Datasets:
TMarcus
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InternVL

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[['11', '10', '12', '9', '8', '13', '7', '14']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.2501e-09, 3.5942e-07, 2.9401e-11, 1.5958e-11, 3.5556e-09,
1.0420e-09, 2.2792e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.2501e-09, 3.5942e-07, 2.9401e-11, 1.5958e-11, 3.5556e-09,
1.0420e-09, 2.2792e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.2501e-09, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(5.9605e-07, device='cuda:1', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is the item facing forward?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
question: ['Is the item facing forward?'], 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([1, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 2.5110e-08, 1.5583e-07, 1.2844e-11, 4.5305e-12, 7.3098e-10,
2.3082e-10, 3.4993e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.5110e-08, 1.5583e-07, 1.2844e-11, 4.5305e-12, 7.3098e-10,
2.3082e-10, 3.4993e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.5110e-08, device='cuda:1', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(2.3842e-07, device='cuda:1', grad_fn=<SubBackward0>)}
tensor([9.6735e-01, 1.8863e-02, 1.6486e-03, 1.1444e-02, 3.9993e-05, 3.6778e-04,
2.5284e-04, 3.0187e-05], device='cuda:2', grad_fn=<SoftmaxBackward0>)
11 *************
['11', '10', '12', '9', '8', '13', '7', '14'] tensor([9.6735e-01, 1.8863e-02, 1.6486e-03, 1.1444e-02, 3.9993e-05, 3.6778e-04,
2.5284e-04, 3.0187e-05], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1., device='cuda:2', grad_fn=<DivBackward0>), False: tensor(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(0., device='cuda:2', grad_fn=<DivBackward0>)}
[2024-10-24 10:21:28,143] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.34 | optimizer_step: 0.32
[2024-10-24 10:21:28,144] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 1207.34 | backward_microstep: 8810.88 | backward_inner_microstep: 1186.22 | backward_allreduce_microstep: 7624.58 | step_microstep: 7.70
[2024-10-24 10:21:28,144] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 1207.35 | backward: 8810.87 | backward_inner: 1186.24 | backward_allreduce: 7624.56 | step: 7.71
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4729/4844 [19:40:11<25:44, 13.43s/it]Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many paper towel rolls are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='How many people are looking straight ahead?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
torch.Size([3, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='What color is the liquid in the container furthest to the left?')
ANSWER1=EVAL(expr='{ANSWER0} == "blue"')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} == 7')
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 paper towel rolls are in the image?'], 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([3, 3, 448, 448]) knan debug pixel values shape
question: ['How many people are looking straight ahead?'], 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([7, 3, 448, 448]) knan debug pixel values shape
tensor([9.9999e-01, 2.8012e-08, 1.3027e-08, 1.9556e-08, 7.0423e-08, 5.7719e-06,
1.6893e-07, 6.0663e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([9.9999e-01, 2.8012e-08, 1.3027e-08, 1.9556e-08, 7.0423e-08, 5.7719e-06,
1.6893e-07, 6.0663e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:2', grad_fn=<DivBackward0>), False: tensor(6.1325e-06, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1921e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='How many women are lined up for a photo?')
ANSWER1=EVAL(expr='{ANSWER0} == 6')
FINAL_ANSWER=RESULT(var=ANSWER1)
question: ['What color is the liquid in the container furthest to the left?'], responses:['yellow']
torch.Size([3, 3, 448, 448])
question: ['How many dogs are in the image?'], responses:['7']
[('yellow', 0.13019233292980176), ('red', 0.12608840659087261), ('green', 0.12436926918223776), ('maroon', 0.12425930516133966), ('pink', 0.12421440410307089), ('mask', 0.12363437991296296), ('orange', 0.12363130058084727), ('color', 0.12361060153886716)]
[['yellow', 'red', 'green', 'maroon', 'pink', 'mask', 'orange', 'color']]
[('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']]
question: ['How many women are lined up for a photo?'], responses:['five']
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
[('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([3, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3402
tensor([3.7894e-07, 2.7142e-01, 2.5083e-02, 2.4470e-04, 7.0274e-01, 1.8400e-04,
6.5085e-05, 2.6590e-04], device='cuda:2', grad_fn=<SoftmaxBackward0>)
feet *************
['7 eleven', 'babies', 'sunrise', 'eating', 'feet', 'candle', 'light', 'floating'] tensor([3.7894e-07, 2.7142e-01, 2.5083e-02, 2.4470e-04, 7.0274e-01, 1.8400e-04,
6.5085e-05, 2.6590e-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(0., device='cuda:2', grad_fn=<MulBackward0>), 'Execute Error': tensor(1., device='cuda:2', grad_fn=<DivBackward0>)}
tensor([9.9984e-01, 4.1337e-05, 2.9916e-07, 1.1868e-04, 1.1605e-08, 1.4435e-09,
6.8772e-09, 1.4240e-08], device='cuda:3', grad_fn=<SoftmaxBackward0>)