text
stringlengths 0
1.16k
|
|---|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
question: ['Is there a black horse in the image?'], responses:['no']
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
[('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
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
tensor([9.9616e-01, 5.4901e-04, 2.3114e-03, 3.6627e-07, 8.5032e-04, 1.0192e-04,
|
2.6671e-05, 2.6126e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
7 *************
|
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.9616e-01, 5.4901e-04, 2.3114e-03, 3.6627e-07, 8.5032e-04, 1.0192e-04,
|
2.6671e-05, 2.6126e-06], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9962, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0038, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='What color is the body of the boat?')
|
ANSWER1=EVAL(expr='{ANSWER0} == "white"')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
|
tensor([1.0000e+00, 1.2147e-06, 5.8779e-08, 7.3392e-08, 1.3701e-08, 1.2348e-08,
|
2.2420e-08, 3.8148e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
2 *************
|
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.2147e-06, 5.8779e-08, 7.3392e-08, 1.3701e-08, 1.2348e-08,
|
2.2420e-08, 3.8148e-08], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.4335e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
|
tensor([1.0000e+00, 7.1232e-10, 3.3671e-07, 1.2159e-11, 1.7911e-11, 2.3128e-08,
|
5.0016e-09, 4.0786e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 7.1232e-10, 3.3671e-07, 1.2159e-11, 1.7911e-11, 2.3128e-08,
|
5.0016e-09, 4.0786e-07], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.1232e-10, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:2', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='How many faucets are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([1, 3, 448, 448])
|
question: ['How many faucets are in the image?'], responses:['1']
|
question: ['What color is the body of the boat?'], responses:['white']
|
[('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([1, 3, 448, 448]) knan debug pixel values shape
|
[('white', 0.12741698904857263), ('black', 0.12562195821587463), ('purple', 0.12482758531934457), ('orange', 0.12467593918870701), ('maroon', 0.12456097552653009), ('color', 0.12448461429606533), ('brown', 0.12421598902969112), ('dark', 0.12419594937521464)]
|
[['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark']]
|
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
|
tensor([1.0000e+00, 2.1266e-09, 3.5011e-07, 1.2449e-11, 1.2620e-10, 5.5432e-09,
|
2.2655e-10, 4.8331e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.1266e-09, 3.5011e-07, 1.2449e-11, 1.2620e-10, 5.5432e-09,
|
2.2655e-10, 4.8331e-07], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.1266e-09, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:1', grad_fn=<SubBackward0>)}
|
tensor([1.0000e+00, 1.7768e-09, 3.7979e-10, 8.7774e-11, 1.5285e-10, 3.2736e-08,
|
2.5360e-07, 4.0640e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.7768e-09, 3.7979e-10, 8.7774e-11, 1.5285e-10, 3.2736e-08,
|
2.5360e-07, 4.0640e-10], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.8914e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
|
tensor([8.9493e-01, 1.5249e-03, 3.1337e-06, 6.9115e-05, 4.4625e-04, 6.2181e-08,
|
1.0259e-01, 4.4137e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
white *************
|
['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark'] tensor([8.9493e-01, 1.5249e-03, 3.1337e-06, 6.9115e-05, 4.4625e-04, 6.2181e-08,
|
1.0259e-01, 4.4137e-04], device='cuda:3', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
|
[2024-10-24 10:47:15,473] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.31 | optimizer_step: 0.32
|
[2024-10-24 10:47:15,474] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4489.97 | backward_microstep: 8186.43 | backward_inner_microstep: 4239.08 | backward_allreduce_microstep: 3947.26 | step_microstep: 7.67
|
[2024-10-24 10:47:15,474] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4489.97 | backward: 8186.42 | backward_inner: 4239.09 | backward_allreduce: 3947.20 | step: 7.69
|
100%|ββββββββββ| 4834/4844 [20:05:59<02:18, 13.90s/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='Is there a flying bird in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=LEFT,question='Is the dog sitting?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='How many lights are hanging above the counter?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 5')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=LEFT,question='How many ferrets are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 5')
|
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([7, 3, 448, 448])
|
question: ['Is there a flying bird in the image?'], responses:['yes']
|
question: ['How many lights are hanging above the counter?'], responses:['11']
|
question: ['How many ferrets are in the image?'], responses:['2']
|
question: ['Is the dog sitting?'], responses:['no']
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.