本文链接：http://blog.csdn.net/thesby/article/details/51264439
caffe的大多数层是由c++写成的，借助于c++的高效性，网络可以快速训练。但是我们有时候需要自己写点输入层以应对各种不同的数据输入，比如你因为是需要在图像中取块而不想写成LMDB，这时候可以考虑使用python直接写一个层。而且输入层不需要GPU加速，所需写起来也比较容易。
python层怎么用
先看一个网上的例子吧（来自http://chrischoy.github.io/research/caffe-python-layer/）
layer {
type: 'Python'
name: 'loss'
top: 'loss'
bottom: 'ipx'
bottom: 'ipy'
python_param {
# the module name -- usually the filename -- that needs to be in $PYTHONPATH
module: 'pyloss'
# the layer name -- the class name in the module
layer: 'EuclideanLossLayer'
}
# set loss weight so Caffe knows this is a loss layer
loss_weight: 1
}这里的type就只有Python一种，然后top，bottom和常见的层是一样的，module就是你的python module名字，一般就是文件名，然后layer就是定义的类的名字。
python层怎么写
这里就以 Fully Convolutional Networks for Semantic Segmentation 论文中公布的代码作为示例，解释python层该怎么写。
import caffe
import numpy as np
from PIL import Image
import random
class VOCSegDataLayer(caffe.Layer):
""" Load (input image, label image) pairs from PASCAL VOC one-at-a-time while reshaping the net to preserve dimensions. Use this to feed data to a fully convolutional network. """
def setup(self, bottom, top):
""" Setup data layer according to parameters: - voc_dir: path to PASCAL VOC year dir - split: train / val / test - mean: tuple of mean values to subtract - randomize: load in random order (default: True) - seed: seed for randomization (default: None / current time) for PASCAL VOC semantic segmentation. example params = dict(voc_dir="/path/to/PASCAL/VOC2011", mean=(104.00698793, 116.66876762, 122.67891434), split="val") """
# config
params = eval(self.param_str)
self.voc_dir = params['voc_dir']
self.split = params['split']
self.mean = np.array(params['mean'])
self.random = params.get('randomize', True)
self.seed = params.get('seed', None)
# two tops: data and label
if len(top) != 2:
raise Exception("Need to define two tops: data and label.")
# data layers have no bottoms
if len(bottom) != 0:
raise Exception("Do not define a bottom.")
# load indices for images and labels
split_f = '{}/ImageSets/Segmentation/{}.txt'.format(self.voc_dir,
self.split)
self.indices = open(split_f, 'r').read().splitlines()
self.idx = 0
# make eval deterministic
if 'train' not in self.split:
self.random = False
# randomization: seed and pick
if self.random:
random.seed(self.seed)
self.idx = random.randint(0, len(self.indices)-1)
def reshape(self, bottom, top):
# load image + label image pair
self.data = self.load_image(self.indices[self.idx])
self.label = self.load_label(self.indices[self.idx])
# reshape tops to fit (leading 1 is for batch dimension)
top[0].reshape(1, *self.data.shape)
top[1].reshape(1, *self.label.shape)
def forward(self, bottom, top):
# assign output
top[0].data[...] = self.data
top[1].data[...] = self.label
# pick next input
if self.random:
self.idx = random.randint(0, len(self.indices)-1)
else:
self.idx += 1
if self.idx == len(self.indices):
self.idx = 0
def backward(self, top, propagate_down, bottom):
pass
def load_image(self, idx):
""" Load input image and preprocess for Caffe: - cast to float - switch channels RGB -> BGR - subtract mean - transpose to channel x height x width order """
im = Image.open('{}/JPEGImages/{}.jpg'.format(self.voc_dir, idx))
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= self.mean
in_ = in_.transpose((2,0,1))
return in_
def load_label(self, idx):
""" Load label image as 1 x height x width integer array of label indices. The leading singleton dimension is required by the loss. """
im = Image.open('{}/SegmentationClass/{}.png'.format(self.voc_dir, idx))
label = np.array(im, dtype=np.uint8)
label = label[np.newaxis, ...]
return label
class SBDDSegDataLayer(caffe.Layer):
""" Load (input image, label image) pairs from the SBDD extended labeling of PASCAL VOC for semantic segmentation one-at-a-time while reshaping the net to preserve dimensions. Use this to feed data to a fully convolutional network. """
def setup(self, bottom, top):
""" Setup data layer according to parameters: - sbdd_dir: path to SBDD `dataset` dir - split: train / seg11valid - mean: tuple of mean values to subtract - randomize: load in random order (default: True) - seed: seed for randomization (default: None / current time) for SBDD semantic segmentation. N.B.segv11alid is the set of segval11 that does not intersect with SBDD. Find it here: https://gist.github.com/shelhamer/edb330760338892d511e. example params = dict(sbdd_dir="/path/to/SBDD/dataset", mean=(104.00698793, 116.66876762, 122.67891434), split="valid") """
# config
params = eval(self.param_str)
self.sbdd_dir = params['sbdd_dir']
self.split = params['split']
self.mean = np.array(params['mean'])
self.random = params.get('randomize', True)
self.seed = params.get('seed', None)
# two tops: data and label
if len(top) != 2:
raise Exception("Need to define two tops: data and label.")
# data layers have no bottoms
if len(bottom) != 0:
raise Exception("Do not define a bottom.")
# load indices for images and labels
split_f = '{}/{}.txt'.format(self.sbdd_dir,
self.split)
self.indices = open(split_f, 'r').read().splitlines()
self.idx = 0
# make eval deterministic
if 'train' not in self.split:
self.random = False
# randomization: seed and pick
if self.random:
random.seed(self.seed)
self.idx = random.randint(0, len(self.indices)-1)
def reshape(self, bottom, top):
# load image + label image pair
self.data = self.load_image(self.indices[self.idx])
self.label = self.load_label(self.indices[self.idx])
# reshape tops to fit (leading 1 is for batch dimension)
top[0].reshape(1, *self.data.shape)
top[1].reshape(1, *self.label.shape)
def forward(self, bottom, top):
# assign output
top[0].data[...] = self.data
top[1].data[...] = self.label
# pick next input
if self.random:
self.idx = random.randint(0, len(self.indices)-1)
else:
self.idx += 1
if self.idx == len(self.indices):
self.idx = 0
def backward(self, top, propagate_down, bottom):
pass
def load_image(self, idx):
""" Load input image and preprocess for Caffe: - cast to float - switch channels RGB -> BGR - subtract mean - transpose to channel x height x width order """
im = Image.open('{}/img/{}.jpg'.format(self.sbdd_dir, idx))
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= self.mean
in_ = in_.transpose((2,0,1))
return in_
def load_label(self, idx):
""" Load label image as 1 x height x width integer array of label indices. The leading singleton dimension is required by the loss. """
import scipy.io
mat = scipy.io.loadmat('{}/cls/{}.mat'.format(self.sbdd_dir, idx))
label = mat['GTcls'][0]['Segmentation'][0].astype(np.uint8)
label = label[np.newaxis, ...]
return label每个类都是层，类的名字就是layer参数的名字。这两个都是数据输入层，由于需要一个data，一个label，所以有两个top，没有bottomo。
类直接继承的是caffe.Layer，然后必须重写setup()，reshape()，forward()，backward()函数，其他的函数可以自己定义，没有限制。
setup()是类启动时该做的事情，比如层所需数据的初始化。
reshape()就是取数据然后把它规范化为四维的矩阵。每次取数据都会调用此函数。
forward()就是网络的前向运行，这里就是把取到的数据往前传递，因为没有其他运算。
backward()就是网络的反馈，data层是没有反馈的，所以这里就直接pass。
PS
这里就把一些资料整合起来，以供参考吧。
1、caffe官网现在开始有了点pycaffe的资料，但是鉴于caffe经常更新，不知道什么时候就把它删除，所需摘录到此。
文件： pyloss.py
import caffe
import numpy as np
class EuclideanLossLayer(caffe.Layer):
""" Compute the Euclidean Loss in the same manner as the C++ EuclideanLossLayer to demonstrate the class interface for developing layers in Python. """
def setup(self, bottom, top):
# check input pair
if len(bottom) != 2:
raise Exception("Need two inputs to compute distance.")
def reshape(self, bottom, top):
# check input dimensions match
if bottom[0].count != bottom[1].count:
raise Exception("Inputs must have the same dimension.")
# difference is shape of inputs
self.diff = np.zeros_like(bottom[0].data, dtype=np.float32)
# loss output is scalar
top[0].reshape(1)
def forward(self, bottom, top):
self.diff[...] = bottom[0].data - bottom[1].data
top[0].data[...] = np.sum(self.diff**2) / bottom[0].num / 2.
def backward(self, top, propagate_down, bottom):
for i in range(2):
if not propagate_down[i]:
continue
if i == 0:
sign = 1
else:
sign = -1
bottom[i].diff[...] = sign * self.diff / bottom[i].num下面这个就是如何使用这个层了：
linreg.prototxt
name: 'LinearRegressionExample'
# define a simple network for linear regression on dummy data
# that computes the loss by a PythonLayer.
layer {
type: 'DummyData'
name: 'x'
top: 'x'
dummy_data_param {
shape: { dim: 10 dim: 3 dim: 2 }
data_filler: { type: 'gaussian' }
}
}
layer {
type: 'DummyData'
name: 'y'
top: 'y'
dummy_data_param {
shape: { dim: 10 dim: 3 dim: 2 }
data_filler: { type: 'gaussian' }
}
}
# include InnerProduct layers for parameters
# so the net will need backward
layer {
type: 'InnerProduct'
name: 'ipx'
top: 'ipx'
bottom: 'x'
inner_product_param {
num_output: 10
weight_filler { type: 'xavier' }
}
}
layer {
type: 'InnerProduct'
name: 'ipy'
top: 'ipy'
bottom: 'y'
inner_product_param {
num_output: 10
weight_filler { type: 'xavier' }
}
}
layer {
type: 'Python'
name: 'loss'
top: 'loss'
bottom: 'ipx'
bottom: 'ipy'
python_param {
# the module name -- usually the filename -- that needs to be in $PYTHONPATH
module: 'pyloss'
# the layer name -- the class name in the module
layer: 'EuclideanLossLayer'
}
# set loss weight so Caffe knows this is a loss layer.
# since PythonLayer inherits directly from Layer, this isn't automatically # known to Caffe loss_weight: 1 }pascal_multilabel_datalayers.py
# imports
import json
import time
import pickle
import scipy.misc
import skimage.io
import caffe
import numpy as np
import os.path as osp
from xml.dom import minidom
from random import shuffle
from threading import Thread
from PIL import Image
from tools import SimpleTransformer
class PascalMultilabelDataLayerSync(caffe.Layer):
""" This is a simple syncronous datalayer for training a multilabel model on PASCAL. """
def setup(self, bottom, top):
self.top_names = ['data', 'label']
# === Read input parameters ===
# params is a python dictionary with layer parameters.
params = eval(self.param_str)
# Check the paramameters for validity.
check_params(params)
# store input as class variables
self.batch_size = params['batch_size']
# Create a batch loader to load the images.
self.batch_loader = BatchLoader(params, None)
# === reshape tops ===
# since we use a fixed input image size, we can shape the data layer
# once. Else, we'd have to do it in the reshape call.
top[0].reshape(
self.batch_size, 3, params['im_shape'][0], params['im_shape'][1])
# Note the 20 channels (because PASCAL has 20 classes.)
top[1].reshape(self.batch_size, 20)
print_info("PascalMultilabelDataLayerSync", params)
def forward(self, bottom, top):
""" Load data. """
for itt in range(self.batch_size):
# Use the batch loader to load the next image.
im, multilabel = self.batch_loader.load_next_image()
# Add directly to the caffe data layer
top[0].data[itt, ...] = im
top[1].data[itt, ...] = multilabel
def reshape(self, bottom, top):
""" There is no need to reshape the data, since the input is of fixed size (rows and columns) """
pass
def backward(self, top, propagate_down, bottom):
""" These layers does not back propagate """
pass
class BatchLoader(object):
""" This class abstracts away the loading of images. Images can either be loaded singly, or in a batch. The latter is used for the asyncronous data layer to preload batches while other processing is performed. """
def __init__(self, params, result):
self.result = result
self.batch_size = params['batch_size']
self.pascal_root = params['pascal_root']
self.im_shape = params['im_shape']
# get list of image indexes.
list_file = params['split'] + '.txt'
self.indexlist = [line.rstrip('\n') for line in open(
osp.join(self.pascal_root, 'ImageSets/Main', list_file))]
self._cur = 0 # current image
# this class does some simple data-manipulations
self.transformer = SimpleTransformer()
print "BatchLoader initialized with {} images".format(
len(self.indexlist))
def load_next_image(self):
""" Load the next image in a batch. """
# Did we finish an epoch?
if self._cur == len(self.indexlist):
self._cur = 0
shuffle(self.indexlist)
# Load an image
index = self.indexlist[self._cur] # Get the image index
image_file_name = index + '.jpg'
im = np.asarray(Image.open(
osp.join(self.pascal_root, 'JPEGImages', image_file_name)))
im = scipy.misc.imresize(im, self.im_shape) # resize
# do a simple horizontal flip as data augmentation
flip = np.random.choice(2)*2-1
im = im[:, ::flip, :]
# Load and prepare ground truth
multilabel = np.zeros(20).astype(np.float32)
anns = load_pascal_annotation(index, self.pascal_root)
for label in anns['gt_classes']:
# in the multilabel problem we don't care how MANY instances
# there are of each class. Only if they are present.
# The "-1" is b/c we are not interested in the background
# class.
multilabel[label - 1] = 1
self._cur += 1
return self.transformer.preprocess(im), multilabel
def load_pascal_annotation(index, pascal_root):
""" This code is borrowed from Ross Girshick's FAST-RCNN code (https://github.com/rbgirshick/fast-rcnn). It parses the PASCAL .xml metadata files. See publication for further details: (http://arxiv.org/abs/1504.08083). Thanks Ross! """
classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
class_to_ind = dict(zip(classes, xrange(21)))
filename = osp.join(pascal_root, 'Annotations', index + '.xml')
# print 'Loading: {}'.format(filename)
def get_data_from_tag(node, tag):
return node.getElementsByTagName(tag)[0].childNodes[0].data
with open(filename) as f:
data = minidom.parseString(f.read())
objs = data.getElementsByTagName('object')
num_objs = len(objs)
boxes = np.zeros((num_objs, 4), dtype=np.uint16)
gt_classes = np.zeros((num_objs), dtype=np.int32)
overlaps = np.zeros((num_objs, 21), dtype=np.float32)
# Load object bounding boxes into a data frame.
for ix, obj in enumerate(objs):
# Make pixel indexes 0-based
x1 = float(get_data_from_tag(obj, 'xmin')) - 1
y1 = float(get_data_from_tag(obj, 'ymin')) - 1
x2 = float(get_data_from_tag(obj, 'xmax')) - 1
y2 = float(get_data_from_tag(obj, 'ymax')) - 1
cls = class_to_ind[
str(get_data_from_tag(obj, "name")).lower().strip()]
boxes[ix, :] = [x1, y1, x2, y2]
gt_classes[ix] = cls
overlaps[ix, cls] = 1.0
overlaps = scipy.sparse.csr_matrix(overlaps)
return {'boxes': boxes,
'gt_classes': gt_classes,
'gt_overlaps': overlaps,
'flipped': False,
'index': index}
def check_params(params):
""" A utility function to check the parameters for the data layers. """
assert 'split' in params.keys(
), 'Params must include split (train, val, or test).'
required = ['batch_size', 'pascal_root', 'im_shape']
for r in required:
assert r in params.keys(), 'Params must include {}'.format(r)
def print_info(name, params):
""" Ouput some info regarding the class """
print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
name,
params['split'],
params['batch_size'],
params['im_shape'])caffenet.py
from __future__ import print_function
from caffe import layers as L, params as P, to_proto
from caffe.proto import caffe_pb2
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, group=group)
return conv, L.ReLU(conv, in_place=True)
def fc_relu(bottom, nout):
fc = L.InnerProduct(bottom, num_output=nout)
return fc, L.ReLU(fc, in_place=True)
def max_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def caffenet(lmdb, batch_size=256, include_acc=False):
data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
# the net itself
conv1, relu1 = conv_relu(data, 11, 96, stride=4)
pool1 = max_pool(relu1, 3, stride=2)
norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
pool2 = max_pool(relu2, 3, stride=2)
norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
pool5 = max_pool(relu5, 3, stride=2)
fc6, relu6 = fc_relu(pool5, 4096)
drop6 = L.Dropout(relu6, in_place=True)
fc7, relu7 = fc_relu(drop6, 4096)
drop7 = L.Dropout(relu7, in_place=True)
fc8 = L.InnerProduct(drop7, num_output=1000)
loss = L.SoftmaxWithLoss(fc8, label)
if include_acc:
acc = L.Accuracy(fc8, label)
return to_proto(loss, acc)
else:
return to_proto(loss)
def make_net():
with open('train.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-train-lmdb'), file=f)
with open('test.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
if __name__ == '__main__':
make_net()tools.py
import numpy as np
class SimpleTransformer:
""" SimpleTransformer is a simple class for preprocessing and deprocessing images for caffe. """
def __init__(self, mean=[128, 128, 128]):
self.mean = np.array(mean, dtype=np.float32)
self.scale = 1.0
def set_mean(self, mean):
""" Set the mean to subtract for centering the data. """
self.mean = mean
def set_scale(self, scale):
""" Set the data scaling. """
self.scale = scale
def preprocess(self, im):
""" preprocess() emulate the pre-processing occuring in the vgg16 caffe prototxt. """
im = np.float32(im)
im = im[:, :, ::-1] # change to BGR
im -= self.mean
im *= self.scale
im = im.transpose((2, 0, 1))
return im
def deprocess(self, im):
""" inverse of preprocess() """
im = im.transpose(1, 2, 0)
im /= self.scale
im += self.mean
im = im[:, :, ::-1] # change to RGB
return np.uint8(im)
class CaffeSolver:
""" Caffesolver is a class for creating a solver.prototxt file. It sets default values and can export a solver parameter file. Note that all parameters are stored as strings. Strings variables are stored as strings in strings. """
def __init__(self, testnet_prototxt_path="testnet.prototxt", trainnet_prototxt_path="trainnet.prototxt", debug=False):
self.sp = {}
# critical:
self.sp['base_lr'] = '0.001'
self.sp['momentum'] = '0.9'
# speed:
self.sp['test_iter'] = '100'
self.sp['test_interval'] = '250'
# looks:
self.sp['display'] = '25'
self.sp['snapshot'] = '2500'
self.sp['snapshot_prefix'] = '"snapshot"' # string withing a string!
# learning rate policy
self.sp['lr_policy'] = '"fixed"'
# important, but rare:
self.sp['gamma'] = '0.1'
self.sp['weight_decay'] = '0.0005'
self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
# pretty much never change these.
self.sp['max_iter'] = '100000'
self.sp['test_initialization'] = 'false'
self.sp['average_loss'] = '25' # this has to do with the display.
self.sp['iter_size'] = '1' # this is for accumulating gradients
if (debug):
self.sp['max_iter'] = '12'
self.sp['test_iter'] = '1'
self.sp['test_interval'] = '4'
self.sp['display'] = '1'
def add_from_file(self, filepath):
""" Reads a caffe solver prototxt file and updates the Caffesolver instance parameters. """
with open(filepath, 'r') as f:
for line in f:
if line[0] == '#':
continue
splitLine = line.split(':')
self.sp[splitLine[0].strip()] = splitLine[1].strip()
def write(self, filepath):
""" Export solver parameters to INPUT "filepath". Sorted alphabetically. """
f = open(filepath, 'w')
for key, value in sorted(self.sp.items()):
if not(type(value) is str):
raise TypeError('All solver parameters must be strings')
f.write('%s: %s\n' % (key, value))