text stringlengths 0 1.16k |
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.8957e-10, 6.7562e-11, 1.8800e-10, 1.1269e-10, 4.9423e-09, |
6.5503e-09, 4.6812e-11], device='cuda:1', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.5503e-09, 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>)} |
question: ['How many insects 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([7, 3, 448, 448]) knan debug pixel values shape |
tensor([1.0000e+00, 7.3802e-08, 4.0126e-08, 8.5767e-09, 8.9345e-10, 4.0831e-09, |
2.7787e-09, 1.1052e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
2 ************* |
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 7.3802e-08, 4.0126e-08, 8.5767e-09, 8.9345e-10, 4.0831e-09, |
2.7787e-09, 1.1052e-09], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.3137e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)} |
[2024-10-24 10:34:32,264] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.37 | optimizer_step: 0.33 |
[2024-10-24 10:34:32,265] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3197.29 | backward_microstep: 10580.82 | backward_inner_microstep: 3019.33 | backward_allreduce_microstep: 7561.36 | step_microstep: 7.96 |
[2024-10-24 10:34:32,265] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3197.29 | backward: 10580.81 | backward_inner: 3019.38 | backward_allreduce: 7561.34 | step: 7.98 |
99%|ββββββββββ| 4782/4844 [19:53:16<15:29, 15.00s/it]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 laptop computer styles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} >= 5') |
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 |
torch.Size([1, 3, 448, 448]) |
ANSWER0=VQA(image=RIGHT,question='Does the bird in the image have its eyes closed?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} <= 3') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='Does the animal in the image have an open mouth?') |
ANSWER1=EVAL(expr='{ANSWER0}') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
torch.Size([1, 3, 448, 448]) |
torch.Size([7, 3, 448, 448]) |
torch.Size([13, 3, 448, 448]) |
question: ['How many laptop computer styles are in the image?'], responses:['1'] |
question: ['Does the animal in the image have an open mouth?'], responses:['no'] |
[('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']] |
[('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']] |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
torch.Size([1, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 326 |
tensor([1.0000e+00, 1.4788e-09, 2.8445e-10, 5.6790e-10, 5.9632e-10, 2.7146e-08, |
5.6586e-08, 2.9190e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>) |
1 ************* |
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4788e-09, 2.8445e-10, 5.6790e-10, 5.9632e-10, 2.7146e-08, |
5.6586e-08, 2.9190e-10], device='cuda:0', grad_fn=<SelectBackward0>) |
tensor([1.0000e+00, 1.9947e-06, 1.3348e-07, 2.6913e-12, 8.8689e-13, 1.7333e-09, |
7.1109e-11, 7.9986e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>) |
no ************* |
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.9947e-06, 1.3348e-07, 2.6913e-12, 8.8689e-13, 1.7333e-09, |
7.1109e-11, 7.9986e-08], device='cuda:2', grad_fn=<SelectBackward0>) |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.8602e-08, 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>)} |
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.9947e-06, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:2', grad_fn=<DivBackward0>)} |
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?') |
ANSWER1=EVAL(expr='{ANSWER0} == 6') |
FINAL_ANSWER=RESULT(var=ANSWER1) |
ANSWER0=VQA(image=RIGHT,question='How many dogs 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]) |
question: ['Does the bird in the image have its eyes closed?'], responses:['no'] |
[('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']] |
torch.Size([7, 3, 448, 448]) knan debug pixel values shape |
question: ['How many bottles are in the image?'], responses:['six'] |
[('7 eleven', 0.1258716720461554), ('dusk', 0.12512990238684168), ('blue', 0.12502287564185594), ('rose', 0.12495109740026594), ('peach', 0.12486403486105606), ('kitten', 0.12474151468778871), ('laundry', 0.12473504457146048), ('sunrise', 0.12468385840457588)] |
[['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise']] |
torch.Size([3, 3, 448, 448]) knan debug pixel values shape |
question: ['How many binders are in the image?'], responses:['five'] |
question: ['How many dogs are in the image?'], responses:['2'] |
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)] |
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']] |
[('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([7, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
torch.Size([13, 3, 448, 448]) knan debug pixel values shape |
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860 |
tensor([4.7311e-06, 8.0086e-04, 2.4099e-03, 1.4239e-01, 2.3293e-01, 1.1254e-04, |
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