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TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0270, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9730, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many animals are on the rock?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
[('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([13, 3, 448, 448])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['How many ferrets 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']]
question: ['How many beetles are in the image?'], responses:['2']
[('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: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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
question: ['How many animals are on the rock?'], 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: 7, images per sample: 7.0, dynamic token length: 1861
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
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: 1861
tensor([6.7380e-01, 2.1875e-01, 5.1615e-02, 3.3322e-02, 1.5739e-02, 3.0996e-03,
3.5124e-03, 1.6875e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([6.7380e-01, 2.1875e-01, 5.1615e-02, 3.3322e-02, 1.5739e-02, 3.0996e-03,
3.5124e-03, 1.6875e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.6738, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.3262, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([0.5954, 0.0246, 0.3657, 0.0027, 0.0009, 0.0028, 0.0009, 0.0070],
device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([0.5954, 0.0246, 0.3657, 0.0027, 0.0009, 0.0028, 0.0009, 0.0070],
device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.5954, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.3657, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0390, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([9.3076e-01, 1.4130e-02, 5.1989e-03, 1.9089e-03, 2.7822e-03, 1.5697e-03,
4.3532e-02, 1.1452e-04], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.3076e-01, 1.4130e-02, 5.1989e-03, 1.9089e-03, 2.7822e-03, 1.5697e-03,
4.3532e-02, 1.1452e-04], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0435, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9565, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many parrots 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([9.5630e-01, 8.2739e-03, 3.6709e-03, 1.2259e-03, 2.0909e-03, 1.1828e-03,
2.7132e-02, 1.2211e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.5630e-01, 8.2739e-03, 3.6709e-03, 1.2259e-03, 2.0909e-03, 1.1828e-03,
2.7132e-02, 1.2211e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9563, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0437, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.7567e-01, 4.8091e-03, 2.1335e-03, 1.1396e-03, 1.3762e-03, 8.7325e-04,
1.3917e-02, 7.9068e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7567e-01, 4.8091e-03, 2.1335e-03, 1.1396e-03, 1.3762e-03, 8.7325e-04,
1.3917e-02, 7.9068e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0243, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9757, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:51:45,812] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.38 | optimizer_gradients: 0.35 | optimizer_step: 0.34
[2024-10-23 14:51:45,812] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3824.28 | backward_microstep: 9967.44 | backward_inner_microstep: 3523.05 | backward_allreduce_microstep: 6444.28 | step_microstep: 9.33
[2024-10-23 14:51:45,812] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3824.30 | backward: 9967.42 | backward_inner: 3523.10 | backward_allreduce: 6444.23 | step: 9.34
1%| | 41/4844 [10:29<21:16:32, 15.95s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Are there people in a shop in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
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 people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there at least one person standing in front of and staring ahead at a row of vending machines?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='Does the sink have a double basin?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])