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ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['How many Canadian geese 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 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
tensor([0.9615, 0.0087, 0.0077, 0.0019, 0.0041, 0.0019, 0.0023, 0.0118], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([0.9615, 0.0087, 0.0077, 0.0019, 0.0041, 0.0019, 0.0023, 0.0118], |
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(0.9615, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0385, device='cuda:2', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
ANSWER0=VQA(image=LEFT,question='Is there a front awning in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 2 has a total capacty of 44.34 GiB of which 414.94 MiB is free. Including non-PyTorch memory, this process has 43.92 GiB memory in use. Of the allocated memory 40.52 GiB is allocated by PyTorch, and 2.77 GiB is reserved by PyTorch but unalloc... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
tensor([8.1799e-01, 7.6144e-02, 4.6174e-02, 3.8271e-02, 1.1318e-02, 5.6900e-03, |
4.1587e-03, 2.5036e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.1799e-01, 7.6144e-02, 4.6174e-02, 3.8271e-02, 1.1318e-02, 5.6900e-03, |
4.1587e-03, 2.5036e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.8180, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1820, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)} |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
[2024-10-22 17:20:33,357] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.25 | optimizer_step: 0.32 |
[2024-10-22 17:20:33,357] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12181.19 | backward_microstep: 11681.89 | backward_inner_microstep: 11676.95 | backward_allreduce_microstep: 4.85 | step_microstep: 95.61 |
[2024-10-22 17:20:33,357] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12181.21 | backward: 11681.88 | backward_inner: 11676.98 | backward_allreduce: 4.83 | step: 95.63 |
0%| | 5/2424 [02:05<16:03:20, 23.89s/it]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 |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is the fragrance bottle a different color than its box?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=RIGHT,question='How many of the ape's feet can be seen in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Do boats float in the water on a sunny day in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many drawers are on the cabinet?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([5, 3, 448, 448]) |
question: ['How many drawers are on the cabinet?'], 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']] |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
question: ['How many of the ape'], 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']] |
question: ['Do boats float in the water on a sunny day in the image?'], responses:['no'] |
question: ['Is the fragrance bottle a different color than its box?'], 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([13, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
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 |
tensor([0.1837, 0.0978, 0.1799, 0.1981, 0.1306, 0.1129, 0.0346, 0.0623], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
6 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.1837, 0.0978, 0.1799, 0.1981, 0.1306, 0.1129, 0.0346, 0.0623], |
device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.1306, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.8694, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
question: ['How many people 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 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
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