Datasets:
TMarcus
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InternVL

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FINAL_ANSWER=RESULT(var=ANSWER1)
[('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])
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
question: ['Does the dispenser on the right have a black base?'], 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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1355
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1352
tensor([0.7014, 0.0096, 0.0061, 0.0094, 0.0025, 0.0085, 0.2581, 0.0045],
device='cuda:1', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([0.7014, 0.0096, 0.0061, 0.0094, 0.0025, 0.0085, 0.2581, 0.0045],
device='cuda:1', grad_fn=<SelectBackward0>)
question: ['How many dogs are in the image?'], responses:['1']
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many birds are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
[('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: 1352
question: ['How many birds 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1353
tensor([1.0000e+00, 8.0854e-10, 4.4576e-10, 7.7437e-10, 5.7358e-11, 4.4135e-11,
5.5847e-12, 1.4899e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.0854e-10, 4.4576e-10, 7.7437e-10, 5.7358e-11, 4.4135e-11,
5.5847e-12, 1.4899e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(4.4576e-10, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(-4.4576e-10, device='cuda:0', grad_fn=<SubBackward0>)}
tensor([1.0000e+00, 2.2602e-06, 1.9405e-08, 1.4006e-07, 3.7040e-09, 2.8168e-09,
1.5317e-08, 4.5338e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 2.2602e-06, 1.9405e-08, 1.4006e-07, 3.7040e-09, 2.8168e-09,
1.5317e-08, 4.5338e-09], 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(2.4460e-06, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 9.1936e-09, 1.5405e-10, 2.0507e-08, 1.2141e-10, 3.4177e-10,
2.6985e-11, 2.1932e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.1936e-09, 1.5405e-10, 2.0507e-08, 1.2141e-10, 3.4177e-10,
2.6985e-11, 2.1932e-08], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.5405e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.5405e-10, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.4219e-10, 3.8047e-11, 9.7535e-11, 4.2112e-11, 4.9435e-09,
4.3635e-09, 4.3721e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.4219e-10, 3.8047e-11, 9.7535e-11, 4.2112e-11, 4.9435e-09,
4.3635e-09, 4.3721e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.4219e-10, 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>)}
[2024-10-24 09:34:05,794] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.43 | optimizer_gradients: 0.37 | optimizer_step: 0.34
[2024-10-24 09:34:05,794] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4432.34 | backward_microstep: 9423.33 | backward_inner_microstep: 4211.57 | backward_allreduce_microstep: 5211.68 | step_microstep: 8.21
[2024-10-24 09:34:05,794] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4432.36 | backward: 9423.33 | backward_inner: 4211.60 | backward_allreduce: 5211.64 | step: 8.22
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Ž| 4540/4844 [18:52:49<1:17:57, 15.39s/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
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Is the laptop facing right?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='What part of the dog can be seen in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == "head"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many animals are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='Is the dog looking towards the camera?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is the laptop facing right?'], 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many animals are in the image?'], responses:['2']
question: ['Is the dog looking towards the camera?'], responses:['yes']
[('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']]
[('yes', 0.1298617250866936), ('congratulations', 0.12464161604141298), ('no', 0.12445222599225532), ('honey', 0.12437056445881921), ('solid', 0.12422595371654564), ('right', 0.12419889376311324), ('candle', 0.12414264780165109), ('chocolate', 0.12410637313950891)]