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tensor([2.9269e-05, 3.0170e-02, 2.0404e-01, 1.1243e-02, 3.1701e-01, 1.3963e-01,
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4.0410e-03, 2.9384e-01], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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virgin *************
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['m', 'santa', 'broom', 'hood', 'virgin', 'batter', 'brand', 'rear'] tensor([2.9269e-05, 3.0170e-02, 2.0404e-01, 1.1243e-02, 3.1701e-01, 1.3963e-01,
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4.0410e-03, 2.9384e-01], device='cuda:0', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=LEFT,question='How many animals 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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ζεηζ¦ηεεΈδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Does the image show an oblong bowl-shaped sink?')
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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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torch.Size([1, 3, 448, 448])
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question: ['Does the image show an oblong bowl-shaped sink?'], 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([1, 3, 448, 448]) knan debug pixel values shape
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 327
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
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dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
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tensor([1.0000e+00, 8.2055e-10, 1.3809e-10, 3.0459e-09, 7.0369e-11, 2.0685e-11,
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1.6267e-11, 3.5210e-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, 8.2055e-10, 1.3809e-10, 3.0459e-09, 7.0369e-11, 2.0685e-11,
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1.6267e-11, 3.5210e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.3809e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.3809e-10, device='cuda:0', grad_fn=<DivBackward0>)}
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question: ['How many animals 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, 1.2212e-09, 3.6381e-10, 4.3882e-10, 2.1712e-10, 5.9269e-08,
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6.6017e-09, 1.8475e-09], 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, 1.2212e-09, 3.6381e-10, 4.3882e-10, 2.1712e-10, 5.9269e-08,
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6.6017e-09, 1.8475e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.9959e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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[2024-10-24 10:40:46,396] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.33 | optimizer_step: 0.32
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[2024-10-24 10:40:46,396] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5158.55 | backward_microstep: 8623.66 | backward_inner_microstep: 4887.11 | backward_allreduce_microstep: 3736.43 | step_microstep: 7.64
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[2024-10-24 10:40:46,396] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5158.55 | backward: 8623.65 | backward_inner: 4887.18 | backward_allreduce: 3736.31 | step: 7.66
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99%|ββββββββββ| 4808/4844 [19:59:30<08:14, 13.74s/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=LEFT,question='What is the posture of the dog in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == "side profile"')
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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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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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ANSWER0=VQA(image=LEFT,question='How many glass jar dispensers 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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Registering EVAL step
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Registering RESULT step
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ANSWER0=VQA(image=RIGHT,question='Is the dog wearing a human-like accessory?')
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ANSWER1=RESULT(var=ANSWER0)
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torch.Size([1, 3, 448, 448])
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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How many orange spoons 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([7, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['What is the posture of the dog in the image?'], responses:['s']
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question: ['How many glass jar dispensers are in the image?'], responses:['1']
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[('s', 0.1292678692397635), ('m', 0.12600113012185044), ('d', 0.125059711934273), ('closet', 0.12412510541505085), ('h', 0.12402688750925138), ('l', 0.12397377741182078), ('tan', 0.12384923745478878), ('striped', 0.12369628091320145)]
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[['s', 'm', 'd', 'closet', 'h', 'l', 'tan', 'striped']]
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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([1, 3, 448, 448]) knan debug pixel values shape
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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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tensor([1.0000e+00, 3.3845e-10, 6.8359e-11, 1.4247e-10, 1.5404e-10, 6.9703e-09,
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6.5503e-09, 2.1624e-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, 3.3845e-10, 6.8359e-11, 1.4247e-10, 1.5404e-10, 6.9703e-09,
|
6.5503e-09, 2.1624e-10], device='cuda:2', grad_fn=<SelectBackward0>)
|
ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.5503e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many vending machines 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([5, 3, 448, 448])
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tensor([1.9243e-04, 4.0765e-06, 8.5836e-04, 6.0729e-02, 3.5552e-05, 3.2583e-04,
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1.6528e-01, 7.7258e-01], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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striped *************
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['s', 'm', 'd', 'closet', 'h', 'l', 'tan', 'striped'] tensor([1.9243e-04, 4.0765e-06, 8.5836e-04, 6.0729e-02, 3.5552e-05, 3.2583e-04,
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1.6528e-01, 7.7258e-01], device='cuda:3', grad_fn=<SelectBackward0>)
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question: ['Is the dog wearing a human-like accessory?'], responses:['yes']
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question: ['How many orange spoons are in the image?'], responses:['0']
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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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ANSWER0=VQA(image=LEFT,question='How many syringes are in the image?')
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