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petrel_client is not installed. If you read data locally instead of from ceph, ignore it.
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Replace train sampler!!
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petrel_client is not installed. Using PIL to load images.
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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=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([13, 3, 448, 448])
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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='How many vases 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=LEFT,question='How many sea lions 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 EVAL step
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Registering RESULT step
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How many people are in the car?')
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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 people are in the car?'], 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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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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question: ['How many sea lions are in the image?'], responses:['3']
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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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: 3, images per sample: 3.0, dynamic token length: 836
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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: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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question: ['How many dogs are in the image?'], responses:['2']
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 836
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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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question: ['How many vases are in the image?'], responses:['1']
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tensor([9.9968e-01, 1.6338e-06, 4.6192e-06, 1.4704e-06, 2.1814e-06, 2.2337e-04,
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8.3464e-05, 9.4052e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9968e-01, 1.6338e-06, 4.6192e-06, 1.4704e-06, 2.1814e-06, 2.2337e-04,
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8.3464e-05, 9.4052e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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最后的概率分布为: {True: tensor(0.0003, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9997, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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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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ANSWER0=VQA(image=LEFT,question='Is there a brown dog sitting on the ground 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([13, 3, 448, 448])
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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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tensor([3.7736e-01, 6.2215e-01, 4.4415e-09, 4.8264e-04, 6.8518e-09, 2.1572e-08,
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2.5074e-07, 4.1156e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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4 *************
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['3', '4', '1', '5', '8', '2', '6', '12'] tensor([3.7736e-01, 6.2215e-01, 4.4415e-09, 4.8264e-04, 6.8518e-09, 2.1572e-08,
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2.5074e-07, 4.1156e-08], device='cuda:1', grad_fn=<SelectBackward0>)
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最后的概率分布为: {True: tensor(4.4415e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the water buffalo facing towards the right?')
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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: ['Is there a brown dog sitting on the ground 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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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: 3401
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question: ['Is the water buffalo facing towards the right?'], 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: 3401
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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: 3402
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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tensor([1.0000e+00, 1.0171e-07, 2.9126e-08, 1.6330e-08, 1.6057e-09, 5.8653e-09,
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3.2682e-09, 4.6414e-09], 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, 1.0171e-07, 2.9126e-08, 1.6330e-08, 1.6057e-09, 5.8653e-09,
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3.2682e-09, 4.6414e-09], device='cuda:3', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
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最后的概率分布为: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.6254e-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 mittens 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([1, 3, 448, 448])
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tensor([1.0000e+00, 2.3375e-10, 5.1895e-12, 1.0003e-11, 2.2896e-11, 2.6047e-09,
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7.0423e-08, 2.1380e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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