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5 *************
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['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.3476, 0.0567, 0.0934, 0.3067, 0.0196, 0.1358, 0.0079, 0.0323],
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device='cuda:2', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
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tensor([6.6448e-01, 3.3412e-01, 2.4019e-04, 1.3032e-04, 2.5970e-04, 1.2729e-04,
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2.1455e-04, 4.2731e-04], 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([6.6448e-01, 3.3412e-01, 2.4019e-04, 1.3032e-04, 2.5970e-04, 1.2729e-04,
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2.1455e-04, 4.2731e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.3341, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.6645, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0014, device='cuda:3', grad_fn=<DivBackward0>)}
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[2024-10-22 17:14:10,922] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.32
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[2024-10-22 17:14:10,922] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6450.13 | backward_microstep: 11415.34 | backward_inner_microstep: 6174.60 | backward_allreduce_microstep: 5240.63 | step_microstep: 7.71
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[2024-10-22 17:14:10,923] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6450.14 | backward: 11415.33 | backward_inner: 6174.62 | backward_allreduce: 5240.61 | step: 7.72
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0%| | 7/4844 [01:54<21:14:06, 15.80s/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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ANSWER0=VQA(image=RIGHT,question='How many white capped bottles are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 16')
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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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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 paper towel rolls 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([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How many pendant style lamps are above the bakery case 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 chimneys 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([7, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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torch.Size([7, 3, 448, 448])
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question: ['How many white capped bottles are in the image?'], responses:['many']
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[('many', 0.12680051474066337), ('few', 0.12559712123098582), ('several', 0.12545126119006317), ('blinds', 0.12452572291517987), ('moss', 0.12441899466837554), ('rainbow', 0.1244056457460399), ('kite', 0.12440323404357946), ('directions', 0.12439750546511286)]
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[['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions']]
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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: 326
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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: 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: 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: 327
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tensor([0.6078, 0.1292, 0.1864, 0.0088, 0.0129, 0.0314, 0.0049, 0.0186],
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device='cuda:0', grad_fn=<SoftmaxBackward0>)
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many *************
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['many', 'few', 'several', 'blinds', 'moss', 'rainbow', 'kite', 'directions'] tensor([0.6078, 0.1292, 0.1864, 0.0088, 0.0129, 0.0314, 0.0049, 0.0186],
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device='cuda:0', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {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=LEFT,question='How many white 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([7, 3, 448, 448])
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question: ['How many pendant style lamps are above the bakery case in the image?'], responses:['2']
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question: ['How many chimneys are in the image?'], responses:['1']
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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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[('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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torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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question: ['How many paper towel rolls 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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question: ['How many white dogs 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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torch.Size([13, 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: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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tensor([4.2138e-01, 2.8963e-01, 1.3680e-01, 7.7949e-02, 5.0256e-02, 1.0549e-02,
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1.3128e-02, 3.0856e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.2138e-01, 2.8963e-01, 1.3680e-01, 7.7949e-02, 5.0256e-02, 1.0549e-02,
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1.3128e-02, 3.0856e-04], device='cuda:1', grad_fn=<SelectBackward0>)
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ๆๅ็ๆฆ็ๅๅธไธบ: {True: tensor(0.9221, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0779, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
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tensor([6.0921e-01, 7.2707e-02, 2.0851e-02, 2.6501e-03, 5.6106e-03, 1.4179e-03,
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2.8748e-01, 7.2847e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([6.0921e-01, 7.2707e-02, 2.0851e-02, 2.6501e-03, 5.6106e-03, 1.4179e-03,
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2.8748e-01, 7.2847e-05], device='cuda:2', grad_fn=<SelectBackward0>)
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ANSWER0=VQA(image=LEFT,question='How many dogs 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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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
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