Datasets:
TMarcus
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InternVL

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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7087e-01, 2.8135e-03, 8.0611e-04, 3.2522e-04, 4.5923e-04, 3.9990e-04,
2.4305e-02, 1.9733e-05], device='cuda:3', grad_fn=<SelectBackward0>)
tensor([9.8179e-01, 3.6529e-03, 1.6206e-03, 8.3367e-04, 9.2174e-04, 8.7424e-04,
1.0246e-02, 5.6277e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.8179e-01, 3.6529e-03, 1.6206e-03, 8.3367e-04, 9.2174e-04, 8.7424e-04,
1.0246e-02, 5.6277e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9818, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0182, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9709, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0291, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the animal in the image just above the seafloor?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
torch.Size([7, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
question: ['Is the animal in the image just above the seafloor?'], responses:['no']
[('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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1863
tensor([5.9710e-01, 1.3610e-02, 4.6052e-02, 1.5750e-03, 3.2485e-04, 3.4022e-01,
8.8308e-04, 2.3772e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([5.9710e-01, 1.3610e-02, 4.6052e-02, 1.5750e-03, 3.2485e-04, 3.4022e-01,
8.8308e-04, 2.3772e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9834, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0166, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([6.5072e-01, 3.4831e-01, 2.0001e-05, 9.3924e-05, 2.1120e-04, 3.2783e-04,
2.8491e-04, 3.2820e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([6.5072e-01, 3.4831e-01, 2.0001e-05, 9.3924e-05, 2.1120e-04, 3.2783e-04,
2.8491e-04, 3.2820e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ANSWER0=VQA(image=RIGHT,question='How many cups are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.3483, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.6507, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0010, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many hartebeests are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many cups are in the image?'], responses:['0']
question: ['How many hartebeests are in the image?'], responses:['1']
[('0', 0.13077743594303964), ('circles', 0.12449813349255197), ('maroon', 0.12428926693968681), ('large', 0.1242263466991631), ('rooster', 0.12409315512763705), ('nuts', 0.12408018414184876), ('beige', 0.1240288472550799), ('bottle', 0.12400663040099273)]
[['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle']]
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([5.9219e-01, 4.0701e-01, 3.9270e-05, 1.2073e-04, 1.4171e-04, 2.4193e-04,
2.4141e-04, 1.6621e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([5.9219e-01, 4.0701e-01, 3.9270e-05, 1.2073e-04, 1.4171e-04, 2.4193e-04,
2.4141e-04, 1.6621e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.4070, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.5922, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0008, device='cuda:3', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1860
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([9.8800e-01, 2.5495e-03, 3.9644e-04, 1.5603e-04, 3.5397e-04, 5.6530e-04,
8.3854e-04, 7.1425e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.8800e-01, 2.5495e-03, 3.9644e-04, 1.5603e-04, 3.5397e-04, 5.6530e-04,
8.3854e-04, 7.1425e-03], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(0.9880, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0120, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([5.9271e-01, 5.1790e-02, 1.1556e-02, 1.7715e-03, 3.4158e-03, 1.1234e-03,
3.3757e-01, 6.7913e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([5.9271e-01, 5.1790e-02, 1.1556e-02, 1.7715e-03, 3.4158e-03, 1.1234e-03,
3.3757e-01, 6.7913e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0697, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9303, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-23 14:55:25,546] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.48 | optimizer_gradients: 0.26 | optimizer_step: 0.32
[2024-10-23 14:55:25,547] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 5121.04 | backward_microstep: 4852.00 | backward_inner_microstep: 4846.49 | backward_allreduce_microstep: 5.42 | step_microstep: 7.68
[2024-10-23 14:55:25,547] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 5121.04 | backward: 4851.99 | backward_inner: 4846.53 | backward_allreduce: 5.40 | step: 7.69
1%| | 55/4844 [14:09<19:08:25, 14.39s/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
ANSWER0=VQA(image=RIGHT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many smart phones are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many oxen are yolked to the cart in the image?')