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1.4771e-07, 2.3189e-08], device='cuda:0', grad_fn=<SelectBackward0>) |
question: ['How many dogs are lying down?'], responses:['1'] |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.0207e-05, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['Is the dog looking toward the camera?'], responses:['yes'] |
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('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([13, 3, 448, 448]) |
[('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 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
question: ['How many dogs are in the image?'], responses:['1'] |
tensor([9.9785e-01, 5.2076e-08, 4.0642e-06, 2.0408e-03, 3.0185e-11, 1.0834e-04, |
1.9224e-08, 7.9397e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
5 ************* |
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9785e-01, 5.2076e-08, 4.0642e-06, 2.0408e-03, 3.0185e-11, 1.0834e-04, |
1.9224e-08, 7.9397e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9978, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0022, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many Samoyed puppies are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
[('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]) |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
question: ['How many Samoyed puppies 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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([1.0000e+00, 4.7081e-10, 1.1392e-10, 1.2209e-10, 1.3331e-10, 4.6441e-09, |
1.3440e-08, 3.5781e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.7081e-10, 1.1392e-10, 1.2209e-10, 1.3331e-10, 4.6441e-09, |
1.3440e-08, 3.5781e-11], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.8960e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='Are the containers in the image empty?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([1.0000e+00, 3.8936e-08, 1.7052e-10, 2.0378e-07, 8.0044e-10, 1.1861e-08, |
2.2049e-10, 4.4267e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.8936e-08, 1.7052e-10, 2.0378e-07, 8.0044e-10, 1.1861e-08, |
2.2049e-10, 4.4267e-08], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.7052e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5746e-07, device='cuda:1', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
tensor([1.0000e+00, 1.7657e-09, 1.0203e-09, 2.5927e-09, 1.8723e-09, 5.9549e-08, |
1.1554e-08, 4.9513e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.7657e-09, 1.0203e-09, 2.5927e-09, 1.8723e-09, 5.9549e-08, |
1.1554e-08, 4.9513e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.7657e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)} |
question: ['Are the containers in the image empty?'], responses:['yes'] |
question: ['How many hyenas 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 |
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 2.7089e-10, 7.5078e-11, 2.1200e-10, 1.2181e-10, 6.1504e-09, |
2.6883e-09, 5.0001e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.7089e-10, 7.5078e-11, 2.1200e-10, 1.2181e-10, 6.1504e-09, |
2.6883e-09, 5.0001e-11], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.6883e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([9.9753e-01, 2.5810e-08, 2.4726e-03, 6.2057e-08, 5.2451e-09, 3.2855e-09, |
6.5016e-10, 4.4331e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9753e-01, 2.5810e-08, 2.4726e-03, 6.2057e-08, 5.2451e-09, 3.2855e-09, |
6.5016e-10, 4.4331e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9975, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0025, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.3574e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 2.5798e-10, 6.3221e-11, 2.8780e-10, 8.9129e-11, 2.4315e-08, |
1.9899e-09, 5.1030e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.5798e-10, 6.3221e-11, 2.8780e-10, 8.9129e-11, 2.4315e-08, |
1.9899e-09, 5.1030e-10], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.7513e-08, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-24 09:45:34,095] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.28 | optimizer_step: 0.32 |
[2024-10-24 09:45:34,095] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5768.35 | backward_microstep: 8099.53 | backward_inner_microstep: 5484.74 | backward_allreduce_microstep: 2614.62 | step_microstep: 7.54 |
[2024-10-24 09:45:34,095] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5768.35 | backward: 8099.52 | backward_inner: 5484.81 | backward_allreduce: 2614.60 | step: 7.55 |
95%|ββββββββββ| 4587/4844 [19:04:17<1:03:09, 14.75s/it]Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering VQA_lavis step |
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