<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[qq_40600539的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/qq_40600539</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; qq_40600539]]></copyright><item><title><![CDATA[pytorch模型转tflite【以EfficientNet-BTS为例】]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/123142541</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/123142541</guid><author>qq_40600539</author><pubDate>Fri, 25 Feb 2022 22:59:59 +0800</pubDate><description><![CDATA[步骤

使用pytorch转tflite需要经过：pytorch -&gt; onnx -&gt; tensorflow -&gt; tflite



配置环境


# ONNX-TensorFlow:  1.8.0   [pip install onnx-tf==1.8.0]
# ONNX:             1.8.0   [pip install onnx==1.8.0]
## TensorFlow:      2.4.0   [pip install tensorflow==2.4.0]
#]]></description><category></category></item><item><title><![CDATA[DDAD 数据集开发日志]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/122573653</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/122573653</guid><author>qq_40600539</author><pubDate>Wed, 19 Jan 2022 09:49:22 +0800</pubDate><description><![CDATA[DDAD是一个自动驾驶数据集，有6个摄像头的RGB数据、lidar点云数据，以及相应的Pose、内参、外参、2d 3d box标注、2d semantic标注。



DDAD数据集：GitHub - TRI-ML/DDAD: Dense Depth for Autonomous Driving (DDAD) dataset.

DDAD数据集调用demo: https://github.com/TRI-ML/DDAD/blob/master/notebooks/DDAD.ipynb



调用DDAD数.]]></description><category></category></item><item><title><![CDATA[深度估计-04-Deep Ordinal Regression Network for Monocular Depth Estimation]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/121908683</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/121908683</guid><author>qq_40600539</author><pubDate>Mon, 13 Dec 2021 17:01:35 +0800</pubDate><description><![CDATA[这是一篇有监督深度估计的文章。在2018年Kitti排行榜上Rank1

参考code (pytorch):https://github.com/dontLoveBugs/DORN_pytorch/

本文做出两方面的贡献：

将深度估计这个回归问题转化为多个离散的二分类问题。
	使用ASPP网络融合多尺度信息


一、Spacing-Increasing Discretization

作者认为，在单目深度估计时，深度越远处，信息量会越少，因此深度大的地方，预测的误差变大的容忍度是比较高的。这可以通过..]]></description><category></category></item><item><title><![CDATA[细粒度识别-01-Graph-based High-Order Relation Discovery for Fine-grained Recognition]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/121196083</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/121196083</guid><author>qq_40600539</author><pubDate>Sun, 07 Nov 2021 20:13:10 +0800</pubDate><description><![CDATA[CVPR2021



提出了一种注意力机制，建模不同channel上的semantic 注意力，然后引入GCN的方法，降维特征，并采用分组学习的方法学习类内特征。



1) Channel-aware relation map



先类似于bilinear attention，特征转置相乘，中间加了几层conv和线性映射，得到HW * HW的postion attention weights. 然后和原有特征做加权，得到C * H* W的Position aware attention，然后和原有特征]]></description><category></category></item><item><title><![CDATA[深度估计-03-MonoIndoor: Towards Good Practice of Self-SupervisedMonocular Depth Estimation for Indoor En]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/120849931</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/120849931</guid><author>qq_40600539</author><pubDate>Tue, 19 Oct 2021 23:13:16 +0800</pubDate><description><![CDATA[MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments

ICCV 2021

Paper:https://arxiv.org/pdf/2107.12429.pdf




]]></description><category></category></item><item><title><![CDATA[深度估计-02-Feature-metric Loss for Self-supervised Learning of Depth and Egomotion]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/120833757</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/120833757</guid><author>qq_40600539</author><pubDate>Mon, 18 Oct 2021 20:56:10 +0800</pubDate><description><![CDATA[ECCV2020

提出问题

1、目前的自监督深度估计大多采用光度误差作为约束方式，当存在低纹理区域时，由于在低纹理区域，即使深度图和位姿矩阵估计得不好，算出来的光度误差也会小，因此，光度误差在此时容易陷入局部最小。

Contribtion

1、提出Feature-metric loss，优化深度估计网络和位姿估计网络。

2、采用FeatureNet, 优化输入深度估计网络的特征。结合正则化损失，优化低纹理区域和局部最小区域。



Related W...]]></description><category></category></item><item><title><![CDATA[深度估计-01-Digging Into Self-Supervised Monocular Depth Estimation]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/120732932</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/120732932</guid><author>qq_40600539</author><pubDate>Wed, 13 Oct 2021 11:42:06 +0800</pubDate><description><![CDATA[ICCV 2019 [paper] [code]

Overview

本文介绍一种自监督的深度估计方法Monodepth2。

太长不看版：

1、提出minmum loss在解决multi-view时存在的遮挡问题，指出应该取multi-view中的最小的光度误差，而不是对误差取平均，这是因为当合成图像某处存在遮挡时候，会造成合成图像产生较大的光度误差，而影响模型训练。

2、提出一种multi-scale采样的方法，对low-level的深度图，采用先upsample到原图大小再进行变换的方法，这样.]]></description><category></category></item><item><title><![CDATA[增量学习-01-Learning a Unified Classifier Incrementally via Rebalancing (CVPR2019)]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/118418033</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/118418033</guid><author>qq_40600539</author><pubDate>Mon, 11 Oct 2021 20:47:41 +0800</pubDate><description><![CDATA[写在前面

目前已经是研一下班学年行至末尾了，感觉自己看了很多方向的论文，但又缺乏梳理，希望以这篇论文作为开始，养成记录、总结的好习惯。

目录

Overview

Motivation

Contribution

Method

Performance



code:

https://github.com/mmasana/FACIL

https://github.com/hshustc/CVPR19_Incremental_Learning



Overview（一句话概括文章方法）



.]]></description><category></category></item><item><title><![CDATA[增量学习-03-Riemannian Walk for Incremental Learning:Understanding Forgetting and Intransigence]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/120692853</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/120692853</guid><author>qq_40600539</author><pubDate>Sun, 10 Oct 2021 22:43:10 +0800</pubDate><description><![CDATA[也许阅读这篇文章需要先阅读EWC 和PI 两篇文献。

We present RWalk, a generalization of EWC++ (our efficient version ofEWC[7]) and Path Integral [26] with a theoretically grounded KL-divergence based perspective.

一些概念：

EWC: regularized by a second-order approximation of the K]]></description><category></category></item><item><title><![CDATA[论文笔记 Fine-Grained Few Shot Learning with Foreground Object Transformation]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/120541514</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/120541514</guid><author>qq_40600539</author><pubDate>Wed, 29 Sep 2021 10:30:39 +0800</pubDate><description><![CDATA[Overview

提出了一种前景提取和姿态变形的方法，可以通过4张输入图像，提取其前景，并重新生成新的姿态图片，从而实现数据增强，构造更多的数据集。方法用于小样本细粒度分类问题。



Related Work

现有的小样本学习方法：

1）initialization based。更好的初始化权重，在新数据上微调。

2）metric learning based。基于度量学习，学习一个相似性度量函数。

3）hallucination based methods。通过生成器，生成新样本进行数据增强.]]></description><category></category></item><item><title><![CDATA[二维数组矩阵与一维数组转换后，index索引变化问题]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/119901041</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/119901041</guid><author>qq_40600539</author><pubDate>Tue, 24 Aug 2021 23:37:44 +0800</pubDate><description><![CDATA[题目：给定H * W的一维数组，指定index，求reshape成二维矩阵后，对应的二维矩阵index（x, y）。




&gt;&gt;&gt; import torch
&gt;&gt;&gt; x = torch.tensor([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16])
&gt;&gt;&gt; x
tensor([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16])
&gt;&gt]]></description><category></category></item><item><title><![CDATA[增量学习-02-Essentials for Class Incremental Learning-CVPR2021]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/118423641</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/118423641</guid><author>qq_40600539</author><pubDate>Sun, 04 Jul 2021 23:23:57 +0800</pubDate><description><![CDATA[Essentials for Class Incremental Learning-CVPR2021

Poster:https://www.youtube.com/watch?v=B2HJH5HGLPw






]]></description><category></category></item><item><title><![CDATA[opencv与numpy中的坐标]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/113407761</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/113407761</guid><author>qq_40600539</author><pubDate>Fri, 29 Jan 2021 22:35:10 +0800</pubDate><description><![CDATA[结论：opencv 中 rectangle函数的x,y坐标与numpy中是相反的。

numpy中image.shape = (h, w)

第一维度代表x，第二维度代表y的话。即横轴是y轴，纵轴是x轴。

而在cv2.rectangle(image, pt1, pt2)函数中，pt1和pt2的坐标也是(x,y)，但是这里的x,y是横轴，纵轴。与numpy相反。其中pt1是左上角顶点，pt2是右下角顶点。


if __name__ == '__main__':
    import cv2
    imp]]></description><category></category></item><item><title><![CDATA[yolov4中route层理解]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/107440460</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/107440460</guid><author>qq_40600539</author><pubDate>Sun, 19 Jul 2020 09:11:53 +0800</pubDate><description><![CDATA[route层是yolo中提出的一个层级概念，在一些文章中也叫做路由层。它的作用是在当前层引出之前卷积所得到的特征层。以yolov4-relu.cfg文件中route层定义为例。



相当于中转作用，不涉及卷积计算。例如上图中的route层，layer = -2代表引出前两层的conv输出的特征图。

（之前一直困惑的是，这个route引出之前的featuremap，那它前一个卷积的输出接到哪里去呢？直接不要了么？在yolov3.cfg文件中，route层前的卷积是yolo层，所以不用考虑接到哪去的问题。）]]></description><category></category></item><item><title><![CDATA[Pytorch填坑]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/105453468</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/105453468</guid><author>qq_40600539</author><pubDate>Sat, 11 Apr 2020 15:36:34 +0800</pubDate><description><![CDATA[记录实验中遇到的pytorch可能的问题及解决办法，持续更新，欢迎大家在评论区补充。



1、UserWarning: Using a target size (torch.Size([64])) that is different to the input size (torch.Size([64, 1])). This will likely lead to incorrect resul...]]></description><category></category></item><item><title><![CDATA[【Pytorch】分割的mask 独热编码转换 scatter  参数理解]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/104273782</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/104273782</guid><author>qq_40600539</author><pubDate>Wed, 12 Feb 2020 09:58:07 +0800</pubDate><description><![CDATA[scatter_(input, dim, index, src)将src中数据根据index中的索引按照dim的方向填进input中。



用于将数据转换为 one hot 独热编码时，代码如下


def to_one_hot(mask, n_class):
    """
    Transform a mask to one hot
    change a mask to n * h*...]]></description><category></category></item><item><title><![CDATA[RuntimeError错误的几个可能原因记录和解决方法]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/103985683</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/103985683</guid><author>qq_40600539</author><pubDate>Wed, 15 Jan 2020 11:24:53 +0800</pubDate><description><![CDATA[在训练时，常遇到RuntimeError错误，提示Memory或者RAM不足，甚至受到buy an new RAM的嘲笑！记录一下所遭遇的报错的原因和解决办法，希望对大家有所帮助，也欢迎大家在评论区补充和纠正。



【Q】DataLoader 函数num_workers 设置过大，线程过多，过多占用Cpu，导致报错memory不足。

【A】改小num_workers 即可， 参考值4 ，8 ...]]></description><category></category></item><item><title><![CDATA[pytorch拼接与保存图片]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/103984533</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/103984533</guid><author>qq_40600539</author><pubDate>Wed, 15 Jan 2020 11:04:36 +0800</pubDate><description><![CDATA[在深度学习训练时，我们常需要对某层的输出或最终模型的预测结果进行输出查看，如featmap、output、predict结果等。在pytorch的设定中有tensor类型变量，不可直接查看和保存图片。

虽然有torchvision.utils.save_image 方法可以方便地保存tensor类型图片，调用方法如下：


from torchvision.utils import save...]]></description><category></category></item><item><title><![CDATA[Python中graphviz库的简单用法]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/103915807</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/103915807</guid><author>qq_40600539</author><pubDate>Thu, 09 Jan 2020 21:12:05 +0800</pubDate><description><![CDATA[import VisualizeGraph as viz

x = Variable(torch.randn(1, 3, args.inWidth, args.inHeight))    # 产生随机X

if args.onGPU == True:
      x = x.cuda()

y = model.forward(x)                #传入网络模型
g =...]]></description><category></category></item><item><title><![CDATA[Python所有关键字含义及用法、数据类型、转义序列]]></title><link>https://blog.youkuaiyun.com/qq_40600539/article/details/86263214</link><guid>https://blog.youkuaiyun.com/qq_40600539/article/details/86263214</guid><author>qq_40600539</author><pubDate>Thu, 10 Jan 2019 23:30:11 +0800</pubDate><description><![CDATA[如题，本文讲解所有关键字。注重全而不详解。也是给自己做一个笔记。全局地浏览有助于了解自己有哪里还不知道的。先放一张图



关键字

    and：逻辑与操作

    del： 关键字del在python中是用来删除变量或者是索引，而不是删除数据本身。而且在Python中del不是方法，而是类似于and,not等系统提供的操作命令。



    from:用于导入模块，如from…impor...]]></description><category></category></item></channel></rss>