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这篇文章主要为大家展示了“基于Pytorch SSD模型的示例分析”，内容简而易懂，条理清晰，希望能够帮助大家解决疑惑，下面让小编带领大家一起研究并学习一下“基于Pytorch SSD模型的示例分析”这篇文章吧。
参考github上SSD实现,对模型进行分析,主要分析模型组成及输入输出大小.SSD网络结构如下图:
每输入的图像有8732个框输出;
import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable #from layers import * from data import voc, coco import os
base = {
'300': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'C', 512, 512, 512, 'M',
512, 512, 512],
'512': [],
}
extras = {
'300': [256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256],
'512': [],
}
mbox = {
'300': [4, 6, 6, 6, 4, 4], # number of boxes per feature map location
'512': [],
}VGG基础网络结构:
def vgg(cfg, i, batch_norm=False): layers = [] in_channels = i for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] elif v == 'C': layers += [nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6) conv7 = nn.Conv2d(1024, 1024, kernel_size=1) layers += [pool5, conv6, nn.ReLU(inplace=True), conv7, nn.ReLU(inplace=True)] return layers
size=300 vgg=vgg(base[str(size)], 3) print(vgg)
输出为:
Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True) Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ReLU(inplace) MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False) Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(6, 6), dilation=(6, 6)) ReLU(inplace) Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1)) ReLU(inplace)
SSD中添加的网络
add_extras函数构建基本的卷积层
def add_extras(cfg, i, batch_norm=False): # Extra layers added to VGG for feature scaling layers = [] in_channels = i flag = False for k, v in enumerate(cfg): if in_channels != 'S': if v == 'S': layers += [nn.Conv2d(in_channels, cfg[k + 1], kernel_size=(1, 3)[flag], stride=2, padding=1)] else: layers += [nn.Conv2d(in_channels, v, kernel_size=(1, 3)[flag])] flag = not flag in_channels = v return layers
extra_layers=add_extras(extras[str(size)], 1024) for layer in extra_layers: print(layer)
输出为:
Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1)) Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1)) Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1)) Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)) Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))
multibox函数得到每个特征图的默认box的位置计算网络和分类得分网络
def multibox(vgg, extra_layers, cfg, num_classes): loc_layers = [] conf_layers = [] vgg_source = [21, -2] for k, v in enumerate(vgg_source): loc_layers += [nn.Conv2d(vgg[v].out_channels, cfg[k] * 4, kernel_size=3, padding=1)] conf_layers += [nn.Conv2d(vgg[v].out_channels, cfg[k] * num_classes, kernel_size=3, padding=1)] for k, v in enumerate(extra_layers[1::2], 2): loc_layers += [nn.Conv2d(v.out_channels, cfg[k] * 4, kernel_size=3, padding=1)] conf_layers += [nn.Conv2d(v.out_channels, cfg[k] * num_classes, kernel_size=3, padding=1)] return vgg, extra_layers, (loc_layers, conf_layers)
base_, extras_, head_ = multibox(vgg(base[str(size)], 3), ## 产生vgg19基本模型 add_extras(extras[str(size)], 1024), mbox[str(size)], num_classes) #mbox[str(size)]为:[4, 6, 6, 6, 4, 4]
得到的输出为:
base_为上述描述的vgg网络,extras_为extra_layers网络,head_为:
([Conv2d(512, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(1024, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(512, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(256, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(256, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(256, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))], [Conv2d(512, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(1024, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(512, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(256, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(256, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), Conv2d(256, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))])
SSD网络及forward函数为:
class SSD(nn.Module): """Single Shot Multibox Architecture The network is composed of a base VGG network followed by the added multibox conv layers. Each multibox layer branches into 1) conv2d for class conf scores 2) conv2d for localization predictions 3) associated priorbox layer to produce default bounding boxes specific to the layer's feature map size. See: https://arxiv.org/pdf/1512.02325.pdf for more details. Args: phase: (string) Can be "test" or "train" size: input image size base: VGG16 layers for input, size of either 300 or 500 extras: extra layers that feed to multibox loc and conf layers head: "multibox head" consists of loc and conf conv layers """ def __init__(self, phase, size, base, extras, head, num_classes): super(SSD, self).__init__() self.phase = phase self.num_classes = num_classes self.cfg = (coco, voc)[num_classes == 21] self.priorbox = PriorBox(self.cfg) self.priors = Variable(self.priorbox.forward(), volatile=True) self.size = size # SSD network self.vgg = nn.ModuleList(base) # Layer learns to scale the l2 normalized features from conv4_3 self.L2Norm = L2Norm(512, 20) self.extras = nn.ModuleList(extras) self.loc = nn.ModuleList(head[0]) self.conf = nn.ModuleList(head[1]) if phase == 'test': self.softmax = nn.Softmax(dim=-1) self.detect = Detect(num_classes, 0, 200, 0.01, 0.45) def forward(self, x): """Applies network layers and ops on input image(s) x. Args: x: input image or batch of images. Shape: [batch,3,300,300]. Return: Depending on phase: test: Variable(tensor) of output class label predictions, confidence score, and corresponding location predictions for each object detected. Shape: [batch,topk,7] train: list of concat outputs from: 1: confidence layers, Shape: [batch*num_priors,num_classes] 2: localization layers, Shape: [batch,num_priors*4] 3: priorbox layers, Shape: [2,num_priors*4] """ sources = list() loc = list() conf = list() # apply vgg up to conv4_3 relu for k in range(23): x = self.vgg[k](x) ##得到的x尺度为[1,512,38,38] s = self.L2Norm(x) sources.append(s) # apply vgg up to fc7 for k in range(23, len(self.vgg)): x = self.vgg[k](x) ##得到的x尺寸为[1,1024,19,19] sources.append(x) # apply extra layers and cache source layer outputs for k, v in enumerate(self.extras): x = F.relu(v(x), inplace=True) if k % 2 == 1: sources.append(x) ''' 上述得到的x输出分别为: torch.Size([1, 512, 10, 10]) torch.Size([1, 256, 5, 5]) torch.Size([1, 256, 3, 3]) torch.Size([1, 256, 1, 1]) ''' # apply multibox head to source layers for (x, l, c) in zip(sources, self.loc, self.conf): loc.append(l(x).permute(0, 2, 3, 1).contiguous()) conf.append(c(x).permute(0, 2, 3, 1).contiguous()) loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1) conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1) if self.phase == "test": output = self.detect( loc.view(loc.size(0), -1, 4), # loc preds self.softmax(conf.view(conf.size(0), -1, self.num_classes)), # conf preds self.priors.type(type(x.data)) # default boxes ) else: output = ( loc.view(loc.size(0), -1, 4), #[1,8732,4] conf.view(conf.size(0), -1, self.num_classes),#[1,8732,21] self.priors ) return output
上述代码中sources中保存的数据输出如下,即用于边框提取的特征图:
torch.Size([1, 512, 38, 38]) torch.Size([1, 1024, 19, 19]) torch.Size([1, 512, 10, 10]) torch.Size([1, 256, 5, 5]) torch.Size([1, 256, 3, 3]) torch.Size([1, 256, 1, 1])
模型输入为
x=Variable(torch.randn(1,3,300,300))
以上是“基于Pytorch SSD模型的示例分析”这篇文章的所有内容，感谢各位的阅读！相信大家都有了一定的了解，希望分享的内容对大家有所帮助，如果还想学习更多知识，欢迎关注亿速云行业资讯频道！
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