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(5.9053e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.3842e-06, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 9.2374e-09, 4.6885e-08, 1.5984e-11, 1.3785e-11, 5.8790e-09,
2.9307e-10, 5.8325e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 9.2374e-09, 4.6885e-08, 1.5984e-11, 1.3785e-11, 5.8790e-09,
2.9307e-10, 5.8325e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(9.2374e-09, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 8')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 3.2887e-06, 9.6256e-08, 2.2159e-08, 2.0292e-09, 7.0127e-10,
5.8717e-09, 5.2823e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 3.2887e-06, 9.6256e-08, 2.2159e-08, 2.0292e-09, 7.0127e-10,
5.8717e-09, 5.2823e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.6258e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many animals 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']]
question: ['How many dogs are in the image?'], responses:['50']
[('50', 0.12746329354121594), ('51', 0.12494443111915052), ('60', 0.12471995183640609), ('55', 0.12470016949940634), ('54', 0.12460076157014638), ('52', 0.12454269500997545), ('44', 0.12453681395238846), ('48', 0.1244918834713108)]
[['50', '51', '60', '55', '54', '52', '44', '48']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
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
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([9.9999e-01, 1.2604e-05, 4.6283e-09, 8.7668e-08, 1.8922e-09, 6.9062e-10,
5.0229e-09, 3.7901e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 1.2604e-05, 4.6283e-09, 8.7668e-08, 1.8922e-09, 6.9062e-10,
5.0229e-09, 3.7901e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.2708e-05, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.6554e-01, 3.5823e-04, 2.0150e-02, 1.0538e-02, 4.9499e-04, 4.4032e-04,
1.6075e-03, 8.6959e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
50 *************
['50', '51', '60', '55', '54', '52', '44', '48'] tensor([9.6554e-01, 3.5823e-04, 2.0150e-02, 1.0538e-02, 4.9499e-04, 4.4032e-04,
1.6075e-03, 8.6959e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:36:21,086] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-24 09:36:21,087] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7031.35 | backward_microstep: 7029.11 | backward_inner_microstep: 6792.23 | backward_allreduce_microstep: 236.72 | step_microstep: 7.48
[2024-10-24 09:36:21,087] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7031.35 | backward: 7029.10 | backward_inner: 6792.30 | backward_allreduce: 236.71 | step: 7.49
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4549/4844 [18:55:04<1:09:32, 14.15s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering VQA_lavis stepRegistering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are the wild dogs feeding on their prey?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)ANSWER0=VQA(image=RIGHT,question='How many neatly folded and stacked bath towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are lying down?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([1, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many people are in the image?'], responses:['δΈ‰']
question: ['How many dogs are lying down?'], responses:['1']
[('biking', 0.12639990046765587), ('geese', 0.1262789403477572), ('cushion', 0.1253965842661667), ('bulldog', 0.1252365705078606), ('striped', 0.12499404846420245), ('floral', 0.12444127054742124), ('stove', 0.12381223353082338), ('dodgers', 0.12344045186811266)]
[['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers']]
[('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([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([0.0012, 0.0078, 0.0621, 0.4839, 0.1806, 0.2281, 0.0209, 0.0154],
device='cuda:3', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([0.0012, 0.0078, 0.0621, 0.4839, 0.1806, 0.2281, 0.0209, 0.0154],
device='cuda:3', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 3.7317e-10, 9.0560e-11, 1.2572e-10, 9.1957e-11, 1.8351e-08,
5.6910e-09, 1.6063e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.7317e-10, 9.0560e-11, 1.2572e-10, 9.1957e-11, 1.8351e-08,
5.6910e-09, 1.6063e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}