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- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import torch.nn.init as init
- from .box_utils import Detect, PriorBox
- class L2Norm(nn.Module):
- def __init__(self, n_channels, scale):
- super(L2Norm, self).__init__()
- self.n_channels = n_channels
- self.gamma = scale or None
- self.eps = 1e-10
- self.weight = nn.Parameter(torch.Tensor(self.n_channels))
- self.reset_parameters()
- def reset_parameters(self):
- init.constant_(self.weight, self.gamma)
- def forward(self, x):
- norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps
- x = torch.div(x, norm)
- out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x
- return out
- class S3FDNet(nn.Module):
- def __init__(self, device='cuda'):
- super(S3FDNet, self).__init__()
- self.device = device
- self.vgg = nn.ModuleList([
- nn.Conv2d(3, 64, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(64, 64, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(2, 2),
- nn.Conv2d(64, 128, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(128, 128, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(2, 2),
-
- nn.Conv2d(128, 256, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(256, 256, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(256, 256, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(2, 2, ceil_mode=True),
-
- nn.Conv2d(256, 512, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(512, 512, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(512, 512, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(2, 2),
- nn.Conv2d(512, 512, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(512, 512, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.Conv2d(512, 512, 3, 1, padding=1),
- nn.ReLU(inplace=True),
- nn.MaxPool2d(2, 2),
- nn.Conv2d(512, 1024, 3, 1, padding=6, dilation=6),
- nn.ReLU(inplace=True),
- nn.Conv2d(1024, 1024, 1, 1),
- nn.ReLU(inplace=True),
- ])
- self.L2Norm3_3 = L2Norm(256, 10)
- self.L2Norm4_3 = L2Norm(512, 8)
- self.L2Norm5_3 = L2Norm(512, 5)
- self.extras = nn.ModuleList([
- nn.Conv2d(1024, 256, 1, 1),
- nn.Conv2d(256, 512, 3, 2, padding=1),
- nn.Conv2d(512, 128, 1, 1),
- nn.Conv2d(128, 256, 3, 2, padding=1),
- ])
-
- self.loc = nn.ModuleList([
- nn.Conv2d(256, 4, 3, 1, padding=1),
- nn.Conv2d(512, 4, 3, 1, padding=1),
- nn.Conv2d(512, 4, 3, 1, padding=1),
- nn.Conv2d(1024, 4, 3, 1, padding=1),
- nn.Conv2d(512, 4, 3, 1, padding=1),
- nn.Conv2d(256, 4, 3, 1, padding=1),
- ])
- self.conf = nn.ModuleList([
- nn.Conv2d(256, 4, 3, 1, padding=1),
- nn.Conv2d(512, 2, 3, 1, padding=1),
- nn.Conv2d(512, 2, 3, 1, padding=1),
- nn.Conv2d(1024, 2, 3, 1, padding=1),
- nn.Conv2d(512, 2, 3, 1, padding=1),
- nn.Conv2d(256, 2, 3, 1, padding=1),
- ])
- self.softmax = nn.Softmax(dim=-1)
- self.detect = Detect()
- def forward(self, x):
- size = x.size()[2:]
- sources = list()
- loc = list()
- conf = list()
- for k in range(16):
- x = self.vgg[k](x)
- s = self.L2Norm3_3(x)
- sources.append(s)
- for k in range(16, 23):
- x = self.vgg[k](x)
- s = self.L2Norm4_3(x)
- sources.append(s)
- for k in range(23, 30):
- x = self.vgg[k](x)
- s = self.L2Norm5_3(x)
- sources.append(s)
- for k in range(30, len(self.vgg)):
- x = self.vgg[k](x)
- 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)
- # apply multibox head to source layers
- loc_x = self.loc[0](sources[0])
- conf_x = self.conf[0](sources[0])
- max_conf, _ = torch.max(conf_x[:, 0:3, :, :], dim=1, keepdim=True)
- conf_x = torch.cat((max_conf, conf_x[:, 3:, :, :]), dim=1)
- loc.append(loc_x.permute(0, 2, 3, 1).contiguous())
- conf.append(conf_x.permute(0, 2, 3, 1).contiguous())
- for i in range(1, len(sources)):
- x = sources[i]
- conf.append(self.conf[i](x).permute(0, 2, 3, 1).contiguous())
- loc.append(self.loc[i](x).permute(0, 2, 3, 1).contiguous())
- features_maps = []
- for i in range(len(loc)):
- feat = []
- feat += [loc[i].size(1), loc[i].size(2)]
- features_maps += [feat]
- 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)
- with torch.no_grad():
- self.priorbox = PriorBox(size, features_maps)
- self.priors = self.priorbox.forward()
- output = self.detect.forward(
- loc.view(loc.size(0), -1, 4),
- self.softmax(conf.view(conf.size(0), -1, 2)),
- self.priors.type(type(x.data)).to(self.device)
- )
- return output
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