Caffe
caffe的单元 Blobs, layers, nets
- Blob
Blob作为caffe的存储及通信单元，是一个要被处理的真实数据（i.e. image batches, model parameters, derivatives for optimization）的封装。
Blob存储图像batch的方式为 (数目N)×(通道K)×(高H)×(宽W)，以行优先方式存储，也就是说，`(n,k,h,w)`物理地址为`((n*K+k)*H+h)*W+w`.
起初， CAFFE 只支持 4-D blob 和 2-D 卷积(NxCxHxW),现在支持 n-D blobs 和 (n-2)-D 卷积。
- Layer
- Data layer
- Image Data
- Database
- HDF Input
- HDF Output
- Input
- Window Data
- Memory Data
- Dummy Data
- Python
- Vision layer
-
Convolution
layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" # learning rate and decay multipliers for the filters param { lr_mult: 1 decay_mult: 1 } # learning rate and decay multipliers for the biases param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 96 # learn 96 filters kernel_size: 11 # each filter is 11x11 stride: 4 # step 4 pixels between each filter application pad: 0 # pad up pixels weight_filler { type: "gaussian" # initialize the filters from a Gaussian std: 0.01 # distribution with stdev 0.01 (default mean: 0) } bias_filler { type: "constant" # initialize the biases to zero (0) value: 0 } } }-
weight_filter type: caffe 中支持的初始化filter类型有
constant, gaussian, positive_unitball, xavier, msra, bilinear, uniform  
默认类型为 constant, 更详细的介绍见 include/caffe/filter.hpp
-
-
Pooling
layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 # pool over a 3x3 region stride: 2 # step two pixels (in the bottom blob) between pooling regions } } -
Spatial Pyramid Pooling(SPP)
-
Local Response Normalization(LRN)
-
Crop
layer { bottom: "A" bottom: "B" top: "C" name: "crop_u1u2" type: "Crop" }CROP层用于裁剪数据。假设A、B层size分别为(20,50,512,512),(20,10,256,256)，则输出层C的size是(20,10,256,256), 更详细解释见crop.
-
Im2col
-
Deconvolution layer(transpose convolution)
same as Convolution layer
- Recurrent Layers
-
Recurrent
-
RNN
-
Long-Short Term Memory(LSTM)
- Common Layers
-
Inner Product - fully connected layer
-
Dropout
-
Embed
- Normalization Layers
-
Local Response Normalization(LRN)
-
Mean Variance Normalization(MVN)
-
Batch Normalization
- Activation Layers
-
ReLU and Leaky-ReLU
-
PReLU
-
ELU
-
Sigmoid
-
TanH
-
Absolute Value
-
Power- f(x)=(shift+scale*x)^power
-
Exp- f(x)=base^(shift+scale*x)
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Log- f(x)=log(x)
-
BNLL- f(x)=log(1+exp(x))
-
Threshold
-
Bias
-
Scale
- Utility Layers
-
Flatter
-
Reshape
-
Batch Reindex
-
Split
-
Concat
layer { bottom: "A" bottom: "B" top: "C" name: "concat_AB_C" type: "Concat" concat_param { axis: 1 } }假设 A、B 的 size 分别为 (n1, c1, h, w), (n2, c2, h, w)，如果 axis=0, 则 C 的 size 为(n1+n2, c1, h, w) 且要求 c1=c2; 如果 axis=1, 则 C 的 size 为 (n1, c1+c2, h, 2) 且要求 n1=n2.
-
Slicing
-
Eltwise
适用于残差学习(Residual Learning)，实现
f(x) + xlayer { bottom: "conv10" bottom: "conv11" top: "Res" name: "Res" type: "Eltwise" eltwise_param { op: SUM coeff: 1 coeff: -1 } } -
Filter/Mask
-
Parameter
-
Reduction
-
Silence
-
ArgMax
-
Softmax
-
Python-allows custom Python layers
- Loss Layers
-
Multinomial Logistic Loss
-
Infogain Loss
-
Softmax with loss
layer{ name: "loss" type: "SoftmaxWithLoss" bottom: "pred" bottom: "label" top: "loss" } -
Euclidean
-
Hinge/Margin
-
Sigmoid Cross-Entropy Loss
layer{ name: "loss" type: "SigmoidCrossEntropyLoss" bottom: "pred" bottom: "label" top: "loss" } -
Accuracy/Top-k layer
-
Contrastive Loss
Multiple loss layers
事实上，一个网络可以包含很多 loss function, 只要它是一个 DAG (directed acyclic graph)(Caffe net本身可以是任何结构的DAG，不一定是线性结构)。 例如:
layers {
name: "recon-loss"
type: "Euclidean"
bottom: "reconstructions"
bottom: "data"
top: "recon-loss"
}
layers {
name: "class-loss"
type: "softmaxWithLoss"
bottom: "class-preds"
bottom: "class-labels"
top: "class-loss"
loss_weight: 100.0
}
表示的 Loss function 就是:
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任何 layer 都可以产生 loss
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- Net
name: "LogReg"
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
data_param {
source: "input_leveldb"
batch_size: 64
}
}
layer {
name: "ip"
type: "InnerProduct"
bottom: "data"
top: "ip"
inner_product_param {
num_output: 2
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip"
bottom: "label"
top: "loss"
}
- Net visualization
~/caffe/python/draw_net.py yout_net.prototxt yoursave.png
- Solver
solver_type: SGD
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solver_type: NESTEROV
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solver_type: ADAGRAD
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- Weight sharing
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caffe 使用问题集锦
-
Q1 训练过程中人为中断训练(ctrl+C)，可否从中断时刻继续训练？
Answer: 可以。
caffe train -solver solver.prototxt -snapshot train_1000.solverstate以上示例表示从第1000步迭代继续训练。Training-and-Resuming
-
Q2 如何可视化卷积层？
- 使用 python 接口可视化卷积层以及做相关的 test ( [jupyter](http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb) ). 不过需要预装 Python 的一些包，如 numpy, scikit-learn等，才能正常 `import caffe`.
sudo apt-get install python-numpy python-matplotlib python-sklearn python-scipy python-skimage python-h5py python-protobuf python-leveldb python-networkx python-nose python-pandas python-gflags Cython ipython
sudo apt-get update
参考 [caffe examples](http://nbviewer.jupyter.org/github/BVLC/caffe/tree/master/examples/), 我们把 .ipynb 文件转换为 .py 文件并用 ipyhon 执行， 如果用 python 执行会出现 [错误](https://stackoverflow.com/questions/32538758/nameerror-name-get-ipython-is-not-defined)。
jupyter nbconvert --to script '00-classfication.ipynb'
ipython 00-classfication.py
但用ipython仍然遇到了一个 ImportError 的错误
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[mnist](http://caffe.berkeleyvision.org/gathered/examples/mnist.html)
-
Q3 什么样的 layer 才能它的 bottom 和 top 可以是相同的名称？
Answer: 目前只有 Relu 层它的上下层可以使用相同名称，因为它是 element-wise 的，所以可以使用 in-place 的操作以节省内存。 (具体)
------------------------------------
caffe matlab接口使用
读取训练好的net参数
caffe.reset_all();
clear; close all;
% settings
model = '*.prototxt'
weights = '*.caffemodel'
% load model using mat_caffe
net = caffe.Net(model, weights, 'test');
在界面中可以看到，net 含有以下参数
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BatchNorm 层的三个blob含义分别为 MEAN, VARIANCE, MOVING AVERAGE FACTOR
caffe python 接口使用
首先我们定义一个简单的卷积网络
name: "myconvnet"
input: "data"
input_dim: 1
input_dim: 1
input_dim: 256
input_dim: 256
layer {
name: "conv"
type: "Convolution"
bottom: "data"
top: "conv"
convolution_param {
num_output: 10
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
为了保证自己定义的网络各层之间的连接没有问题，我们可以将它可视化来检查网络，看它是什么样子的。 这需要安装一些依赖的包
$ pip insall pydot
$ sudo apt-get install graphviz libgraphviz-dev
$ pip install pygraphviz
然后，就可以用 caffe 自带的 python 脚本画出自定义的网络
$ python /path/to/caffe/python/draw_net.py myconvnet.prototxt myconvnet.png
打开 myconvnet.png 就可以看到画出的网络
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下面说一说怎么用 Python 调用训练好的网络来做测试。首先，创建一个 net 对象来容纳我们的卷积网络:
impoort sys
sys.path.insert(0, '/path/to/caffe/python')
import numpy as np
import cv2
from pylab import * #画图
import caffe
#initialize
caffe.set_device(1) #指定使用哪一块GPU
caffe.set_mode_gpu() #指定GPU计算
model_def = 'deploy.prototxt' #给定网络模型
model_weight = 'net.cafffemodel' #给定参数
net = caffe.Net(model_def, model_weight, caffe.TEST) #给定phase = TEST, 那么网络只会向前计算，不会 backpropagation
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