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1.1184e-07, 1.0495e-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([9.0485e-01, 9.5093e-02, 5.9727e-05, 6.9782e-08, 3.5562e-07, 2.7295e-09,
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1.1184e-07, 1.0495e-09], device='cuda:2', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(6.9782e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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question: ['How many 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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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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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: 3397
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dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
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tensor([9.9872e-01, 1.6844e-04, 6.1975e-05, 1.0122e-05, 1.0877e-04, 1.5188e-05,
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9.1084e-04, 2.0669e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
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7 *************
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['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.9872e-01, 1.6844e-04, 6.1975e-05, 1.0122e-05, 1.0877e-04, 1.5188e-05,
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9.1084e-04, 2.0669e-07], 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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tensor([1.0000e+00, 1.2771e-10, 3.2290e-11, 9.4903e-11, 6.2214e-11, 8.0204e-09,
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1.9212e-09, 1.1149e-10], 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, 1.2771e-10, 3.2290e-11, 9.4903e-11, 6.2214e-11, 8.0204e-09,
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1.9212e-09, 1.1149e-10], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1.9212e-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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[2024-10-24 10:32:21,186] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.36 | optimizer_step: 0.34
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[2024-10-24 10:32:21,186] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7067.45 | backward_microstep: 10613.34 | backward_inner_microstep: 6804.67 | backward_allreduce_microstep: 3808.54 | step_microstep: 8.29
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[2024-10-24 10:32:21,186] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7067.46 | backward: 10613.33 | backward_inner: 6804.74 | backward_allreduce: 3808.53 | step: 8.30
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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='Does the image have pink flowers inside of a vase?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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99%|ββββββββββ| 4773/4844 [19:51:04<19:18, 16.32s/it]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='Is the drain in the bottom of the basin visible?')
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FINAL_ANSWER=RESULT(var=ANSWER0)
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torch.Size([1, 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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torch.Size([1, 3, 448, 448])
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ANSWER0=VQA(image=RIGHT,question='How many graduation students are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} == 3')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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ANSWER0=VQA(image=RIGHT,question='How many blue and yellow-orange parrots are in the image?')
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ANSWER1=EVAL(expr='{ANSWER0} >= 5')
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FINAL_ANSWER=RESULT(var=ANSWER1)
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torch.Size([3, 3, 448, 448])
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torch.Size([13, 3, 448, 448])
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question: ['Does the image have pink flowers inside of a vase?'], responses:['yes']
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question: ['Is the drain in the bottom of the basin visible?'], 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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[('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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torch.Size([1, 3, 448, 448]) knan debug pixel values shape
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question: ['How many blue and yellow-orange parrots are in the image?'], responses:['six']
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[('7 eleven', 0.1258716720461554), ('dusk', 0.12512990238684168), ('blue', 0.12502287564185594), ('rose', 0.12495109740026594), ('peach', 0.12486403486105606), ('kitten', 0.12474151468778871), ('laundry', 0.12473504457146048), ('sunrise', 0.12468385840457588)]
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[['7 eleven', 'dusk', 'blue', 'rose', 'peach', 'kitten', 'laundry', 'sunrise']]
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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: 844
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tensor([1.0000e+00, 5.1763e-09, 2.7264e-06, 4.1737e-08, 1.0381e-10, 3.8346e-11,
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9.8644e-11, 4.2613e-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, 5.1763e-09, 2.7264e-06, 4.1737e-08, 1.0381e-10, 3.8346e-11,
|
9.8644e-11, 4.2613e-08], device='cuda:3', grad_fn=<SelectBackward0>)
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tensor([1.0000e+00, 3.0502e-09, 1.8367e-08, 1.2919e-09, 1.2797e-10, 2.4369e-11,
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5.6676e-12, 1.0747e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
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yes *************
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['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.0502e-09, 1.8367e-08, 1.2919e-09, 1.2797e-10, 2.4369e-11,
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5.6676e-12, 1.0747e-09], device='cuda:1', grad_fn=<SelectBackward0>)
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ζεηζ¦ηεεΈδΈΊ: dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
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{True: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(2.7264e-06, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.3458e-07, device='cuda:3', grad_fn=<SubBackward0>)}
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ANSWER0=VQA(image=LEFT,question='How many birds 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([1, 3, 448, 448])
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ζεηζ¦ηεεΈδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.8367e-08, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.8367e-08, device='cuda:1', grad_fn=<SubBackward0>)}
|
ANSWER0=VQA(image=RIGHT,question='How many chimps 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: 3, images per sample: 3.0, dynamic token length: 843
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question: ['How many birds are in the image?'], responses:['11']
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torch.Size([13, 3, 448, 448])
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[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
|
[['11', '10', '12', '9', '8', '13', '7', '14']]
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 843
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torch.Size([1, 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: 844
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
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dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 844
|
tensor([9.6237e-01, 1.0576e-03, 7.3554e-03, 1.2631e-04, 2.5179e-07, 2.1262e-02,
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