text stringlengths 0 1.16k |
|---|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([9.9441e-01, 8.7888e-04, 2.7651e-04, 8.9641e-05, 1.4342e-04, 1.2446e-04, |
4.0639e-03, 8.9307e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9441e-01, 8.7888e-04, 2.7651e-04, 8.9641e-05, 1.4342e-04, 1.2446e-04, |
4.0639e-03, 8.9307e-06], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.9944, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0056, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many boars are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.86 GiB. GPU 2 has a total capacty of 44.34 GiB of which 5.69 GiB is free. Including non-PyTorch memory, this process has 38.63 GiB memory in use. Of the allocated memory 36.19 GiB is allocated by PyTorch, and 1.81 GiB is reserved by PyTorch but unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
tensor([9.4674e-01, 1.4376e-02, 1.5152e-03, 3.6712e-02, 3.8308e-04, 1.4047e-04, |
1.2816e-04, 8.8281e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.4674e-01, 1.4376e-02, 1.5152e-03, 3.6712e-02, 3.8308e-04, 1.4047e-04, |
1.2816e-04, 8.8281e-06], device='cuda:0', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.9467, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0533, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['How many boars 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 |
tensor([6.9094e-01, 3.0660e-01, 1.7713e-04, 1.9471e-04, 2.0072e-04, 1.3235e-03, |
3.3687e-04, 2.2250e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.9094e-01, 3.0660e-01, 1.7713e-04, 1.9471e-04, 2.0072e-04, 1.3235e-03, |
3.3687e-04, 2.2250e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.3066, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.6909, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0025, device='cuda:1', grad_fn=<SubBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 0 has a total capacty of 44.34 GiB of which 1.92 GiB is free. Including non-PyTorch memory, this process has 42.40 GiB memory in use. Of the allocated memory 39.98 GiB is allocated by PyTorch, and 1.81 GiB is reserved by PyTorch but unallocat... |
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str' |
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998} |
tensor([0.3225, 0.2350, 0.0979, 0.1161, 0.0107, 0.1689, 0.0465, 0.0024], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
3 ************* |
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([0.3225, 0.2350, 0.0979, 0.1161, 0.0107, 0.1689, 0.0465, 0.0024], |
device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.4107, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5893, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-22 17:31:29,524] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.31 | optimizer_step: 0.33 |
[2024-10-22 17:31:29,525] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 11590.74 | backward_microstep: 12524.15 | backward_inner_microstep: 9445.55 | backward_allreduce_microstep: 3078.39 | step_microstep: 7.72 |
[2024-10-22 17:31:29,525] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 11590.76 | backward: 12524.14 | backward_inner: 9445.70 | backward_allreduce: 3078.38 | step: 7.74 |
1%|▏ | 33/2424 [13:01<15:11:45, 22.88s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is the right-hand sink rectangular rather than rounded?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is the roof pink on the structure in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=LEFT,question='Does the train have any round windows?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([3, 3, 448, 448]) |
question: ['Is the right-hand sink rectangular rather than rounded?'], 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([1, 3, 448, 448]) knan debug pixel values shape |
question: ['How many animals are in the image?'], responses:['30'] |
[('30', 0.12740713819081037), ('29', 0.12530514884086683), ('40', 0.1249276424885007), ('28', 0.12486301766888525), ('31', 0.12483184010065636), ('32', 0.12430090544871905), ('26', 0.12425497754646514), ('35', 0.12410932971509633)] |
[['30', '29', '40', '28', '31', '32', '26', '35']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
tensor([5.2341e-01, 8.1789e-03, 4.6189e-01, 8.3017e-04, 1.1025e-03, 3.1875e-03, |
1.4999e-04, 1.2471e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.2341e-01, 8.1789e-03, 4.6189e-01, 8.3017e-04, 1.1025e-03, 3.1875e-03, |
1.4999e-04, 1.2471e-03], device='cuda:3', grad_fn=<SelectBackward0>) |
最后的概率分布为: {True: tensor(0.5234, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(0.4619, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0147, device='cuda:3', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many pandas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['Is the roof pink on the structure 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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([0.2605, 0.1167, 0.1550, 0.1172, 0.0443, 0.0803, 0.1000, 0.1259], |
device='cuda:1', grad_fn=<SoftmaxBackward0>) |
30 ************* |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.