Datasets:
TMarcus
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InternVL

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2.4135e-04, 4.5158e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3627, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.6365, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many birds are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
question: ['How many birds are in the image?'], responses:['10']
[('10', 0.1277249466426885), ('11', 0.12579928416580372), ('12', 0.12560051978633632), ('8', 0.1247991444010043), ('9', 0.12459861387933152), ('26', 0.12389435171102943), ('13', 0.12388731669200545), ('6', 0.12369582272180085)]
[['10', '11', '12', '8', '9', '26', '13', '6']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
tensor([5.6457e-01, 1.8916e-02, 4.1304e-01, 1.2637e-03, 2.3978e-04, 1.1150e-03,
1.0709e-04, 7.4774e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([5.6457e-01, 1.8916e-02, 4.1304e-01, 1.2637e-03, 2.3978e-04, 1.1150e-03,
1.0709e-04, 7.4774e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.5646, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.4130, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0224, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many snow plows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([0.1788, 0.1473, 0.1403, 0.1341, 0.1776, 0.0256, 0.1221, 0.0741],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
10 *************
['10', '11', '12', '8', '9', '26', '13', '6'] tensor([0.1788, 0.1473, 0.1403, 0.1341, 0.1776, 0.0256, 0.1221, 0.0741],
device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Is the dog in the left image facing right?'], responses:['no']
[('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']]
question: ['How many snow plows 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([13, 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: 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: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([8.8006e-01, 1.1910e-01, 5.6476e-05, 6.4826e-05, 6.4626e-05, 3.3666e-04,
1.7767e-04, 1.3160e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.8006e-01, 1.1910e-01, 5.6476e-05, 6.4826e-05, 6.4626e-05, 3.3666e-04,
1.7767e-04, 1.3160e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1191, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.8801, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0008, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Does the bottle in the image have a wooden look?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
question: ['Does the bottle in the image have a wooden look?'], responses:['no']
[('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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([7.8968e-01, 4.1945e-02, 1.6426e-02, 4.6953e-03, 6.4323e-03, 3.0330e-03,
1.3753e-01, 2.5403e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([7.8968e-01, 4.1945e-02, 1.6426e-02, 4.6953e-03, 6.4323e-03, 3.0330e-03,
1.3753e-01, 2.5403e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.7897, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.2103, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([8.6666e-01, 1.3291e-01, 3.4312e-05, 4.3202e-05, 3.1659e-05, 7.6179e-05,
1.9915e-04, 5.2824e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([8.6666e-01, 1.3291e-01, 3.4312e-05, 4.3202e-05, 3.1659e-05, 7.6179e-05,
1.9915e-04, 5.2824e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1329, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.8667, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0004, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([5.6141e-01, 4.3722e-01, 4.2092e-05, 1.2464e-04, 1.3181e-04, 6.7151e-04,
3.6966e-04, 2.4815e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.6141e-01, 4.3722e-01, 4.2092e-05, 1.2464e-04, 1.3181e-04, 6.7151e-04,
3.6966e-04, 2.4815e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4372, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.5614, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0014, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the door open?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 2.92 GiB. GPU 1 has a total capacty of 44.34 GiB of which 548.94 MiB is free. Including non-PyTorch memory, this process has 43.79 GiB memory in use. Of the allocated memory 40.77 GiB is allocated by PyTorch, and 2.38 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}
[2024-10-22 17:27:25,214] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.45 | optimizer_gradients: 0.28 | optimizer_step: 0.32
[2024-10-22 17:27:25,215] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 10927.83 | backward_microstep: 13045.24 | backward_inner_microstep: 10443.92 | backward_allreduce_microstep: 2601.14 | step_microstep: 9.24
[2024-10-22 17:27:25,215] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 10927.85 | backward: 13045.23 | backward_inner: 10443.99 | backward_allreduce: 2601.13 | step: 9.26
1%| | 22/2424 [08:57<16:15:29, 24.37s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
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='Is there a person in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')