Datasets:
TMarcus
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InternVL

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question: ['How many acorns 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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
tensor([1.0000e+00, 2.1266e-09, 3.6118e-07, 4.2114e-11, 2.6197e-11, 1.0930e-08,
6.5770e-10, 2.1412e-07], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.1266e-09, 3.6118e-07, 4.2114e-11, 2.6197e-11, 1.0930e-08,
6.5770e-10, 2.1412e-07], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.1266e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many dogs are wearing a collar?'], responses:['1']
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
[('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: 5, images per sample: 5.0, dynamic token length: 1349
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1349
tensor([9.8083e-01, 1.9111e-02, 5.7105e-05, 1.1497e-06, 1.9849e-06, 2.4767e-08,
6.0580e-07, 6.6574e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.8083e-01, 1.9111e-02, 5.7105e-05, 1.1497e-06, 1.9849e-06, 2.4767e-08,
6.0580e-07, 6.6574e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9808, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(0.0192, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.4247e-10, 3.4913e-11, 9.6296e-11, 1.0244e-10, 3.0718e-08,
2.9756e-09, 3.5144e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4247e-10, 3.4913e-11, 9.6296e-11, 1.0244e-10, 3.0718e-08,
2.9756e-09, 3.5144e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(3.4422e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:08:23,535] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.26 | optimizer_step: 0.31
[2024-10-24 10:08:23,535] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4488.20 | backward_microstep: 9389.17 | backward_inner_microstep: 4234.80 | backward_allreduce_microstep: 5154.30 | step_microstep: 7.22
[2024-10-24 10:08:23,536] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4488.20 | backward: 9389.16 | backward_inner: 4234.82 | backward_allreduce: 5154.29 | step: 7.24
97%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‹| 4676/4844 [19:27:07<40:26, 14.44s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
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='Is the wolf facing towards the left?')
FINAL_ANSWER=RESULT(var=ANSWER0)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does brocolli sit in a white bowl in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the image contain a white wooden cabinet?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=LEFT,question='How many flutes are 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([13, 3, 448, 448])
question: ['Is the wolf facing towards the left?'], responses:['no']
question: ['Does brocolli sit in a white bowl in the image?'], responses:['yes']
question: ['How many flutes are in the image?'], responses:['2']
[('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']]
[('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']]
[('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
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
question: ['Does the image contain a white wooden cabinet?'], 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([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3397
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3398
tensor([1.0000e+00, 2.9990e-09, 8.6477e-08, 2.2320e-12, 2.0021e-12, 2.8593e-09,
1.0011e-10, 8.7391e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.9990e-09, 8.6477e-08, 2.2320e-12, 2.0021e-12, 2.8593e-09,
1.0011e-10, 8.7391e-08], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 2.8684e-09, 9.1271e-11, 2.5225e-09, 1.8259e-10, 4.8285e-11,
3.5514e-12, 6.7260e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.8684e-09, 9.1271e-11, 2.5225e-09, 1.8259e-10, 4.8285e-11,
3.5514e-12, 6.7260e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.9990e-09, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:2', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(9.1271e-11, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(-9.1271e-11, device='cuda:1', grad_fn=<DivBackward0>)}
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')