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[('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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
[('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']] |
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 |
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 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
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([0.2049, 0.1306, 0.1375, 0.1940, 0.0490, 0.1547, 0.0317, 0.0975], |
device='cuda:0', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2049, 0.1306, 0.1375, 0.1940, 0.0490, 0.1547, 0.0317, 0.0975], |
device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.0490, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9510, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['How many monkeys 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([13, 3, 448, 448]) knan debug pixel values shape |
tensor([0.6511, 0.2027, 0.0995, 0.0123, 0.0030, 0.0085, 0.0070, 0.0160], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
metal ************* |
['metal', 'glass', 'steel', 'iron', 'rust', 'fur', 'stone', 'wine'] tensor([0.6511, 0.2027, 0.0995, 0.0123, 0.0030, 0.0085, 0.0070, 0.0160], |
device='cuda:2', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the animal in the image lying down?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 2 has a total capacty of 44.34 GiB of which 5.43 GiB is free. Including non-PyTorch memory, this process has 38.89 GiB memory in use. Of the allocated memory 36.21 GiB is allocated by PyTorch, and 2.06 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([8.6105e-01, 8.5310e-02, 1.4825e-02, 3.1360e-02, 4.8097e-03, 1.2159e-03, |
1.3780e-03, 5.5515e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.6105e-01, 8.5310e-02, 1.4825e-02, 3.1360e-02, 4.8097e-03, 1.2159e-03, |
1.3780e-03, 5.5515e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.8924, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1076, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
question: ['How many wolves are in the image?'], responses:['1'] |
[('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([3, 3, 448, 448]) knan debug pixel values shape |
tensor([9.8173e-01, 8.4937e-03, 2.5904e-03, 6.2139e-03, 4.9400e-04, 2.8797e-04, |
1.6950e-04, 2.1962e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.8173e-01, 8.4937e-03, 2.5904e-03, 6.2139e-03, 4.9400e-04, 2.8797e-04, |
1.6950e-04, 2.1962e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.9817, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0183, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many pillows are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 1 has a total capacty of 44.34 GiB of which 122.94 MiB is free. Including non-PyTorch memory, this process has 44.21 GiB memory in use. Of the allocated memory 40.52 GiB is allocated by PyTorch, and 3.05 GiB is reserved by PyTorch but unalloc... |
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([9.7128e-01, 4.6403e-03, 1.8164e-03, 7.0602e-04, 8.8375e-04, 4.9190e-04, |
2.0158e-02, 2.5730e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7128e-01, 4.6403e-03, 1.8164e-03, 7.0602e-04, 8.8375e-04, 4.9190e-04, |
2.0158e-02, 2.5730e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.9713, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0287, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-22 17:32:18,717] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.30 | optimizer_step: 0.33 |
[2024-10-22 17:32:18,717] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8272.64 | backward_microstep: 15834.53 | backward_inner_microstep: 7866.39 | backward_allreduce_microstep: 7967.71 | step_microstep: 7.59 |
[2024-10-22 17:32:18,717] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8272.66 | backward: 15834.52 | backward_inner: 7866.71 | backward_allreduce: 7967.68 | step: 7.61 |
1%|▏ | 35/2424 [13:50<15:44:11, 23.71s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is the ferret seen coming out of a hole?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many striped pillows are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Are there white flowers in a vase in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many wine glasses are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([5, 3, 448, 448]) |
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