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[2024-10-24 10:20:50,343] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9049.50 | backward_microstep: 8720.93 | backward_inner_microstep: 8715.01 | backward_allreduce_microstep: 5.83 | step_microstep: 7.56 |
[2024-10-24 10:20:50,343] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9049.51 | backward: 8720.92 | backward_inner: 8715.04 | backward_allreduce: 5.81 | step: 7.57 |
98%|ββββββββββ| 4726/4844 [19:39:34<31:20, 15.93s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=RIGHT,question='Are the dogs sitting on grass?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many dogs in the image are wearing a collar?') |
ANSWER1=EVAL(expr='{ANSWER0} == 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=LEFT,question='How many water buffaloes are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Are there flowers in a transparent vase in the image?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Are the dogs sitting on grass?'], responses:['no'] |
question: ['How many dogs in the image are wearing a collar?'], responses:['0'] |
question: ['How many water buffaloes are in the image?'], responses:['40'] |
[('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']] |
[('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']] |
[('40', 0.12638022987124733), ('39', 0.12509919407251455), ('42', 0.12494223232783619), ('41', 0.12482626048065008), ('45', 0.12479694604159434), ('38', 0.12473125094691345), ('47', 0.1246423477331973), ('32', 0.1245815385260468)] |
[['40', '39', '42', '41', '45', '38', '47', '32']] |
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 |
question: ['Are there flowers in a transparent vase in the image?'], responses:['yes'] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
[('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']] |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863 |
torch.Size([13, 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 |
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([1.0000e+00, 1.3176e-10, 2.6888e-07, 1.5582e-12, 1.6201e-12, 4.8267e-09, |
9.0150e-11, 9.4123e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.3176e-10, 2.6888e-07, 1.5582e-12, 1.6201e-12, 4.8267e-09, |
9.0150e-11, 9.4123e-07], device='cuda:2', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.2060e-06, 8.0371e-08, 1.1431e-11, 4.4707e-07, 9.9074e-09, |
4.5034e-07, 1.0619e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
0 ************* |
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 1.2060e-06, 8.0371e-08, 1.1431e-11, 4.4707e-07, 9.9074e-09, |
4.5034e-07, 1.0619e-06], device='cuda:3', grad_fn=<SelectBackward0>) |
tensor([0.7789, 0.0249, 0.0067, 0.0266, 0.0934, 0.0090, 0.0521, 0.0084], |
device='cuda:0', grad_fn=<SoftmaxBackward0>) |
40 ************* |
['40', '39', '42', '41', '45', '38', '47', '32'] tensor([0.7789, 0.0249, 0.0067, 0.0266, 0.0934, 0.0090, 0.0521, 0.0084], |
device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3176e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many boats are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.2187e-06, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the dog wearing a collar?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
torch.Size([7, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Does the parrot in the image have a red head?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([3, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['Does the parrot in the image have a red head?'], 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: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 843 |
question: ['How many boats are in the image?'], responses:['50'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
[('50', 0.12746329354121594), ('51', 0.12494443111915052), ('60', 0.12471995183640609), ('55', 0.12470016949940634), ('54', 0.12460076157014638), ('52', 0.12454269500997545), ('44', 0.12453681395238846), ('48', 0.1244918834713108)] |
[['50', '51', '60', '55', '54', '52', '44', '48']] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841 |
tensor([1.0000e+00, 3.8702e-09, 1.9172e-10, 1.3886e-08, 3.4291e-10, 4.7450e-10, |
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