Datasets:
TMarcus
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InternVL

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3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9997e-01, 3.3213e-05, 3.5688e-08, 7.9325e-09, 5.3237e-11, 2.1986e-08,
1.1997e-10, 1.9977e-09], 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(3.5688e-08, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:48:50,820] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.34 | optimizer_step: 0.33
[2024-10-24 09:48:50,820] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5083.06 | backward_microstep: 8708.03 | backward_inner_microstep: 4811.82 | backward_allreduce_microstep: 3896.10 | step_microstep: 7.74
[2024-10-24 09:48:50,821] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5083.06 | backward: 8708.02 | backward_inner: 4811.85 | backward_allreduce: 3896.08 | step: 7.75
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4599/4844 [19:07:34<1:05:29, 16.04s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
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=RIGHT,question='How many silver vending machines are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many multi-colored parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many creatures are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 8')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many men are standing in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([3, 3, 448, 448])
question: ['How many multi-colored parrots are in the image?'], responses:['3']
question: ['How many men are standing in the image?'], responses:['0']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
[('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
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
question: ['How many silver vending machines are in the image?'], responses:['1']
question: ['How many creatures are in the image?'], responses:['100']
[('100', 0.1277092174007614), ('120', 0.12519936731884676), ('88', 0.12483671971182599), ('80', 0.12474858811112934), ('60', 0.12457749608485191), ('99', 0.1243465850330014), ('90', 0.12430147627057883), ('101', 0.12428055006900451)]
[['100', '120', '88', '80', '60', '99', '90', '101']]
[('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: 837
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
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: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 838
dynamic ViT batch size: 3, images per sample: 3.0, dynamic token length: 839
tensor([1.0000e+00, 3.6883e-07, 7.5947e-09, 4.1864e-10, 1.1876e-11, 1.7977e-07,
7.3886e-11, 7.8828e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 3.6883e-07, 7.5947e-09, 4.1864e-10, 1.1876e-11, 1.7977e-07,
7.3886e-11, 7.8828e-10], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.7333e-06, 2.6507e-08, 2.5128e-11, 1.1866e-07, 3.9131e-08,
1.7195e-07, 1.8459e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 1.7333e-06, 2.6507e-08, 2.5128e-11, 1.1866e-07, 3.9131e-08,
1.7195e-07, 1.8459e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.5947e-09, 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>)}
ANSWER0=VQA(image=LEFT,question='How many rodents are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.2650e-06, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the lock silver metal?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is the lock silver metal?'], 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
tensor([1.0000e+00, 2.2369e-09, 1.4247e-10, 2.2824e-09, 3.6954e-10, 1.8582e-10,
9.6096e-12, 2.9985e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.2369e-09, 1.4247e-10, 2.2824e-09, 3.6954e-10, 1.8582e-10,
9.6096e-12, 2.9985e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.4247e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.4247e-10, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 3.7323e-09, 5.9983e-10, 2.3539e-09, 1.3623e-09, 2.1144e-08,
3.5411e-08, 5.6453e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)