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question: ['How many wolves 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([7, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 1.8294e-10, 3.8047e-11, 1.1717e-10, 4.7712e-11, 4.4298e-09,
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1.5558e-09, 3.7939e-11], 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, 1.8294e-10, 3.8047e-11, 1.1717e-10, 4.7712e-11, 4.4298e-09,
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1.5558e-09, 3.7939e-11], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.4095e-09, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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[2024-10-24 10:38:32,742] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.30 | optimizer_step: 0.32
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[2024-10-24 10:38:32,743] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3811.71 | backward_microstep: 10067.28 | backward_inner_microstep: 3532.36 | backward_allreduce_microstep: 6534.79 | step_microstep: 7.71
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[2024-10-24 10:38:32,743] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3811.73 | backward: 10067.27 | backward_inner: 3532.38 | backward_allreduce: 6534.78 | step: 7.72
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99%|ββββββββββ| 4798/4844 [19:57:16<11:07, 14.52s/it]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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ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} <= 4')
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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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ANSWER0=VQA(image=LEFT,question='How many rolls of paper towels are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 6')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='Does the bookshelf cover an entire right-angle corner?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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ANSWER0=VQA(image=LEFT,question='What are the sled dogs doing?')
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ANSWER1=EVAL(expr='{ANSWER0} == "taking a break"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([4, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['How many binders are in the image?'], responses:['37']
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[('37', 0.12602601760154822), ('38', 0.12520142583637134), ('39', 0.12518201874785773), ('36', 0.12516664760231044), ('47', 0.12478763564484581), ('42', 0.12462790950563608), ('41', 0.12453088059191597), ('46', 0.12447746446951438)]
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[['37', '38', '39', '36', '47', '42', '41', '46']]
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torch.Size([4, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
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question: ['What are the sled dogs doing?'], responses:['running']
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question: ['Does the bookshelf cover an entire right-angle corner?'], responses:['yes']
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[('running', 0.12933164657582716), ('kicking', 0.12519228146951575), ('talking', 0.12468272401050465), ('shopping', 0.12439383412649963), ('waving', 0.1242157080242936), ('throwing', 0.12417530658671574), ('falling', 0.12401927267239059), ('feeding', 0.12398922653425282)]
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[['running', 'kicking', 'talking', 'shopping', 'waving', 'throwing', 'falling', 'feeding']]
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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: 4, images per sample: 4.0, dynamic token length: 1093
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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: 4, images per sample: 4.0, dynamic token length: 1093
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
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question: ['How many rolls of paper towels are in the image?'], responses:['1']
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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
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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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dynamic ViT batch size: 4, images per sample: 4.0, dynamic token length: 1093
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tensor([0.5657, 0.0161, 0.1350, 0.0600, 0.1358, 0.0082, 0.0448, 0.0343],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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37 *************
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['37', '38', '39', '36', '47', '42', '41', '46'] tensor([0.5657, 0.0161, 0.1350, 0.0600, 0.1358, 0.0082, 0.0448, 0.0343],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Is the sky 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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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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torch.Size([13, 3, 448, 448])
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tensor([9.8600e-01, 7.2237e-03, 1.2158e-03, 5.8390e-05, 4.4137e-03, 6.9762e-04,
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3.1785e-04, 6.9201e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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running *************
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['running', 'kicking', 'talking', 'shopping', 'waving', 'throwing', 'falling', 'feeding'] tensor([9.8600e-01, 7.2237e-03, 1.2158e-03, 5.8390e-05, 4.4137e-03, 6.9762e-04,
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3.1785e-04, 6.9201e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 3.3860e-09, 6.0236e-08, 9.6343e-09, 2.3928e-11, 3.5463e-11,
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9.5106e-12, 1.5916e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.3860e-09, 6.0236e-08, 9.6343e-09, 2.3928e-11, 3.5463e-11,
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9.5106e-12, 1.5916e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(6.0236e-08, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.8974e-08, device='cuda:3', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='Can the shelf unit stand on its own?')
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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 rodents 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([5, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['Is the sky visible 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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question: ['Can the shelf unit stand on its own?'], responses:['yes']
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torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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