Datasets:
TMarcus
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InternVL

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6.7430e-02, 4.1171e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([8.7444e-01, 2.8109e-02, 1.3706e-02, 4.8859e-03, 7.1062e-03, 3.9086e-03,
6.7430e-02, 4.1171e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.1256, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.8744, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many binders are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['How many dogs 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
question: ['How many binders are 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']]
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
tensor([6.4529e-01, 3.6404e-02, 7.7135e-03, 3.0475e-01, 3.1801e-03, 1.2575e-03,
1.2838e-03, 1.2271e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.4529e-01, 3.6404e-02, 7.7135e-03, 3.0475e-01, 3.1801e-03, 1.2575e-03,
1.2838e-03, 1.2271e-04], device='cuda:2', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6952, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3048, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Can you see the lamp in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
tensor([9.5083e-01, 8.7571e-03, 4.8350e-03, 2.0759e-03, 2.8410e-03, 1.8060e-03,
2.8713e-02, 1.4430e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5083e-01, 8.7571e-03, 4.8350e-03, 2.0759e-03, 2.8410e-03, 1.8060e-03,
2.8713e-02, 1.4430e-04], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([4.6084e-01, 1.6952e-01, 5.8545e-02, 2.5903e-01, 3.3738e-02, 8.2708e-03,
9.6686e-03, 3.8927e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ:
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([4.6084e-01, 1.6952e-01, 5.8545e-02, 2.5903e-01, 3.3738e-02, 8.2708e-03,
9.6686e-03, 3.8927e-04], device='cuda:0', grad_fn=<SelectBackward0>)
{True: tensor(0.9508, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0492, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4608, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5392, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the goat laying down?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
tensor([9.0359e-01, 4.4981e-02, 9.4297e-03, 3.7294e-02, 2.8759e-03, 8.7658e-04,
9.0464e-04, 5.1583e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.0359e-01, 4.4981e-02, 9.4297e-03, 3.7294e-02, 2.8759e-03, 8.7658e-04,
9.0464e-04, 5.1583e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9036, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0964, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', 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)
torch.Size([13, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 2 has a total capacty of 44.34 GiB of which 5.32 GiB is free. Including non-PyTorch memory, this process has 39.00 GiB memory in use. Of the allocated memory 36.23 GiB is allocated by PyTorch, and 2.14 GiB is reserved by PyTorch but unallocat...
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}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 1 has a total capacty of 44.34 GiB of which 3.38 GiB is free. Including non-PyTorch memory, this process has 40.95 GiB memory in use. Of the allocated memory 38.11 GiB is allocated by PyTorch, and 2.20 GiB is reserved by PyTorch but unallocat...
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}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 5.85 GiB. GPU 3 has a total capacty of 44.34 GiB of which 3.96 GiB is free. Including non-PyTorch memory, this process has 40.36 GiB memory in use. Of the allocated memory 38.07 GiB is allocated by PyTorch, and 1.73 GiB is reserved by PyTorch but unallocat...
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 17:20:09,379] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-22 17:20:09,379] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 9610.22 | backward_microstep: 10485.01 | backward_inner_microstep: 9200.49 | backward_allreduce_microstep: 1284.03 | step_microstep: 7.34
[2024-10-22 17:20:09,379] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 9610.24 | backward: 10485.00 | backward_inner: 9200.51 | backward_allreduce: 1284.02 | step: 7.35
0%| | 4/2424 [01:41<16:01:50, 23.85s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Are the two pins touching each other?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a plant in one of the vases?')
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
ANSWER0=VQA(image=LEFT,question='How many dogs are standing on grass in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Do the golf balls in the left image look noticeably darker and grayer than those in the right image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])