text
stringlengths 0
1.16k
|
|---|
question: ['How many dogs are in the image?'], responses:['1']
|
question: ['Is the bottle on the right pink?'], 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
|
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
|
question: ['What color is the dog on the right?'], responses:['white']
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
|
[('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([1, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
tensor([1.0000e+00, 3.3779e-10, 7.7461e-11, 1.7051e-10, 1.4135e-10, 7.3021e-09,
|
8.4108e-09, 1.6891e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
|
1 *************
|
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.3779e-10, 7.7461e-11, 1.7051e-10, 1.4135e-10, 7.3021e-09,
|
8.4108e-09, 1.6891e-10], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(8.4108e-09, 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, 2.9990e-09, 2.0414e-07, 9.7508e-13, 1.6788e-11, 1.1533e-09,
|
8.1448e-11, 2.5528e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
|
no *************
|
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.9990e-09, 2.0414e-07, 9.7508e-13, 1.6788e-11, 1.1533e-09,
|
8.1448e-11, 2.5528e-07], device='cuda:0', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.9990e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:0', grad_fn=<SubBackward0>)}
|
tensor([2.1136e-01, 8.4128e-04, 2.1821e-05, 3.8382e-03, 5.7073e-04, 8.3987e-06,
|
7.8333e-01, 3.3542e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
|
brown *************
|
['white', 'black', 'purple', 'orange', 'maroon', 'color', 'brown', 'dark'] tensor([2.1136e-01, 8.4128e-04, 2.1821e-05, 3.8382e-03, 5.7073e-04, 8.3987e-06,
|
7.8333e-01, 3.3542e-05], 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 10:31:45,592] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.21 | optimizer_step: 0.30
|
[2024-10-24 10:31:45,592] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5075.74 | backward_microstep: 5101.36 | backward_inner_microstep: 4960.91 | backward_allreduce_microstep: 140.35 | step_microstep: 7.45
|
[2024-10-24 10:31:45,592] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5075.74 | backward: 5101.35 | backward_inner: 4960.95 | backward_allreduce: 140.33 | step: 7.46
|
98%|ββββββββββ| 4771/4844 [19:50:29<18:00, 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 golf balls are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='How many birds are in the image?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 1')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=RIGHT,question='Is the mashed potato on a white bowl?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
ANSWER0=VQA(image=LEFT,question='How many white sails are up on the boat?')
|
ANSWER1=EVAL(expr='{ANSWER0} == 3')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([7, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
torch.Size([13, 3, 448, 448])
|
question: ['Is the mashed potato on a white bowl?'], responses:['yes']
|
[('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([7, 3, 448, 448]) knan debug pixel values shape
|
question: ['How many golf balls are in the image?'], responses:['12']
|
question: ['How many birds are in the image?'], responses:['2']
|
question: ['How many white sails are up on the boat?'], responses:['0']
|
[('12', 0.1271623397239889), ('11', 0.12513993889798333), ('10', 0.1250472536472656), ('8', 0.12474319370676636), ('6', 0.12462998196332449), ('26', 0.12450301801207538), ('47', 0.1243904847365581), ('13', 0.12438378931203788)]
|
[['12', '11', '10', '8', '6', '26', '47', '13']]
|
[('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']]
|
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
|
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
|
tensor([1.0000e+00, 3.0657e-09, 3.3315e-11, 5.0738e-09, 1.2377e-10, 1.0754e-10,
|
1.1015e-11, 1.5483e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
|
yes *************
|
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.0657e-09, 3.3315e-11, 5.0738e-09, 1.2377e-10, 1.0754e-10,
|
1.1015e-11, 1.5483e-08], device='cuda:1', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.3315e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.3315e-11, device='cuda:1', grad_fn=<DivBackward0>)}
|
ANSWER0=VQA(image=LEFT,question='Are the bottles in the image unopened?')
|
ANSWER1=EVAL(expr='{ANSWER0}')
|
FINAL_ANSWER=RESULT(var=ANSWER1)
|
torch.Size([13, 3, 448, 448])
|
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.