Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
question: ['Does the image contain a human hand?'], responses:['yes']
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
[('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']]
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
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
tensor([1.0000e+00, 4.4524e-06, 1.2744e-08, 1.3260e-08, 1.0034e-10, 1.6885e-07,
5.9961e-10, 2.8965e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([1.0000e+00, 4.4524e-06, 1.2744e-08, 1.3260e-08, 1.0034e-10, 1.6885e-07,
5.9961e-10, 2.8965e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.2744e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 3.6684e-09, 8.6079e-11, 2.3359e-08, 5.1743e-09, 5.7682e-10,
2.3780e-11, 2.8661e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 3.6684e-09, 8.6079e-11, 2.3359e-08, 5.1743e-09, 5.7682e-10,
2.3780e-11, 2.8661e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(8.6079e-11, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-8.6079e-11, device='cuda:3', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 3.1924e-09, 2.5330e-07, 1.1087e-12, 4.8665e-13, 4.6853e-09,
8.8622e-11, 5.8919e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.1924e-09, 2.5330e-07, 1.1087e-12, 4.8665e-13, 4.6853e-09,
8.8622e-11, 5.8919e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.1924e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(8.3447e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.9999e-01, 9.2772e-08, 7.4112e-06, 4.3541e-07, 8.3976e-09, 9.8331e-09,
3.8168e-07, 2.0276e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9999e-01, 9.2772e-08, 7.4112e-06, 4.3541e-07, 8.3976e-09, 9.8331e-09,
3.8168e-07, 2.0276e-06], device='cuda:2', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=LEFT,question='How many pandas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(7.4112e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.9600e-06, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Can you see the lamp in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many pandas 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['Can you see the lamp in the image?'], responses:['yes']
[('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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 8.0716e-10, 1.3621e-10, 3.1356e-10, 2.3197e-10, 4.0304e-09,
1.7258e-08, 5.5370e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 8.0716e-10, 1.3621e-10, 3.1356e-10, 2.3197e-10, 4.0304e-09,
1.7258e-08, 5.5370e-11], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(2.2832e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.0731e-08, 6.9041e-10, 4.2906e-08, 2.5955e-10, 7.4225e-09,
1.5361e-10, 9.4247e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0731e-08, 6.9041e-10, 4.2906e-08, 2.5955e-10, 7.4225e-09,
1.5361e-10, 9.4247e-09], 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(6.9041e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1852e-07, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:01:28,036] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.34 | optimizer_step: 0.32
[2024-10-24 10:01:28,036] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3107.11 | backward_microstep: 14711.35 | backward_inner_microstep: 3034.25 | backward_allreduce_microstep: 11677.03 | step_microstep: 7.82
[2024-10-24 10:01:28,036] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3107.13 | backward: 14711.34 | backward_inner: 3034.27 | backward_allreduce: 11677.02 | step: 7.83
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4649/4844 [19:20:11<54:18, 16.71s/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=LEFT,question='What is the color of the background?')
ANSWER1=EVAL(expr='{ANSWER0} == "brown"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many pillows are leaning against each other?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['5']
[('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([3, 3, 448, 448]) knan debug pixel values shape