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| import torch | |
| import torch.nn as nn | |
| 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(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] | |