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>) |
最后的概率分布为: {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>) |
最后的概率分布为: {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>) |
最后的概率分布为: {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>) |
最后的概率分布为: {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 unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
最后的概率分布为: {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>) |
最后的概率分布为: {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 unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
最后的概率分布为: {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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