Split (1)

train · 714k rows

text stringlengths 0 1.16k
ANSWER0=VQA(image=LEFT,question='What color is the stingray?')
ANSWER1=EVAL(expr='{ANSWER0} == "black"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
最后的概率分布为: {True: tensor(2.7036e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many ferrets are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['What color is the stingray?'], responses:['gray']
[('gray', 0.1265382865690464), ('blue', 0.12487099708489514), ('cord', 0.12481778563179191), ('woods', 0.12480249232797942), ('teddy', 0.12479520368375643), ('harley', 0.12475495450660062), ('shadow', 0.1247269487809549), ('swan', 0.12469333141497527)]
[['gray', 'blue', 'cord', 'woods', 'teddy', 'harley', 'shadow', 'swan']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many pillows are in the image?'], responses:['4']
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
tensor([3.5563e-01, 6.4434e-01, 1.0391e-08, 1.4520e-06, 3.0388e-06, 4.2985e-06,
3.0140e-06, 2.3210e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
blue *************
['gray', 'blue', 'cord', 'woods', 'teddy', 'harley', 'shadow', 'swan'] tensor([3.5563e-01, 6.4434e-01, 1.0391e-08, 1.4520e-06, 3.0388e-06, 4.2985e-06,
3.0140e-06, 2.3210e-05], device='cuda:2', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
question: ['How many ferrets are in the image?'], responses:['3']
[('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']]
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
tensor([9.9456e-01, 5.4328e-03, 2.7971e-06, 1.8463e-09, 8.3742e-07, 1.4711e-07,
7.2672e-09, 4.4146e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9456e-01, 5.4328e-03, 2.7971e-06, 1.8463e-09, 8.3742e-07, 1.4711e-07,
7.2672e-09, 4.4146e-08], device='cuda:0', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.0054, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9946, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9999e-01, 1.3818e-05, 3.6817e-07, 2.8168e-09, 3.1079e-11, 7.7714e-08,
4.6446e-11, 9.2568e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 1.3818e-05, 3.6817e-07, 2.8168e-09, 3.1079e-11, 7.7714e-08,
4.6446e-11, 9.2568e-09], device='cuda:1', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(3.6817e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:27:43,791] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| optimizer_allgather: 1.37 \| optimizer_gradients: 0.33 \| optimizer_step: 0.32
[2024-10-24 10:27:43,792] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward_microstep: 6417.38 \| backward_microstep: 11356.16 \| backward_inner_microstep: 6187.41 \| backward_allreduce_microstep: 5168.46 \| step_microstep: 10.71
[2024-10-24 10:27:43,792] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) \| forward: 6417.38 \| backward: 11356.14 \| backward_inner: 6187.57 \| backward_allreduce: 5168.39 \| step: 10.73

98%\|█████████▊\| 4755/4844 [19:46:27<21:28, 14.48s/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=LEFT,question='How many zebras are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many convertible vehicles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many different animal species are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many zebras are in the image?'], responses:['four']
question: ['How many convertible vehicles are in the image?'], responses:['2']
question: ['How many bottles are in the image?'], responses:['3']
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)]
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']]
[('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']]
[('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']]
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: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many different animal species are in the image?'], responses:['1']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]

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