Datasets:
TMarcus
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InternVL

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[('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([9.9981e-01, 2.5839e-09, 6.2181e-05, 1.2552e-04, 3.0699e-10, 6.1759e-07,
2.0577e-09, 5.0769e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
5 *************
['5', '8', '4', '6', '3', '7', '11', '9'] tensor([9.9981e-01, 2.5839e-09, 6.2181e-05, 1.2552e-04, 3.0699e-10, 6.1759e-07,
2.0577e-09, 5.0769e-09], 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([9.9365e-01, 5.5504e-03, 7.9962e-04, 7.1077e-08, 1.8620e-06, 1.1537e-07,
1.3623e-06, 7.4915e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9365e-01, 5.5504e-03, 7.9962e-04, 7.1077e-08, 1.8620e-06, 1.1537e-07,
1.3623e-06, 7.4915e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: question: ['How many dogs are in the image?'], responses:['five']
{True: tensor(1.4851e-06, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
[('7 eleven', 0.1264466744091217), ('babies', 0.124977990347662), ('sunrise', 0.12490143984830117), ('eating', 0.1247676656843781), ('feet', 0.12475702323703439), ('candle', 0.12473210928138137), ('light', 0.12472650705175181), ('floating', 0.12469059014036947)]
[['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating']]
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([3.6084e-05, 7.2562e-01, 1.8574e-02, 1.6511e-03, 2.5375e-01, 8.9786e-05,
1.8531e-04, 9.6125e-05], device='cuda:1', grad_fn=<SoftmaxBackward0>)
babies *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([3.6084e-05, 7.2562e-01, 1.8574e-02, 1.6511e-03, 2.5375e-01, 8.9786e-05,
1.8531e-04, 9.6125e-05], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), False: tensor(0., device='cuda:1', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:03:02,785] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.31 | optimizer_step: 0.32
[2024-10-24 10:03:02,785] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 1190.01 | backward_microstep: 12547.04 | backward_inner_microstep: 1203.82 | backward_allreduce_microstep: 11343.04 | step_microstep: 7.38
[2024-10-24 10:03:02,785] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 1190.01 | backward: 12547.04 | backward_inner: 1203.91 | backward_allreduce: 11343.02 | step: 7.39
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4655/4844 [19:21:46<47:25, 15.05s/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 VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the convertible in the image have its top off?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='Are there pajama-clad models in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many brand labels are showing on the pencil pouch?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many drawers are on the cabinet in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
torch.Size([3, 3, 448, 448])
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['Are there pajama-clad models 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([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
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: 328
question: ['Does the convertible in the image have its top off?'], responses:['yes']
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
[('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']]
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 328
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 329
tensor([1.0000e+00, 8.1392e-08, 2.4337e-08, 4.1865e-08, 1.7828e-09, 1.6206e-08,
5.8324e-10, 2.6379e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 8.1392e-08, 2.4337e-08, 4.1865e-08, 1.7828e-09, 1.6206e-08,
5.8324e-10, 2.6379e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(2.4337e-08, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.4872e-08, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['How many brand labels are showing on the pencil pouch?'], responses:['1']
torch.Size([13, 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']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.0372e-08, 4.0586e-10, 2.8216e-09, 2.3993e-11, 8.1178e-11,
4.1050e-11, 1.5638e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.0372e-08, 4.0586e-10, 2.8216e-09, 2.3993e-11, 8.1178e-11,
4.1050e-11, 1.5638e-09], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), False: tensor(4.0586e-10, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(-4.0586e-10, device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many drawers are on the cabinet in the image?'], responses:['2']
ANSWER0=VQA(image=LEFT,question='How many frames are on the wall in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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([13, 3, 448, 448])