Datasets:
TMarcus
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InternVL

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['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 5.0216e-10, 2.8945e-11, 5.1599e-11, 3.1297e-11, 6.7534e-09,
2.2380e-07, 1.6905e-10], device='cuda:3', grad_fn=<SelectBackward0>)
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1347
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(7.0343e-09, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:3', grad_fn=<DivBackward0>)}
question: ['How many dogs are in the image?'], responses:['1']
ANSWER0=VQA(image=RIGHT,question='How many flower-shaped anemones are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
[('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])
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1347
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1348
tensor([1.0000e+00, 9.5381e-09, 8.5074e-11, 5.9044e-08, 3.0507e-10, 3.6381e-10,
6.8605e-11, 5.8762e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 9.5381e-09, 8.5074e-11, 5.9044e-08, 3.0507e-10, 3.6381e-10,
6.8605e-11, 5.8762e-09], 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(8.5074e-11, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1912e-07, device='cuda:0', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many hyenas are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
tensor([9.6943e-01, 6.6808e-03, 2.0300e-06, 5.0787e-03, 1.4321e-02, 9.1604e-04,
3.3593e-03, 2.0830e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
100 *************
['100', '120', '88', '80', '60', '99', '90', '101'] tensor([9.6943e-01, 6.6808e-03, 2.0300e-06, 5.0787e-03, 1.4321e-02, 9.1604e-04,
3.3593e-03, 2.0830e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='How many towels are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
question: ['How many flower-shaped anemones are in the image?'], responses:['2']
question: ['How many hyenas are in the image?'], responses:['7']
question: ['How many towels are in the image?'], responses:['7']
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]
[('7', 0.12828776251745355), ('8', 0.1258361832781132), ('11', 0.12481772898325143), ('5', 0.124759881092759), ('9', 0.12447036165452931), ('10', 0.1239759375399529), ('6', 0.12393017600998846), ('12', 0.12392196892395223)]
[['7', '8', '11', '5', '9', '10', '6', '12']]
[('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
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
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1861
tensor([8.7433e-01, 4.2728e-04, 2.0561e-02, 6.3378e-02, 5.5375e-03, 1.6879e-03,
3.3921e-02, 1.5710e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
7 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([8.7433e-01, 4.2728e-04, 2.0561e-02, 6.3378e-02, 5.5375e-03, 1.6879e-03,
3.3921e-02, 1.5710e-04], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0339, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0.9661, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.4235e-10, 1.1448e-10, 3.2868e-10, 1.9171e-10, 3.4852e-08,
3.9421e-09, 4.3454e-10], device='cuda:1', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.4235e-10, 1.1448e-10, 3.2868e-10, 1.9171e-10, 3.4852e-08,
3.9421e-09, 4.3454e-10], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:1', grad_fn=<DivBackward0>), False: tensor(4.0106e-08, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
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([2.9643e-01, 1.5093e-05, 1.4722e-06, 5.8357e-01, 7.2350e-06, 1.5908e-06,
1.1997e-01, 4.8622e-08], device='cuda:0', grad_fn=<SoftmaxBackward0>)
5 *************
['7', '8', '11', '5', '9', '10', '6', '12'] tensor([2.9643e-01, 1.5093e-05, 1.4722e-06, 5.8357e-01, 7.2350e-06, 1.5908e-06,
1.1997e-01, 4.8622e-08], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0., device='cuda:0', grad_fn=<MulBackward0>), False: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([9.9993e-01, 6.6052e-05, 6.4116e-08, 2.0175e-08, 5.3459e-09, 2.3672e-10,
5.9638e-09, 2.5002e-10], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([9.9993e-01, 6.6052e-05, 6.4116e-08, 2.0175e-08, 5.3459e-09, 2.3672e-10,
5.9638e-09, 2.5002e-10], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.9999, device='cuda:3', grad_fn=<DivBackward0>), False: tensor(6.6148e-05, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
[2024-10-24 10:28:25,755] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.36 | optimizer_step: 0.35
[2024-10-24 10:28:25,755] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 4478.86 | backward_microstep: 6940.14 | backward_inner_microstep: 4217.85 | backward_allreduce_microstep: 2722.20 | step_microstep: 7.77
[2024-10-24 10:28:25,755] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 4478.87 | backward: 6940.13 | backward_inner: 4217.88 | backward_allreduce: 2722.10 | step: 7.79
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4758/4844 [19:47:09<20:04, 14.01s/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 EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='Does the dessert contain berries?')
ANSWER1=EVAL(expr='not {ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='Do the windows in the image have dark brown shades?')
ANSWER1=EVAL(expr='{ANSWER0}')