Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
[('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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
[('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: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
tensor([0.2049, 0.1306, 0.1375, 0.1940, 0.0490, 0.1547, 0.0317, 0.0975],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([0.2049, 0.1306, 0.1375, 0.1940, 0.0490, 0.1547, 0.0317, 0.0975],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0490, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9510, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many monkeys 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([0.6511, 0.2027, 0.0995, 0.0123, 0.0030, 0.0085, 0.0070, 0.0160],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
metal *************
['metal', 'glass', 'steel', 'iron', 'rust', 'fur', 'stone', 'wine'] tensor([0.6511, 0.2027, 0.0995, 0.0123, 0.0030, 0.0085, 0.0070, 0.0160],
device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the animal in the image lying down?')
ANSWER1=EVAL(expr='{ANSWER0}')
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 5.43 GiB is free. Including non-PyTorch memory, this process has 38.89 GiB memory in use. Of the allocated memory 36.21 GiB is allocated by PyTorch, and 2.06 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
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}
tensor([8.6105e-01, 8.5310e-02, 1.4825e-02, 3.1360e-02, 4.8097e-03, 1.2159e-03,
1.3780e-03, 5.5515e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([8.6105e-01, 8.5310e-02, 1.4825e-02, 3.1360e-02, 4.8097e-03, 1.2159e-03,
1.3780e-03, 5.5515e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8924, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1076, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many wolves are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many wolves 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([9.8173e-01, 8.4937e-03, 2.5904e-03, 6.2139e-03, 4.9400e-04, 2.8797e-04,
1.6950e-04, 2.1962e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.8173e-01, 8.4937e-03, 2.5904e-03, 6.2139e-03, 4.9400e-04, 2.8797e-04,
1.6950e-04, 2.1962e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9817, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0183, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 1 has a total capacty of 44.34 GiB of which 122.94 MiB is free. Including non-PyTorch memory, this process has 44.21 GiB memory in use. Of the allocated memory 40.52 GiB is allocated by PyTorch, and 3.05 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
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}
tensor([9.7128e-01, 4.6403e-03, 1.8164e-03, 7.0602e-04, 8.8375e-04, 4.9190e-04,
2.0158e-02, 2.5730e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7128e-01, 4.6403e-03, 1.8164e-03, 7.0602e-04, 8.8375e-04, 4.9190e-04,
2.0158e-02, 2.5730e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9713, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0287, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-22 17:32:18,717] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.30 | optimizer_step: 0.33
[2024-10-22 17:32:18,717] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 8272.64 | backward_microstep: 15834.53 | backward_inner_microstep: 7866.39 | backward_allreduce_microstep: 7967.71 | step_microstep: 7.59
[2024-10-22 17:32:18,717] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 8272.66 | backward: 15834.52 | backward_inner: 7866.71 | backward_allreduce: 7967.68 | step: 7.61
1%|▏ | 35/2424 [13:50<15:44:11, 23.71s/it]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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is the ferret seen coming out of a hole?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many striped pillows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Are there white flowers in a vase in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many wine glasses are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([5, 3, 448, 448])