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Model	Image Size	Target Size	Block Size	Total Time(sec)	GPU Memory(MB)
models-cunet	200x200	400x400	400/200/100	0.93/0.30/0.33	615/615/173
models-cunet	400x400	800x800	400/200/100	0.78/0.71/0.78	2408/615/174
models-cunet	1000x1000	2000x2000	400/200/100	3.16/3.21/3.53	2416/618/175
models-cunet	2000x2000	4000x4000	400/200/100	11.40/11.98/13.86	2420/669/193
models-cunet	4000x4000	8000x8000	400/200/100	44.33/47.15/54.76	2452/644/197
models-upconv_7_anime_style_art_rgb	200x200	400x400	400/200/100	0.16/0.16/0.15	459/459/119
models-upconv_7_anime_style_art_rgb	400x400	800x800	400/200/100	0.43/0.37/0.37	1741/460/119
models-upconv_7_anime_style_art_rgb	1000x1000	2000x2000	400/200/100	1.62/1.59/1.67	1764/462/120

nihui

$ ./benchmark/benchncnn
thread_policy_set error 46
loop_count = 4
num_threads = 8
powersave = 0
gpu_device = -1
cooling_down = 1
          squeezenet  min =    5.64  max =    6.24  avg =    5.88
     squeezenet_int8  min =    8.93  max =    8.97  avg =    8.94
           mobilenet  min =    8.86  max =    8.99  avg =    8.91

nihui

#!/bin/sh
# Use socat to proxy git through an HTTP CONNECT firewall.
# Useful if you are trying to clone git:// from inside a company.
# Requires that the proxy allows CONNECT to port 9418.
#
# Save this file as gitproxy somewhere in your path (e.g., ~/bin) and then run
#   chmod +x gitproxy
#   git config --global core.gitproxy gitproxy
#
# More details at http://tinyurl.com/8xvpny

nihui

-- Remove the history from 
rm -rf .git

-- recreate the repos from the current content only
git init
git add .
git commit -m "Initial commit"

-- push to the github remote repos ensuring you overwrite history
git remote add origin [email protected]:<YOUR ACCOUNT>/<YOUR REPOS>.git

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# delete local tag '12345'
git tag -d 12345
# delete remote tag '12345' (eg, GitHub version too)
git push origin :refs/tags/12345
# alternative approach
git push --delete origin tagName
git tag -d tagName

nihui

# 256x256
data = batch.data[0].as_in_context(ctx)

# random crop resize to 224x224
data_elems = nd.split(data, num_outputs=batch_size, axis=0)
for b in range(batch_size):
    xy0 = random.randint(0, 32)
    xy1 = random.randint(256-32, 256)
    cropped = nd.slice(data_elems[b], begin=(None,None,xy0,xy0), end=(None,None,xy1,xy1))

nihui

#include <opencv2/core/core.hpp>

static cv::Mat get_affine_transform(const cv::Point2f* a, const cv::Point2f* b, int count)
{
    double sa[4][4]={{0.}};
    double sb[4]={0.};
    double m[4];
    cv::Mat A( 4, 4, CV_64F, sa );
    cv::Mat B( 4, 1, CV_64F, sb );
    cv::Mat MM( 4, 1, CV_64F, m );

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/*!
*  Copyright (c) 2019 by Contributors
* \file op.h
* \brief definition of all the operators
* \author Chuntao Hong, Xin Li
*/

#ifndef MXNET_CPP_OP_H_
#define MXNET_CPP_OP_H_

nihui

using namespace ncnn;

class TaskQueue
{
public:
    TaskQueue()
    {
    }

nihui

#ifndef MYREC_H
#define MYREC_H

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#include <string>
#include <vector>