This is used to have a bunch of ZeroRPC clients and workers talking to each other.
WARNING: this is not compatible with heartbeats and streaming!
Clients connect to the "in" side of the hub.
Workers connect to the "out" side of the hub.
Guide for merging accelerated convolution to Caffe. | |
==== | |
*Yuanjun Xiong* | |
--- | |
[TOC] |
CHECK_EQ(mdb_cursor_get(mdb_cursor_, &mdb_key_, | |
&mdb_value_, MDB_GET_CURRENT), MDB_SUCCESS); | |
datum.ParseFromArray(mdb_value_.mv_data, | |
mdb_value_.mv_size); | |
LOG(INFO)<<"Read "<<item_id<<" "<<(char*)mdb_key_.mv_data; |
int ReadVectorToDatum(float* data_ptr, int data_len, Datum* datum){ | |
//reshape and clear data | |
datum->set_channels(data_len); | |
datum->set_height(1); | |
datum->set_width(1); | |
datum->set_label(0); | |
datum->clear_data(); | |
datum->clear_float_data(); | |
<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<!-- Le styles --> | |
<link href="../bootstrap/css/bootstrap.css" rel="stylesheet"> | |
<script type="text/javascript" src="http://ajax.googleapis.com/ajax/libs/jquery/1.7/jquery.js"></script> | |
</head> | |
<body> |
__author__ = 'alex' | |
# from pyspark import SparkContext, SparkConf | |
import nltk | |
from nltk.corpus import stopwords | |
sw = stopwords.words('english') | |
tk = nltk.tokenize.WordPunctTokenizer() |
youtube-dl -f 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/bestvideo+bestaudio' \ | |
--merge-output-format mp4 \ | |
"http://www.youtube.com/watch?v=P9pzm5b6FFY" | |
# This command downloads the best available quality video together with the best audio. Then it combines them with the post-processor. |
%% This part goes in preamble | |
\newcommand{\dummyfig}[1]{ | |
\centering | |
\fbox{ | |
\begin{minipage}[c][0.33\textheight][c]{0.5\textwidth} | |
\centering{#1} | |
\end{minipage} | |
} | |
} |
import cv2 | |
import sys | |
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.') | |
assert minor_ver >= 2, "Must use opencv 3.2.x up" | |
if __name__ == '__main__' : | |
# Set up tracker. | |
# Instead of MIL, you can also use |
This gist holds the Caffe style model spec for the CVPR'15 paper
Recognize Complex Events from Static Images by Fusing Deep Channels
The model has two channels, one for appearance analysis, the other one for detection bounding box analysis.
The appearcance analysis channel has the similar structure of the AlexNet and thus is initialized using a model pretrained on ImageNet.