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torch.Size([7, 3, 448, 448]) |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 0 has a total capacty of 44.34 GiB of which 2.18 GiB is free. Including non-PyTorch memory, this process has 42.15 GiB memory in use. Of the allocated memory 40.53 GiB is allocated by PyTorch, and 1020.53 MiB 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' |
ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
tensor([9.0192e-01, 1.9313e-02, 8.0502e-03, 2.7776e-03, 4.1757e-03, 2.2177e-03, |
6.1376e-02, 1.6628e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.0192e-01, 1.9313e-02, 8.0502e-03, 2.7776e-03, 4.1757e-03, 2.2177e-03, |
6.1376e-02, 1.6628e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0614, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9386, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 1.17 GiB. GPU 3 has a total capacty of 44.34 GiB of which 344.94 MiB is free. Including non-PyTorch memory, this process has 43.99 GiB memory in use. Of the allocated memory 40.98 GiB is allocated by PyTorch, and 2.45 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' |
tensor([9.6061e-01, 7.8066e-03, 3.4634e-03, 1.7931e-03, 2.2344e-03, 1.3802e-03, |
2.2591e-02, 1.2513e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6061e-01, 7.8066e-03, 3.4634e-03, 1.7931e-03, 2.2344e-03, 1.3802e-03, |
2.2591e-02, 1.2513e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0394, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9606, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
[2024-10-22 17:27:01,215] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.27 | optimizer_step: 0.32 |
[2024-10-22 17:27:01,215] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12381.86 | backward_microstep: 13029.20 | backward_inner_microstep: 10766.75 | backward_allreduce_microstep: 2262.13 | step_microstep: 7.72 |
[2024-10-22 17:27:01,215] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12381.88 | backward: 13029.19 | backward_inner: 10766.78 | backward_allreduce: 2262.10 | step: 7.73 |
1%| | 21/2424 [08:33<16:22:11, 24.52s/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 sledding dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
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 |
ANSWER0=VQA(image=RIGHT,question='Is there a female wearing a pink bikini?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the animal in the image on the right standing on all fours?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 5') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([11, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is there a female wearing a pink bikini?'], 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 |
question: ['Is the animal in the image on the right standing on all fours?'], responses:['no'] |
[('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']] |
tensor([8.5146e-01, 2.3168e-02, 1.2267e-01, 9.3737e-04, 1.1642e-04, 6.8445e-04, |
3.9597e-05, 9.2298e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.5146e-01, 2.3168e-02, 1.2267e-01, 9.3737e-04, 1.1642e-04, 6.8445e-04, |
3.9597e-05, 9.2298e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
question: ['How many sledding dogs are in the image?'], responses:['2'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8515, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.1227, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0259, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many boats are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('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([11, 3, 448, 448]) knan debug pixel values shape |
torch.Size([13, 3, 448, 448]) |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['How many dogs are in the image?'], responses:['5'] |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891 |
[('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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892 |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891 |
question: ['How many boats are in the image?'], responses:['1'] |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2891 |
[('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']] |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892 |
dynamic ViT batch size: 11, images per sample: 11.0, dynamic token length: 2892 |
tensor([6.1779e-01, 3.8137e-01, 9.2330e-06, 1.0100e-04, 1.2259e-04, 3.8036e-04, |
2.0958e-04, 1.5572e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.1779e-01, 3.8137e-01, 9.2330e-06, 1.0100e-04, 1.2259e-04, 3.8036e-04, |
2.0958e-04, 1.5572e-05], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3814, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.6178, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is there a pug lying on its back in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
question: ['Is there a pug lying on its back in the image?'], responses:['no'] |
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