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1.0145e-08, 8.4428e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.1957e-10, 1.4472e-10, 2.9810e-10, 2.2414e-10, 2.4337e-08,
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1.0145e-08, 8.4428e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.6414e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many dogs 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([7, 3, 448, 448])
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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tensor([1.0000e+00, 2.8113e-10, 7.9920e-11, 1.4136e-10, 5.0799e-11, 1.2237e-08,
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2.7199e-09, 2.7263e-10], 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, 2.8113e-10, 7.9920e-11, 1.4136e-10, 5.0799e-11, 1.2237e-08,
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2.7199e-09, 2.7263e-10], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(2.8113e-10, 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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question: ['How many dogs are in the image?'], responses:['2']
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[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
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[['2', '3', '4', '1', '5', '8', '7', '29']]
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 9.7215e-07, 7.1124e-07, 3.4518e-09, 2.5452e-09, 6.4488e-09,
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4.0355e-09, 1.5166e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 9.7215e-07, 7.1124e-07, 3.4518e-09, 2.5452e-09, 6.4488e-09,
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4.0355e-09, 1.5166e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.7000e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 1.9751e-07, 1.6727e-08, 6.6156e-08, 4.2931e-10, 1.0243e-09,
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1.3730e-09, 9.3398e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.9751e-07, 1.6727e-08, 6.6156e-08, 4.2931e-10, 1.0243e-09,
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1.3730e-09, 9.3398e-11], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.8331e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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[2024-10-24 10:44:52,116] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.26 | optimizer_step: 0.31
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[2024-10-24 10:44:52,117] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5155.21 | backward_microstep: 8864.28 | backward_inner_microstep: 4869.08 | backward_allreduce_microstep: 3995.14 | step_microstep: 7.23
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[2024-10-24 10:44:52,117] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5155.21 | backward: 8864.27 | backward_inner: 4869.09 | backward_allreduce: 3995.13 | step: 7.24
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100%|ββββββββββ| 4824/4844 [20:03:35<04:55, 14.77s/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 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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ANSWER0=VQA(image=RIGHT,question='Are the primate's fangs fully shown?')
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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='Are the roll bars visible in the image?')
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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='How many colorful parrots 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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ANSWER0=VQA(image=RIGHT,question='Does the image contain a green and yellow train?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([3, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([3, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many colorful parrots are in the image?'], responses:['3']
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question: ['Does the image contain a green and yellow train?'], responses:['no']
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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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[('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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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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torch.Size([3, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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question: ['Are the roll bars visible in the image?'], responses:['yes']
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question: ['Are the primate'], responses:['yes']
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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[('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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[('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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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
|
tensor([9.9830e-01, 1.7002e-03, 7.1256e-09, 9.9109e-08, 5.4809e-11, 3.7332e-08,
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2.9621e-10, 3.0636e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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3 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9830e-01, 1.7002e-03, 7.1256e-09, 9.9109e-08, 5.4809e-11, 3.7332e-08,
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2.9621e-10, 3.0636e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(7.1256e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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tensor([1.0000e+00, 2.4617e-10, 3.9680e-07, 2.4323e-12, 2.8411e-11, 3.2295e-09,
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1.8029e-10, 7.7788e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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no *************
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