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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865 |
tensor([4.7601e-01, 2.9483e-02, 4.9060e-01, 1.2110e-03, 1.9515e-04, 9.7798e-04, |
1.4793e-04, 1.3803e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([4.7601e-01, 2.9483e-02, 4.9060e-01, 1.2110e-03, 1.9515e-04, 9.7798e-04, |
1.4793e-04, 1.3803e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4760, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.4906, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0334, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many arches are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([6.7045e-01, 2.6022e-02, 3.0075e-01, 1.3238e-03, 1.4883e-04, 4.7973e-04, |
1.0540e-04, 7.1840e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.7045e-01, 2.6022e-02, 3.0075e-01, 1.3238e-03, 1.4883e-04, 4.7973e-04, |
1.0540e-04, 7.1840e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.6705, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.3008, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0288, device='cuda:2', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 0 has a total capacty of 44.34 GiB of which 1.07 GiB is free. Including non-PyTorch memory, this process has 43.25 GiB memory in use. Of the allocated memory 40.79 GiB is allocated by PyTorch, and 1.85 GiB is reserved by PyTorch but unallocat... |
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 2.93 GiB. GPU 2 has a total capacty of 44.34 GiB of which 1.05 GiB is free. Including non-PyTorch memory, this process has 43.28 GiB memory in use. Of the allocated memory 40.77 GiB is allocated by PyTorch, and 1.87 GiB is reserved by PyTorch but unallocat... |
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 17:22:34,766] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.33 |
[2024-10-22 17:22:34,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12751.29 | backward_microstep: 10965.44 | backward_inner_microstep: 10693.59 | backward_allreduce_microstep: 271.75 | step_microstep: 8.86 |
[2024-10-22 17:22:34,767] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12751.31 | backward: 10965.43 | backward_inner: 10693.62 | backward_allreduce: 271.73 | step: 8.87 |
0%| | 10/2424 [04:07<16:13:49, 24.20s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
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='How many mostly black dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is there a blue seating area near the books in the image?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=RIGHT,question='How many men are working on the roof of the house?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many horned animals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([3, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is there a blue seating area near the books in the image?'], responses:['no'] |
question: ['How many horned animals are in the image?'], responses:['1'] |
[('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']] |
[('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']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
question: ['How many mostly black dogs are in the image?'], responses:['1'] |
[('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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([5.6177e-01, 4.3750e-01, 1.1427e-05, 1.1786e-04, 1.8298e-04, 1.7538e-04, |
2.1789e-04, 2.6700e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.6177e-01, 4.3750e-01, 1.1427e-05, 1.1786e-04, 1.8298e-04, 1.7538e-04, |
2.1789e-04, 2.6700e-05], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([8.1985e-01, 2.9868e-02, 1.3672e-02, 3.1464e-03, 4.8750e-03, 2.6846e-03, |
1.2573e-01, 1.7865e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.1985e-01, 2.9868e-02, 1.3672e-02, 3.1464e-03, 4.8750e-03, 2.6846e-03, |
1.2573e-01, 1.7865e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.4375, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.5618, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0007, device='cuda:2', grad_fn=<SubBackward0>)} |
question: ['How many men are working on the roof of the house?'], responses:['1'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0299, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9701, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many power poles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many elephants are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
[('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']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400 |
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