Yusn kdplus

android qq聊天记录导出大致流程

tested on android 6 tencent qq

$ sqlite3 872222222-IndexQQMsg.db

Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation [Paper]
- Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz
- CVPR 2018 (splotlight)
Video frame synthesis using deep voxel flow [Paper] [Code]
- Z. Liu, R. Yeh, X. Tang, Y. Liu, and A. Agarwala.
- ICCV 2017
Video frame interpolation via adaptive separable convolution. [Paper] [Code]

注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。

启动新会话：

tmux [new -s 会话名 -n 窗口名]

恢复会话：


	# = = = = = include = = = = = #

	library(MASS)

	# = = = = = function = = = = = #

	Obj_func <- function(Y,B,A,Lambda){
	MAT <- t(Y) %% Y - Y %% t(A) %% solve(A%%t(A)+Lambda) %% t(Y%%t(A)) - Lambda
	return <- sum(diag(MAT))

	require 'nn'

	local vgg = nn.Sequential()
	-- building block
	local function ConvBNReLU(nInputPlane, nOutputPlane)
	vgg:add(nn.SpatialConvolution(nInputPlane, nOutputPlane, 3,3, 1,1, 1,1))
	vgg:add(nn.SpatialBatchNormalization(nOutputPlane,1e-3))
	vgg:add(nn.ReLU(true))
	return vgg
	end

	m = nn.ParallelTable()
	layer = nn.SparseLinear(inputSize,outputSize)
	m:add(nn.Sequential():add(layer):add(nn.Reshape(1,outputSize)))
	for i=2,batchSize do
	local repLayer = layer:clone('weight', 'bias', 'gradWeight', 'gradBias')
	m:add(nn.Sequential():add(repLayer):add(nn.Reshape(1,outputSize)))
	end
	batchLayer = nn.Sequential():add(m):add(nn.JoinTable(1))

	------- AlexNet: Using my own weight initialization
	model = nn.Sequential()
	model:add(cudnn.SpatialConvolution(3,96,11,11,4,4,2,2))
	model.modules[#model.modules].weight:normal(0, 0.01)
	model.modules[#model.modules].bias:fill(0)
	model:add(cudnn.ReLU())
	model:add(inn.SpatialCrossResponseNormalization(5, 0.0001, 0.75, 1))
	model:add(nn.SpatialMaxPooling(3,3,2,2))
	model:add(cudnn.SpatialConvolution(96,256,5,5,1,1,2,2))
	model.modules[#model.modules].weight:normal(0, 0.01)

	"""
	L1-penalized minimization using the feature sign search algorithm.
	"""

	import logging
	import numpy as np
	log = logging.getLogger("feature_sign")
	log.setLevel(logging.INFO)