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TMarcus
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InternVL

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ANSWER0=VQA(image=LEFT,question='Is the body of the hyena facing left?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1354
tensor([1.0000e+00, 2.0568e-10, 7.7461e-11, 2.0408e-10, 3.6954e-10, 1.7803e-08,
5.2224e-09, 2.7825e-10], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.0568e-10, 7.7461e-11, 2.0408e-10, 3.6954e-10, 1.7803e-08,
5.2224e-09, 2.7825e-10], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.4160e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Is the body of the hyena facing left?'], 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([13, 3, 448, 448]) knan debug pixel values shape
tensor([9.9999e-01, 5.2035e-07, 3.1556e-07, 7.0422e-08, 1.6873e-08, 3.3988e-06,
5.6514e-07, 4.3572e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9999e-01, 5.2035e-07, 3.1556e-07, 7.0422e-08, 1.6873e-08, 3.3988e-06,
5.6514e-07, 4.3572e-07], device='cuda:1', grad_fn=<SelectBackward0>)
tensor([2.3132e-01, 5.4962e-02, 7.0359e-01, 1.7837e-07, 2.8723e-03, 4.6462e-05,
7.2173e-03, 1.3877e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
4 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([2.3132e-01, 5.4962e-02, 7.0359e-01, 1.7837e-07, 2.8723e-03, 4.6462e-05,
7.2173e-03, 1.3877e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.2035e-07, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7088e-06, device='cuda:1', grad_fn=<DivBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.7837e-07, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many gorillas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Is a person holding the dog in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Is a person holding the dog 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: ['How many gorillas 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([1.0000e+00, 7.2658e-09, 3.6377e-11, 1.1220e-07, 4.8929e-10, 5.4618e-10,
1.2370e-10, 3.0882e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.2658e-09, 3.6377e-11, 1.1220e-07, 4.8929e-10, 5.4618e-10,
1.2370e-10, 3.0882e-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(3.6377e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1917e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.6684e-09, 2.8780e-10, 3.6484e-09, 2.0148e-11, 2.7036e-10,
4.6884e-11, 1.6221e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.6684e-09, 2.8780e-10, 3.6484e-09, 2.0148e-11, 2.7036e-10,
4.6884e-11, 1.6221e-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(2.8780e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-2.8780e-10, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 6.8771e-10, 1.0262e-10, 1.1439e-10, 7.9813e-11, 1.5419e-08,
1.6468e-08, 3.5094e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 6.8771e-10, 1.0262e-10, 1.1439e-10, 7.9813e-11, 1.5419e-08,
1.6468e-08, 3.5094e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(3.3222e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:18:25,908] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.33
[2024-10-24 10:18:25,908] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3137.91 | backward_microstep: 14646.59 | backward_inner_microstep: 2904.67 | backward_allreduce_microstep: 11741.77 | step_microstep: 7.74
[2024-10-24 10:18:25,908] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3137.92 | backward: 14646.58 | backward_inner: 2904.74 | backward_allreduce: 11741.75 | step: 7.76
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4716/4844 [19:37:09<32:45, 15.36s/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
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many lipsticks are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the right image feature multiple canoes heading forward at a right angle?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Are there multiple animals in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([5, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many balloons are inside the party shop or balloon store?')
ANSWER1=EVAL(expr='{ANSWER0} >= 15')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Are there multiple animals 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many lipsticks are in the image?'], responses:['5']