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(1.7357e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 8.0577e-09, 2.9023e-06, 5.0955e-10, 8.2921e-12, 1.8398e-11,
5.9408e-11, 8.1924e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.0577e-09, 2.9023e-06, 5.0955e-10, 8.2921e-12, 1.8398e-11,
5.9408e-11, 8.1924e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(2.9023e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.1270e-08, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:27:25,990] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.42 | optimizer_gradients: 0.32 | optimizer_step: 0.32
[2024-10-24 10:27:25,991] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2486.96 | backward_microstep: 11294.74 | backward_inner_microstep: 2193.22 | backward_allreduce_microstep: 9101.39 | step_microstep: 7.97
[2024-10-24 10:27:25,991] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2486.96 | backward: 11294.73 | backward_inner: 2193.26 | backward_allreduce: 9101.36 | step: 7.98
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4754/4844 [19:46:09<19:35, 13.06s/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 the animal in the image standing on its hind legs?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='What color is the flower in the white vase?')
ANSWER1=EVAL(expr='{ANSWER0} == "yellow"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Do both images show kneepads modelled on human legs?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the banana flower purple?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Do both images show kneepads modelled on human legs?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['What color is the flower in the white vase?'], responses:['yellow']
question: ['Is the banana flower purple?'], responses:['no']
question: ['Is the animal in the image standing on its hind legs?'], responses:['no']
[('yellow', 0.13019233292980176), ('red', 0.12608840659087261), ('green', 0.12436926918223776), ('maroon', 0.12425930516133966), ('pink', 0.12421440410307089), ('mask', 0.12363437991296296), ('orange', 0.12363130058084727), ('color', 0.12361060153886716)]
[['yellow', 'red', 'green', 'maroon', 'pink', 'mask', 'orange', 'color']]
[('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']]
[('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([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
tensor([1.0000e+00, 1.3440e-08, 2.3695e-07, 1.6330e-12, 1.1333e-11, 1.9706e-10,
2.9387e-10, 3.1684e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.3440e-08, 2.3695e-07, 1.6330e-12, 1.1333e-11, 1.9706e-10,
2.9387e-10, 3.1684e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.3440e-08, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['How many dogs are in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
tensor([9.9999e-01, 1.2740e-05, 6.2251e-08, 6.1929e-09, 1.3264e-10, 4.4527e-08,
2.8942e-10, 1.5279e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9999e-01, 1.2740e-05, 6.2251e-08, 6.1929e-09, 1.3264e-10, 4.4527e-08,
2.8942e-10, 1.5279e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.2762e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
tensor([9.9643e-01, 1.0286e-03, 6.2233e-04, 3.9603e-04, 1.4949e-03, 9.4843e-09,
2.3446e-05, 1.1183e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yellow *************
['yellow', 'red', 'green', 'maroon', 'pink', 'mask', 'orange', 'color'] tensor([9.9643e-01, 1.0286e-03, 6.2233e-04, 3.9603e-04, 1.4949e-03, 9.4843e-09,
2.3446e-05, 1.1183e-07], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.8780e-10, 2.6489e-07, 2.8496e-11, 1.8181e-11, 1.9855e-08,
6.3743e-10, 5.8691e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.8780e-10, 2.6489e-07, 2.8496e-11, 1.8181e-11, 1.9855e-08,
6.3743e-10, 5.8691e-07], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.7036e-10, 1.8750e-07, 2.0006e-12, 2.3794e-11, 6.5651e-09,
5.7781e-10, 1.0284e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.7036e-10, 1.8750e-07, 2.0006e-12, 2.3794e-11, 6.5651e-09,
5.7781e-10, 1.0284e-06], device='cuda:1', 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>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.8780e-10, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:0', grad_fn=<DivBackward0>)}