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[2024-10-24 10:40:32,586] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7103.28 | backward: 6801.77 | backward_inner: 6767.82 | backward_allreduce: 33.78 | step: 10.93
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99%|ββββββββββ| 4807/4844 [19:59:16<08:27, 13.71s/it]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='How many mountain goats 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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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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ANSWER0=VQA(image=RIGHT,question='Are the golfballs in the image in shadow?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Are any hands holding the wine glasses?')
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ANSWER1=EVAL(expr='not {ANSWER0}')
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FINAL_ANSWER=RESULT(var=ANSWER1)torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How are the llamas standing?')
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ANSWER1=EVAL(expr='{ANSWER0} == "with their sides touching"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Are the golfballs in the image in shadow?'], 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([1, 3, 448, 448]) knan debug pixel values shape
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question: ['Are any hands holding the wine glasses?'], 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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tensor([1.0000e+00, 2.0817e-08, 6.2553e-08, 9.1990e-11, 1.2048e-10, 5.9939e-09,
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2.4010e-10, 1.2758e-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, 2.0817e-08, 6.2553e-08, 9.1990e-11, 1.2048e-10, 5.9939e-09,
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2.4010e-10, 1.2758e-07], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.0817e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many instruments 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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torch.Size([3, 3, 448, 448])
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question: ['How many instruments 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([3, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 2.9067e-09, 2.3453e-07, 5.9178e-09, 7.7088e-09, 2.2349e-07,
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2.1288e-08, 2.5971e-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, 2.9067e-09, 2.3453e-07, 5.9178e-09, 7.7088e-09, 2.2349e-07,
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2.1288e-08, 2.5971e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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question: ['How many mountain goats are in the image?'], responses:['1']
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(2.9067e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:3', grad_fn=<DivBackward0>)}
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question: ['How are the llamas standing?'], responses:['st']
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ANSWER0=VQA(image=LEFT,question='How many antelopes 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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[('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([3, 3, 448, 448])
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[('m', 0.12581326269018167), ('santa', 0.1253264086837026), ('broom', 0.12500836712840702), ('hood', 0.12488960815771866), ('virgin', 0.12482165846042056), ('batter', 0.12480295794283948), ('brand', 0.12468266634423), ('rear', 0.1246550705925001)]
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[['m', 'santa', 'broom', 'hood', 'virgin', 'batter', 'brand', 'rear']]
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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question: ['How many antelopes are in the image?'], responses:['1']
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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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([3, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 4.2868e-10, 2.1724e-10, 2.8444e-10, 3.4851e-10, 2.9349e-08,
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9.3829e-09, 3.1698e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.2868e-10, 2.1724e-10, 2.8444e-10, 3.4851e-10, 2.9349e-08,
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9.3829e-09, 3.1698e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.0328e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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tensor([1.0000e+00, 3.0997e-10, 4.3792e-11, 8.6074e-11, 5.4072e-11, 2.8157e-09,
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5.5159e-09, 4.5719e-11], 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, 3.0997e-10, 4.3792e-11, 8.6074e-11, 5.4072e-11, 2.8157e-09,
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5.5159e-09, 4.5719e-11], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.3553e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
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tensor([1.0000e+00, 2.8893e-10, 5.4928e-11, 1.2870e-10, 6.5210e-11, 1.0955e-08,
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3.2937e-09, 1.9646e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.8893e-10, 5.4928e-11, 1.2870e-10, 6.5210e-11, 1.0955e-08,
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3.2937e-09, 1.9646e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.4983e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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