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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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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):
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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)):
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x = self.vgg[k](x)
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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)
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if k % 2 == 1:
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sources.append(x)
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loc_x = self.loc[0](sources[0])
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conf_x = self.conf[0](sources[0])
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max_conf, _ = torch.max(conf_x[:, 0:3, :, :], dim=1, keepdim=True)
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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())
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conf.append(conf_x.permute(0, 2, 3, 1).contiguous())
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for i in range(1, len(sources)):
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x = sources[i]
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conf.append(self.conf[i](x).permute(0, 2, 3, 1).contiguous())
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loc.append(self.loc[i](x).permute(0, 2, 3, 1).contiguous())
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features_maps = []
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for i in range(len(loc)):
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feat = []
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feat += [loc[i].size(1), loc[i].size(2)]
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features_maps += [feat]
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loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1)
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conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1)
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with torch.no_grad():
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self.priorbox = PriorBox(size, features_maps)
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self.priors = self.priorbox.forward()
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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)
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)
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return output
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