Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 1.8582e-10, 8.3112e-07, 1.8255e-11, 8.5650e-10, 3.6936e-08,
1.6099e-09, 4.1106e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.8582e-10, 8.3112e-07, 1.8255e-11, 8.5650e-10, 3.6936e-08,
1.6099e-09, 4.1106e-07], device='cuda:0', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 2.7853e-07, 2.0612e-09, 5.3687e-07, 3.3779e-10, 1.6478e-10,
4.0586e-10, 3.6587e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 2.7853e-07, 2.0612e-09, 5.3687e-07, 3.3779e-10, 1.6478e-10,
4.0586e-10, 3.6587e-11], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8582e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image contain a chair?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.8153e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a person in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many pandas are in the image?'], responses:['2']
question: ['How many seals are in the image?'], responses:['4']
[('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']]
[('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']]
question: ['Is there a person in the image?'], 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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Does the image contain a chair?'], responses:['no']
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([8.9384e-01, 1.1724e-04, 1.0604e-01, 9.6016e-10, 3.7492e-08, 3.7134e-09,
7.9274e-09, 1.0182e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([8.9384e-01, 1.1724e-04, 1.0604e-01, 9.6016e-10, 3.7492e-08, 3.7134e-09,
7.9274e-09, 1.0182e-08], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.7265e-06, 8.2332e-08, 1.9556e-08, 7.2353e-10, 4.6532e-10,
1.0045e-09, 1.8024e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.7265e-06, 8.2332e-08, 1.9556e-08, 7.2353e-10, 4.6532e-10,
1.0045e-09, 1.8024e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.9274e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.8307e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.8352e-08, 9.4998e-11, 1.4359e-07, 3.6731e-09, 1.3953e-09,
8.4954e-11, 1.8009e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.8352e-08, 9.4998e-11, 1.4359e-07, 3.6731e-09, 1.3953e-09,
8.4954e-11, 1.8009e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(9.4998e-11, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.3832e-07, device='cuda:3', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([1.0000e+00, 4.3543e-10, 5.7150e-07, 2.7036e-10, 1.6682e-09, 1.4629e-07,
2.5074e-09, 3.0910e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.3543e-10, 5.7150e-07, 2.7036e-10, 1.6682e-09, 1.4629e-07,
2.5074e-09, 3.0910e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.3543e-10, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.0729e-06, device='cuda:0', grad_fn=<SubBackward0>)}
[2024-10-24 09:39:26,397] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.46 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-24 09:39:26,397] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9061.43 | backward_microstep: 8747.18 | backward_inner_microstep: 8741.38 | backward_allreduce_microstep: 5.68 | step_microstep: 7.58
[2024-10-24 09:39:26,397] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9061.43 | backward: 8747.17 | backward_inner: 8741.40 | backward_allreduce: 5.66 | step: 7.59
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4561/4844 [18:58:10<1:12:20, 15.34s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT 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='Is the mouth of the dog open?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there a bed with mostly solid white pillows?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the image show a mirror over the sink?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is there a visible orange vegetable in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([5, 3, 448, 448])