text stringlengths 0 1.16k |
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0271, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9729, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([9.2996e-01, 1.1346e-02, 5.3578e-03, 2.1634e-03, 2.9589e-03, 1.7769e-03, |
4.6287e-02, 1.4539e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.2996e-01, 1.1346e-02, 5.3578e-03, 2.1634e-03, 2.9589e-03, 1.7769e-03, |
4.6287e-02, 1.4539e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.0237, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9763, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3393 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394 |
tensor([8.2918e-01, 1.4683e-02, 1.5338e-01, 1.6671e-03, 8.7692e-05, 2.9467e-04, |
1.3775e-04, 5.7093e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.2918e-01, 1.4683e-02, 1.5338e-01, 1.6671e-03, 8.7692e-05, 2.9467e-04, |
1.3775e-04, 5.7093e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8292, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1534, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0174, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
tensor([8.0291e-01, 2.6661e-02, 1.6830e-01, 7.9991e-04, 1.5460e-04, 3.5563e-04, |
6.3776e-05, 7.5329e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.0291e-01, 2.6661e-02, 1.6830e-01, 7.9991e-04, 1.5460e-04, 3.5563e-04, |
6.3776e-05, 7.5329e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8029, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1683, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0288, device='cuda:3', grad_fn=<DivBackward0>)} |
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 0 has a total capacty of 44.34 GiB of which 1.01 GiB is free. Including non-PyTorch memory, this process has 43.31 GiB memory in use. Of the allocated memory 40.70 GiB is allocated by PyTorch, and 2.00 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 17:24:59,116] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.32 |
[2024-10-22 17:24:59,116] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12813.79 | backward_microstep: 11494.00 | backward_inner_microstep: 10687.08 | backward_allreduce_microstep: 806.65 | step_microstep: 7.66 |
[2024-10-22 17:24:59,117] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12813.81 | backward: 11493.99 | backward_inner: 10687.16 | backward_allreduce: 806.47 | step: 7.68 |
1%| | 16/2424 [06:31<16:08:40, 24.14s/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='Does the laptop on the right display the tiles from the operating system Windows?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is the dog looking toward the camera?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the drummer wearing a blue and white shirt?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=LEFT,question='How many dogs are standing in the grass?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Is the drummer wearing a blue and white shirt?'], 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 dog looking toward the camera?'], 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([8.6410e-01, 2.3322e-02, 1.0975e-01, 1.0560e-03, 1.1733e-04, 3.4943e-04, |
2.9020e-05, 1.2676e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.6410e-01, 2.3322e-02, 1.0975e-01, 1.0560e-03, 1.1733e-04, 3.4943e-04, |
2.9020e-05, 1.2676e-03], device='cuda:1', grad_fn=<SelectBackward0>) |
question: ['How many dogs are standing in the grass?'], responses:['2'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8641, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(0.1098, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0261, device='cuda:1', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the animal holding food?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
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([13, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['Does the laptop on the right display the tiles from the operating system Windows?'], 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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403 |
tensor([8.3385e-01, 1.8874e-02, 1.4465e-01, 1.1085e-03, 1.0186e-04, 6.8897e-04, |
4.8068e-05, 6.8228e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.3385e-01, 1.8874e-02, 1.4465e-01, 1.1085e-03, 1.0186e-04, 6.8897e-04, |
4.8068e-05, 6.8228e-04], device='cuda:3', grad_fn=<SelectBackward0>) |
question: ['Is the animal holding food?'], responses:['yes'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.8338, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1447, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0215, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is a person pushing the dispenser?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
[('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']] |
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