Datasets:
TMarcus
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InternVL

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[('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([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
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([9.9996e-01, 2.6422e-05, 9.8097e-06, 3.0242e-08, 7.1640e-10, 1.0172e-07,
2.1291e-09, 4.9810e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([9.9996e-01, 2.6422e-05, 9.8097e-06, 3.0242e-08, 7.1640e-10, 1.0172e-07,
2.1291e-09, 4.9810e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(4.9810e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9999e-01, 3.2739e-10, 1.1159e-11, 5.3235e-11, 2.2193e-11, 1.4276e-08,
1.0783e-05, 8.2595e-11], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9999e-01, 3.2739e-10, 1.1159e-11, 5.3235e-11, 2.2193e-11, 1.4276e-08,
1.0783e-05, 8.2595e-11], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 7.7845e-08, 1.1359e-10, 4.2205e-08, 8.6689e-11, 2.4235e-10,
8.9486e-11, 3.7458e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.7845e-08, 1.1359e-10, 4.2205e-08, 8.6689e-11, 2.4235e-10,
8.9486e-11, 3.7458e-08], 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(1.1359e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1910e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the train facing left?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0783e-05, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many boxes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([7.2777e-01, 2.7223e-01, 1.2127e-07, 1.0059e-10, 4.2517e-11, 7.0313e-09,
2.5336e-10, 1.6350e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([7.2777e-01, 2.7223e-01, 1.2127e-07, 1.0059e-10, 4.2517e-11, 7.0313e-09,
2.5336e-10, 1.6350e-08], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([13, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.2722, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.7278, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there a white corner shelf in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Is there a white corner shelf 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([7, 3, 448, 448]) knan debug pixel values shape
question: ['Is the train facing left?'], responses:['yes']
question: ['How many boxes are in the image?'], responses:['0']
[('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']]
[('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']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 5.2475e-09, 3.3125e-10, 3.7254e-08, 2.4959e-10, 1.7731e-10,
7.0985e-11, 9.5226e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.2475e-09, 3.3125e-10, 3.7254e-08, 2.4959e-10, 1.7731e-10,
7.0985e-11, 9.5226e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.3125e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.3125e-10, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.9242e-01, 8.8087e-09, 7.5773e-03, 6.2466e-09, 6.7839e-11, 4.8585e-10,
3.2140e-10, 3.0142e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9242e-01, 8.8087e-09, 7.5773e-03, 6.2466e-09, 6.7839e-11, 4.8585e-10,
3.2140e-10, 3.0142e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9924, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0076, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.5193e-08, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9991e-01, 8.1230e-05, 1.6727e-06, 3.1212e-09, 1.5248e-06, 2.7326e-07,
3.0307e-06, 4.8221e-06], device='cuda:1', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9991e-01, 8.1230e-05, 1.6727e-06, 3.1212e-09, 1.5248e-06, 2.7326e-07,
3.0307e-06, 4.8221e-06], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0.9999, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.2506e-05, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 09:42:35,794] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.29 | optimizer_step: 0.32
[2024-10-24 09:42:35,794] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3140.75 | backward_microstep: 14609.40 | backward_inner_microstep: 2990.20 | backward_allreduce_microstep: 11619.14 | step_microstep: 7.57
[2024-10-24 09:42:35,794] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3140.75 | backward: 14609.39 | backward_inner: 2990.20 | backward_allreduce: 11619.14 | step: 7.59
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4575/4844 [19:01:19<1:09:42, 15.55s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
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='How many dogs are sitting together on a piece of furniture?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many water buffaloes are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is the penguin looking down?')