759e-03, 1.5568e-03,
|
4.2078e-01, 8.4552e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([4.3334e-01, 1.0954e-01, 2.5216e-02, 3.1070e-03, 6.3759e-03, 1.5568e-03,
|
4.2078e-01, 8.4552e-05], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
|
{True: tensor(0.4208, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5792, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
|
tensor([0.7898, 0.0870, 0.0364, 0.0064, 0.0083, 0.0253, 0.0210, 0.0258],
|
device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
black *************
|
['black', 'white', 'dark', 'purple', 'orange', 'red', 'maroon', 'blue'] tensor([0.7898, 0.0870, 0.0364, 0.0064, 0.0083, 0.0253, 0.0210, 0.0258],
|
device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='Does the image contain a tree house?')
|
FINAL_ANSWER=RESULT(var=ANSWER0)
|
torch.Size([7, 3, 448, 448])
|
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.20 GiB. GPU 0 has a total capacty of 44.34 GiB of which 3.20 GiB is free. Including non-PyTorch memory, this process has 41.13 GiB memory in use. Of the allocated memory 38.63 GiB is allocated by PyTorch, and 1.88 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([9.8177e-01, 2.0182e-03, 1.5950e-03, 6.8146e-04, 1.2954e-03, 1.2288e-03,
|
2.0303e-03, 9.3771e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
0 *************
|
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8177e-01, 2.0182e-03, 1.5950e-03, 6.8146e-04, 1.2954e-03, 1.2288e-03,
|
2.0303e-03, 9.3771e-03], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0.9818, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0182, device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 3 has a total capacty of 44.34 GiB of which 1.76 GiB is free. Including non-PyTorch memory, this process has 42.57 GiB memory in use. Of the allocated memory 40.55 GiB is allocated by PyTorch, and 1.46 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}
|
[2024-10-22 17:20:56,883] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.32
|
[2024-10-22 17:20:56,884] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 11919.90 | backward_microstep: 11580.66 | backward_inner_microstep: 10141.59 | backward_allreduce_microstep: 1438.67 | step_microstep: 7.65
|
[2024-10-22 17:20:56,884] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 11919.92 | backward: 11580.64 | backward_inner: 10141.75 | backward_allreduce: 1438.65 | step: 7.66
|
0%| | 6/2424 [02:29<15:57:53, 23.77s/it]Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=RIGHT,question='How many sets of measuring utensils are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=RIGHT,question='Does the image show a hound standing on thick green grass?')
|
ANSWER1=RESULT(var=ANSWER0)
|
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.