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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
tensor([9.8511e-01, 1.4125e-03, 2.2239e-03, 3.4409e-04, 1.3988e-03, 3.6797e-04, |
4.6263e-04, 8.6851e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8511e-01, 1.4125e-03, 2.2239e-03, 3.4409e-04, 1.3988e-03, 3.6797e-04, |
4.6263e-04, 8.6851e-03], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9851, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0149, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([9.7082e-01, 5.9555e-03, 2.9016e-03, 1.5491e-03, 1.7591e-03, 1.2266e-03, |
1.5692e-02, 9.8459e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7082e-01, 5.9555e-03, 2.9016e-03, 1.5491e-03, 1.7591e-03, 1.2266e-03, |
1.5692e-02, 9.8459e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
{True: tensor(0.0157, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9843, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
question: ['How many wolves 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: 1861 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([9.6839e-01, 3.1129e-02, 2.1214e-05, 4.3962e-05, 1.9677e-04, 8.5077e-05, |
1.0771e-04, 2.3391e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.6839e-01, 3.1129e-02, 2.1214e-05, 4.3962e-05, 1.9677e-04, 8.5077e-05, |
1.0771e-04, 2.3391e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0311, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9684, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0005, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([0.4085, 0.0495, 0.1170, 0.2526, 0.0207, 0.1193, 0.0062, 0.0261], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.4085, 0.0495, 0.1170, 0.2526, 0.0207, 0.1193, 0.0062, 0.0261], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.4085, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5915, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How are the pencils laying?') |
ANSWER1=EVAL(expr='{ANSWER0} == "points facing down and slightly left"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 2 has a total capacty of 44.34 GiB of which 778.94 MiB is free. Including non-PyTorch memory, this process has 43.56 GiB memory in use. Of the allocated memory 40.78 GiB is allocated by PyTorch, and 2.15 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' |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
[2024-10-22 17:27:48,886] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.29 | optimizer_step: 0.32 |
[2024-10-22 17:27:48,886] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10265.85 | backward_microstep: 13381.08 | backward_inner_microstep: 9813.65 | backward_allreduce_microstep: 3567.36 | step_microstep: 7.96 |
[2024-10-22 17:27:48,887] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10265.87 | backward: 13381.07 | backward_inner: 9813.67 | backward_allreduce: 3567.16 | step: 7.97 |
1%| | 23/2424 [09:21<16:06:44, 24.16s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
ANSWER0=VQA(image=RIGHT,question='How many people are wearing graduation caps in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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='Do the sails in the image have the color white on them?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Does the dessert contain any berries?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is there any animal in the water?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Do the sails in the image have the color white on them?'], responses:['no'] |
question: ['Does the dessert contain any berries?'], responses:['yes'] |
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many people are wearing graduation caps 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']] |
question: ['Is there any animal in the water?'], responses:['no'] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)] |
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([9.2803e-01, 7.1562e-02, 3.2194e-06, 3.4914e-05, 5.4308e-05, 9.8693e-05, |
2.0402e-04, 1.3851e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
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