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