text stringlengths 0 1.16k |
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1.3381e-01, 2.4893e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.1644e-01, 2.3941e-02, 1.0624e-02, 3.9043e-03, 6.0502e-03, 4.9831e-03, |
1.3381e-01, 2.4893e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8164, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1836, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
question: ['How many dogs are in the image?'], responses:['3'] |
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)] |
[['3', '4', '1', '5', '8', '2', '6', '12']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([9.1900e-01, 8.0300e-02, 1.5652e-05, 7.5536e-05, 1.7854e-04, 1.7527e-04, |
2.1692e-04, 3.7212e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.1900e-01, 8.0300e-02, 1.5652e-05, 7.5536e-05, 1.7854e-04, 1.7527e-04, |
2.1692e-04, 3.7212e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0803, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9190, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many golf balls are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
tensor([9.8758e-01, 1.6317e-03, 9.8126e-04, 2.8854e-04, 1.3306e-03, 3.4758e-04, |
3.8276e-03, 4.0147e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8758e-01, 1.6317e-03, 9.8126e-04, 2.8854e-04, 1.3306e-03, 3.4758e-04, |
3.8276e-03, 4.0147e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0.9876, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0124, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many zebras are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['How many golf balls 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 |
tensor([6.0776e-01, 3.8336e-02, 2.0523e-02, 7.8919e-03, 8.3188e-04, 3.2098e-01, |
3.2473e-03, 4.2559e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([6.0776e-01, 3.8336e-02, 2.0523e-02, 7.8919e-03, 8.3188e-04, 3.2098e-01, |
3.2473e-03, 4.2559e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6585, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.3415, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many shoes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.86 GiB. GPU 1 has a total capacty of 44.34 GiB of which 5.53 GiB is free. Including non-PyTorch memory, this process has 38.80 GiB memory in use. Of the allocated memory 36.24 GiB is allocated by PyTorch, and 1.92 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([8.9908e-01, 4.2042e-02, 1.0303e-02, 4.2058e-02, 3.7902e-03, 1.4834e-03, |
1.1197e-03, 1.2520e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.9908e-01, 4.2042e-02, 1.0303e-02, 4.2058e-02, 3.7902e-03, 1.4834e-03, |
1.1197e-03, 1.2520e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8991, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1009, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 3 has a total capacty of 44.34 GiB of which 3.45 GiB is free. Including non-PyTorch memory, this process has 40.88 GiB memory in use. Of the allocated memory 38.10 GiB is allocated by PyTorch, and 2.23 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 17:30:19,021] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.30 | optimizer_step: 0.32 |
[2024-10-22 17:30:19,021] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7642.63 | backward_microstep: 13619.16 | backward_inner_microstep: 7208.20 | backward_allreduce_microstep: 6410.71 | step_microstep: 7.82 |
[2024-10-22 17:30:19,021] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7642.65 | backward: 13619.15 | backward_inner: 7208.30 | backward_allreduce: 6410.70 | step: 7.83 |
1%| | 30/2424 [11:51<13:59:28, 21.04s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many weights does the rack hold?') |
ANSWER1=EVAL(expr='{ANSWER0} > 12') |
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([1, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='Is the vending machine green?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the grassy area in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many dogs are lying down?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many weights does the rack hold?'], responses:['12'] |
[('12', 0.1271623397239889), ('11', 0.12513993889798333), ('10', 0.1250472536472656), ('8', 0.12474319370676636), ('6', 0.12462998196332449), ('26', 0.12450301801207538), ('47', 0.1243904847365581), ('13', 0.12438378931203788)] |
[['12', '11', '10', '8', '6', '26', '47', '13']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
question: ['Is the vending machine green?'], responses:['yes'] |
[('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 |
tensor([0.3176, 0.0514, 0.2312, 0.1730, 0.0899, 0.0770, 0.0208, 0.0391], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
12 ************* |
['12', '11', '10', '8', '6', '26', '47', '13'] tensor([0.3176, 0.0514, 0.2312, 0.1730, 0.0899, 0.0770, 0.0208, 0.0391], |
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