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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
tensor([7.9498e-01, 1.1453e-01, 2.4008e-02, 5.4101e-02, 8.0272e-03, 1.9098e-03, |
2.3736e-03, 6.8425e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.9498e-01, 1.1453e-01, 2.4008e-02, 5.4101e-02, 8.0272e-03, 1.9098e-03, |
2.3736e-03, 6.8425e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.7950, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2050, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([0.1630, 0.1254, 0.1603, 0.1617, 0.1602, 0.0347, 0.0914, 0.1034], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
10 ************* |
['10', '11', '12', '8', '9', '26', '13', '6'] tensor([0.1630, 0.1254, 0.1603, 0.1617, 0.1602, 0.0347, 0.0914, 0.1034], |
device='cuda:2', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([9.8959e-01, 1.4607e-03, 1.2284e-03, 2.0534e-04, 9.4250e-04, 2.7740e-04, |
1.2083e-03, 5.0849e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8959e-01, 1.4607e-03, 1.2284e-03, 2.0534e-04, 9.4250e-04, 2.7740e-04, |
1.2083e-03, 5.0849e-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(0.9896, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0104, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is a rodent eating pasta in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
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 3.88 GiB is free. Including non-PyTorch memory, this process has 40.44 GiB memory in use. Of the allocated memory 38.10 GiB is allocated by PyTorch, and 1.71 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 17:28:57,050] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.28 | optimizer_step: 0.32 |
[2024-10-22 17:28:57,051] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8941.20 | backward_microstep: 10928.38 | backward_inner_microstep: 8522.65 | backward_allreduce_microstep: 2405.63 | step_microstep: 7.50 |
[2024-10-22 17:28:57,051] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8941.21 | backward: 10928.37 | backward_inner: 8522.67 | backward_allreduce: 2405.61 | step: 7.52 |
1%| | 26/2424 [10:29<15:13:59, 22.87s/it]Registering VQA_lavis step |
Registering EVAL stepRegistering VQA_lavis step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
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 sets of measuring utensils are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many rolls of paper towels are in the package?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many puppies are lying down in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many objects are standing straight up in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 9') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many puppies are lying down in the image?'], responses:['3'] |
question: ['How many rolls of paper towels are in the package?'], responses:['13'] |
question: ['How many objects are standing straight up in the image?'], responses:['5'] |
[('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']] |
[('13', 0.12770862924411772), ('14', 0.12534395389083108), ('21', 0.12493249815266858), ('12', 0.12491814916612239), ('11', 0.12461120999761086), ('27', 0.12444592740053353), ('15', 0.12414436865504584), ('29', 0.1238952634930699)] |
[['13', '14', '21', '12', '11', '27', '15', '29']] |
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)] |
[['5', '8', '4', '6', '3', '7', '11', '9']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
question: ['How many sets of measuring utensils are in the image?'], responses:['5'] |
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)] |
[['5', '8', '4', '6', '3', '7', '11', '9']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
tensor([0.3802, 0.0662, 0.0936, 0.0178, 0.0022, 0.4309, 0.0081, 0.0010], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.3802, 0.0662, 0.0936, 0.0178, 0.0022, 0.4309, 0.0081, 0.0010], |
device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([0.4789, 0.0834, 0.0221, 0.2482, 0.0838, 0.0110, 0.0631, 0.0094], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
13 ************* |
['13', '14', '21', '12', '11', '27', '15', '29'] tensor([0.4789, 0.0834, 0.0221, 0.2482, 0.0838, 0.0110, 0.0631, 0.0094], |
device='cuda:3', grad_fn=<SelectBackward0>) |
tensor([0.2074, 0.0852, 0.1650, 0.1714, 0.1257, 0.1376, 0.0314, 0.0763], |
device='cuda:0', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2074, 0.0852, 0.1650, 0.1714, 0.1257, 0.1376, 0.0314, 0.0763], |
device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
最后的概率分布为: {True: tensor(0.0936, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9064, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
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