Datasets:
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InternVL

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ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.7323e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-06, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
question: ['How many dogs are outside in the image?'], responses:['ε››']
question: ['How many people are in the image?'], responses:['0']
[('geese', 0.12791273653846358), ('cushion', 0.12632164867635856), ('biking', 0.12559214056053666), ('bulldog', 0.12532071672327474), ('striped', 0.12486304389654934), ('goose', 0.12402122964730407), ('vegetable', 0.12318440383239601), ('dodgers', 0.12278408012511692)]
[['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers']]
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['What color are the citrus fruits growing on the tree?'], responses:['orange']
[('orange', 0.12675952683011027), ('purple', 0.12551481365388975), ('vanilla', 0.12482510860547383), ('cinnamon', 0.12472774302623701), ('maroon', 0.12470081889874104), ('lemon', 0.12449279608493648), ('lime', 0.1244923535449387), ('black', 0.12448683935567285)]
[['orange', 'purple', 'vanilla', 'cinnamon', 'maroon', 'lemon', 'lime', 'black']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['How many dogs are in the image?'], responses:['1']
[('1', 0.12829009354978346), ('3', 0.12529928082343206), ('4', 0.12464806219229535), ('8', 0.12460015878893425), ('6', 0.12451220062887247), ('12', 0.124338487048427), ('2', 0.12420459433498025), ('47', 0.12410712263327517)]
[['1', '3', '4', '8', '6', '12', '2', '47']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([6.2652e-04, 1.0853e-02, 3.1780e-05, 3.4085e-01, 4.6210e-02, 6.0580e-03,
5.8909e-01, 6.2768e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
vegetable *************
['geese', 'cushion', 'biking', 'bulldog', 'striped', 'goose', 'vegetable', 'dodgers'] tensor([6.2652e-04, 1.0853e-02, 3.1780e-05, 3.4085e-01, 4.6210e-02, 6.0580e-03,
5.8909e-01, 6.2768e-03], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([9.9992e-01, 6.4913e-05, 6.2612e-07, 1.3525e-08, 4.1586e-06, 2.1174e-07,
7.9551e-06, 3.8546e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9992e-01, 6.4913e-05, 6.2612e-07, 1.3525e-08, 4.1586e-06, 2.1174e-07,
7.9551e-06, 3.8546e-06], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='Is there a zipper in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(8.1778e-05, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog on a wooden floor?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the dog on a wooden floor?'], responses:['no']
[('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)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([1.0000e+00, 1.4166e-09, 3.3595e-07, 1.0219e-12, 9.4783e-13, 1.3295e-09,
1.9512e-10, 1.9849e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.4166e-09, 3.3595e-07, 1.0219e-12, 9.4783e-13, 1.3295e-09,
1.9512e-10, 1.9849e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.4166e-09, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:3', grad_fn=<SubBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
question: ['Is there a zipper in the image?'], responses:['yes']
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]
[['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([9.9891e-01, 2.3546e-05, 1.2904e-04, 2.8135e-04, 1.7973e-04, 4.5751e-04,
1.0400e-05, 5.4867e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
orange *************
['orange', 'purple', 'vanilla', 'cinnamon', 'maroon', 'lemon', 'lime', 'black'] tensor([9.9891e-01, 2.3546e-05, 1.2904e-04, 2.8135e-04, 1.7973e-04, 4.5751e-04,
1.0400e-05, 5.4867e-06], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {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>)}
ANSWER0=VQA(image=RIGHT,question='How many cups are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3396
tensor([1.0000e+00, 2.0488e-10, 5.1600e-11, 1.6527e-10, 9.1982e-11, 1.2622e-08,
2.2286e-09, 1.1715e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.0488e-10, 5.1600e-11, 1.6527e-10, 9.1982e-11, 1.2622e-08,
2.2286e-09, 1.1715e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.2286e-09, 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>)}
question: ['How many cups are in the image?'], responses:['0']
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.4521e-09, 4.3204e-10, 1.5402e-08, 4.0592e-11, 3.0334e-11,
1.1658e-11, 3.9355e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.4521e-09, 4.3204e-10, 1.5402e-08, 4.0592e-11, 3.0334e-11,
1.1658e-11, 3.9355e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.3204e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.3204e-10, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.9998e-01, 1.6817e-05, 1.3007e-07, 6.0840e-10, 1.4023e-06, 1.5834e-07,
3.6828e-07, 5.7799e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9998e-01, 1.6817e-05, 1.3007e-07, 6.0840e-10, 1.4023e-06, 1.5834e-07,
3.6828e-07, 5.7799e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.9431e-05, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:00:20,712] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.37 | optimizer_step: 0.33
[2024-10-24 10:00:20,712] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5076.91 | backward_microstep: 8744.64 | backward_inner_microstep: 4937.83 | backward_allreduce_microstep: 3806.71 | step_microstep: 7.78
[2024-10-24 10:00:20,712] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5076.92 | backward: 8744.64 | backward_inner: 4937.86 | backward_allreduce: 3806.69 | step: 7.79
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4645/4844 [19:19:04<49:22, 14.89s/it]Registering VQA_lavis step
Registering EVAL step