Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
1.16k
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.2233, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.7753, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(0.0015, device='cuda:2', grad_fn=<SubBackward0>)}
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: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
tensor([0.3827, 0.1206, 0.0368, 0.4210, 0.0223, 0.0077, 0.0082, 0.0007],
device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([0.3827, 0.1206, 0.0368, 0.4210, 0.0223, 0.0077, 0.0082, 0.0007],
device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4210, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.5790, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many rodents are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many rodents are in the image?'], responses:['1']
tensor([7.2282e-01, 6.7233e-02, 9.7243e-03, 1.9451e-01, 3.4672e-03, 9.5859e-04,
1.1933e-03, 8.6256e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([7.2282e-01, 6.7233e-02, 9.7243e-03, 1.9451e-01, 3.4672e-03, 9.5859e-04,
1.1933e-03, 8.6256e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8055, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.1945, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many chimps are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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])
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
tensor([9.1584e-01, 1.6766e-02, 6.5671e-03, 2.7311e-03, 3.9817e-03, 2.2444e-03,
5.1658e-02, 2.0783e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.1584e-01, 1.6766e-02, 6.5671e-03, 2.7311e-03, 3.9817e-03, 2.2444e-03,
5.1658e-02, 2.0783e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0842, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.9158, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
Encountered ExecuteError: CUDA out of memory. Tried to allocate 3.21 GiB. GPU 3 has a total capacty of 44.34 GiB of which 2.04 GiB is free. Including non-PyTorch memory, this process has 42.28 GiB memory in use. Of the allocated memory 40.52 GiB is allocated by PyTorch, and 1.21 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 17:25:47,560] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.23 | optimizer_step: 0.31
[2024-10-22 17:25:47,560] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 12243.55 | backward_microstep: 11754.27 | backward_inner_microstep: 11748.45 | backward_allreduce_microstep: 5.62 | step_microstep: 7.82
[2024-10-22 17:25:47,560] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 12243.55 | backward: 11754.26 | backward_inner: 11748.58 | backward_allreduce: 5.61 | step: 7.83
1%| | 18/2424 [07:19<16:08:54, 24.16s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
ANSWER0=VQA(image=LEFT,question='Is a slice being lifted off the pizza in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
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 food being served in a white dish?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the animal in the image standing on all fours?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is a slice being lifted off the pizza in the image?'], 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is the food being served in a white dish?'], responses:['yes']
question: ['How many people are in the image?'], responses:['4']
[('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']]
[('4', 0.12804651361935848), ('5', 0.12521071898947128), ('3', 0.12515925906184908), ('8', 0.12489091845155219), ('6', 0.1245383468146311), ('1', 0.12441141527606933), ('2', 0.12403713327181662), ('11', 0.12370569451525179)]
[['4', '5', '3', '8', '6', '1', '2', '11']]
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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1865
tensor([6.0687e-01, 3.9183e-01, 7.2778e-05, 1.7290e-04, 1.2966e-04, 3.4735e-04,
4.7665e-04, 1.0349e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.0687e-01, 3.9183e-01, 7.2778e-05, 1.7290e-04, 1.2966e-04, 3.4735e-04,
4.7665e-04, 1.0349e-04], device='cuda:2', grad_fn=<SelectBackward0>)
question: ['Is the animal in the image standing on all fours?'], responses:['yes']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3918, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.6069, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0013, device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862