text
stringlengths 0
1.16k
|
|---|
tensor([1.0000e+00, 2.9220e-09, 2.1387e-10, 8.8773e-09, 1.3594e-10, 7.5824e-10,
|
1.6436e-11, 3.1001e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.9220e-09, 2.1387e-10, 8.8773e-09, 1.3594e-10, 7.5824e-10,
|
1.6436e-11, 3.1001e-08], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(2.1387e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-2.1387e-10, device='cuda:3', grad_fn=<SubBackward0>)}
|
[2024-10-24 10:45:48,858] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.25 | optimizer_step: 0.32
|
[2024-10-24 10:45:48,859] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7056.06 | backward_microstep: 6845.58 | backward_inner_microstep: 6781.18 | backward_allreduce_microstep: 64.31 | step_microstep: 7.38
|
[2024-10-24 10:45:48,859] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7056.06 | backward: 6845.57 | backward_inner: 6781.21 | backward_allreduce: 64.25 | step: 7.39
|
100%|ββββββββββ| 4828/4844 [20:04:32<03:56, 14.80s/it]Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering VQA_lavis step
|
Registering VQA_lavis step
|
Registering EVAL step
|
Registering RESULT step
|
Registering EVAL step
|
Registering RESULT step
|
ANSWER0=VQA(image=RIGHT,question='How many locks with a chain are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=LEFT,question='How many graduation students are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='How many monkeys are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} > 4')
|
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: ['How many graduation students are in the image?'], responses:['1']
|
question: ['How many monkeys are in the image?'], responses:['3']
|
question: ['How many animals are in the image?'], responses:['7']
|
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
|
[['3', '4', '1', '5', '8', '2', '6', '12']]
|
[('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']]
|
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
|
[['7', '8', '11', '5', '9', '10', '6', '12']]
|
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: 1861
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
question: ['How many locks with a chain are in the image?'], responses:['1']
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
|
[('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: 7, images per sample: 7.0, dynamic token length: 1861
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
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
|
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([9.1079e-01, 1.9934e-03, 7.5506e-02, 1.3380e-08, 7.9134e-03, 3.7043e-03,
|
5.4470e-06, 9.0645e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
7 *************
|
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.1079e-01, 1.9934e-03, 7.5506e-02, 1.3380e-08, 7.9134e-03, 3.7043e-03,
|
5.4470e-06, 9.0645e-05], device='cuda:1', grad_fn=<SelectBackward0>)
|
tensor([9.9998e-01, 4.0015e-10, 7.3183e-12, 2.8495e-11, 9.3966e-12, 3.1401e-09,
|
1.8925e-05, 2.5776e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9998e-01, 4.0015e-10, 7.3183e-12, 2.8495e-11, 9.3966e-12, 3.1401e-09,
|
1.8925e-05, 2.5776e-11], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
|
tensor([9.9987e-01, 1.3171e-04, 9.9422e-08, 1.8387e-08, 4.5338e-11, 3.0613e-08,
|
1.3362e-10, 1.1026e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
3 *************
|
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9987e-01, 1.3171e-04, 9.9422e-08, 1.8387e-08, 4.5338e-11, 3.0613e-08,
|
1.3362e-10, 1.1026e-08], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(3.6112e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='Do the skunks in the image have their tails up?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(9.9422e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
|
torch.Size([7, 3, 448, 448])
|
ANSWER0=VQA(image=LEFT,question='How many white bowls are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
question: ['How many dogs are in the image?'], responses:['2']
|
question: ['Do the skunks in the image have their tails up?'], responses:['no']
|
[('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']]
|
[('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
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.