Comments (5)
Thanks for the release of DVQA.
I have run the eval.py on my workstation, which has the following specs. But somehow I always meet the error "CUDA out of memory" when evaluating 720p and 1080p videos. Only videos with the resolution of 540p or lower worked fine . I am wondering if there is any configuration I can modify to evaluate 720p/1080p videos on my workstation, or simply I just need to upgrade the hardware.OS: Windows 10 home CPU: Intel i5-10400 RAM: 16GB GPU: RTX2060 with 6GB memory SSD: 256GB
Looking forward to your reply, Thanks a lot.
Hello, have you reproduced the DVQA code? If it is reproduced, how do you put your LIVE data set? I put all the data sets in a folder, and an error occurred: RuntimeError: The size of tensor a (35) must match the size of tensor b (109) at non-singleton dimension 2. If you know how to solve it, please reply. Looking forward to your reply, Thanks a lot.
from dvqa.
Thanks for the release of DVQA.
I have run the eval.py on my workstation, which has the following specs. But somehow I always meet the error "CUDA out of memory" when evaluating 720p and 1080p videos. Only videos with the resolution of 540p or lower worked fine . I am wondering if there is any configuration I can modify to evaluate 720p/1080p videos on my workstation, or simply I just need to upgrade the hardware.OS: Windows 10 home CPU: Intel i5-10400 RAM: 16GB GPU: RTX2060 with 6GB memory SSD: 256GB
Looking forward to your reply, Thanks a lot.
I have no experience about the exact memory requirements for 720p and 1080p videos. Besides upgrading hardware, one quick workaround is to reduce the number of frames per sample. The GPU memory requirement is increasing linearly with frame number.
from dvqa.
Thanks for the release of DVQA.
I have run the eval.py on my workstation, which has the following specs. But somehow I always meet the error "CUDA out of memory" when evaluating 720p and 1080p videos. Only videos with the resolution of 540p or lower worked fine . I am wondering if there is any configuration I can modify to evaluate 720p/1080p videos on my workstation, or simply I just need to upgrade the hardware.
OS: Windows 10 home CPU: Intel i5-10400 RAM: 16GB GPU: RTX2060 with 6GB memory SSD: 256GB
Looking forward to your reply, Thanks a lot.Hello, have you reproduced the DVQA code? If it is reproduced, how do you put your LIVE data set? I put all the data sets in a folder, and an error occurred: RuntimeError: The size of tensor a (35) must match the size of tensor b (109) at non-singleton dimension 2. If you know how to solve it, please reply. Looking forward to your reply, Thanks a lot.
A script is provided under https://github.com/Tencent/DVQA/blob/master/dataset/LIVE/prep_live_score.py. It will output a json file that could be directly used by the train or eval scripts.
Unfortunately, I am not able to figure out what happens given the limited information provided. Have you decoded the mp4 streams to YUV?
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哥,有没有数据集给一份呢
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Related Issues (20)
- the performance when using some filters (USM) is not good HOT 1
- ”dis" and "ref" in dataset json file HOT 3
- 请问有小白使用DVQA的使用指南吗? HOT 12
- 为什么对视频的评分需要的内存如此之大! HOT 4
- RuntimeError: The size of tensor a (109) must match the size of tensor b (71) at non-singleton dimension 2 HOT 3
- The performance on LIVE-VQA dataset. HOT 6
- 有预训练好的模型吗,想看看效果
- The question of the training setting of C3DVQA HOT 10
- dataset中json里的mos值是怎么获取?
- How to output the number of FLOPs of this model?
- 请问您有csiq数据集的下载地址吗,找了好久,都没找到。可以给个网盘地址或者发送到邮箱[email protected]。非常感谢。 HOT 5
- How is the MOS label in the dataset videoset mapped? This paper only uses video in yuv format, if only mp4 format is used, is there any performance impact? HOT 2
- Could you provide the download link of video quality datasets? HOT 2
- 训练一个自己的DVQA模型,理想的样本数量是多少? HOT 2
- a question about our eval results HOT 2
- The file(.json) about the dataset information of the LIVE and CSIQ HOT 2
- any pretrained models? HOT 1
- could you introduce some about the dataset size, training environments, training speed HOT 2
- How to use it by CPU? HOT 1
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