Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
1.16k
[['2', '3', '4', '1', '5', '8', '7', '29']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 9.2781e-09, 3.3840e-11, 1.1441e-08, 7.8117e-10, 4.8957e-10,
5.5936e-11, 2.2391e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.2781e-09, 3.3840e-11, 1.1441e-08, 7.8117e-10, 4.8957e-10,
5.5936e-11, 2.2391e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.3840e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.3840e-11, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([1.0000e+00, 1.4450e-07, 5.0616e-09, 6.3126e-08, 9.1462e-10, 6.9581e-10,
1.3623e-09, 2.7859e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.4450e-07, 5.0616e-09, 6.3126e-08, 9.1462e-10, 6.9581e-10,
1.3623e-09, 2.7859e-10], 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(2.1594e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many dogs are in the image?'], responses:['δΈ‰']
[('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']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.9682e-01, 8.9113e-06, 7.1304e-08, 3.1727e-03, 3.1641e-08, 2.9749e-09,
2.5030e-08, 4.2654e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9682e-01, 8.9113e-06, 7.1304e-08, 3.1727e-03, 3.1641e-08, 2.9749e-09,
2.5030e-08, 4.2654e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.1304e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([3.6947e-04, 2.3185e-03, 3.8355e-02, 6.3025e-01, 1.0116e-01, 6.6076e-02,
3.7279e-03, 1.5774e-01], device='cuda:3', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([3.6947e-04, 2.3185e-03, 3.8355e-02, 6.3025e-01, 1.0116e-01, 6.6076e-02,
3.7279e-03, 1.5774e-01], device='cuda:3', 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>)}
[2024-10-24 09:56:56,604] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.35 | optimizer_step: 0.33
[2024-10-24 09:56:56,605] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3164.10 | backward_microstep: 14622.80 | backward_inner_microstep: 3024.96 | backward_allreduce_microstep: 11597.67 | step_microstep: 7.48
[2024-10-24 09:56:56,605] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3164.12 | backward: 14622.79 | backward_inner: 3025.05 | backward_allreduce: 11597.65 | step: 7.49
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4632/4844 [19:15:40<51:05, 14.46s/it]Registering VQA_lavis 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 food sitting in?')
ANSWER1=EVAL(expr='{ANSWER0} == "brown plate"')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a child pulling a dog on a sled in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Is the toe of the shoe pointed to the left?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the phone in the slide out position?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([5, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['What is the food sitting in?'], responses:['plate']
[('plate', 0.12673014572769747), ('bowl', 0.1248969376466481), ('delivery', 0.12487111978025815), ('container', 0.12476829192613588), ('blending', 0.12471798969044892), ('doll', 0.12470041906034778), ('hazy', 0.12466699410259863), ('sliding', 0.12464810206586503)]
[['plate', 'bowl', 'delivery', 'container', 'blending', 'doll', 'hazy', 'sliding']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
question: ['Is the toe of the shoe pointed to the left?'], 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']]
tensor([8.8971e-01, 1.0243e-01, 1.9896e-06, 7.6878e-03, 6.0332e-06, 1.2307e-04,
1.7157e-05, 1.9160e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
plate *************
['plate', 'bowl', 'delivery', 'container', 'blending', 'doll', 'hazy', 'sliding'] tensor([8.8971e-01, 1.0243e-01, 1.9896e-06, 7.6878e-03, 6.0332e-06, 1.2307e-04,
1.7157e-05, 1.9160e-05], 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>)}
ANSWER0=VQA(image=LEFT,question='How many cylindrical pencil cases are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
question: ['Is the phone in the slide out position?'], 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['How many cylindrical pencil cases 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['Is there a child pulling a dog on a sled 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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3403
tensor([1.0000e+00, 3.4661e-07, 4.2714e-08, 5.9644e-09, 8.4914e-08, 3.5410e-08,
8.3153e-07, 9.2572e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.4661e-07, 4.2714e-08, 5.9644e-09, 8.4914e-08, 3.5410e-08,