Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 5.6916e-07, 5.1655e-08, 3.2732e-10, 1.6243e-06, 3.6128e-08,
2.8410e-07, 5.8569e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([1.0000e+00, 5.6916e-07, 5.1655e-08, 3.2732e-10, 1.6243e-06, 3.6128e-08,
2.8410e-07, 5.8569e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:3', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(3.2187e-06, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:36:07,003] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.24 | optimizer_step: 0.32
[2024-10-24 09:36:07,003] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3860.28 | backward_microstep: 7639.44 | backward_inner_microstep: 3556.95 | backward_allreduce_microstep: 4082.39 | step_microstep: 7.41
[2024-10-24 09:36:07,003] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3860.28 | backward: 7639.43 | backward_inner: 3556.99 | backward_allreduce: 4082.35 | step: 7.42
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4548/4844 [18:54:50<1:09:54, 14.17s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis stepRegistering 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='Is there a rodent sitting on the grass in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Does the animal in the image have an open mouth?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='Does an animal sit atop a rocky outcropping in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many objects are standing straight up in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 9')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([11, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Does an animal sit atop a rocky outcropping 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 objects are standing straight up 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']]
question: ['Is there a rodent sitting on the grass in the image?'], responses:['no']
question: ['Does the animal in the image have an open mouth?'], 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([11, 3, 448, 448]) knan debug pixel values shape
[('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: 3401
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([1.0000e+00, 2.3576e-09, 3.2612e-10, 7.1990e-09, 1.8013e-11, 4.5537e-11,
3.6055e-11, 2.8537e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 2.3576e-09, 3.2612e-10, 7.1990e-09, 1.8013e-11, 4.5537e-11,
3.6055e-11, 2.8537e-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(3.2612e-10, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-3.2612e-10, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many wine glasses are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many wine glasses are in the image?'], responses:['1']
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
[('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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
tensor([1.0000e+00, 3.6382e-10, 7.3913e-11, 1.9779e-10, 1.3166e-10, 1.6681e-08,
3.6458e-09, 2.0922e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 3.6382e-10, 7.3913e-11, 1.9779e-10, 1.3166e-10, 1.6681e-08,
3.6458e-09, 2.0922e-10], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.6458e-09, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([9.7831e-01, 5.7598e-04, 1.2315e-02, 9.4963e-04, 2.0566e-07, 4.5304e-03,
1.3411e-06, 3.3146e-03], device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.7831e-01, 5.7598e-04, 1.2315e-02, 9.4963e-04, 2.0566e-07, 4.5304e-03,
1.3411e-06, 3.3146e-03], 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(1.5468e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many baboons are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 5')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
question: ['How many baboons 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 5.9053e-10, 1.0469e-06, 2.4839e-12, 1.0008e-11, 2.4671e-08,
4.9207e-10, 1.2450e-06], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 5.9053e-10, 1.0469e-06, 2.4839e-12, 1.0008e-11, 2.4671e-08,
4.9207e-10, 1.2450e-06], device='cuda:0', grad_fn=<SelectBackward0>)