text stringlengths 0 1.16k |
|---|
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 unallo... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
ๆๅ็ๆฆ็ๅๅธไธบ: {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>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {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 unalloc... |
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>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {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>)} |
ๆๅ็ๆฆ็ๅๅธไธบ: {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'] |
ๆๅ็ๆฆ็ๅๅธไธบ: {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>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {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'] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.