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question: ['Are multiple tracks visible in the image?'], responses:['no']
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[('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)]
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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3406
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
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question: ['How many mittens are in the image?'], responses:['3']
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[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
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[['3', '4', '1', '5', '8', '2', '6', '12']]
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3407
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 9.6427e-10, 1.6789e-06, 2.8838e-10, 3.5812e-11, 2.3018e-12,
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5.5242e-12, 8.1162e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.6427e-10, 1.6789e-06, 2.8838e-10, 3.5812e-11, 2.3018e-12,
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5.5242e-12, 8.1162e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.6789e-06, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.0007e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many cheetahs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 2')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([13, 3, 448, 448])
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tensor([1.0000e+00, 2.8737e-07, 1.7427e-07, 1.2573e-10, 2.1279e-10, 4.6030e-09,
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1.8877e-10, 1.6932e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.8737e-07, 1.7427e-07, 1.2573e-10, 2.1279e-10, 4.6030e-09,
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1.8877e-10, 1.6932e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.8737e-07, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.9802e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How many cheetahs are in the image?'], responses:['1']
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[('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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[['1', '3', '4', '8', '6', '12', '2', '47']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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tensor([9.6265e-01, 9.7517e-06, 3.9031e-07, 7.7309e-09, 3.2138e-10, 3.7344e-02,
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9.0368e-09, 4.7621e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.6265e-01, 9.7517e-06, 3.9031e-07, 7.7309e-09, 3.2138e-10, 3.7344e-02,
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9.0368e-09, 4.7621e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.9031e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
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tensor([1.0000e+00, 3.6417e-10, 5.2005e-11, 5.8923e-11, 5.5322e-11, 7.6531e-09,
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3.7323e-09, 6.1331e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.6417e-10, 5.2005e-11, 5.8923e-11, 5.5322e-11, 7.6531e-09,
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3.7323e-09, 6.1331e-11], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.1977e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-24 10:47:43,408] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.46 | optimizer_gradients: 0.25 | optimizer_step: 0.32
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[2024-10-24 10:47:43,408] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9079.19 | backward_microstep: 8740.93 | backward_inner_microstep: 8735.04 | backward_allreduce_microstep: 5.78 | step_microstep: 7.63
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[2024-10-24 10:47:43,408] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9079.20 | backward: 8740.92 | backward_inner: 8735.08 | backward_allreduce: 5.75 | step: 7.64
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100%|ββββββββββ| 4836/4844 [20:06:27<01:54, 14.28s/it]Registering VQA_lavis step
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Registering VQA_lavis step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering EVAL step
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Registering RESULT step
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Registering VQA_lavis step
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Registering EVAL step
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Registering RESULT step
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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Does the image contain at least one pair of legs?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Do the doors in the image open to a grassy area?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=RIGHT,question='How many black labs are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 1')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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torch.Size([5, 3, 448, 448])
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torch.Size([11, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many binders are in the image?'], responses:['4']
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[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
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[['4', '5', '3', '8', '6', '1', '2', '11']]
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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question: ['Does the image contain at least one pair of legs?'], responses:['yes']
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
|
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
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[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
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tensor([8.9900e-01, 1.2613e-04, 1.0087e-01, 7.3383e-09, 7.1255e-08, 7.8395e-07,
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5.0390e-07, 3.4737e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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4 *************
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