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InternVL

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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: 1861
tensor([9.8511e-01, 1.4125e-03, 2.2239e-03, 3.4409e-04, 1.3988e-03, 3.6797e-04,
4.6263e-04, 8.6851e-03], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8511e-01, 1.4125e-03, 2.2239e-03, 3.4409e-04, 1.3988e-03, 3.6797e-04,
4.6263e-04, 8.6851e-03], device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0.9851, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0149, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([9.7082e-01, 5.9555e-03, 2.9016e-03, 1.5491e-03, 1.7591e-03, 1.2266e-03,
1.5692e-02, 9.8459e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7082e-01, 5.9555e-03, 2.9016e-03, 1.5491e-03, 1.7591e-03, 1.2266e-03,
1.5692e-02, 9.8459e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
{True: tensor(0.0157, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9843, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
question: ['How many wolves are in the image?'], responses:['5']
[('5', 0.12793059870235002), ('8', 0.12539646467821697), ('4', 0.12509737486793587), ('6', 0.12470234839853608), ('3', 0.12467331676337925), ('7', 0.12441254825093238), ('11', 0.12401867309944531), ('9', 0.12376867523920407)]
[['5', '8', '4', '6', '3', '7', '11', '9']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([9.6839e-01, 3.1129e-02, 2.1214e-05, 4.3962e-05, 1.9677e-04, 8.5077e-05,
1.0771e-04, 2.3391e-05], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.6839e-01, 3.1129e-02, 2.1214e-05, 4.3962e-05, 1.9677e-04, 8.5077e-05,
1.0771e-04, 2.3391e-05], device='cuda:0', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.0311, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9684, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0005, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([0.4085, 0.0495, 0.1170, 0.2526, 0.0207, 0.1193, 0.0062, 0.0261],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.4085, 0.0495, 0.1170, 0.2526, 0.0207, 0.1193, 0.0062, 0.0261],
device='cuda:2', grad_fn=<SelectBackward0>)
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: tensor(0.4085, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.5915, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How are the pencils laying?')
ANSWER1=EVAL(expr='{ANSWER0} == "points facing down and slightly left"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.93 GiB. GPU 2 has a total capacty of 44.34 GiB of which 778.94 MiB is free. Including non-PyTorch memory, this process has 43.56 GiB memory in use. Of the allocated memory 40.78 GiB is allocated by PyTorch, and 2.15 GiB is reserved by PyTorch but unalloc...
Encountered TypeError: unsupported operand type(s) for +: 'NoneType' and 'str'
ๆœ€ๅŽ็š„ๆฆ‚็Ž‡ๅˆ†ๅธƒไธบ: {True: 1e-09, False: 1e-09, 'Execute Error': 0.999999998}
[2024-10-22 17:27:48,886] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-22 17:27:48,886] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10265.85 | backward_microstep: 13381.08 | backward_inner_microstep: 9813.65 | backward_allreduce_microstep: 3567.36 | step_microstep: 7.96
[2024-10-22 17:27:48,887] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10265.87 | backward: 13381.07 | backward_inner: 9813.67 | backward_allreduce: 3567.16 | step: 7.97
1%| | 23/2424 [09:21<16:06:44, 24.16s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
ANSWER0=VQA(image=RIGHT,question='How many people are wearing graduation caps in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Do the sails in the image have the color white on them?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Does the dessert contain any berries?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is there any animal in the water?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Do the sails in the image have the color white on them?'], responses:['no']
question: ['Does the dessert contain any berries?'], responses:['yes']
[('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']]
[('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
question: ['How many people are wearing graduation caps 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']]
question: ['Is there any animal in the water?'], responses:['no']
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
[('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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3399
tensor([9.2803e-01, 7.1562e-02, 3.2194e-06, 3.4914e-05, 5.4308e-05, 9.8693e-05,
2.0402e-04, 1.3851e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)