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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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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.6919e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.6919e-10, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Are there pelicans in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([5, 3, 448, 448])
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['Are there pelicans in the image?'], responses:['no']
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tensor([1.0000e+00, 3.0289e-08, 3.5177e-07, 9.5435e-12, 1.2968e-10, 1.7250e-08,
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2.5070e-10, 4.4867e-07], device='cuda:0', 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, 3.0289e-08, 3.5177e-07, 9.5435e-12, 1.2968e-10, 1.7250e-08,
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2.5070e-10, 4.4867e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 1.4450e-07, 1.1496e-08, 1.4761e-08, 2.6826e-10, 7.8828e-10,
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6.7434e-10, 1.7319e-10], 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, 1.4450e-07, 1.1496e-08, 1.4761e-08, 2.6826e-10, 7.8828e-10,
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6.7434e-10, 1.7319e-10], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.0289e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.7486e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the car on the right light blue?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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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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ζεηζ¦ηεεΈδΈΊ: torch.Size([3, 3, 448, 448])
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{True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.7266e-07, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='How many hounds 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([7, 3, 448, 448])
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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question: ['Is the car on the right light blue?'], 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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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: 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: 838
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question: ['How many hounds are in the image?'], responses:['δΈ']
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
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[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
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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: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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tensor([9.9999e-01, 3.3195e-09, 2.5426e-07, 1.3114e-09, 1.2846e-08, 1.0175e-06,
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1.8138e-07, 4.0761e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9999e-01, 3.3195e-09, 2.5426e-07, 1.3114e-09, 1.2846e-08, 1.0175e-06,
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1.8138e-07, 4.0761e-06], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.3195e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.6028e-06, device='cuda:0', grad_fn=<SubBackward0>)}
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tensor([1.0000e+00, 7.5826e-10, 2.5894e-07, 2.0207e-11, 4.5100e-11, 2.4325e-08,
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6.9892e-10, 3.4646e-07], device='cuda:1', 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, 7.5826e-10, 2.5894e-07, 2.0207e-11, 4.5100e-11, 2.4325e-08,
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6.9892e-10, 3.4646e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(7.5826e-10, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([1.6861e-04, 2.0103e-03, 5.7227e-02, 7.2853e-01, 1.2419e-01, 4.1800e-02,
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4.1267e-03, 4.1944e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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bulldog *************
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['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([1.6861e-04, 2.0103e-03, 5.7227e-02, 7.2853e-01, 1.2419e-01, 4.1800e-02,
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4.1267e-03, 4.1944e-02], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {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>)}
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tensor([1.0000e+00, 1.5588e-09, 4.0737e-07, 2.3723e-09, 1.8252e-08, 2.9839e-07,
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2.9288e-08, 5.7151e-07], device='cuda:2', 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, 1.5588e-09, 4.0737e-07, 2.3723e-09, 1.8252e-08, 2.9839e-07,
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2.9288e-08, 5.7151e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.5588e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.3113e-06, device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-24 10:41:31,862] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.38 | optimizer_step: 0.35
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[2024-10-24 10:41:31,863] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5718.02 | backward_microstep: 8130.89 | backward_inner_microstep: 5454.63 | backward_allreduce_microstep: 2676.17 | step_microstep: 7.76
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[2024-10-24 10:41:31,863] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5718.02 | backward: 8130.88 | backward_inner: 5454.67 | backward_allreduce: 2676.16 | step: 7.77
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99%|ββββββββββ| 4811/4844 [20:00:15<08:02, 14.63s/it]Registering VQA_lavis 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 VQA_lavis 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='Is the stairway bordered with glass panels?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=RIGHT,question='Is there an animal laying bleeding in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='Is there a silver lamp with white lights 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=LEFT,question='How many frames are on the wall in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} > 5')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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