Datasets:
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InternVL

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no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 1.8767e-09, 2.4387e-07, 2.1803e-12, 3.0594e-11, 1.1919e-08,
3.2066e-10, 4.6860e-07], device='cuda:3', grad_fn=<SelectBackward0>)
torch.Size([7, 3, 448, 448])
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.8767e-09, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(7.1526e-07, device='cuda:3', grad_fn=<SubBackward0>)}
ANSWER0=VQA(image=RIGHT,question='Does the image show a hound standing on thick green grass?')
ANSWER1=RESULT(var=ANSWER0)
torch.Size([13, 3, 448, 448])
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
question: ['Is the dog in side profile?'], 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
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3400
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
question: ['Does the image show a hound standing on thick green grass?'], 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']]
dynamic ViT batch size: 13, images per sample: 13.0, dynamic token length: 3401
torch.Size([13, 3, 448, 448]) knan debug pixel values shape
tensor([1.0000e+00, 1.3332e-09, 7.3382e-07, 1.1462e-09, 6.9536e-12, 2.0804e-12,
3.3440e-11, 1.4519e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
yes *************
['yes', 'congratulations', 'no', 'honey', 'solid', 'right', 'candle', 'chocolate'] tensor([1.0000e+00, 1.3332e-09, 7.3382e-07, 1.1462e-09, 6.9536e-12, 2.0804e-12,
3.3440e-11, 1.4519e-09], device='cuda:0', grad_fn=<SelectBackward0>)
tensor([9.9999e-01, 4.7137e-08, 1.3033e-07, 1.0861e-10, 3.0979e-06, 6.4033e-09,
1.0223e-06, 1.8143e-06], device='cuda:2', grad_fn=<SoftmaxBackward0>)
0 *************
['0', 'circles', 'maroon', 'large', 'rooster', 'nuts', 'beige', 'bottle'] tensor([9.9999e-01, 4.7137e-08, 1.3033e-07, 1.0861e-10, 3.0979e-06, 6.4033e-09,
1.0223e-06, 1.8143e-06], device='cuda:2', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.0000, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(7.3382e-07, device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(-1.8565e-08, device='cuda:0', grad_fn=<DivBackward0>)}
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(6.1989e-06, device='cuda:2', grad_fn=<DivBackward0>)}
ANSWER0=VQA(image=LEFT,question='What color is the dispenser button?')
ANSWER1=EVAL(expr='{ANSWER0} == "light gray"')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([1, 3, 448, 448])
ANSWER0=VQA(image=RIGHT,question='How many basins are set in the counter?')
ANSWER1=EVAL(expr='{ANSWER0} == 2')
FINAL_ANSWER=RESULT(var=ANSWER1)
torch.Size([5, 3, 448, 448])
question: ['What color is the dispenser button?'], responses:['sil']
[('jal', 0.12711127546139203), ('asics', 0.1250181807174628), ('pug', 0.12498902974083527), ('camo', 0.12476128011675007), ('ge', 0.1245824295519601), ('can', 0.12453509855707018), ('kia', 0.12453205050659558), ('vent', 0.12447065534793383)]
[['jal', 'asics', 'pug', 'camo', 'ge', 'can', 'kia', 'vent']]
torch.Size([1, 3, 448, 448]) knan debug pixel values shape
tensor([9.9999e-01, 1.1479e-05, 9.2896e-08, 8.7403e-13, 6.2128e-13, 1.0446e-10,
4.3746e-11, 4.5815e-08], device='cuda:1', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([9.9999e-01, 1.1479e-05, 9.2896e-08, 8.7403e-13, 6.2128e-13, 1.0446e-10,
4.3746e-11, 4.5815e-08], device='cuda:1', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(1.1479e-05, device='cuda:1', grad_fn=<DivBackward0>), False: tensor(1.0000, device='cuda:1', grad_fn=<DivBackward0>), 'Execute Error': tensor(1.7881e-07, device='cuda:1', grad_fn=<DivBackward0>)}
question: ['How many basins are set in the counter?'], responses:['1']
tensor([1.6590e-02, 4.7141e-01, 1.2354e-02, 3.2400e-01, 1.1884e-02, 8.4353e-05,
5.6522e-02, 1.0716e-01], device='cuda:2', grad_fn=<SoftmaxBackward0>)
asics *************
['jal', 'asics', 'pug', 'camo', 'ge', 'can', 'kia', 'vent'] tensor([1.6590e-02, 4.7141e-01, 1.2354e-02, 3.2400e-01, 1.1884e-02, 8.4353e-05,
5.6522e-02, 1.0716e-01], 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>)}
[('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([5, 3, 448, 448]) knan debug pixel values shape
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
dynamic ViT batch size: 5, images per sample: 5.0, dynamic token length: 1350
tensor([1.0000e+00, 2.7458e-09, 7.7038e-10, 3.6135e-09, 2.4264e-09, 1.6278e-07,
3.7065e-08, 1.0968e-09], device='cuda:0', grad_fn=<SoftmaxBackward0>)
1 *************
['1', '3', '4', '8', '6', '12', '2', '47'] tensor([1.0000e+00, 2.7458e-09, 7.7038e-10, 3.6135e-09, 2.4264e-09, 1.6278e-07,
3.7065e-08, 1.0968e-09], device='cuda:0', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(3.7065e-08, device='cuda:0', grad_fn=<DivBackward0>), False: tensor(1., device='cuda:0', grad_fn=<DivBackward0>), 'Execute Error': tensor(0., device='cuda:0', grad_fn=<DivBackward0>)}
tensor([1.0000e+00, 2.7036e-10, 6.1874e-07, 1.8503e-12, 1.5990e-12, 2.3611e-09,
3.0080e-10, 3.9817e-07], device='cuda:3', grad_fn=<SoftmaxBackward0>)
no *************
['no', 'yes', 'no smoking', 'gone', 'man', 'meow', 'kia', 'no clock'] tensor([1.0000e+00, 2.7036e-10, 6.1874e-07, 1.8503e-12, 1.5990e-12, 2.3611e-09,
3.0080e-10, 3.9817e-07], device='cuda:3', grad_fn=<SelectBackward0>)
ζœ€εŽηš„ζ¦‚ηŽ‡εˆ†εΈƒδΈΊ: {True: tensor(2.7036e-10, device='cuda:3', grad_fn=<UnbindBackward0>), False: tensor(1.0000, device='cuda:3', grad_fn=<UnbindBackward0>), 'Execute Error': tensor(9.5367e-07, device='cuda:3', grad_fn=<SubBackward0>)}
[2024-10-24 10:30:10,558] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | optimizer_allgather: 1.35 | optimizer_gradients: 0.28 | optimizer_step: 0.32
[2024-10-24 10:30:10,558] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward_microstep: 6434.12 | backward_microstep: 7550.89 | backward_inner_microstep: 6200.74 | backward_allreduce_microstep: 1349.95 | step_microstep: 7.58
[2024-10-24 10:30:10,559] [INFO] [logging.py:96:log_dist] [Rank 0] rank=0 time (ms) | forward: 6434.13 | backward: 7550.88 | backward_inner: 6200.89 | backward_allreduce: 1349.94 | step: 7.59
98%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–Š| 4765/4844 [19:48:54<18:50, 14.31s/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=RIGHT,question='Does the image show a laptop displayed like an inverted book with its pages fanning out?')
FINAL_ANSWER=RESULT(var=ANSWER0)
ANSWER0=VQA(image=RIGHT,question='How many white dogs are in the image?')