SFD_pytorch/net_s3fd.py at master · leopd/SFD_pytorch · GitHub

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import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F

class L2Norm(nn.Module):
    def __init__(self,n_channels, scale=1.0):
        super(L2Norm,self).__init__()
        self.n_channels = n_channels
        self.scale = scale
        self.eps = 1e-10
        self.weight = nn.Parameter(torch.Tensor(self.n_channels))
        self.weight.data *= 0.0
        self.weight.data += self.scale

    def forward(self, x):
        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt()+self.eps
        x = x / norm * self.weight.view(1,-1,1,1)
        return x

class S3fd_Model(nn.Module):
    def __init__(self):
        super(s3fd, self).__init__()
        self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1)

        self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)

        self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
        self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)

        self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)

        self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)
        self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1)

        self.fc6     = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3)
        self.fc7     = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)

        self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0)
        self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)

        self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0)
        self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)

        self.conv3_3_norm = L2Norm(256,scale=10)
        self.conv4_3_norm = L2Norm(512,scale=8)
        self.conv5_3_norm = L2Norm(512,scale=5)

        self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
        self.conv3_3_norm_mbox_loc  = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)
        self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
        self.conv4_3_norm_mbox_loc  = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
        self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
        self.conv5_3_norm_mbox_loc  = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)

        self.fc7_mbox_conf     = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1)
        self.fc7_mbox_loc      = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1)
        self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1)
        self.conv6_2_mbox_loc  = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1)
        self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1)
        self.conv7_2_mbox_loc  = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1)

    def forward(self, x):
        h = F.relu(self.conv1_1(x))
        h = F.relu(self.conv1_2(h))
        h = F.max_pool2d(h, 2, 2)

        h = F.relu(self.conv2_1(h))
        h = F.relu(self.conv2_2(h))
        h = F.max_pool2d(h, 2, 2)

        h = F.relu(self.conv3_1(h))
        h = F.relu(self.conv3_2(h))
        h = F.relu(self.conv3_3(h)); f3_3 = h
        h = F.max_pool2d(h, 2, 2)

        h = F.relu(self.conv4_1(h))
        h = F.relu(self.conv4_2(h))
        h = F.relu(self.conv4_3(h)); f4_3 = h
        h = F.max_pool2d(h, 2, 2)

        h = F.relu(self.conv5_1(h))
        h = F.relu(self.conv5_2(h))
        h = F.relu(self.conv5_3(h)); f5_3 = h
        h = F.max_pool2d(h, 2, 2)

        h = F.relu(self.fc6(h))
        h = F.relu(self.fc7(h));     ffc7 = h
        h = F.relu(self.conv6_1(h))
        h = F.relu(self.conv6_2(h)); f6_2 = h
        h = F.relu(self.conv7_1(h))
        h = F.relu(self.conv7_2(h)); f7_2 = h

        f3_3 = self.conv3_3_norm(f3_3)
        f4_3 = self.conv4_3_norm(f4_3)
        f5_3 = self.conv5_3_norm(f5_3)

        cls1 = self.conv3_3_norm_mbox_conf(f3_3)
        reg1 = self.conv3_3_norm_mbox_loc(f3_3)
        cls2 = self.conv4_3_norm_mbox_conf(f4_3)
        reg2 = self.conv4_3_norm_mbox_loc(f4_3)
        cls3 = self.conv5_3_norm_mbox_conf(f5_3)
        reg3 = self.conv5_3_norm_mbox_loc(f5_3)
        cls4 = self.fc7_mbox_conf(ffc7)
        reg4 = self.fc7_mbox_loc(ffc7)
        cls5 = self.conv6_2_mbox_conf(f6_2)
        reg5 = self.conv6_2_mbox_loc(f6_2)
        cls6 = self.conv7_2_mbox_conf(f7_2)
        reg6 = self.conv7_2_mbox_loc(f7_2)

        # max-out background label
        chunk = torch.chunk(cls1,4,1)
        bmax  = torch.max(torch.max(chunk[0],chunk[1]),chunk[2])
        cls1  = torch.cat([bmax,chunk[3]],dim=1)

        return [cls1,reg1,cls2,reg2,cls3,reg3,cls4,reg4,cls5,reg5,cls6,reg6]

s3fd = S3fd_Model