Datasets:
TMarcus
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InternVL

Dataset card Data Studio Files
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0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 3.7234e-06, 1.2334e-07, 1.7191e-09, 5.8385e-08, 3.9991e-08,
7.3873e-07, 4.5686e-07], device='cuda:3', grad_fn=<SelectBackward0>)
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(5.0068e-06, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many Samoyed puppies are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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']]
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
question: ['How many Samoyed puppies are in the image?'], 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3394
tensor([1.0000e+00, 1.4106e-09, 1.9977e-09, 1.7347e-09, 1.3843e-11, 2.7619e-11,
2.7123e-11, 6.4805e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.4106e-09, 1.9977e-09, 1.7347e-09, 1.3843e-11, 2.7619e-11,
2.7123e-11, 6.4805e-10], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.6788e-07, 1.1683e-08, 1.3177e-10, 8.2043e-07, 3.8420e-09,
4.8706e-08, 3.9832e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 1.6788e-07, 1.1683e-08, 1.3177e-10, 8.2043e-07, 3.8420e-09,
4.8706e-08, 3.9832e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.9977e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.9977e-09, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: ANSWER0=VQA(image=RIGHT,question='How many throw cushions are on the sofa?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
{True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.5497e-06, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
tensor([9.9996e-01, 4.2615e-05, 8.3702e-07, 6.8891e-09, 1.4349e-10, 1.2870e-07,
4.1996e-10, 7.0834e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9996e-01, 4.2615e-05, 8.3702e-07, 6.8891e-09, 1.4349e-10, 1.2870e-07,
4.1996e-10, 7.0834e-08], 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(4.3659e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3395
tensor([1.0000e+00, 2.3589e-08, 5.7986e-08, 1.0986e-11, 2.7516e-12, 5.2564e-10,
2.6828e-10, 4.5888e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.3589e-08, 5.7986e-08, 1.0986e-11, 2.7516e-12, 5.2564e-10,
2.6828e-10, 4.5888e-08], device='cuda:0', grad_fn=<SelectBackward0>)
question: ['How many throw cushions are on the sofa?'], responses:['3']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.3589e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['How many towels are in the image?'], responses:['2']
[('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']]
[('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([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.7068e-01, 2.9312e-02, 7.9919e-08, 3.6167e-06, 8.6051e-10, 2.4468e-08,
1.5161e-08, 1.1270e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.7068e-01, 2.9312e-02, 7.9919e-08, 3.6167e-06, 8.6051e-10, 2.4468e-08,
1.5161e-08, 1.1270e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9707, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0293, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.9955e-01, 3.1499e-04, 1.2548e-07, 1.3978e-04, 9.3786e-09, 3.1972e-09,
3.8271e-08, 2.8844e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9955e-01, 3.1499e-04, 1.2548e-07, 1.3978e-04, 9.3786e-09, 3.1972e-09,
3.8271e-08, 2.8844e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0003, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9997, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 09:44:20,727] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.53 | optimizer_gradients: 0.27 | optimizer_step: 0.32
[2024-10-24 09:44:20,727] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5775.62 | backward_microstep: 11926.77 | backward_inner_microstep: 5462.84 | backward_allreduce_microstep: 6463.85 | step_microstep: 8.22
[2024-10-24 09:44:20,728] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5775.62 | backward: 11926.76 | backward_inner: 5462.86 | backward_allreduce: 6463.79 | step: 8.24
95%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4582/4844 [19:03:04<1:08:02, 15.58s/it]Registering VQA_lavis step
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 stepANSWER0=VQA(image=LEFT,question='Is the animal in the image looking into the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Are people walking past the shop?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)