text stringlengths 0 1.16k |
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tensor([7.9906e-01, 2.0997e-02, 1.7779e-01, 1.1181e-03, 6.6398e-05, 2.4941e-04, |
7.3311e-05, 6.4740e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.9906e-01, 2.0997e-02, 1.7779e-01, 1.1181e-03, 6.6398e-05, 2.4941e-04, |
7.3311e-05, 6.4740e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
torch.Size([7, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.7991, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1778, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0232, device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['How many dogs are standing on grass in the image?'], responses:['1'] |
ANSWER0=VQA(image=LEFT,question='Is there a front awning in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many children are in the image?'], responses:['0'] |
question: ['How many sled dogs are in the image?'], responses:['5'] |
[('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']] |
[('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']] |
question: ['Is there a front awning in the image?'], 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 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
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: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
tensor([9.6689e-01, 6.1198e-03, 2.6318e-03, 1.2408e-03, 1.7526e-03, 1.1887e-03, |
2.0067e-02, 1.1022e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6689e-01, 6.1198e-03, 2.6318e-03, 1.2408e-03, 1.7526e-03, 1.1887e-03, |
2.0067e-02, 1.1022e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9669, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0331, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is there a bird in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
tensor([0.2970, 0.0793, 0.1393, 0.2608, 0.0352, 0.1299, 0.0133, 0.0452], |
device='cuda:2', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2970, 0.0793, 0.1393, 0.2608, 0.0352, 0.1299, 0.0133, 0.0452], |
device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.1393, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8607, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([5.9443e-01, 1.7622e-02, 3.8379e-01, 1.6942e-03, 1.4479e-04, 4.3967e-04, |
2.4717e-04, 1.6231e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.9443e-01, 1.7622e-02, 3.8379e-01, 1.6942e-03, 1.4479e-04, 4.3967e-04, |
2.4717e-04, 1.6231e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.5944, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3838, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0218, device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['Is there a bird in the image?'], 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([0.9613, 0.0087, 0.0078, 0.0019, 0.0041, 0.0019, 0.0022, 0.0122], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([0.9613, 0.0087, 0.0078, 0.0019, 0.0041, 0.0019, 0.0022, 0.0122], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0.9613, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0387, device='cuda:3', 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([7, 3, 448, 448]) |
tensor([9.0361e-01, 2.0232e-02, 7.4173e-02, 1.3935e-03, 5.4900e-05, 1.8930e-04, |
3.6977e-05, 3.0932e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.0361e-01, 2.0232e-02, 7.4173e-02, 1.3935e-03, 5.4900e-05, 1.8930e-04, |
3.6977e-05, 3.0932e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9036, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0742, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0222, device='cuda:1', grad_fn=<DivBackward0>)} |
question: ['How many dogs are in the image?'], responses:['2'] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
tensor([9.2601e-01, 2.4677e-02, 5.3367e-03, 4.0686e-02, 1.7870e-03, 7.9033e-04, |
6.5608e-04, 5.5262e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.2601e-01, 2.4677e-02, 5.3367e-03, 4.0686e-02, 1.7870e-03, 7.9033e-04, |
6.5608e-04, 5.5262e-05], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9260, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0740, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-23 14:43:42,205] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.33 |
[2024-10-23 14:43:42,206] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3187.67 | backward_microstep: 10857.34 | backward_inner_microstep: 3014.31 | backward_allreduce_microstep: 7842.93 | step_microstep: 7.54 |
[2024-10-23 14:43:42,206] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3187.68 | backward: 10857.33 | backward_inner: 3014.35 | backward_allreduce: 7842.92 | step: 7.56 |
0%| | 9/4844 [02:26<21:04:48, 15.70s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Are the two pins touching each other?') |
ANSWER1=EVAL(expr='not {ANSWER0}') |
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