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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>) |
ζεηζ¦ηεεΈδΈΊ: {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 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([9.4674e-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>) |
ζεηζ¦ηεεΈδΈΊ: {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>) |
ζεηζ¦ηεεΈδΈΊ: {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 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([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>) |
ζεηζ¦ηεεΈδΈΊ: {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%|β | 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>) |
ζεηζ¦ηεεΈδΈΊ: {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 ************* |
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