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摘要: 论文: https://arxiv.org/pdf/1804.02815.pdf 主页:http://mmlab.ie.cuhk.edu.hk/projects/SFTGAN/ 代码:https://github.com/xinntao/SFTGAN 贡献点: 提出了SFT层 In this pap 阅读全文
posted @ 2021-02-02 16:42 wioponsen 阅读(392) 评论(0) 推荐(0)
摘要: ref: https://linux.cn/article-5139-1.html dmidecode 最常用的选项就是用 -t 或者 --type 来限定关键字: bios, system, baseboard, chassis, processor, memory, cache, connect 阅读全文
posted @ 2021-02-02 13:17 wioponsen 阅读(391) 评论(0) 推荐(0)
摘要: #include <time.h> void get_time_str(char* name) { time_t timep; struct tm *p; time(&timep); p = gmtime(&timep); clock_t t = clock(); int ms = t * 1000 阅读全文
posted @ 2020-12-21 09:30 wioponsen 阅读(2718) 评论(0) 推荐(0)
摘要: 替换代码: import tensorflow as tf 修改为: import tensorflow.compat.v1 as tf tf.disable_v2_behavior() 阅读全文
posted @ 2020-11-20 16:56 wioponsen 阅读(758) 评论(0) 推荐(0)
摘要: 关于Wasserstein GAN的讲解,深入浅出,很棒,收藏! 【知乎】令人拍案叫绝的Wasserstein GAN 代码实现: https://github.com/martinarjovsky/WassersteinGAN 阅读全文
posted @ 2020-11-17 17:29 wioponsen 阅读(86) 评论(0) 推荐(0)
摘要: SSIM 公式含义: \[ SSIM(x,y)=[l(x,y)]^{\alpha}[c(x,y)]^{\beta}[s(x,y)]^{\gamma} \] 亮度: \[ l(x,y) = \frac{2\mu_x\mu_y+c_1}{\mu_x^2 + \mu_y^2 + c_1} \] 对比度: 阅读全文
posted @ 2020-11-13 14:47 wioponsen 阅读(459) 评论(0) 推荐(0)
摘要: 代码 def data_iterator(tfrecords, batch_size=2, shuffle=True, train=True, num_parallel_reads=3): # 声明TFRecordDataset dataset = tf.data.TFRecordDataset(t 阅读全文
posted @ 2020-11-13 14:36 wioponsen 阅读(679) 评论(0) 推荐(0)
摘要: ref:https://blog.csdn.net/u010454261/article/details/71268325 问题描述: error while loading shared libraries: libcudart.so.8.0: cannot open shared object 阅读全文
posted @ 2020-10-29 11:08 wioponsen 阅读(841) 评论(0) 推荐(0)
摘要: ref: https://blog.csdn.net/weixin_43593330/article/details/108491755 在设置optimizer时, 只需要参数分为两个部分, 并分别给定不同的学习率lr。 base_params = list(map(id, net.backbon 阅读全文
posted @ 2020-10-27 15:34 wioponsen 阅读(3642) 评论(1) 推荐(2)
摘要: ref: https://nervanasystems.github.io/distiller/knowledge_distillation.html (For details on how to train a model with knowledge distillation in Distil 阅读全文
posted @ 2020-10-26 09:41 wioponsen 阅读(396) 评论(0) 推荐(0)
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