Datasets:
TMarcus
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InternVL

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tensor([1.0000e+00, 5.4192e-09, 1.4503e-10, 2.4106e-09, 3.0287e-10, 2.4788e-11,
2.5540e-11, 1.8754e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 5.4192e-09, 1.4503e-10, 2.4106e-09, 3.0287e-10, 2.4788e-11,
2.5540e-11, 1.8754e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9998, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(0.0002, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Is there a dog walking on the pavement?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(5.3158e-08, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(8.9407e-07, device='cuda:2', grad_fn=<SubBackward0>)}
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.4503e-10, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.4503e-10, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many zipper pouches are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=LEFT,question='How many hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([7, 3, 448, 448])
question: ['Is there a dog walking on the pavement?'], 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']]
question: ['How many zipper pouches are in the image?'], responses:['1']
question: ['How many hyenas are in the image?'], responses:['2']
[('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']]
[('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
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([1.0000e+00, 3.2358e-10, 7.2222e-07, 1.0712e-10, 7.0702e-10, 2.0847e-07,
1.6339e-08, 1.7899e-06], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 3.2358e-10, 7.2222e-07, 1.0712e-10, 7.0702e-10, 2.0847e-07,
1.6339e-08, 1.7899e-06], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.2358e-10, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(2.7418e-06, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([9.4653e-01, 1.2319e-05, 1.9900e-07, 1.4488e-09, 2.8365e-09, 6.6034e-09,
5.3455e-02, 1.8237e-11], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.4653e-01, 1.2319e-05, 1.9900e-07, 1.4488e-09, 2.8365e-09, 6.6034e-09,
5.3455e-02, 1.8237e-11], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2529e-05, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 1.5382e-07, 1.2502e-09, 2.3589e-08, 3.1119e-10, 9.2208e-11,
7.1223e-10, 7.2843e-11], device='cuda:0', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.5382e-07, 1.2502e-09, 2.3589e-08, 3.1119e-10, 9.2208e-11,
7.1223e-10, 7.2843e-11], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.5382e-07, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
[2024-10-24 09:56:00,868] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.25 | optimizer_step: 0.32
[2024-10-24 09:56:00,868] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 7141.33 | backward_microstep: 6758.02 | backward_inner_microstep: 6752.80 | backward_allreduce_microstep: 5.10 | step_microstep: 7.43
[2024-10-24 09:56:00,868] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 7141.35 | backward: 6758.01 | backward_inner: 6752.86 | backward_allreduce: 5.07 | step: 7.44
96%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Œ| 4628/4844 [19:14:44<52:49, 14.67s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many warthogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many perfume bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Which way is the bird facing?')
ANSWER1=EVAL(expr='{ANSWER0} == "left"')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=LEFT,question='How many glasses are the desserts being served in?')
ANSWER1=EVAL(expr='{ANSWER0} == 3')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many warthogs are in the image?'], responses:['3']
[('3', 0.12809209985493852), ('4', 0.12520382509374006), ('1', 0.1251059160028928), ('5', 0.12483070991268265), ('8', 0.12458076282181878), ('2', 0.12413212281858195), ('6', 0.1241125313968017), ('12', 0.12394203209854344)]
[['3', '4', '1', '5', '8', '2', '6', '12']]
torch.Size([3, 3, 448, 448]) knan debug pixel values shape
question: ['Which way is the bird facing?'], responses:['right']
[('right', 0.12743553739412528), ('right 1', 0.12490968573275477), ('straight', 0.12485251094891832), ('floating', 0.12468075392646753), ('flip', 0.12467791878738273), ('backwards', 0.12452118816110067), ('serious', 0.12447626064603681), ('working', 0.12444614440321403)]
[['right', 'right 1', 'straight', 'floating', 'flip', 'backwards', 'serious', 'working']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([9.9999e-01, 5.6495e-06, 2.0092e-08, 3.5795e-09, 4.3008e-11, 6.4213e-08,
5.5102e-11, 3.4255e-09], device='cuda:3', grad_fn=<SoftmaxBackward0>)