caffe学习笔记2：Fine-tuning一个类别识别的预处理的网

Fine-tuning一个预处理的网用于类型识别（Fine-tuning a Pretrained Network for Style Recognition）

本文原文地址here

在这个实验中，我们我们探索一个通用的方法，这个方法在现实世界的应用中非常的有用：使用一个提前训练的caffe网络，并且使用自定义的数据来fine-tune参数。
这个方法的优点就是，提前训练好的我那个落是从一个非常大的图像数据集中学习到的，中层的网可以捕获一般视觉表象的语义信息。考虑把它作为十分强大的通用的可视化特征，这个特征你可以看作是一个黑盒。最重要的是（on top of that），需要一些相对少量的数据在目标任务上有一个很好的表现。
首先，我们需要准备数据。这个包含下面几个步骤：
1. 通过提供的shell scripts得到ImageNet ilsvrc预训练的模型。
2. 下载Flickr style数据集的一个子集用于这个demo。
3. 编译下载的Flickr数据到caffe是数据格式。

caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)

import sys
sys.path.insert(0, caffe_root + 'python')
import caffe

caffe.set_device(0)
caffe.set_mode_gpu()

import numpy as np
from pylab import *
%matplotlib inline
import tempfile

# Helper function for deprocessing preprocessed images, e.g., for display.
def deprocess_net_image(image):
    image = image.copy()              # don't modify destructively
    image = image[::-1]               # BGR -> RGB
    image = image.transpose(1, 2, 0)  # CHW -> HWC
    image += [123, 117, 104]          # (approximately) undo mean subtraction

    # clamp values in [0, 255]
    image[image < 0], image[image > 255] = 0, 255

    # round and cast from float32 to uint8
    image = np.round(image)
    image = np.require(image, dtype=np.uint8)

    return image

1、建立和数据集下载

下载数据要求这些执行

• get_ilsvrc_aux.sh去下载ImageNet数据均值和标签等
• download_model_binary.py去下载提前训练好的参考模型
• finetune_flickr_style/assemble_data.py下载style训练和测试数据

我们执行这些之后将下载所有数据集中的一小部分：从80k的图像中仅仅获得2000幅图像，从20个style类别中获得了5个。（为了获取所有的数据集可以在接下来的设置中设置full_dataset=True）

# Download just a small subset of the data for this exercise.
# (2000 of 80K images, 5 of 20 labels.)
# To download the entire dataset, set `full_dataset = True`.
full_dataset = False
if full_dataset:
    NUM_STYLE_IMAGES = NUM_STYLE_LABELS = -1
else:
    NUM_STYLE_IMAGES = 2000
    NUM_STYLE_LABELS = 5

# This downloads the ilsvrc auxiliary data (mean file, etc),
# and a subset of 2000 images for the style recognition task.
import os
os.chdir(caffe_root)  # run scripts from caffe root
!data/ilsvrc12/get_ilsvrc_aux.sh
!scripts/download_model_binary.py models/bvlc_reference_caffenet
!python examples/finetune_flickr_style/assemble_data.py \
    --workers=-1 --seed=1701 \
    --images=$NUM_STYLE_IMAGES --label=$NUM_STYLE_LABELS
# back to examples
os.chdir('examples')

Downloading…
–2016-02-24 00:28:36– http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz
Resolving dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)… 169.229.222.251
Connecting to dl.caffe.berkeleyvision.org (dl.caffe.berkeleyvision.org)|169.229.222.251|:80… connected.
HTTP request sent, awaiting response… 200 OK
Length: 17858008 (17M) [application/octet-stream]
Saving to: ‘caffe_ilsvrc12.tar.gz’
100%[======================================>] 17,858,008 112MB/s in 0.2s
2016-02-24 00:28:36 (112 MB/s) - ‘caffe_ilsvrc12.tar.gz’ saved [17858008/17858008]
Unzipping…
Done.
Model already exists.
Downloading 2000 images with 7 workers…
Writing train/val for 1996 successfully downloaded images.

定义weights，设置我们刚刚下载过的ImageNet预训练的权值路径，确保它存在。

import os
weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
assert os.path.exists(weights)

从ilsvrc12/synset_words.txt加载1000幅ImageNet标签，并且从finetune_flickr_style/style_names.txt加载5个style标签。

# Load ImageNet labels to imagenet_labels
imagenet_label_file = caffe_root + 'data/ilsvrc12/synset_words.txt'
imagenet_labels = list(np.loadtxt(imagenet_label_file, str, delimiter='\t'))
assert len(imagenet_labels) == 1000
print 'Loaded ImageNet labels:\n', '\n'.join(imagenet_labels[:10] + ['...'])

# Load style labels to style_labels
style_label_file = caffe_root + 'examples/finetune_flickr_style/style_names.txt'
style_labels = list(np.loadtxt(style_label_file, str, delimiter='\n'))
if NUM_STYLE_LABELS > 0:
    style_labels = style_labels[:NUM_STYLE_LABELS]
print '\nLoaded style labels:\n', ', '.join(style_labels)

2、定义和运行网络

我们开始通过定义caffenet，一个函数来初始化CaffeNet结构（一个在AlexNet的小的变种），以参数指定数据和输出类的数量。

from caffe import layers as L
from caffe import params as P

weight_param = dict(lr_mult=1, decay_mult=1)
bias_param   = dict(lr_mult=2, decay_mult=0)
learned_param = [weight_param, bias_param]

frozen_param = [dict(lr_mult=0)] * 2

def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1, param=learned_param, weight_filler=dict(type='gaussian', std=0.01), bias_filler=dict(type='constant', value=0.1)):
    conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
                         num_output=nout, pad=pad, group=group,
                         param=param, weight_filler=weight_filler,
                         bias_filler=bias_filler)
    return conv, L.ReLU(conv, in_place=True)

def fc_relu(bottom, nout, param=learned_param, weight_filler=dict(type='gaussian', std=0.005), bias_filler=dict(type='constant', value=0.1)):
    fc = L.InnerProduct(bottom, num_output=nout, param=param,
                        weight_filler=weight_filler,
                        bias_filler=bias_filler)
    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(data, label=None, train=True, num_classes=1000, classifier_name='fc8', learn_all=False):
    """Returns a NetSpec specifying CaffeNet, following the original proto text specification (./models/bvlc_reference_caffenet/train_val.prototxt)."""
    n = caffe.NetSpec()
    n.data = data
    param = learned_param if learn_all else frozen_param
    n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param)
    n.pool1 = max_pool(n.relu1, 3, stride=2)
    n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75)
    n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param)
    n.pool2 = max_pool(n.relu2, 3, stride=2)
    n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75)
    n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param)
    n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param)
    n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param)
    n.pool5 = max_pool(n.relu5, 3, stride=2)
    n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param)
    if train:
        n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True)
    else:
        fc7input = n.relu6
    n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param)
    if train:
        n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True)
    else:
        fc8input = n.relu7
    # always learn fc8 (param=learned_param)
    fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param)
    # give fc8 the name specified by argument `classifier_name`
    n.__setattr__(classifier_name, fc8)
    if not train:
        n.probs = L.Softmax(fc8)
    if label is not None:
        n.label = label
        n.loss = L.SoftmaxWithLoss(fc8, n.label)
        n.acc = L.Accuracy(fc8, n.label)
    # write the net to a temporary file and return its filename
    with tempfile.NamedTemporaryFile(delete=False) as f:
        f.write(str(n.to_proto()))
        return f.name

现在，让我们创建一个CaffeNet网络，把没有标签的“dummy data”作为输入，允许我们从外部去设置输入图像，并且看看预测成ImageNet的什么类别。

dummy_data = L.DummyData(shape=dict(dim=[1, 3, 227, 227]))
imagenet_net_filename = caffenet(data=dummy_data, train=False)
imagenet_net = caffe.Net(imagenet_net_filename, weights, caffe.TEST)

定义一个函数style_net这会调用caffenet。

这个新的网络也会有CaffeNet的结构，输入和输出不同。

• 输入是我们下载的Flickr style，被一个ImageData层所输入。
• 输出是一个20类的分布而不是原始1000个imageNet类
• 分类层被重新命名为fc8_flickr代替fc8，告诉Caffe不是从预训练的ImageNet模型加载原始的分类（fc8）权值。

def style_net(train=True, learn_all=False, subset=None):
    if subset is None:
        subset = 'train' if train else 'test'
    source = caffe_root + 'data/flickr_style/%s.txt' % subset
    transform_param = dict(mirror=train, crop_size=227,
        mean_file=caffe_root + 'data/ilsvrc12/imagenet_mean.binaryproto')
    style_data, style_label = L.ImageData(
        transform_param=transform_param, source=source,
        batch_size=50, new_height=256, new_width=256, ntop=2)
    return caffenet(data=style_data, label=style_label, train=train,
                    num_classes=NUM_STYLE_LABELS,
                    classifier_name='fc8_flickr',
                    learn_all=learn_all)