Datasets:
TMarcus
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InternVL

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[('50', 0.12746329354121594), ('51', 0.12494443111915052), ('60', 0.12471995183640609), ('55', 0.12470016949940634), ('54', 0.12460076157014638), ('52', 0.12454269500997545), ('44', 0.12453681395238846), ('48', 0.1244918834713108)]
[['50', '51', '60', '55', '54', '52', '44', '48']]
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
question: ['How many wild dogs 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']]
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
torch.Size([7, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
dynamic ViT batch size: 7, images per sample: 7.0, dynamic token length: 1862
tensor([9.6592e-01, 3.2329e-04, 2.4080e-02, 6.9289e-03, 2.7243e-04, 3.1554e-04,
7.2661e-04, 1.4372e-03], device='cuda:0', grad_fn=<SoftmaxBackward0>)
50 *************
['50', '51', '60', '55', '54', '52', '44', '48'] tensor([9.6592e-01, 3.2329e-04, 2.4080e-02, 6.9289e-03, 2.7243e-04, 3.1554e-04,
7.2661e-04, 1.4372e-03], 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(1.1921e-07, device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 5.8382e-08, 1.7357e-09, 1.4761e-08, 1.1974e-10, 2.9810e-10,
4.6348e-10, 6.5624e-11], device='cuda:3', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 5.8382e-08, 1.7357e-09, 1.4761e-08, 1.1974e-10, 2.9810e-10,
4.6348e-10, 6.5624e-11], 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(7.5826e-08, device='cuda:3', grad_fn=<DivBackward0>), 'Execute Error': tensor(5.9605e-08, device='cuda:3', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 4.6652e-08, 9.0915e-11, 7.6767e-08, 1.4653e-09, 2.1266e-09,
2.7520e-10, 6.7677e-09], device='cuda:2', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 4.6652e-08, 9.0915e-11, 7.6767e-08, 1.4653e-09, 2.1266e-09,
2.7520e-10, 6.7677e-09], 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(9.0915e-11, device='cuda:2', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.1912e-07, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image have a plain white background?')
FINAL_ANSWER=RESULT(var=ANSWER0)
torch.Size([1, 3, 448, 448])
tensor([1.0000e+00, 1.8660e-08, 8.3629e-08, 3.3265e-08, 1.3384e-10, 6.2259e-09,
2.9686e-10, 1.2349e-09], device='cuda:1', grad_fn=<SoftmaxBackward0>)
2 *************
['2', '3', '4', '1', '5', '8', '7', '29'] tensor([1.0000e+00, 1.8660e-08, 8.3629e-08, 3.3265e-08, 1.3384e-10, 6.2259e-09,
2.9686e-10, 1.2349e-09], 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(1.4345e-07, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:1', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='Is there a barber pole in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([7, 3, 448, 448])
question: ['Does the image have a plain white background?'], 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([9.9883e-01, 1.1695e-03, 2.6801e-07, 2.2870e-11, 3.9591e-11, 6.9448e-10,
5.1156e-10, 5.9390e-08], device='cuda:2', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9883e-01, 1.1695e-03, 2.6801e-07, 2.2870e-11, 3.9591e-11, 6.9448e-10,
5.1156e-10, 5.9390e-08], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(0.0012, device='cuda:2', grad_fn=<UnbindBackward0>), False: tensor(0.9988, device='cuda:2', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(3.5763e-07, device='cuda:2', grad_fn=<SubBackward0>)}
question: ['Is there a barber pole in the image?'], 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([7, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.8190e-09, 6.5952e-07, 6.9581e-10, 2.9078e-09, 3.6654e-08,
9.1095e-09, 1.9135e-07], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.8190e-09, 6.5952e-07, 6.9581e-10, 2.9078e-09, 3.6654e-08,
9.1095e-09, 1.9135e-07], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8190e-09, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:1', grad_fn=<DivBackward0>)}
[2024-10-24 10:28:57,331] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.36 | optimizer_gradients: 0.34 | optimizer_step: 0.32
[2024-10-24 10:28:57,332] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 3812.98 | backward_microstep: 10016.44 | backward_inner_microstep: 3510.47 | backward_allreduce_microstep: 6505.89 | step_microstep: 7.57
[2024-10-24 10:28:57,332] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 3813.00 | backward: 10016.43 | backward_inner: 3510.48 | backward_allreduce: 6505.88 | step: 7.58
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4760/4844 [19:47:41<20:38, 14.74s/it]Registering VQA_lavis step
Registering VQA_lavis step
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=LEFT,question='How many wine bottles are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} >= 4')
FINAL_ANSWER=RESULT(var=ANSWER1)
Registering VQA_lavis step
Registering EVAL step
Registering RESULT step
ANSWER0=VQA(image=RIGHT,question='Is there a human hand near a laptop in the image?')
ANSWER1=EVAL(expr='{ANSWER0}')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many monkeys are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} > 7')
FINAL_ANSWER=RESULT(var=ANSWER1)
ANSWER0=VQA(image=RIGHT,question='How many wild dogs are in the image?')
ANSWER1=EVAL(expr='{ANSWER0} <= 1')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
torch.Size([13, 3, 448, 448])
question: ['How many wine bottles are in the image?'], responses:['four']
question: ['How many monkeys are in the image?'], responses:['δΈ‰']