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no *************
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['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.1562e-09, 4.6040e-07, 8.3103e-11, 1.4545e-10, 6.2026e-08,
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1.7757e-09, 1.3411e-06], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.1562e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9073e-06, device='cuda:1', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,question='Is the restaurant empty?')
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ANSWER1=EVAL(expr='{ANSWER0}')
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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: 1867
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torch.Size([7, 3, 448, 448])
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
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tensor([7.3390e-04, 1.5323e-02, 9.2348e-06, 1.9614e-01, 1.3026e-01, 9.3070e-04,
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6.5592e-01, 6.8502e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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vegetable *************
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['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([7.3390e-04, 1.5323e-02, 9.2348e-06, 1.9614e-01, 1.3026e-01, 9.3070e-04,
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6.5592e-01, 6.8502e-04], device='cuda:3', grad_fn=<SelectBackward0>)
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dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1867
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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=RIGHT,question='What is the shape of the sink?')
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ANSWER1=EVAL(expr='{ANSWER0} == "rectangular"')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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question: ['Is the restaurant empty?'], responses:['no']
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torch.Size([5, 3, 448, 448])
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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: 7, images per sample: 7.0, dynamic token length: 1867
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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: 1867
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question: ['What is the shape of the sink?'], responses:['oval']
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tensor([1.0000e+00, 5.9767e-08, 6.4624e-08, 1.9444e-07, 1.0861e-09, 7.6559e-09,
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2.9658e-09, 3.6533e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
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2 *************
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['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.9767e-08, 6.4624e-08, 1.9444e-07, 1.0861e-09, 7.6559e-09,
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2.9658e-09, 3.6533e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 3.6744e-09, 3.9870e-07, 2.9927e-10, 9.4988e-09, 3.2863e-08,
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5.2206e-09, 5.2447e-07], device='cuda:0', 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, 3.6744e-09, 3.9870e-07, 2.9927e-10, 9.4988e-09, 3.2863e-08,
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5.2206e-09, 5.2447e-07], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(7.9986e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=RIGHT,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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[('oval', 0.12822836161278967), ('mural', 0.1247496604372442), ('plastic', 0.12462060981640453), ('wooden', 0.12453418095774828), ('sydney', 0.1245046057807645), ('ceramic', 0.12448011413450115), ('glazed', 0.12445528920190407), ('evergreen', 0.12442717805864358)]
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[['oval', 'mural', 'plastic', 'wooden', 'sydney', 'ceramic', 'glazed', 'evergreen']]
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.6744e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:0', grad_fn=<DivBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many insects 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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torch.Size([13, 3, 448, 448])
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torch.Size([5, 3, 448, 448]) knan debug pixel values shape
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question: ['How many dogs are in the image?'], responses:['1']
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question: ['How many insects are in the image?'], responses:['1']
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tensor([1.0000e+00, 1.1979e-07, 4.7624e-07, 1.9280e-11, 4.6939e-11, 1.1366e-09,
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3.6668e-10, 4.6156e-07], device='cuda:1', 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, 1.1979e-07, 4.7624e-07, 1.9280e-11, 4.6939e-11, 1.1366e-09,
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3.6668e-10, 4.6156e-07], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.1979e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:1', 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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tensor([9.9193e-01, 9.8027e-05, 1.2029e-03, 7.1617e-04, 3.1363e-04, 3.1540e-03,
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2.5546e-03, 2.6846e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
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oval *************
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['oval', 'mural', 'plastic', 'wooden', 'sydney', 'ceramic', 'glazed', 'evergreen'] tensor([9.9193e-01, 9.8027e-05, 1.2029e-03, 7.1617e-04, 3.1363e-04, 3.1540e-03,
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2.5546e-03, 2.6846e-05], device='cuda:3', grad_fn=<SelectBackward0>)
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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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[('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([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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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: 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: 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: 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, 2.2067e-10, 3.6590e-11, 9.7917e-11, 6.8287e-11, 7.0817e-09,
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3.1184e-09, 1.2564e-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, 2.2067e-10, 3.6590e-11, 9.7917e-11, 6.8287e-11, 7.0817e-09,
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3.1184e-09, 1.2564e-10], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.0749e-08, 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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tensor([1.0000e+00, 2.4192e-09, 3.9646e-10, 6.1156e-10, 4.8005e-10, 1.4972e-08,
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3.1742e-08, 1.5633e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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1 *************
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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.4192e-09, 3.9646e-10, 6.1156e-10, 4.8005e-10, 1.4972e-08,
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3.1742e-08, 1.5633e-09], device='cuda:0', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(3.1742e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
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[2024-10-24 10:37:25,596] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.25 | optimizer_step: 0.31
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[2024-10-24 10:37:25,596] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7069.17 | backward_microstep: 6796.59 | backward_inner_microstep: 6791.29 | backward_allreduce_microstep: 5.20 | step_microstep: 7.60
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[2024-10-24 10:37:25,596] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7069.19 | backward: 6796.58 | backward_inner: 6791.32 | backward_allreduce: 5.19 | step: 7.61
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99%|ββββββββββ| 4793/4844 [19:56:09<13:29, 15.88s/it]Registering VQA_lavis 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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