text stringlengths 0 1.16k |
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.7185e-10, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1904e-07, device='cuda:1', grad_fn=<SubBackward0>)} |
ζεηζ¦ηεεΈδΈΊ: ANSWER0=VQA(image=RIGHT,question='What color is the jellyfish?') |
ANSWER1=EVAL(expr='{ANSWER0} == "pink"') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
{True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.7312e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.7312e-11, device='cuda:0', grad_fn=<DivBackward0>)} |
tensor([9.9897e-01, 1.0324e-03, 1.8824e-07, 2.2532e-07, 8.0158e-09, 1.1751e-08, |
4.2153e-08, 1.4507e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9897e-01, 1.0324e-03, 1.8824e-07, 2.2532e-07, 8.0158e-09, 1.1751e-08, |
4.2153e-08, 1.4507e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
ANSWER0=VQA(image=RIGHT,question='How many seals are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 2') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([3, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9990, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0010, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
tensor([1.0000e+00, 8.6284e-08, 2.1145e-08, 1.6894e-07, 1.3256e-09, 2.8841e-09, |
2.5903e-09, 2.5999e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.6284e-08, 2.1145e-08, 1.6894e-07, 1.3256e-09, 2.8841e-09, |
2.5903e-09, 2.5999e-10], device='cuda:3', grad_fn=<SelectBackward0>) |
torch.Size([13, 3, 448, 448]) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.1449e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='Is the panda nibbling something?') |
FINAL_ANSWER=RESULT(var=ANSWER0) |
torch.Size([7, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
question: ['How many seals 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([3, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
question: ['How many dogs are in the image?'], responses:['1'] |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
question: ['Is the panda nibbling something?'], responses:['yes'] |
[('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']] |
[('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: 3, images per sample: 3.0, dynamic token length: 836 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836 |
tensor([9.9951e-01, 4.8785e-04, 5.8355e-08, 2.1014e-07, 2.7722e-09, 2.1046e-10, |
3.0746e-09, 1.0681e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9951e-01, 4.8785e-04, 5.8355e-08, 2.1014e-07, 2.7722e-09, 2.1046e-10, |
3.0746e-09, 1.0681e-09], device='cuda:0', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9995, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0005, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)} |
question: ['What color is the jellyfish?'], responses:['blue'] |
[('blue', 0.12610723189030773), ('kitten', 0.12505925935446505), ('iris', 0.12496487399785434), ('lemon', 0.12480860793572608), ('cherry', 0.12478264542061647), ('bright', 0.12478001416316817), ('peach', 0.12475640037922975), ('cookie', 0.12474096685863247)] |
[['blue', 'kitten', 'iris', 'lemon', 'cherry', 'bright', 'peach', 'cookie']] |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 2.0092e-10, 7.1639e-11, 1.6657e-10, 1.1094e-10, 2.0159e-08, |
2.2726e-09, 2.1217e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.0092e-10, 7.1639e-11, 1.6657e-10, 1.1094e-10, 2.0159e-08, |
2.2726e-09, 2.1217e-10], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.0092e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
tensor([1.0000e+00, 2.8478e-08, 1.3356e-11, 7.4614e-08, 2.0530e-10, 1.8767e-09, |
7.5982e-11, 2.2280e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>) |
yes ************* |
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.8478e-08, 1.3356e-11, 7.4614e-08, 2.0530e-10, 1.8767e-09, |
7.5982e-11, 2.2280e-08], device='cuda:3', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.3356e-11, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1920e-07, device='cuda:3', grad_fn=<SubBackward0>)} |
tensor([9.9825e-01, 2.4183e-06, 1.4051e-06, 3.6758e-04, 1.5985e-04, 1.5241e-04, |
1.0649e-03, 1.1170e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>) |
blue ************* |
['blue', 'kitten', 'iris', 'lemon', 'cherry', 'bright', 'peach', 'cookie'] tensor([9.9825e-01, 2.4183e-06, 1.4051e-06, 3.6758e-04, 1.5985e-04, 1.5241e-04, |
1.0649e-03, 1.1170e-07], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)} |
[2024-10-24 09:46:04,557] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.26 | optimizer_step: 0.31 |
[2024-10-24 09:46:04,557] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5736.30 | backward_microstep: 11916.91 | backward_inner_microstep: 5444.51 | backward_allreduce_microstep: 6472.32 | step_microstep: 7.68 |
[2024-10-24 09:46:04,557] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5736.32 | backward: 11916.90 | backward_inner: 5444.53 | backward_allreduce: 6472.30 | step: 7.70 |
95%|ββββββββββ| 4589/4844 [19:04:48<1:04:39, 15.21s/it]Registering VQA_lavis step |
Registering VQA_lavis step |
Registering EVAL step |
Registering RESULT step |
Registering EVAL step |
Registering RESULT step |
ANSWER0=VQA(image=LEFT,question='Does the left image show an overlapping, upright row of at least three color versions of a pencil case style?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
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='How many wolves are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} > 1') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
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