Datasets:
TMarcus
/
InternVL

Dataset card Data Studio Files
xet
Community
1
text
stringlengths
0
1.16k
1.9591e-09, 3.1112e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 8.4946e-08, 1.0386e-08, 4.0126e-08, 3.4716e-10, 1.5589e-09,
1.9591e-09, 3.1112e-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(1.3963e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many saxes is the musician playing?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([1.0000e+00, 2.7104e-09, 1.2117e-09, 7.1734e-09, 5.7548e-11, 2.5544e-11,
3.1524e-11, 3.3632e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.7104e-09, 1.2117e-09, 7.1734e-09, 5.7548e-11, 2.5544e-11,
3.1524e-11, 3.3632e-09], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.2117e-09, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.2117e-09, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many goats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
question: ['How many goats are in the image?'], responses:['2']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
[('2', 0.12961991198727602), ('3', 0.12561270547489775), ('4', 0.12556127085987287), ('1', 0.1254920833223361), ('5', 0.12407835939022728), ('8', 0.124024076973589), ('7', 0.12288810153923228), ('29', 0.12272349045256851)]
[['2', '3', '4', '1', '5', '8', '7', '29']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
question: ['How many saxes is the musician playing?'], 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([9.9999e-01, 2.6165e-07, 8.0256e-09, 5.0937e-06, 7.4141e-10, 1.2612e-09,
3.5061e-09, 2.4040e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9999e-01, 2.6165e-07, 8.0256e-09, 5.0937e-06, 7.4141e-10, 1.2612e-09,
3.5061e-09, 2.4040e-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(5.3691e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 2.3582e-10, 7.3338e-11, 1.6852e-10, 1.0022e-10, 9.9825e-09,
2.3356e-09, 1.1076e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.3582e-10, 7.3338e-11, 1.6852e-10, 1.0022e-10, 9.9825e-09,
2.3356e-09, 1.1076e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.3356e-09, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.5870e-07, 2.2027e-09, 6.4121e-08, 4.3036e-10, 2.1893e-10,
1.0599e-09, 2.1914e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.5870e-07, 2.2027e-09, 6.4121e-08, 4.3036e-10, 2.1893e-10,
1.0599e-09, 2.1914e-10], 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(6.4121e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many dogs 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
tensor([1.0000e+00, 1.8874e-10, 2.9401e-11, 8.9155e-11, 5.7550e-11, 9.5180e-09,
1.7222e-09, 1.1532e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.8874e-10, 2.9401e-11, 8.9155e-11, 5.7550e-11, 9.5180e-09,
1.7222e-09, 1.1532e-10], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 3.7099e-10, 7.2484e-11, 2.6769e-11, 2.1848e-11, 5.0850e-09,
4.0525e-07, 5.6542e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.7099e-10, 7.2484e-11, 2.6769e-11, 2.1848e-11, 5.0850e-09,
4.0525e-07, 5.6542e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1720e-08, 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>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.0525e-07, 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>)}
[2024-10-24 10:05:09,652] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.51 | optimizer_gradients: 0.24 | optimizer_step: 0.30
[2024-10-24 10:05:09,652] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5177.40 | backward_microstep: 8677.07 | backward_inner_microstep: 4957.41 | backward_allreduce_microstep: 3719.59 | step_microstep: 7.62
[2024-10-24 10:05:09,652] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5177.41 | backward: 8677.06 | backward_inner: 4957.43 | backward_allreduce: 3719.58 | step: 7.63
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4663/4844 [19:23:53<48:03, 15.93s/it]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='How many boats are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Does the left image show mashed potatoes in a round bowl with fluted edges?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many orange golf balls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)