Datasets:
TMarcus
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InternVL

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torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
question: ['Is the bed canopy closed?'], responses:['yes']
question: ['How many pillows are in the image?'], responses:['4']
[('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']]
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
question: ["Are some of the crab's legs red?"], 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: 3399
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([9.0449e-01, 9.5347e-02, 6.7619e-05, 3.3226e-07, 8.1390e-05, 6.8343e-10,
3.3358e-10, 1.3264e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.0449e-01, 9.5347e-02, 6.7619e-05, 3.3226e-07, 8.1390e-05, 6.8343e-10,
3.3358e-10, 1.3264e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.7620e-05, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.2626e-08, 3.8232e-07, 1.1879e-11, 4.6024e-11, 3.7377e-09,
1.4011e-10, 2.2489e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.2626e-08, 3.8232e-07, 1.1879e-11, 4.6024e-11, 3.7377e-09,
1.4011e-10, 2.2489e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2626e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.3702e-08, 3.8890e-08, 1.3345e-08, 2.3859e-10, 1.3177e-10,
8.8917e-11, 3.4769e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.3702e-08, 3.8890e-08, 1.3345e-08, 2.3859e-10, 1.3177e-10,
8.8917e-11, 3.4769e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.8890e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.8890e-08, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.7972e-09, 4.4952e-06, 6.3512e-08, 1.6306e-10, 1.2117e-09,
5.1925e-10, 2.8328e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.7972e-09, 4.4952e-06, 6.3512e-08, 1.6306e-10, 1.2117e-09,
5.1925e-10, 2.8328e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.4952e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5398e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:17:12,180] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.39 | optimizer_step: 0.33
[2024-10-24 10:17:12,180] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5189.93 | backward_microstep: 8656.17 | backward_inner_microstep: 4989.69 | backward_allreduce_microstep: 3666.39 | step_microstep: 8.14
[2024-10-24 10:17:12,180] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5189.94 | backward: 8656.16 | backward_inner: 4989.72 | backward_allreduce: 3666.36 | step: 8.16
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4711/4844 [19:35:55<31:46, 14.33s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many water buffalos are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many bottles in the image have a silver cap?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many people are sitting near piles of paper goods?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the shelving unit empty?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many people are sitting near piles of paper goods?'], 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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
question: ['Is the shelving unit empty?'], responses:['yes']
question: ['How many bottles in the image have a silver cap?'], responses:['1']
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
[('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']]
[('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']]
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 840
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 841
tensor([1.0000e+00, 1.9837e-06, 3.9175e-08, 6.3837e-10, 1.4800e-07, 5.4930e-09,
2.1842e-07, 7.7682e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 1.9837e-06, 3.9175e-08, 6.3837e-10, 1.4800e-07, 5.4930e-09,
2.1842e-07, 7.7682e-07], device='cuda:0', grad_fn=<SelectBackward0>)