pytorch深度学习以图搜图
文章目录
- 前言
- 一、特征提取网络
- 二、数据库图像特征提取
- 三、特征比对计算
- 总结
前言
最近项目上有一些图像相似性的问题需要研究，之前用传统基于特征点方法还是有一些劣势。想了一下写一篇简易的关于使用神经网络来做图像搜索的文章，图像搜索本质是输入一张图像，从数据库查找到和他最相似的图像并排序返回。最关键的环节就是两张图像相似度的度量。本文方法感觉和孪生神经网络没有什么本质区别，都是输入到同一个网络然后计算相似性，不过本文是将数据库大量图像特征提前计算保存下来，也可以节省计算时间。本文一共三部分代码。
一、特征提取网络
可以使用在一些公开数据集(比如imagenet，cifar)上面训练好的模型作为特征提取器，例如vgg16或者resnet这些，都有很好的特征提取能力。如果是特定垂类的数据建议重新训练一下。我这里是重新训练了一个图像二分类模型，backbone使用了华为的G-ghostnet
代码：
# G_ghostnet_extract.py
import numpy as np
import torch
import torch.nn as nn
__all__ = ['regnetx_032']
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class Bottleneck(nn.Module):
expansion = 1
__constants__ = ['downsample']
def __init__(self, inplanes, planes, stride=1, downsample=None, group_width=1,
dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = planes * self.expansion
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, width // min(width, group_width), dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes)
self.bn3 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class LambdaLayer(nn.Module):
def __init__(self, lambd):
super(LambdaLayer, self).__init__()
self.lambd = lambd
def forward(self, x):
return self.lambd(x)
class Stage(nn.Module):
def __init__(self, block, inplanes, planes, group_width, blocks, stride=1, dilate=False, cheap_ratio=0.5):
super(Stage, self).__init__()
norm_layer = nn.BatchNorm2d
downsample = None
self.dilation = 1
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
conv1x1(inplanes, planes, stride),
norm_layer(planes),
)
self.base = block(inplanes, planes, stride, downsample, group_width,
previous_dilation, norm_layer)
self.end = block(planes, planes, group_width=group_width,
dilation=self.dilation,
norm_layer=norm_layer)
group_width = int(group_width * 0.75)
raw_planes = int(planes * (1 - cheap_ratio) / group_width) * group_width
cheap_planes = planes - raw_planes
self.cheap_planes = cheap_planes
self.raw_planes = raw_planes
self.merge = nn.Sequential(
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(planes + raw_planes * (blocks - 2), cheap_planes,
kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(cheap_planes),
nn.ReLU(inplace=True),
nn.Conv2d(cheap_planes, cheap_planes, kernel_size=1, bias=False),
nn.BatchNorm2d(cheap_planes),
# nn.ReLU(inplace=True),
)
self.cheap = nn.Sequential(
nn.Conv2d(cheap_planes, cheap_planes,
kernel_size=1, stride=1, bias=False),
nn.BatchNorm2d(cheap_planes),
# nn.ReLU(inplace=True),
)
self.cheap_relu = nn.ReLU(inplace=True)
layers = []
downsample = nn.Sequential(
LambdaLayer(lambda x: x[:, :raw_planes])
)
layers = []
layers.append(block(raw_planes, raw_planes, 1, downsample, group_width,
self.dilation, norm_layer))
inplanes = raw_planes
for _ in range(2, blocks - 1):
layers.append(block(inplanes, raw_planes, group_width=group_width,
dilation=self.dilation,
norm_layer=norm_layer))
self.layers = nn.Sequential(*layers)
def forward(self, input):
x0 = self.base(input)
m_list = [x0]
e = x0[:, :self.raw_planes]
for l in self.layers:
e = l(e)
m_list.append(e)
m = torch.cat(m_list, 1)
m = self.merge(m)
c = x0[:, self.raw_planes:]
c = self.cheap_relu(self.cheap(c) + m)
x = torch.cat((e, c), 1)
x = self.end(x)
return x
class RegNet(nn.Module):
def __init__(self, block, layers, widths, num_classes=1000, zero_init_residual=True,
group_width=1, replace_stride_with_dilation=None,
norm_layer=None):
super(RegNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 32
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False, False]
if len(replace_stride_with_dilation) != 4:
raise ValueError("replace_stride_with_dilation should be None "
"or a 4-element tuple, got {}".format(replace_stride_with_dilation))
self.group_width = group_width
self.conv1 = nn.Conv2d(1, self.inplanes, kernel_size=3, stride=2, padding=1,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.layer1 = self._make_layer(block, widths[0], layers[0], stride=2,
dilate=replace_stride_with_dilation[0])
self.inplanes = widths[0]
if layers[1] > 2:
self.layer2 = Stage(block, self.inplanes, widths[1], group_width, layers[1], stride=2,
dilate=replace_stride_with_dilation[1], cheap_ratio=0.5)
else:
self.layer2 = self._make_layer(block, widths[1], layers[1], stride=2,
dilate=replace_stride_with_dilation[1])
self.inplanes = widths[1]
self.layer3 = Stage(block, self.inplanes, widths[2], group_width, layers[2], stride=2,
dilate=replace_stride_with_dilation[2], cheap_ratio=0.5)
self.inplanes = widths[2]
if layers[3] > 2:
self.layer4 = Stage(block, self.inplanes, widths[3], group_width, layers[3], stride=2,
dilate=replace_stride_with_dilation[3], cheap_ratio=0.5)
else:
self.layer4 = self._make_layer(block, widths[3], layers[3], stride=2,
dilate=replace_stride_with_dilation[3])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(0.2)
self.fc = nn.Linear(widths[-1] * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
# if zero_init_residual:
# for m in self.modules():
# if isinstance(m, Bottleneck):
# nn.init.constant_(m.bn3.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes, stride),
norm_layer(planes),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.group_width,
previous_dilation, norm_layer))
self.inplanes = planes
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, group_width=self.group_width,
dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def _forward_impl(self, x):
# See note [TorchScript super()]
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
# x = x.cpu() # 如果用cuda取消这一行的注释,否则下面numpy计算会报错
LA = np.linalg.norm(x[0]) # 二范数l2 = sqrt(x1^2+x2^2+x3^)
norm_feat = np.array(x[0]) / LA # 01之间
return norm_feat
# x = self.dropout(x)
# x = self.fc(x)
# return x
def forward(self, x):
return self._forward_impl(x)
def regnetx_032(*kwargs):
return RegNet(Bottleneck, [2, 6, 15, 2], [96, 192, 432, 1008], group_width=48, num_classes=2, *kwargs)
这部分代码基本与开源一致改动就几行，_forward_impl函数flatten后直接获取到特征向量输出，regnetx_032是传参主函数num_classes=2。
训练时这部分代码还是按照正常的流程来做：
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.dropout(x)
x = self.fc(x)
return x
二、数据库图像特征提取
计算多个图像的特征并保存成npz文件。
代码：
# save_features.py
import torch
from torchvision import transforms
from PIL import Image
from G_ghostnet_extract import regnetx_032
import numpy as np
from glob import glob
def predict(savefileName, imgs):
device = torch.device('cpu')
torch.set_grad_enabled(False)
model = regnetx_032()
model.load_state_dict(torch.load('./ghostnet-10-regular.pth'), False) # 加载模型
model.eval()
model = model.to(device)
transform_data = transforms.Compose([transforms.ToTensor()])
featuresList = []
namesList = []
img_list = sorted(glob(imgs+"/*"))
for img_path in img_list:
img = Image.open(img_path).convert("L") # 我这里是训练的灰度图
img = img.resize((112, 112), 0) # 输入大小我这里是112,与训练一致就行
img = transform_data(img).unsqueeze(0)
img = img.to(device)
output = model(img) # 输出特征
featuresList.append(output)
namesList.append(img_path.split('/')[-1])
featuresList = np.array(featuresList)
namesList = np.string_(namesList) # np.string_得到的是bytes类型,比str更省空间
np.savez(savefileName, featuresList, namesList)
if __name__ == "__main__":
savefileName = "features" # 保存特征的名称
imgs = "imgs" # 输入图像文件夹,这个文件夹下放了很多图片
predict(savefileName, imgs)
执行之后会在当前路径得到features.npz文件，这就是imgs文件夹里面所有图像保存的特征。
三、特征比对计算
此时输入一个图像获取特征，再与加载进来的图像特征文件计算相似性返回结果
代码：
# load_features.py
import time
import numpy as np
import torch
from torchvision import transforms
from PIL import Image
from G_ghostnet_extract import regnetx_032
def cal_feature(img_path):
transform_data = transforms.Compose([transforms.ToTensor()])
img = Image.open(img_path).convert("L")
img = img.resize((112, 112), 0)
img = transform_data(img).unsqueeze(0)
img = img.to(device)
output = model(img) # 输出特征
return output
if __name__ == "__main__":
device = torch.device('cpu')
torch.set_grad_enabled(False)
model = regnetx_032()
model.load_state_dict(torch.load('./ghostnet-10-regular.pth'), False)
model.eval()
model = model.to(device)
start = time.time()
savefileName = "features"
readNpy = np.load(savefileName+".npz") # 加载之前计算的图像集特征文件
# np.savez会自动命名参数arr_0, arr_1,也可以自己传入
features = readNpy["arr_0"] # 特征向量
names = readNpy["arr_1"] # 图像名称
imgFeature = cal_feature("./imgs/1.tif") # 输入待查找的图像获取特征
scores = np.dot(imgFeature, features.T) # 计算得分0-1之间,越靠近1越相似
# 得分从大到小排序
sortID = np.argsort(scores)[::-1]
sort_score = scores[sortID]
returnNum = 200 # 搜索出200张相似度最高的图片
imgList = []
for i, index in enumerate(sortID[0:returnNum]):
imgList.append(names[index].decode('utf-8'))
print("name:{}, score:{}".format(names[index].decode('utf-8'), sort_score[i]))
print("花费时间", time.time()-start)
print("返回top %d图像排序list" % returnNum, imgList)
从imgs取一张图1.tif测试，两张图一样所以计算出来的score为1，后面的相似度依次排序
返回top200，230张图像的数据cpu计算时间大概80ms左右，感觉还是不错的。
总结
本文主要写了一下如何使用神经网络做图像搜索，其本质是和批量特征比对得到相似性，应该也可以用于人脸识别，商品搜索等内容，因为没做过上述实际场景的项目就不多描述，如果写的有错误的地方望指正交流，感谢！！！