Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 2.0267e-07, 1.5415e-06, 4.9200e-09, 2.9686e-08, 6.1186e-08,
8.2497e-09, 7.3405e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.0267e-07, 1.5415e-06, 4.9200e-09, 2.9686e-08, 6.1186e-08,
8.2497e-09, 7.3405e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.0267e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3246e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.7290e-13, 9.9911e-01, 6.6687e-06, 8.2344e-04, 5.5688e-05, 2.4182e-06,
2.9875e-06, 2.1702e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([1.7290e-13, 9.9911e-01, 6.6687e-06, 8.2344e-04, 5.5688e-05, 2.4182e-06,
2.9875e-06, 2.1702e-07], device='cuda:2', grad_fn=<SelectBackward0>)
torch.Size([5, 3, 448, 448])
tensor([8.9370e-01, 2.1868e-04, 2.3133e-03, 1.1878e-02, 6.4717e-04, 6.8751e-04,
1.7952e-04, 9.0377e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>)
pan *************
['pan', 'fireplace', 'pedestal', 'coaster', 'mantle', 'quilt', 'moss', 'grill'] tensor([8.9370e-01, 2.1868e-04, 2.3133e-03, 1.1878e-02, 6.4717e-04, 6.8751e-04,
1.7952e-04, 9.0377e-02], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9992, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0008, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.2398e-05, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many baby animals 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:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many cases are in the image?'], responses:['four']
[('7 eleven', 0.12650899275575006), ('4', 0.125210025275264), ('first', 0.12483048280083887), ('3', 0.12473532336671392), ('5', 0.1247268629491862), ('dark', 0.12470563072493092), ('forward', 0.12466964370422237), ('bag', 0.12461303842309367)]
[['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['How many baby animals are in the image?'], responses:['4']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
[('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']]
question: ['How many animals 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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([7, 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, 1.2091e-08, 5.2114e-10, 1.0904e-07, 4.0495e-10, 1.8190e-09,
2.2144e-10, 3.7926e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.2091e-08, 5.2114e-10, 1.0904e-07, 4.0495e-10, 1.8190e-09,
2.2144e-10, 3.7926e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(5.2114e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1869e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([5.2818e-14, 9.3342e-01, 6.9233e-06, 6.6508e-02, 3.8364e-05, 4.0113e-07,
1.8047e-05, 4.0208e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
4 *************
['7 eleven', '4', 'first', '3', '5', 'dark', 'forward', 'bag'] tensor([5.2818e-14, 9.3342e-01, 6.9233e-06, 6.6508e-02, 3.8364e-05, 4.0113e-07,
1.8047e-05, 4.0208e-06], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9334, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0665, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.9445e-05, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.8883e-01, 1.0985e-02, 1.7756e-04, 4.3824e-09, 1.0017e-05, 2.2512e-10,
4.0604e-10, 2.0702e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.8883e-01, 1.0985e-02, 1.7756e-04, 4.3824e-09, 1.0017e-05, 2.2512e-10,
4.0604e-10, 2.0702e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0002, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9998, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.3764e-09, 2.1388e-10, 2.9926e-10, 1.4695e-10, 1.8368e-08,
9.0942e-09, 7.3437e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.3764e-09, 2.1388e-10, 2.9926e-10, 1.4695e-10, 1.8368e-08,
9.0942e-09, 7.3437e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.0233e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:22:52,302] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 09:22:52,303] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3874.42 | backward_microstep: 6123.42 | backward_inner_microstep: 3515.18 | backward_allreduce_microstep: 2608.14 | step_microstep: 7.37
[2024-10-24 09:22:52,303] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3874.44 | backward: 6123.41 | backward_inner: 3515.23 | backward_allreduce: 2608.09 | step: 7.38
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4494/4844 [18:41:36<1:13:55, 12.67s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the phone in the slide out position?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image show a glass with a straw in it?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='How many small bags are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Is the animal turned directly toward the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])