Datasets:
TMarcus
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InternVL

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tensor([9.9945e-01, 1.6528e-06, 3.8049e-07, 5.5278e-04, 4.8651e-09, 5.7774e-09,
2.5438e-09, 5.5760e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9945e-01, 1.6528e-06, 3.8049e-07, 5.5278e-04, 4.8651e-09, 5.7774e-09,
2.5438e-09, 5.5760e-11], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 6.5622e-10, 1.6657e-10, 2.0566e-10, 1.4022e-10, 1.6969e-08,
6.4488e-09, 2.4667e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.5622e-10, 1.6657e-10, 2.0566e-10, 1.4022e-10, 1.6969e-08,
6.4488e-09, 2.4667e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.8332e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.4833e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many empty containers are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many empty containers 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.9532e-09, 2.5219e-07, 2.6882e-09, 1.6936e-09, 7.0170e-08,
5.4136e-09, 2.4706e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 8.9532e-09, 2.5219e-07, 2.6882e-09, 1.6936e-09, 7.0170e-08,
5.4136e-09, 2.4706e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.9532e-09, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:2', grad_fn=<SubBackward0>)}
question: ['How many hyenas are in the image?'], responses:['five']
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([9.9999e-01, 8.0163e-07, 1.1197e-07, 2.2453e-09, 1.3848e-06, 6.5055e-09,
2.7792e-07, 1.2182e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 8.0163e-07, 1.1197e-07, 2.2453e-09, 1.3848e-06, 6.5055e-09,
2.7792e-07, 1.2182e-05], 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(1.4782e-05, device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.5766e-09, 6.5200e-01, 2.1710e-02, 4.5845e-04, 3.2523e-01, 6.7269e-05,
3.4578e-04, 1.8014e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
babies *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([1.5766e-09, 6.5200e-01, 2.1710e-02, 4.5845e-04, 3.2523e-01, 6.7269e-05,
3.4578e-04, 1.8014e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 10:32:57,520] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.41 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-24 10:32:57,520] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7061.61 | backward_microstep: 6750.56 | backward_inner_microstep: 6744.80 | backward_allreduce_microstep: 5.66 | step_microstep: 7.55
[2024-10-24 10:32:57,520] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7061.63 | backward: 6750.55 | backward_inner: 6744.81 | backward_allreduce: 5.65 | step: 7.56
99%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4776/4844 [19:51:41<15:35, 13.76s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many hairless chimps are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many pieces of food are on the dish?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are lying on the ground?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many people are in the image?'], responses:['4']
[('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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([9.9943e-01, 5.1929e-04, 5.1417e-05, 2.4178e-09, 1.1604e-07, 1.5158e-10,
5.4159e-10, 1.1855e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
4 *************
['4', '5', '3', '8', '6', '1', '2', '11'] tensor([9.9943e-01, 5.1929e-04, 5.1417e-05, 2.4178e-09, 1.1604e-07, 1.5158e-10,
5.4159e-10, 1.1855e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(5.1417e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many whole pizzas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])