Datasets:
TMarcus
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InternVL

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1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.7323e-09, 5.9983e-10, 2.3539e-09, 1.3623e-09, 2.1144e-08,
3.5411e-08, 5.6453e-10], device='cuda:3', grad_fn=<SelectBackward0>)
question: ['How many rodents are in the image?'], responses:['11']
tensor([9.6271e-01, 1.6455e-02, 2.1118e-06, 3.4297e-03, 4.7139e-03, 3.6710e-03,
4.0586e-03, 4.9615e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: tensor([9.6271e-01, 1.6455e-02, 2.1118e-06, 3.4297e-03, 4.7139e-03, 3.6710e-03,
4.0586e-03, 4.9615e-03], device='cuda:1', grad_fn=<SelectBackward0>)
{True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.5168e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
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(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([3, 3, 448, 448])
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['How many people 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
tensor([1.0000e+00, 2.8737e-07, 1.5382e-07, 3.5411e-08, 1.3730e-09, 1.8914e-09,
7.4880e-09, 5.9053e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.8737e-07, 1.5382e-07, 3.5411e-08, 1.3730e-09, 1.8914e-09,
7.4880e-09, 5.9053e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.6516e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many animals 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([8.4127e-01, 4.0298e-04, 3.0666e-02, 8.0425e-06, 7.9823e-09, 7.7014e-02,
9.3611e-08, 5.0640e-02], device='cuda:2', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([8.4127e-01, 4.0298e-04, 3.0666e-02, 8.0425e-06, 7.9823e-09, 7.7014e-02,
9.3611e-08, 5.0640e-02], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9972e-01, 2.7869e-04, 1.7022e-08, 3.7697e-07, 1.9597e-08, 9.2569e-09,
1.1598e-07, 4.2197e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9972e-01, 2.7869e-04, 1.7022e-08, 3.7697e-07, 1.9597e-08, 9.2569e-09,
1.1598e-07, 4.2197e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9997, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0003, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 09:49:04,718] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.32 | optimizer_step: 0.33
[2024-10-24 09:49:04,718] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 1843.09 | backward_microstep: 12031.09 | backward_inner_microstep: 1690.51 | backward_allreduce_microstep: 10340.47 | step_microstep: 7.84
[2024-10-24 09:49:04,719] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 1843.09 | backward: 12031.08 | backward_inner: 1690.56 | backward_allreduce: 10340.40 | step: 7.86
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4600/4844 [19:07:48<1:02:36, 15.40s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the golfball in the image have a square-shaped design?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image show birds standing in grass?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='How many guinea pigs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many mirrors hang over the sinks?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does the golfball in the image have a square-shaped design?'], 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 332
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
question: ['Does the image show birds standing in grass?'], responses:['no']
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
[('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']]
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
tensor([6.2633e-04, 3.6232e-09, 9.9937e-01, 4.7253e-10, 3.3566e-13, 2.7486e-13,
1.8867e-11, 1.1439e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([6.2633e-04, 3.6232e-09, 9.9937e-01, 4.7253e-10, 3.3566e-13, 2.7486e-13,
1.8867e-11, 1.1439e-09], device='cuda:0', grad_fn=<SelectBackward0>)