text stringlengths 0 1.16k |
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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 unallocat... |
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 unallocat... |
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 |
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