text stringlengths 0 1.16k |
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torch.Size([1, 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']] |
[('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']] |
question: ['How many black chow dogs are in the image?'], responses:['0'] |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
torch.Size([1, 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: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329 |
question: ['How many red balloons are in the image?'], responses:['0'] |
tensor([1.0000e+00, 1.6913e-07, 3.8276e-08, 1.6917e-10, 1.3957e-06, 4.2896e-09, |
1.1249e-07, 8.2465e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 1.6913e-07, 3.8276e-08, 1.6917e-10, 1.3957e-06, 4.2896e-09, |
1.1249e-07, 8.2465e-07], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.5034e-06, device='cuda:0', grad_fn=<DivBackward0>)} |
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)] |
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 3.6202e-10, 4.5537e-11, 1.4247e-10, 7.1068e-11, 5.9564e-09, |
3.3983e-09, 1.2498e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.6202e-10, 4.5537e-11, 1.4247e-10, 7.1068e-11, 5.9564e-09, |
3.3983e-09, 1.2498e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0101e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 3.6382e-10, 5.9857e-11, 1.4081e-10, 9.6778e-11, 3.9107e-09, |
6.9728e-09, 5.1876e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.6382e-10, 5.9857e-11, 1.4081e-10, 9.6778e-11, 3.9107e-09, |
6.9728e-09, 5.1876e-11], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.1597e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Does the car on the right have its top down?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many fish are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
tensor([9.9999e-01, 3.8206e-06, 1.3935e-07, 1.0599e-09, 4.1140e-08, 3.1867e-08, |
1.6688e-07, 8.3463e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 3.8206e-06, 1.3935e-07, 1.0599e-09, 4.1140e-08, 3.1867e-08, |
1.6688e-07, 8.3463e-07], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.0068e-06, device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['Does the car on the right have its top down?'], responses:['yes'] |
question: ['How many fish are in the image?'], responses:['1'] |
[('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']] |
[('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 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 5.9103e-09, 1.1861e-08, 2.6303e-09, 5.3172e-12, 2.1849e-11, |
1.3781e-11, 1.2600e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.9103e-09, 1.1861e-08, 2.6303e-09, 5.3172e-12, 2.1849e-11, |
1.3781e-11, 1.2600e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.1861e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1861e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 1.4904e-09, 2.4617e-10, 7.0673e-10, 2.9229e-10, 6.8230e-08, |
4.4763e-08, 7.8958e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4904e-09, 2.4617e-10, 7.0673e-10, 2.9229e-10, 6.8230e-08, |
4.4763e-08, 7.8958e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.1652e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-24 09:31:37,322] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.26 | optimizer_step: 0.31 |
[2024-10-24 09:31:37,322] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 1315.71 | backward_microstep: 8746.87 | backward_inner_microstep: 1183.73 | backward_allreduce_microstep: 7563.08 | step_microstep: 7.26 |
[2024-10-24 09:31:37,322] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 1315.71 | backward: 8746.86 | backward_inner: 1183.74 | backward_allreduce: 7563.06 | step: 7.27 |
94%|ββββββββββ| 4531/4844 [18:50:21<1:07:34, 12.95s/it]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='How many yellowish anemone are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 8') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Is someone holding the crab?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many wine bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Is the dog's head laying down in the left picture?') |
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