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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.7220e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-24 09:30:47,920] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.35 | optimizer_step: 0.33 |
[2024-10-24 09:30:47,921] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3206.73 | backward_microstep: 10721.37 | backward_inner_microstep: 3003.57 | backward_allreduce_microstep: 7717.70 | step_microstep: 7.77 |
[2024-10-24 09:30:47,921] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3206.74 | backward: 10721.36 | backward_inner: 3003.62 | backward_allreduce: 7717.67 | step: 7.78 |
93%|ββββββββββ| 4527/4844 [18:49:31<1:25:52, 16.25s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is there at least one orange cap visible in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='How many binders are standing vertically in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 4') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Can you clearly see the label that designates which knee this pad goes on?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is the ferret seen coming out of a hole?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many binders are standing vertically in the image?'], responses:['1'] |
question: ['Is there at least one orange cap visible in the image?'], responses:['yes'] |
[('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']] |
[('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 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
question: ['Can you clearly see the label that designates which knee this pad goes on?'], responses:['no'] |
tensor([1.0000e+00, 4.9937e-08, 1.9363e-09, 3.9643e-10, 3.8507e-09, 1.4740e-08, |
3.3265e-08, 3.8206e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.9937e-08, 1.9363e-09, 3.9643e-10, 3.8507e-09, 1.4740e-08, |
3.3265e-08, 3.8206e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 4.2999e-09, 3.3158e-08, 6.2294e-09, 4.1984e-10, 1.7095e-10, |
3.2656e-11, 2.2349e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.2999e-09, 3.3158e-08, 6.2294e-09, 4.1984e-10, 1.7095e-10, |
3.2656e-11, 2.2349e-09], device='cuda:3', grad_fn=<SelectBackward0>) |
[('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']] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.1306e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.3158e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.3158e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([5, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1356 |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1356 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1357 |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1356 |
question: ['Is the ferret seen coming out of a hole?'], responses:['no'] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1356 |
question: ['How many dogs are in the image?'], responses:['7'] |
[('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']] |
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)] |
[['7', '8', '11', '5', '9', '10', '6', '12']] |
question: ['How many wolves are in the image?'], responses:['5'] |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1357 |
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)] |
[['5', '8', '4', '6', '3', '7', '11', '9']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1357 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1357 |
tensor([1.0000e+00, 6.2149e-08, 7.8909e-07, 3.6382e-10, 1.5205e-09, 2.5793e-08, |
1.0151e-09, 1.0831e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 6.2149e-08, 7.8909e-07, 3.6382e-10, 1.5205e-09, 2.5793e-08, |
1.0151e-09, 1.0831e-06], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.2149e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9670e-06, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the dog on the right have its mouth wide open?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
question: ['Does the dog on the right have its mouth wide open?'], 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([7, 3, 448, 448]) knan debug pixel values shape |
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 |
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