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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([7, 3, 448, 448])
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question: ['How many people are standing on the platform near the train?'], 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: 3400
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question: ['Is the dog in the image against a white background?'], 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: 3400
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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: 3400
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tensor([1.0000e+00, 5.6750e-09, 8.7774e-11, 5.1736e-09, 4.3716e-10, 5.1306e-10,
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5.6539e-11, 1.2264e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.6750e-09, 8.7774e-11, 5.1736e-09, 4.3716e-10, 5.1306e-10,
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5.6539e-11, 1.2264e-08], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(8.7774e-11, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-8.7774e-11, device='cuda:2', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Does the image contain a person wearing a black blazer?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([1, 3, 448, 448])
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tensor([9.8756e-01, 1.2433e-02, 1.9203e-07, 4.3023e-06, 2.3659e-08, 2.6339e-09,
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4.6317e-08, 1.0216e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.8756e-01, 1.2433e-02, 1.9203e-07, 4.3023e-06, 2.3659e-08, 2.6339e-09,
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4.6317e-08, 1.0216e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0.9876, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0124, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['Does the image contain a person wearing a black blazer?'], responses:['no']
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ANSWER0=VQA(image=LEFT,question='Is there a vase of flowers in the image?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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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([7, 3, 448, 448])
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torch.Size([1, 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: 3400
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tensor([1.0000e+00, 1.3308e-09, 4.4459e-07, 2.5201e-10, 3.2768e-09, 1.1431e-07,
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9.3161e-10, 2.3072e-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, 1.3308e-09, 4.4459e-07, 2.5201e-10, 3.2768e-09, 1.1431e-07,
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9.3161e-10, 2.3072e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.3308e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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question: ['Is there a vase of flowers 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: 3400
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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 1.7357e-09, 1.1945e-06, 2.9694e-10, 3.8193e-10, 9.9986e-08,
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8.8288e-09, 1.8528e-06], 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, 1.7357e-09, 1.1945e-06, 2.9694e-10, 3.8193e-10, 9.9986e-08,
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8.8288e-09, 1.8528e-06], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.7357e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.0994e-06, device='cuda:1', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
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tensor([9.9998e-01, 9.3447e-07, 6.8367e-07, 2.8949e-07, 5.5390e-08, 3.7987e-07,
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1.5631e-05, 5.3353e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9998e-01, 9.3447e-07, 6.8367e-07, 2.8949e-07, 5.5390e-08, 3.7987e-07,
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1.5631e-05, 5.3353e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.7979e-05, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the bottle on the right have a blue label?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([7, 3, 448, 448])
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tensor([1.0000e+00, 1.0124e-09, 3.9247e-07, 9.8125e-10, 1.7255e-09, 3.7591e-07,
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2.6339e-08, 2.8094e-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, 1.0124e-09, 3.9247e-07, 9.8125e-10, 1.7255e-09, 3.7591e-07,
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2.6339e-08, 2.8094e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0124e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['Does the bottle on the right have a blue label?'], responses:['yes']
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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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
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tensor([1.0000e+00, 4.4316e-09, 4.2292e-09, 3.6834e-09, 4.5265e-11, 2.2543e-11,
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4.6773e-12, 4.9338e-09], 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, 4.4316e-09, 4.2292e-09, 3.6834e-09, 4.5265e-11, 2.2543e-11,
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4.6773e-12, 4.9338e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(4.2292e-09, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-4.2292e-09, device='cuda:0', grad_fn=<SubBackward0>)}
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[2024-10-24 10:44:38,072] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.44 | optimizer_gradients: 0.38 | optimizer_step: 0.33
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[2024-10-24 10:44:38,072] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7127.60 | backward_microstep: 6796.44 | backward_inner_microstep: 6790.62 | backward_allreduce_microstep: 5.71 | step_microstep: 12.55
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[2024-10-24 10:44:38,073] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7127.61 | backward: 6796.43 | backward_inner: 6790.64 | backward_allreduce: 5.65 | step: 12.56
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100%|ββββββββββ| 4823/4844 [20:03:21<05:16, 15.08s/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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