Datasets:
TMarcus
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InternVL

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[['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock']]
question: ['How many binders are in the image?'], responses:['1']
question: ['Is the dispenser round?'], responses:['no']
[('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']]
[('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([1, 3, 448, 448]) knan debug pixel values shape
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 322
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
dynamic ViT batch size: 1, images per sample: 1.0, dynamic token length: 323
tensor([1.0000e+00, 1.2883e-09, 1.7456e-10, 2.4234e-10, 1.9918e-10, 1.7701e-08,
1.5870e-07, 1.9820e-10], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 1.2883e-09, 1.7456e-10, 2.4234e-10, 1.9918e-10, 1.7701e-08,
1.5870e-07, 1.9820e-10], device='cuda:2', grad_fn=<SelectBackward0>)
tensor([1.0000e+00, 1.3730e-09, 3.5762e-07, 4.6519e-12, 3.6197e-12, 1.8144e-09,
7.2227e-11, 3.9613e-07], device='cuda:0', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.3730e-09, 3.5762e-07, 4.6519e-12, 3.6197e-12, 1.8144e-09,
7.2227e-11, 3.9613e-07], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.3730e-09, device='cuda:0', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:0', grad_fn=<SubBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.7850e-07, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
question: ['How many sails does the boat in the image have?'], responses:['1']
[('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([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.2637e-09, 1.0627e-07, 4.0186e-11, 4.5524e-11, 3.1826e-09,
3.3619e-10, 3.9058e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.2637e-09, 1.0627e-07, 4.0186e-11, 4.5524e-11, 3.1826e-09,
3.3619e-10, 3.9058e-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(2.2637e-09, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(4.7684e-07, device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many ducks are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['How many ducks 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, 6.5131e-08, 2.6109e-08, 8.5449e-09, 5.4619e-10, 2.4474e-09,
1.4725e-09, 3.5988e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 6.5131e-08, 2.6109e-08, 8.5449e-09, 5.4619e-10, 2.4474e-09,
1.4725e-09, 3.5988e-09], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(8.5449e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:1', grad_fn=<DivBackward0>)}
tensor([9.9999e-01, 7.3074e-09, 4.0355e-09, 2.9462e-10, 1.4221e-09, 8.4945e-08,
7.8893e-06, 1.2971e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9999e-01, 7.3074e-09, 4.0355e-09, 2.9462e-10, 1.4221e-09, 8.4945e-08,
7.8893e-06, 1.2971e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(4.0355e-09, 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>)}
ANSWER0=VQA(image=RIGHT,question='Is the sky visible in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([3, 3, 448, 448])
question: ['Is the sky visible 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([3, 3, 448, 448]) knan debug pixel values shape
tensor([9.9998e-01, 9.7226e-09, 2.2828e-05, 7.6262e-08, 1.1444e-08, 2.5250e-09,
6.1883e-10, 8.6206e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([9.9998e-01, 9.7226e-09, 2.2828e-05, 7.6262e-08, 1.1444e-08, 2.5250e-09,
6.1883e-10, 8.6206e-09], 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(2.2828e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7940e-07, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 09:40:54,706] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.35 | optimizer_step: 0.33
[2024-10-24 09:40:54,707] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 1187.80 | backward_microstep: 10175.90 | backward_inner_microstep: 1191.95 | backward_allreduce_microstep: 8983.86 | step_microstep: 7.90
[2024-10-24 09:40:54,707] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 1187.81 | backward: 10175.89 | backward_inner: 1191.97 | backward_allreduce: 8983.84 | step: 7.91
94%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–| 4568/4844 [18:59:38<54:50, 11.92s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Does the image show a set of measuring spoons?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Does the image show a ladder leaned up against the front of a stocked bookshelf?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many parrots are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])