Split (1)

train · 714k rows

text stringlengths 0 1.16k
5.8157e-05, 1.2676e-03], device='cuda:0', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.7111, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2637, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0253, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the left image feature a barn style door made of weathered-look horizontal wood boards?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many chimpanzees are in the image?'], responses:['5']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.22 GiB. GPU 0 has a total capacty of 44.34 GiB of which 1.06 GiB is free. Including non-PyTorch memory, this process has 43.26 GiB memory in use. Of the allocated memory 40.56 GiB is allocated by PyTorch, and 2.09 GiB is reserved by PyTorch but unallocat...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
tensor([0.1860, 0.1344, 0.1143, 0.1859, 0.0636, 0.1613, 0.0567, 0.0978],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.1860, 0.1344, 0.1143, 0.1859, 0.0636, 0.1613, 0.0567, 0.0978],
device='cuda:1', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.0636, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9364, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-22 17:19:49,259] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| optimizer_allgather: 1.36 \| optimizer_gradients: 0.37 \| optimizer_step: 0.33
[2024-10-22 17:19:49,260] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward_microstep: 12288.91 \| backward_microstep: 12509.25 \| backward_inner_microstep: 10684.06 \| backward_allreduce_microstep: 1824.77 \| step_microstep: 7.80
[2024-10-22 17:19:49,260] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward: 12288.93 \| backward: 12509.24 \| backward_inner: 10684.28 \| backward_allreduce: 1824.72 \| step: 7.82

0%\| \| 3/2424 [01:21<17:40:19, 26.28s/it]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 white capped bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 16')
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='Is there an unworn knee pad to the right of a model's legs?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the sleepwear feature a Disney Princess theme on the front?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many pendant style lamps are above the bakery case in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many white capped bottles are in the image?'], responses:['many']
question: ['Does the sleepwear feature a Disney Princess theme on the front?'], responses:['no']
[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
[('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: 326
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
question: ['Is there an unworn knee pad to the right of a model'], responses:['yes']
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
[('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']]
tensor([0.6039, 0.1310, 0.1869, 0.0089, 0.0136, 0.0314, 0.0049, 0.0195],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
many *************
['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.6039, 0.1310, 0.1869, 0.0089, 0.0136, 0.0314, 0.0049, 0.0195],
device='cuda:0', grad_fn=<SelectBackward0>)
tensor([5.6103e-01, 4.3699e-01, 4.2379e-04, 1.2735e-04, 3.0892e-04, 5.4178e-04,
2.2134e-04, 3.5507e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.6103e-01, 4.3699e-01, 4.2379e-04, 1.2735e-04, 3.0892e-04, 5.4178e-04,
2.2134e-04, 3.5507e-04], device='cuda:3', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the mouth of the dog open?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
最后的概率分布为: {True: tensor(0.4370, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5610, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0020, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many paper towel rolls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many pendant style lamps are above the bakery case in the image?'], responses:['2']
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
[('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([5.0163e-01, 2.4089e-02, 4.7123e-01, 8.8055e-04, 1.5856e-04, 9.0009e-04,
2.1315e-04, 8.9567e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.0163e-01, 2.4089e-02, 4.7123e-01, 8.8055e-04, 1.5856e-04, 9.0009e-04,
2.1315e-04, 8.9567e-04], device='cuda:2', grad_fn=<SelectBackward0>)

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.