Datasets:
TMarcus
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InternVL

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yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([7.7914e-01, 1.9911e-02, 1.9700e-01, 1.7878e-03, 1.0029e-04, 5.1458e-04,
3.7752e-05, 1.5059e-03], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7791, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1970, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0239, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many kids are holding pillows in the image?'], responses:['2']
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1864
question: ['How many animals are in the image?'], responses:['1']
tensor([8.1682e-01, 1.8225e-01, 2.7560e-05, 8.3327e-05, 1.4315e-04, 3.6947e-04,
2.6904e-04, 2.9660e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.1682e-01, 1.8225e-01, 2.7560e-05, 8.3327e-05, 1.4315e-04, 3.6947e-04,
2.6904e-04, 2.9660e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1823, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8168, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0009, device='cuda:0', grad_fn=<DivBackward0>)}
[('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']]
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
tensor([0.3966, 0.1003, 0.0396, 0.4339, 0.0168, 0.0058, 0.0063, 0.0006],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.3966, 0.1003, 0.0396, 0.4339, 0.0168, 0.0058, 0.0063, 0.0006],
device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3966, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6034, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many hyenas 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([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: 837
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: 837
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: 837
tensor([9.7378e-01, 4.3707e-03, 1.8219e-03, 6.7012e-04, 8.6058e-04, 6.0760e-04,
1.7836e-02, 5.3070e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7378e-01, 4.3707e-03, 1.8219e-03, 6.7012e-04, 8.6058e-04, 6.0760e-04,
1.7836e-02, 5.3070e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0262, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9738, 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 zebras are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
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: 837
tensor([8.4332e-01, 2.6274e-02, 9.9711e-03, 4.5535e-03, 5.5048e-03, 2.9246e-03,
1.0722e-01, 2.3536e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.4332e-01, 2.6274e-02, 9.9711e-03, 4.5535e-03, 5.5048e-03, 2.9246e-03,
1.0722e-01, 2.3536e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1072, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8928, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many zebras 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
tensor([5.6221e-01, 2.3293e-02, 4.1132e-01, 1.5315e-03, 1.2447e-04, 6.0886e-04,
1.4363e-04, 7.6975e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6221e-01, 2.3293e-02, 4.1132e-01, 1.5315e-03, 1.2447e-04, 6.0886e-04,
1.4363e-04, 7.6975e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.5622, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.4113, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0265, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog facing left?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.92 GiB. GPU 2 has a total capacty of 44.34 GiB of which 972.94 MiB is free. Including non-PyTorch memory, this process has 43.38 GiB memory in use. Of the allocated memory 40.77 GiB is allocated by PyTorch, and 1.97 GiB is reserved by PyTorch but unalloc...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
tensor([9.6361e-01, 6.4921e-03, 2.3884e-03, 1.0255e-03, 1.3185e-03, 9.8591e-04,
2.4124e-02, 5.7483e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6361e-01, 6.4921e-03, 2.3884e-03, 1.0255e-03, 1.3185e-03, 9.8591e-04,
2.4124e-02, 5.7483e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9636, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0364, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-22 17:22:11,020] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.32 | optimizer_step: 0.33
[2024-10-22 17:22:11,020] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10812.84 | backward_microstep: 14712.44 | backward_inner_microstep: 10220.94 | backward_allreduce_microstep: 4490.95 | step_microstep: 7.87
[2024-10-22 17:22:11,021] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10812.86 | backward: 14712.43 | backward_inner: 10221.40 | backward_allreduce: 4490.93 | step: 7.89
0%| | 9/2424 [03:43<16:22:28, 24.41s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there at least one dog standing on all fours in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step