Datasets:
TMarcus
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InternVL

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yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([8.9755e-01, 1.2301e-02, 8.8873e-02, 3.2378e-04, 6.1256e-05, 1.6077e-04,
1.2779e-05, 7.2216e-04], device='cuda:3', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.0889, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.8975, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0136, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the girl wearing primarily gray pajamas?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
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
tensor([9.6772e-01, 5.9367e-03, 2.4747e-03, 1.1678e-03, 1.4097e-03, 1.1252e-03,
2.0084e-02, 7.8647e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6772e-01, 5.9367e-03, 2.4747e-03, 1.1678e-03, 1.4097e-03, 1.1252e-03,
2.0084e-02, 7.8647e-05], device='cuda:0', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.9677, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0323, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Is the girl wearing primarily gray pajamas?'], responses:['no']
ANSWER0=VQA(image=LEFT,question='How many seals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
[('no', 0.1313955057270409), ('yes', 0.12592208734904367), ('no smoking', 0.12472972590078177), ('gone', 0.12376514658020793), ('man', 0.12367833016285167), ('meow', 0.1235796378467502), ('kia', 0.12347643720898455), ('no clock', 0.12345312922433942)]
[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many seals 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
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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([9.2761e-01, 1.1679e-02, 5.0227e-03, 2.0913e-03, 2.8617e-03, 1.4309e-03,
4.9154e-02, 1.4684e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.2761e-01, 1.1679e-02, 5.0227e-03, 2.0913e-03, 2.8617e-03, 1.4309e-03,
4.9154e-02, 1.4684e-04], device='cuda:2', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.0492, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9508, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([5.4613e-01, 4.5275e-01, 3.4255e-05, 9.9135e-05, 3.3128e-04, 3.9236e-04,
2.4418e-04, 1.9416e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.4613e-01, 4.5275e-01, 3.4255e-05, 9.9135e-05, 3.3128e-04, 3.9236e-04,
2.4418e-04, 1.9416e-05], device='cuda:3', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.4528, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5461, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0011, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([13, 3, 448, 448])
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([9.6855e-01, 5.7580e-03, 2.4750e-03, 1.3095e-03, 1.7494e-03, 1.2077e-03,
1.8883e-02, 7.1559e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.6855e-01, 5.7580e-03, 2.4750e-03, 1.3095e-03, 1.7494e-03, 1.2077e-03,
1.8883e-02, 7.1559e-05], device='cuda:0', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.9685, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0315, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 3 has a total capacty of 44.34 GiB of which 706.94 MiB is free. Including non-PyTorch memory, this process has 43.63 GiB memory in use. Of the allocated memory 40.76 GiB is allocated by PyTorch, and 2.32 GiB is reserved by PyTorch but unalloc...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
最后的概率分布为: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
[2024-10-22 17:28:37,155] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-22 17:28:37,156] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12421.99 | backward_microstep: 11653.13 | backward_inner_microstep: 11647.79 | backward_allreduce_microstep: 5.27 | step_microstep: 7.67
[2024-10-22 17:28:37,156] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12422.01 | backward: 11653.12 | backward_inner: 11647.81 | backward_allreduce: 5.26 | step: 7.68
1%| | 25/2424 [10:09<16:05:20, 24.14s/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=LEFT,question='How many rolls of paper towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is the dog against a white background?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are all the balls in the image white?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many rolls of paper towels are in the image?'], responses:['1']
question: ['How many animals are in the image?'], responses:['4']
[('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']]
[('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']]