Datasets:
TMarcus
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.7094e-09, 4.5447e-07, 5.1415e-09, 1.5213e-08, 7.7561e-07,
8.6764e-09, 1.6163e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.7094e-09, 4.5447e-07, 5.1415e-09, 1.5213e-08, 7.7561e-07,
8.6764e-09, 1.6163e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.7094e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.4305e-06, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 1.6694e-07, 3.5384e-08, 3.5658e-08, 5.2053e-10, 3.7295e-09,
2.1464e-09, 1.8093e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.6694e-07, 3.5384e-08, 3.5658e-08, 5.2053e-10, 3.7295e-09,
2.1464e-09, 1.8093e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.5384e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.0303e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:23:06,299] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-24 09:23:06,300] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7063.62 | backward_microstep: 6911.69 | backward_inner_microstep: 6784.17 | backward_allreduce_microstep: 127.44 | step_microstep: 7.68
[2024-10-24 09:23:06,300] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7063.63 | backward: 6911.68 | backward_inner: 6784.20 | backward_allreduce: 127.41 | step: 7.70
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4495/4844 [18:41:50<1:16:01, 13.07s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are some pizzas raised on stands in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='What color are the vases?')
ANSWER1=EVAL(expr='{ANSWER0} == "silver"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many flute parts are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many seals are in the image?'], responses:['2']
[('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([3, 3, 448, 448]) knan debug pixel values shape
question: ['Are some pizzas raised on stands in the image?'], responses:['yes']
question: ['What color are the vases?'], responses:['green']
[('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']]
[('green', 0.1326115459908909), ('yellow', 0.12668030247077625), ('red', 0.12551779073733718), ('wild', 0.12324669870262604), ('orange and blue', 0.12319974118412196), ('bronze', 0.1230515752050065), ('pink', 0.12286305245049417), ('red white blue', 0.12282929325874692)]
[['green', 'yellow', 'red', 'wild', 'orange and blue', 'bronze', 'pink', 'red white blue']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
tensor([1.0000e+00, 2.9357e-08, 8.5432e-09, 7.4225e-09, 1.4303e-10, 4.9922e-10,
4.4749e-10, 7.0433e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.9357e-08, 8.5432e-09, 7.4225e-09, 1.4303e-10, 4.9922e-10,
4.4749e-10, 7.0433e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.4225e-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>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many flute parts are in the image?'], responses:['2']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([1.0000e+00, 6.2073e-09, 8.3278e-10, 7.2237e-09, 8.6878e-12, 8.2457e-11,
5.6574e-12, 8.9415e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.2073e-09, 8.3278e-10, 7.2237e-09, 8.6878e-12, 8.2457e-11,
5.6574e-12, 8.9415e-09], device='cuda:0', grad_fn=<SelectBackward0>)
question: ['How many dogs are in the image?'], responses:['2']
tensor([9.9955e-01, 2.5958e-06, 1.5679e-05, 6.4103e-07, 3.2355e-05, 2.2139e-04,
1.6226e-04, 1.5543e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
green *************
['green', 'yellow', 'red', 'wild', 'orange and blue', 'bronze', 'pink', 'red white blue'] tensor([9.9955e-01, 2.5958e-06, 1.5679e-05, 6.4103e-07, 3.2355e-05, 2.2139e-04,
1.6226e-04, 1.5543e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(8.3278e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.3278e-10, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many pug dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {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>)}
[('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']]
ANSWER0=VQA(image=RIGHT,question='How many cheetahs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')