Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 2.9990e-09, 5.4092e-07, 2.8556e-10, 6.6183e-10, 2.5847e-07,
1.8205e-09, 3.1725e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.9990e-09, 5.4092e-07, 2.8556e-10, 6.6183e-10, 2.5847e-07,
1.8205e-09, 3.1725e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(3.6164e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.6164e-11, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.9990e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many brown pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([5.5165e-01, 6.1966e-04, 4.1999e-01, 6.5678e-05, 1.3716e-02, 1.2445e-02,
2.5677e-04, 1.2556e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([5.5165e-01, 6.1966e-04, 4.1999e-01, 6.5678e-05, 1.3716e-02, 1.2445e-02,
2.5677e-04, 1.2556e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='Is there a hunter posing near the wild pig?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a cabinet behind the toilet in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([5, 3, 448, 448])
question: ['How many brown pillows 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([1.0000e+00, 8.2724e-07, 2.9521e-07, 1.2771e-10, 1.0801e-07, 2.5221e-08,
2.0188e-06, 2.9742e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
question: ['Is there a cabinet behind the toilet in the image?'], responses:['yes']0
*************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 8.2724e-07, 2.9521e-07, 1.2771e-10, 1.0801e-07, 2.5221e-08,
2.0188e-06, 2.9742e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5763e-06, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
[('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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([1.0000e+00, 1.8582e-10, 4.7722e-11, 1.4247e-10, 8.1164e-11, 1.6205e-08,
1.9977e-09, 1.6904e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.8582e-10, 4.7722e-11, 1.4247e-10, 8.1164e-11, 1.6205e-08,
1.9977e-09, 1.6904e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8829e-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>)}
question: ['Is there a hunter posing near the wild pig?'], 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.4709e-09, 8.2332e-08, 1.8172e-08, 2.7570e-11, 6.8359e-11,
2.4252e-10, 2.7579e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.4709e-09, 8.2332e-08, 1.8172e-08, 2.7570e-11, 6.8359e-11,
2.4252e-10, 2.7579e-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(8.2332e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.6877e-08, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.7584e-08, 1.4931e-10, 3.4199e-08, 5.6206e-11, 6.3716e-11,
9.1087e-11, 3.6081e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.7584e-08, 1.4931e-10, 3.4199e-08, 5.6206e-11, 6.3716e-11,
9.1087e-11, 3.6081e-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(1.4931e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1906e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:52:07,134] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.27 | optimizer_step: 0.31
[2024-10-24 09:52:07,135] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3776.29 | backward_microstep: 10052.16 | backward_inner_microstep: 3503.97 | backward_allreduce_microstep: 6548.12 | step_microstep: 7.59
[2024-10-24 09:52:07,135] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3776.30 | backward: 10052.15 | backward_inner: 3503.99 | backward_allreduce: 6548.10 | step: 7.60
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4612/4844 [19:10:50<54:55, 14.20s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL stepRegistering EVAL step
Registering RESULT stepRegistering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many knee braces are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image on all fours?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is someone watching TV while sitting on a couch?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['1']