Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
tensor([9.5152e-01, 3.5691e-04, 4.6818e-05, 1.2992e-04, 2.2162e-04, 1.8335e-05,
4.7703e-02, 8.5454e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.5152e-01, 3.5691e-04, 4.6818e-05, 1.2992e-04, 2.2162e-04, 1.8335e-05,
4.7703e-02, 8.5454e-07], device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
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(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['Which direction are the dogs heading?'], responses:['right']
[('right', 0.12743553739412528), ('right 1', 0.12490968573275477), ('straight', 0.12485251094891832), ('floating', 0.12468075392646753), ('flip', 0.12467791878738273), ('backwards', 0.12452118816110067), ('serious', 0.12447626064603681), ('working', 0.12444614440321403)]
[['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working']]
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
tensor([2.7290e-08, 5.4630e-01, 9.7096e-03, 1.4017e-03, 4.4202e-01, 2.8714e-04,
7.7839e-05, 2.0747e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
babies *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([2.7290e-08, 5.4630e-01, 9.7096e-03, 1.4017e-03, 4.4202e-01, 2.8714e-04,
7.7839e-05, 2.0747e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.2483e-06, 1.3027e-08, 1.7430e-07, 2.6157e-09, 5.2728e-10,
2.1941e-09, 3.0159e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.2483e-06, 1.3027e-08, 1.7430e-07, 2.6157e-09, 5.2728e-10,
2.1941e-09, 3.0159e-10], 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(1.7430e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.8597e-01, 1.0789e-05, 4.9619e-03, 5.0761e-06, 3.8280e-06, 8.6713e-03,
3.6381e-04, 8.1825e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
right *************
['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working'] tensor([9.8597e-01, 1.0789e-05, 4.9619e-03, 5.0761e-06, 3.8280e-06, 8.6713e-03,
3.6381e-04, 8.1825e-06], 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>)}
[2024-10-24 10:17:36,122] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.39 | optimizer_gradients: 0.37 | optimizer_step: 0.33
[2024-10-24 10:17:36,122] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3821.00 | backward_microstep: 6036.20 | backward_inner_microstep: 3520.10 | backward_allreduce_microstep: 2516.01 | step_microstep: 8.00
[2024-10-24 10:17:36,123] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3821.02 | backward: 6036.19 | backward_inner: 3520.12 | backward_allreduce: 2515.98 | step: 8.01
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4713/4844 [19:36:19<28:15, 12.94s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
ANSWER0=VQA(image=RIGHT,question='Is the needle connected to the syringe?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is there a lock with the key inside the locking mechanism in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many people are inside the store?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are there flowers on the bathroom counter?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is there a lock with the key inside the locking mechanism 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']]
question: ['Is the needle connected to the syringe?'], responses:['no']
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['Are there flowers on the bathroom counter?'], responses:['yes']
[('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']]
[('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']]
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: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
question: ['How many people are inside the store?'], 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([1.0000e+00, 4.7379e-07, 2.0055e-07, 3.0636e-10, 3.4444e-12, 1.1359e-09,
1.1474e-09, 1.7158e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.7379e-07, 2.0055e-07, 3.0636e-10, 3.4444e-12, 1.1359e-09,
1.1474e-09, 1.7158e-07], device='cuda:1', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.7379e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many windows are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
tensor([1.0000e+00, 5.6028e-09, 1.3268e-07, 1.0588e-10, 5.7682e-13, 1.2840e-09,
7.0426e-10, 6.4674e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************