text stringlengths 0 1.16k |
|---|
tensor([6.6743e-01, 3.3160e-01, 6.2741e-05, 1.2985e-04, 7.4728e-05, 1.4166e-04, |
5.1933e-04, 4.7270e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.6743e-01, 3.3160e-01, 6.2741e-05, 1.2985e-04, 7.4728e-05, 1.4166e-04, |
5.1933e-04, 4.7270e-05], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3316, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6674, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
question: ['Is there a flying bird in the image?'], 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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([8.1667e-01, 1.9891e-02, 1.6082e-01, 1.1578e-03, 8.4228e-05, 2.8979e-04, |
4.7773e-05, 1.0398e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.1667e-01, 1.9891e-02, 1.6082e-01, 1.1578e-03, 8.4228e-05, 2.8979e-04, |
4.7773e-05, 1.0398e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8167, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.1608, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0225, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([6.2514e-01, 3.7312e-01, 6.1991e-05, 1.3154e-04, 1.2416e-04, 1.0271e-03, |
3.1753e-04, 8.0362e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.2514e-01, 3.7312e-01, 6.1991e-05, 1.3154e-04, 1.2416e-04, 1.0271e-03, |
3.1753e-04, 8.0362e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.3731, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.6251, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0017, device='cuda:3', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many dogs are in the image?'], responses:['3'] |
question: ['How many pandas are in the image?'], responses:['1'] |
[('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']] |
[('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']] |
tensor([8.9916e-01, 1.9814e-02, 7.8530e-02, 1.6341e-03, 9.6868e-05, 3.2054e-04, |
5.0132e-05, 3.9163e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9916e-01, 1.9814e-02, 7.8530e-02, 1.6341e-03, 9.6868e-05, 3.2054e-04, |
5.0132e-05, 3.9163e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8992, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0785, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0223, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the image contain a flower?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
question: ['Does the image contain a flower?'], 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']] |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([0.5540, 0.1915, 0.0178, 0.0484, 0.0039, 0.1678, 0.0157, 0.0010], |
device='cuda:0', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.5540, 0.1915, 0.0178, 0.0484, 0.0039, 0.1678, 0.0157, 0.0010], |
device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7396, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2604, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the baby seal lying down?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
tensor([9.4175e-01, 1.0139e-02, 4.2268e-03, 1.4144e-03, 2.1923e-03, 1.3056e-03, |
3.8867e-02, 1.0060e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4175e-01, 1.0139e-02, 4.2268e-03, 1.4144e-03, 2.1923e-03, 1.3056e-03, |
3.8867e-02, 1.0060e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0389, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9611, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is there a barber pole in the image?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([1, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is there a barber pole in the image?'], 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']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
tensor([4.8372e-01, 5.1493e-01, 2.3439e-05, 1.9839e-04, 4.8162e-04, 1.7930e-04, |
4.3626e-04, 3.7234e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([4.8372e-01, 5.1493e-01, 2.3439e-05, 1.9839e-04, 4.8162e-04, 1.7930e-04, |
4.3626e-04, 3.7234e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5149, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.4837, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0014, device='cuda:3', grad_fn=<SubBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 0 has a total capacty of 44.34 GiB of which 932.94 MiB is free. Including non-PyTorch memory, this process has 43.41 GiB memory in use. Of the allocated memory 40.69 GiB is allocated by PyTorch, and 2.11 GiB is reserved by PyTorch but unalloc... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 1.17 GiB. GPU 2 has a total capacty of 44.34 GiB of which 186.94 MiB is free. Including non-PyTorch memory, this process has 44.14 GiB memory in use. Of the allocated memory 40.99 GiB is allocated by PyTorch, and 2.52 GiB is reserved by PyTorch but unalloc... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
ζεηζ¦ηεεΈδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
[2024-10-22 17:28:13,055] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.32 |
[2024-10-22 17:28:13,055] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12795.50 | backward_microstep: 11348.89 | backward_inner_microstep: 10722.72 | backward_allreduce_microstep: 625.82 | step_microstep: 7.52 |
[2024-10-22 17:28:13,055] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12795.52 | backward: 11348.88 | backward_inner: 10722.96 | backward_allreduce: 625.81 | step: 7.53 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.