Tianfei Zhou tfzhou

MobileNet Inference Time

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Using NCSDK 1.12.00, lastest as of April 2018

Version	Average Inference Time

Learn the register macro used in caffe.cpp

#include <iostream>                                                                                                                                                                                          
 #include <map>                                                                                                                                                                                               
 #include <string>                                                                                                                                                                                            
                                                                                                                                                                                                              
 using namespace std;

How to create a custom Caffe layer in Python?

This tutorial will guide through the steps to create a simple custom layer for Caffe using python. By the end of it, there are some examples of custom layers.

- Why would I want to do that?

Usually you would create a custom layer to implement a funcionality that isn't available in Caffe, tuning it for your requirements.

- What will I need?

Probably just Python and Caffe instaled.

- Is there any downside?

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

	import torch
	import torch.nn as nn
	from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

	seqs = ['gigantic_string','tiny_str','medium_str']

	# make <pad> idx 0
	vocab = ['<pad>'] + sorted(set(''.join(seqs)))

	# make model

	#!/usr/bin/env python

	'''Caffe ResNet NetSpec example.

	Compatible with Kaiming He's pre-trained models.

	https://github.com/KaimingHe/deep-residual-networks
	'''

	import sys

	void CVMatToDatum(const cv::Mat& cv_img, Datum* datum, int depth)
	{
	if (depth == CV_8U) {
	CVMatToDatum(cv_img, datum);
	return;
	}

	CHECK(cv_img.depth() == CV_32F) << "data type must be 32bit float";

	datum->set_channels(cv_img.channels());

	#include <opencv2/opencv.hpp>

	#include <iostream>
	#include <vector>
	#include <cmath>
	#include <assert.h>

	using namespace std;
	using namespace cv;