text stringlengths 0 1.16k |
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
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: 1860 |
tensor([9.1103e-01, 1.6760e-02, 7.0251e-02, 1.0674e-03, 6.2371e-05, 2.0985e-04, |
6.0408e-05, 5.6099e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.1103e-01, 1.6760e-02, 7.0251e-02, 1.0674e-03, 6.2371e-05, 2.0985e-04, |
6.0408e-05, 5.6099e-04], device='cuda:2', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9110, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0703, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0187, device='cuda:2', grad_fn=<DivBackward0>)} |
question: ['How many collies 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']] |
question: ['How many cups are in the image?'], responses:['0'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861 |
tensor([9.0485e-01, 2.4156e-02, 6.9767e-02, 6.2941e-04, 7.9574e-05, 3.4278e-04, |
3.9048e-05, 1.3372e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.0485e-01, 2.4156e-02, 6.9767e-02, 6.2941e-04, 7.9574e-05, 3.4278e-04, |
3.9048e-05, 1.3372e-04], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.0698, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9049, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0254, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([7.8382e-01, 5.0108e-02, 1.2279e-02, 1.4499e-01, 4.5165e-03, 2.1350e-03, |
2.0027e-03, 1.5070e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.8382e-01, 5.0108e-02, 1.2279e-02, 1.4499e-01, 4.5165e-03, 2.1350e-03, |
2.0027e-03, 1.5070e-04], device='cuda:1', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.8550, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.1450, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
tensor([9.8187e-01, 3.5334e-03, 6.6523e-04, 2.9887e-04, 4.9929e-04, 8.5846e-04, |
1.3640e-03, 1.0906e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8187e-01, 3.5334e-03, 6.6523e-04, 2.9887e-04, 4.9929e-04, 8.5846e-04, |
1.3640e-03, 1.0906e-02], device='cuda:3', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0.9819, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0181, device='cuda:3', grad_fn=<DivBackward0>)} |
[2024-10-22 17:29:37,686] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.33 |
[2024-10-22 17:29:37,687] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8197.24 | backward_microstep: 9267.88 | backward_inner_microstep: 7908.72 | backward_allreduce_microstep: 1358.96 | step_microstep: 7.82 |
[2024-10-22 17:29:37,687] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8197.26 | backward: 9267.87 | backward_inner: 7908.86 | backward_allreduce: 1358.94 | step: 7.83 |
1%| | 28/2424 [11:09<14:11:07, 21.31s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT 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=RIGHT,question='Are seats available in the reading area?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Do the mashed potatoes have a spoon handle visibly sticking out of them?') |
ANSWER1=RESULT(var=ANSWER0) |
ANSWER0=VQA(image=LEFT,question='How many dogs are standing on grass?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='Are there trees in the background of the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Do the mashed potatoes have a spoon handle visibly sticking out of them?'], 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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 847 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
question: ['Are seats available in the reading area?'], responses:['no'] |
question: ['How many dogs are standing on grass?'], 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']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
[('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']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844 |
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: 3, images per sample: 3.0, dynamic token length: 844 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 845 |
tensor([5.6062e-01, 1.6246e-02, 4.1958e-01, 1.2906e-03, 1.4644e-04, 5.3781e-04, |
4.0041e-05, 1.5417e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6062e-01, 1.6246e-02, 4.1958e-01, 1.2906e-03, 1.4644e-04, 5.3781e-04, |
4.0041e-05, 1.5417e-03], device='cuda:0', grad_fn=<SelectBackward0>) |
ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.5606, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(0.4196, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0198, device='cuda:0', grad_fn=<SubBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Is the middle child sitting criss cross?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
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