text stringlengths 0 1.16k |
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[['1', '3', '4', '8', '6', '12', '2', '47']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([0.1986, 0.1409, 0.2048, 0.1927, 0.1210, 0.0368, 0.0035, 0.1016], |
device='cuda:3', grad_fn=<SoftmaxBackward0>) |
10 ************* |
['12', '11', '10', '8', '6', '26', '47', '13'] tensor([0.1986, 0.1409, 0.2048, 0.1927, 0.1210, 0.0368, 0.0035, 0.1016], |
device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([6.7985e-01, 7.6551e-02, 6.3176e-02, 2.1168e-02, 2.5644e-02, 1.4546e-02, |
1.1852e-01, 5.5232e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.7985e-01, 7.6551e-02, 6.3176e-02, 2.1168e-02, 2.5644e-02, 1.4546e-02, |
1.1852e-01, 5.5232e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.1185, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8815, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397 |
tensor([9.2378e-01, 8.5673e-03, 6.6918e-02, 1.7198e-04, 5.1825e-05, 1.3301e-04, |
1.7138e-05, 3.5971e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.2378e-01, 8.5673e-03, 6.6918e-02, 1.7198e-04, 5.1825e-05, 1.3301e-04, |
1.7138e-05, 3.5971e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9238, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0669, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0093, device='cuda:0', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is a rodent eating pasta in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([13, 3, 448, 448]) |
question: ['Is a rodent eating pasta 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([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399 |
tensor([3.8399e-01, 1.7019e-02, 5.9473e-01, 2.8559e-03, 1.2895e-04, 2.9224e-04, |
1.7162e-04, 8.0880e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
no ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([3.8399e-01, 1.7019e-02, 5.9473e-01, 2.8559e-03, 1.2895e-04, 2.9224e-04, |
1.7162e-04, 8.0880e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.3840, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5947, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0213, device='cuda:0', grad_fn=<DivBackward0>)} |
[2024-10-23 14:54:43,867] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.44 | optimizer_gradients: 0.25 | optimizer_step: 0.33 |
[2024-10-23 14:54:43,867] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9005.88 | backward_microstep: 8738.52 | backward_inner_microstep: 8732.10 | backward_allreduce_microstep: 6.32 | step_microstep: 10.16 |
[2024-10-23 14:54:43,867] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9005.88 | backward: 8738.51 | backward_inner: 8732.12 | backward_allreduce: 6.29 | step: 10.17 |
1%| | 52/4844 [13:27<21:45:39, 16.35s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='How many puppies are lying down in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many rolls of paper towels are in the package?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many laptops are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='How many objects are standing straight up in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 9') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
question: ['How many laptops 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([3, 3, 448, 448]) knan debug pixel values shape |
question: ['How many puppies are lying down in the image?'], responses:['2'] |
question: ['How many rolls of paper towels are in the package?'], responses:['13'] |
question: ['How many objects are standing straight up in the image?'], responses:['5'] |
[('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']] |
[('13', 0.12770862924411772), ('14', 0.12534395389083108), ('21', 0.12493249815266858), ('12', 0.12491814916612239), ('11', 0.12461120999761086), ('27', 0.12444592740053353), ('15', 0.12414436865504584), ('29', 0.1238952634930699)] |
[['13', '14', '21', '12', '11', '27', '15', '29']] |
[('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 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
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([9.9106e-01, 1.0901e-03, 4.5437e-04, 2.0146e-04, 2.5082e-04, 2.5559e-04, |
6.6778e-03, 1.4567e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9106e-01, 1.0901e-03, 4.5437e-04, 2.0146e-04, 2.5082e-04, 2.5559e-04, |
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