Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
1.16k
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many cases are in the image?'], responses:['6']
[('6', 0.12794147189263105), ('8', 0.12539492259598553), ('12', 0.12539359088927945), ('5', 0.12471292164321114), ('4', 0.12443617393590153), ('1', 0.12417386497855347), ('11', 0.12398049124372558), ('3', 0.12396656282071232)]
[['6', '8', '12', '5', '4', '1', '11', '3']]
torch.Size([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
tensor([1.0000e+00, 4.3036e-10, 9.9462e-11, 2.6202e-10, 1.6269e-10, 1.3649e-08,
7.1941e-09, 3.4435e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 4.3036e-10, 9.9462e-11, 2.6202e-10, 1.6269e-10, 1.3649e-08,
7.1941e-09, 3.4435e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.2142e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
tensor([7.7210e-01, 1.1082e-04, 1.2930e-06, 2.2768e-01, 1.0260e-04, 2.6468e-09,
2.9020e-06, 6.3176e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
6 *************
['6', '8', '12', '5', '4', '1', '11', '3'] tensor([7.7210e-01, 1.1082e-04, 1.2930e-06, 2.2768e-01, 1.0260e-04, 2.6468e-09,
2.9020e-06, 6.3176e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.6468e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.9067e-09, 1.9474e-10, 5.6693e-09, 7.3876e-11, 3.1609e-10,
3.0559e-11, 1.8970e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.9067e-09, 1.9474e-10, 5.6693e-09, 7.3876e-11, 3.1609e-10,
3.0559e-11, 1.8970e-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(1.9474e-10, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-1.9474e-10, device='cuda:3', grad_fn=<SubBackward0>)}
[2024-10-24 09:31:27,240] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.37 | optimizer_gradients: 0.20 | optimizer_step: 0.30
[2024-10-24 09:31:27,241] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6407.69 | backward_microstep: 6328.63 | backward_inner_microstep: 6178.93 | backward_allreduce_microstep: 149.64 | step_microstep: 7.20
[2024-10-24 09:31:27,241] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6407.69 | backward: 6328.62 | backward_inner: 6178.94 | backward_allreduce: 149.63 | step: 7.21
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4530/4844 [18:50:11<1:14:13, 14.18s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many women are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
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 the sink shaped like a bowl?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([1, 3, 448, 448])
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many birds are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['How many women are in the image?'], responses:['five']
question: ['Is the sink shaped like a bowl?'], 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([1, 3, 448, 448]) knan debug pixel values shape
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
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([9.9999e-01, 1.9972e-09, 1.4739e-05, 1.7578e-09, 9.5320e-12, 2.1974e-12,
4.9522e-11, 2.4121e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9999e-01, 1.9972e-09, 1.4739e-05, 1.7578e-09, 9.5320e-12, 2.1974e-12,
4.9522e-11, 2.4121e-09], device='cuda:1', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 324
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.4739e-05, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.3027e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many red balloons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 325
torch.Size([7, 3, 448, 448])
tensor([8.1054e-11, 3.0122e-01, 7.0089e-02, 1.1874e-04, 6.2799e-01, 1.0822e-04,
2.1659e-04, 2.5774e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
feet *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([8.1054e-11, 3.0122e-01, 7.0089e-02, 1.1874e-04, 6.2799e-01, 1.0822e-04,
2.1659e-04, 2.5774e-04], device='cuda:0', grad_fn=<SelectBackward0>)
question: ['How many parrots are in the image?'], responses:['1']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: question: ['How many birds are in the image?'], responses:['1']
{True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many black chow dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)