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[['17', '18', '19', '21', '16', '23', '27', '20']] |
question: ['How many dogs 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([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
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: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324 |
tensor([1.0000e+00, 1.9935e-10, 6.3220e-11, 1.9470e-10, 7.6215e-11, 1.1963e-08, |
2.3174e-09, 1.8604e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.9935e-10, 6.3220e-11, 1.9470e-10, 7.6215e-11, 1.1963e-08, |
2.3174e-09, 1.8604e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.3174e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['How many 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([13, 3, 448, 448]) knan debug pixel values shape |
tensor([0.2706, 0.1341, 0.2284, 0.0593, 0.0122, 0.1393, 0.0484, 0.1077], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
17 ************* |
['17', '18', '19', '21', '16', '23', '27', '20'] tensor([0.2706, 0.1341, 0.2284, 0.0593, 0.0122, 0.1393, 0.0484, 0.1077], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many loaves of bread are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([1.0000e+00, 4.7851e-06, 1.2445e-07, 1.2254e-11, 1.6206e-12, 6.3630e-10, |
9.4265e-11, 3.0102e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.7851e-06, 1.2445e-07, 1.2254e-11, 1.6206e-12, 6.3630e-10, |
9.4265e-11, 3.0102e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(4.7851e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the marmot eating something?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['Is the marmot eating something?'], 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 |
question: ['How many loaves of bread are in the image?'], responses:['100'] |
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)] |
[['100', '120', '88', '80', '60', '99', '90', '101']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 4.5278e-10, 5.6011e-11, 9.8583e-11, 9.6920e-11, 2.4092e-09, |
6.0581e-09, 3.2483e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.5278e-10, 5.6011e-11, 9.8583e-11, 9.6920e-11, 2.4092e-09, |
6.0581e-09, 3.2483e-11], device='cuda:1', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 3.5730e-08, 9.7920e-11, 1.2157e-07, 1.6576e-10, 1.2501e-09, |
4.9948e-10, 1.4575e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.5730e-08, 9.7920e-11, 1.2157e-07, 1.6576e-10, 1.2501e-09, |
4.9948e-10, 1.4575e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.0581e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(9.7920e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1911e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
tensor([8.9512e-01, 1.2416e-02, 6.3305e-06, 2.1281e-02, 6.0886e-02, 2.7335e-04, |
9.9112e-03, 1.0815e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
100 ************* |
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([8.9512e-01, 1.2416e-02, 6.3305e-06, 2.1281e-02, 6.0886e-02, 2.7335e-04, |
9.9112e-03, 1.0815e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-24 09:55:29,216] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.33 |
[2024-10-24 09:55:29,216] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 1184.62 | backward_microstep: 12630.34 | backward_inner_microstep: 1197.10 | backward_allreduce_microstep: 11433.15 | step_microstep: 7.93 |
[2024-10-24 09:55:29,216] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 1184.64 | backward: 12630.33 | backward_inner: 1197.13 | backward_allreduce: 11433.13 | step: 7.94 |
95%|ββββββββββ| 4626/4844 [19:14:13<50:13, 13.82s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Does the laptop on the right image have a black background?') |
ANSWER1=RESULT(var=ANSWER0) |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is there a barber pole in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is there a ladder leaning against the bookcase in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Is there a silver lid resting against a container in the image?') |
ANSWER1=RESULT(var=ANSWER0) |
torch.Size([1, 3, 448, 448]) |
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