Datasets:
TMarcus
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InternVL

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no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.3947e-01, 6.0058e-02, 3.1162e-05, 9.0254e-05, 1.0511e-04, 8.4534e-05,
1.1984e-04, 4.4697e-05], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([8.3894e-01, 1.2741e-02, 1.6850e-02, 1.5668e-03, 2.4380e-04, 1.2866e-01,
7.1864e-04, 2.7724e-04], device='cuda:0', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.3894e-01, 1.2741e-02, 1.6850e-02, 1.5668e-03, 2.4380e-04, 1.2866e-01,
7.1864e-04, 2.7724e-04], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.8389, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.1611, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0601, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.9395, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0005, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
tensor([9.8839e-01, 1.7923e-03, 7.4704e-04, 3.0865e-04, 3.9902e-04, 3.0514e-04,
8.0331e-03, 2.2735e-05], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.8839e-01, 1.7923e-03, 7.4704e-04, 3.0865e-04, 3.9902e-04, 3.0514e-04,
8.0331e-03, 2.2735e-05], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9884, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0116, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many pigs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['How many animals 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 pigs 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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([9.5921e-01, 5.0628e-03, 3.4939e-02, 4.8173e-04, 2.0824e-05, 8.6529e-05,
5.7109e-06, 1.9500e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.5921e-01, 5.0628e-03, 3.4939e-02, 4.8173e-04, 2.0824e-05, 8.6529e-05,
5.7109e-06, 1.9500e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9592, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0349, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0.0059, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many warthogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([9.9597e-01, 7.0730e-04, 2.6019e-04, 1.1532e-04, 1.5777e-04, 1.5111e-04,
2.6280e-03, 1.0057e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9597e-01, 7.0730e-04, 2.6019e-04, 1.1532e-04, 1.5777e-04, 1.5111e-04,
2.6280e-03, 1.0057e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9960, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0.0040, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([8.2834e-01, 5.6369e-02, 9.2004e-03, 5.0819e-03, 3.5646e-04, 9.8931e-02,
1.5499e-03, 1.7386e-04], device='cuda:3', grad_fn=<SoftmaxBackward0>)
3 *************
['3', '4', '1', '5', '8', '2', '6', '12'] tensor([8.2834e-01, 5.6369e-02, 9.2004e-03, 5.0819e-03, 3.5646e-04, 9.8931e-02,
1.5499e-03, 1.7386e-04], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0989, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.9011, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many warthogs 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.7466e-01, 4.6567e-03, 2.1996e-03, 1.2135e-03, 1.4651e-03, 9.4187e-04,
1.4799e-02, 6.7870e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.7466e-01, 4.6567e-03, 2.1996e-03, 1.2135e-03, 1.4651e-03, 9.4187e-04,
1.4799e-02, 6.7870e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9895, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.0105, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-23 14:55:54,244] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.35 | optimizer_step: 0.32
[2024-10-23 14:55:54,245] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 2513.27 | backward_microstep: 16161.90 | backward_inner_microstep: 2249.09 | backward_allreduce_microstep: 13912.74 | step_microstep: 7.93
[2024-10-23 14:55:54,245] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 2513.28 | backward: 16161.89 | backward_inner: 2249.10 | backward_allreduce: 13912.73 | step: 7.94
1%| | 57/4844 [14:38<19:37:33, 14.76s/it]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 hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the middle child sitting criss cross?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many boats are sailing in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many hyenas are laying on the ground in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the middle child sitting criss cross?'], responses:['no']
question: ['How many hyenas are in the image?'], responses:['1']
question: ['How many hyenas are laying on the ground in the image?'], responses:['2']
question: ['How many boats are sailing in the image?'], responses:['1']