Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Is the picture in color?'], 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([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
question: ['Are the sails furled in the image?'], responses:['yes']
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1858
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1859
tensor([1.0000e+00, 1.2698e-09, 7.6875e-07, 5.6349e-10, 4.7813e-09, 3.4505e-08,
1.5083e-09, 1.5063e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.2698e-09, 7.6875e-07, 5.6349e-10, 4.7813e-09, 3.4505e-08,
1.5083e-09, 1.5063e-06], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 5.6586e-08, 5.1214e-09, 2.8454e-08, 6.3853e-10, 8.3269e-10,
8.6564e-10, 6.3592e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.6586e-08, 5.1214e-09, 2.8454e-08, 6.3853e-10, 8.3269e-10,
8.6564e-10, 6.3592e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2698e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.2650e-06, device='cuda:0', grad_fn=<SubBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.3134e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is the dog in the image laying down?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([3.2731e-05, 1.1702e-03, 3.7470e-02, 7.6226e-01, 1.0844e-01, 4.5051e-02,
3.6136e-03, 4.1962e-02], device='cuda:3', grad_fn=<SoftmaxBackward0>)
bulldog *************
['biking', 'geese', 'cushion', 'bulldog', 'striped', 'floral', 'stove', 'dodgers'] tensor([3.2731e-05, 1.1702e-03, 3.7470e-02, 7.6226e-01, 1.0844e-01, 4.5051e-02,
3.6136e-03, 4.1962e-02], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many humans are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
tensor([1.0000e+00, 7.1060e-09, 4.3204e-10, 4.5190e-09, 6.5218e-11, 3.8128e-10,
3.6049e-11, 2.3889e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 7.1060e-09, 4.3204e-10, 4.5190e-09, 6.5218e-11, 3.8128e-10,
3.6049e-11, 2.3889e-09], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(4.3204e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.3204e-10, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['Is the dog in the image laying down?'], 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']]
question: ['How many humans are in the image?'], responses:['11']
[('11', 0.12740768001087358), ('10', 0.12548679249075975), ('12', 0.12538137681693887), ('9', 0.12485855662563465), ('8', 0.12469919178932766), ('13', 0.12431757055023795), ('7', 0.12396146028399917), ('14', 0.1238873714322284)]
[['11', '10', '12', '9', '8', '13', '7', '14']]
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, 4.7450e-10, 2.3562e-07, 2.1176e-11, 3.0628e-11, 2.0047e-08,
2.6872e-10, 4.5240e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 4.7450e-10, 2.3562e-07, 2.1176e-11, 3.0628e-11, 2.0047e-08,
2.6872e-10, 4.5240e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.7450e-10, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.4995e-01, 1.0449e-03, 4.4013e-03, 4.9358e-04, 1.1603e-05, 3.6834e-02,
1.6184e-03, 5.6463e-03], device='cuda:3', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.4995e-01, 1.0449e-03, 4.4013e-03, 4.9358e-04, 1.1603e-05, 3.6834e-02,
1.6184e-03, 5.6463e-03], 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(0., device='cuda:3', grad_fn=<MulBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:29:58,338] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.33 | optimizer_step: 0.33
[2024-10-24 09:29:58,338] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4447.95 | backward_microstep: 13475.64 | backward_inner_microstep: 4231.38 | backward_allreduce_microstep: 9244.12 | step_microstep: 7.53
[2024-10-24 09:29:58,338] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4447.95 | backward: 13475.63 | backward_inner: 4231.40 | backward_allreduce: 9244.10 | step: 7.54
93%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4524/4844 [18:48:42<1:28:41, 16.63s/it]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=RIGHT,question='Are books hanging on the wall in rectangular boxes?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is there a person standing near the entrance of the store?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the right image show a "Whataburger" cup sitting on a surface?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='Is there a mostly black dog leaping through the air in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([5, 3, 448, 448])
torch.Size([3, 3, 448, 448])