Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1866
tensor([1.0000e+00, 8.3335e-09, 8.6778e-09, 5.2493e-09, 1.1680e-11, 5.0012e-11,
8.0998e-12, 8.8480e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.3335e-09, 8.6778e-09, 5.2493e-09, 1.1680e-11, 5.0012e-11,
8.0998e-12, 8.8480e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(8.6778e-09, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-8.6778e-09, device='cuda:0', grad_fn=<DivBackward0>)}
question: ['Is there a dog laying in the grass?'], 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 animal holding food?'], 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([1.0000e+00, 6.7972e-08, 1.1142e-08, 1.5382e-08, 7.1006e-11, 1.3809e-10,
6.3637e-10, 1.5532e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 6.7972e-08, 1.1142e-08, 1.5382e-08, 7.1006e-11, 1.3809e-10,
6.3637e-10, 1.5532e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1142e-08, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.0807e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([1.0000e+00, 1.1524e-08, 6.9040e-10, 1.1489e-08, 1.8659e-11, 1.4931e-10,
1.2905e-10, 7.9336e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.1524e-08, 6.9040e-10, 1.1489e-08, 1.8659e-11, 1.4931e-10,
1.2905e-10, 7.9336e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(6.9040e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-6.9040e-10, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many people are in the image?'], responses:['7']
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([9.7268e-01, 2.5665e-03, 1.5857e-02, 1.2004e-04, 7.3471e-03, 7.3535e-04,
6.4893e-04, 4.8914e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([9.7268e-01, 2.5665e-03, 1.5857e-02, 1.2004e-04, 7.3471e-03, 7.3535e-04,
6.4893e-04, 4.8914e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.3012e-08, 8.7775e-11, 9.3631e-08, 9.3282e-11, 1.5558e-09,
1.1319e-10, 1.4242e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.3012e-08, 8.7775e-11, 9.3631e-08, 9.3282e-11, 1.5558e-09,
1.1319e-10, 1.4242e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(8.7775e-11, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1912e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:00:06,869] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.37 | optimizer_step: 0.34
[2024-10-24 10:00:06,869] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3159.01 | backward_microstep: 10784.96 | backward_inner_microstep: 3006.60 | backward_allreduce_microstep: 7778.23 | step_microstep: 10.10
[2024-10-24 10:00:06,869] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3159.01 | backward: 10784.95 | backward_inner: 3006.66 | backward_allreduce: 7778.19 | step: 10.12
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4644/4844 [19:18:50<51:07, 15.34s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Are there any cream colored Chow Chow puppies 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
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dogs are outside in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='What color are the citrus fruits growing on the tree?')
ANSWER1=EVAL(expr='{ANSWER0} == "yellow"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many people are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are there any cream colored Chow Chow puppies in the image?'], 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']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 331
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 330
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 331
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 331
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 331
tensor([1.0000e+00, 3.7323e-09, 1.4440e-06, 1.6818e-11, 1.1833e-10, 2.4167e-08,
7.4950e-10, 9.7473e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.7323e-09, 1.4440e-06, 1.6818e-11, 1.1833e-10, 2.4167e-08,
7.4950e-10, 9.7473e-07], device='cuda:0', grad_fn=<SelectBackward0>)