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TMarcus
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InternVL

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ANSWER0=VQA(image=RIGHT,question='Is the restaurant empty?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['What are the vultures doing in the image?'], responses:['standing']
[('standing', 0.12723967049623947), ('sitting', 0.12550921268440737), ('kneeling', 0.12533132135475653), ('floating', 0.12470854046371647), ('movement', 0.12442871056656102), ('moving', 0.12438049520499413), ('falling', 0.12421897137183824), ('leaning', 0.1241830778574868)]
[['standing', 'sitting', 'kneeling', 'floating', 'movement', 'moving', 'falling', 'leaning']]
question: ['Is the restaurant empty?'], 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([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
question: ['How many apes are in the image?'], responses:['2']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857
tensor([7.7672e-01, 2.2253e-01, 1.3707e-05, 1.0045e-04, 2.3444e-04, 1.2844e-04,
2.5347e-04, 1.6909e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.7672e-01, 2.2253e-01, 1.3707e-05, 1.0045e-04, 2.3444e-04, 1.2844e-04,
2.5347e-04, 1.6909e-05], device='cuda:3', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.2225, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7767, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0007, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1857
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
tensor([0.5022, 0.2633, 0.1019, 0.0386, 0.0011, 0.0454, 0.0408, 0.0067],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
standing *************
['standing', 'sitting', 'kneeling', 'floating', 'movement', 'moving', 'falling', 'leaning'] tensor([0.5022, 0.2633, 0.1019, 0.0386, 0.0011, 0.0454, 0.0408, 0.0067],
device='cuda:1', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([4.4858e-01, 2.6222e-02, 5.2190e-01, 1.0738e-03, 2.1260e-04, 7.1573e-04,
1.8352e-04, 1.1115e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([4.4858e-01, 2.6222e-02, 5.2190e-01, 1.0738e-03, 2.1260e-04, 7.1573e-04,
1.8352e-04, 1.1115e-03], device='cuda:0', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
最后的概率分布为: {True: tensor(0.4486, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5219, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0295, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many vending machines are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 1 has a total capacty of 44.34 GiB of which 3.25 GiB is free. Including non-PyTorch memory, this process has 41.07 GiB memory in use. Of the allocated memory 38.09 GiB is allocated by PyTorch, and 2.35 GiB is reserved by PyTorch but unallocat...
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}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.86 GiB. GPU 0 has a total capacty of 44.34 GiB of which 3.62 GiB is free. Including non-PyTorch memory, this process has 40.70 GiB memory in use. Of the allocated memory 38.07 GiB is allocated by PyTorch, and 2.02 GiB is reserved by PyTorch but unallocat...
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}
tensor([7.7485e-01, 3.1878e-02, 5.9159e-03, 1.8346e-01, 1.9750e-03, 9.0224e-04,
9.0580e-04, 1.1310e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.7485e-01, 3.1878e-02, 5.9159e-03, 1.8346e-01, 1.9750e-03, 9.0224e-04,
9.0580e-04, 1.1310e-04], device='cuda:2', grad_fn=<SelectBackward0>)
最后的概率分布为: {True: tensor(0.9583, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0417, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Are there red oars in the image?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 2 has a total capacty of 44.34 GiB of which 1.81 GiB is free. Including non-PyTorch memory, this process has 42.52 GiB memory in use. Of the allocated memory 40.55 GiB is allocated by PyTorch, and 1.34 GiB is reserved by PyTorch but unallocat...
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 17:29:20,196] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-22 17:29:20,196] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10997.81 | backward_microstep: 12121.38 | backward_inner_microstep: 8841.30 | backward_allreduce_microstep: 3279.89 | step_microstep: 7.79
[2024-10-22 17:29:20,196] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10997.84 | backward: 12121.37 | backward_inner: 8841.32 | backward_allreduce: 3279.76 | step: 7.80
1%| | 27/2424 [10:52<15:16:54, 22.95s/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=RIGHT,question='How many humans are holding cell phones in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many green and yellow balloons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many power poles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Are there triangular pennants on display in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)