diff options
Diffstat (limited to 'r_facelib/detection/retinaface/retinaface_net.py')
| -rw-r--r-- | r_facelib/detection/retinaface/retinaface_net.py | 196 |
1 files changed, 196 insertions, 0 deletions
diff --git a/r_facelib/detection/retinaface/retinaface_net.py b/r_facelib/detection/retinaface/retinaface_net.py new file mode 100644 index 0000000..c52535e --- /dev/null +++ b/r_facelib/detection/retinaface/retinaface_net.py @@ -0,0 +1,196 @@ +import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def conv_bn(inp, oup, stride=1, leaky=0):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_bn_no_relu(inp, oup, stride):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
+ nn.BatchNorm2d(oup),
+ )
+
+
+def conv_bn1X1(inp, oup, stride, leaky=0):
+ return nn.Sequential(
+ nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True))
+
+
+def conv_dw(inp, oup, stride, leaky=0.1):
+ return nn.Sequential(
+ nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False),
+ nn.BatchNorm2d(inp),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True),
+ nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
+ nn.BatchNorm2d(oup),
+ nn.LeakyReLU(negative_slope=leaky, inplace=True),
+ )
+
+
+class SSH(nn.Module):
+
+ def __init__(self, in_channel, out_channel):
+ super(SSH, self).__init__()
+ assert out_channel % 4 == 0
+ leaky = 0
+ if (out_channel <= 64):
+ leaky = 0.1
+ self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1)
+
+ self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky)
+ self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+ self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky)
+ self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1)
+
+ def forward(self, input):
+ conv3X3 = self.conv3X3(input)
+
+ conv5X5_1 = self.conv5X5_1(input)
+ conv5X5 = self.conv5X5_2(conv5X5_1)
+
+ conv7X7_2 = self.conv7X7_2(conv5X5_1)
+ conv7X7 = self.conv7x7_3(conv7X7_2)
+
+ out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1)
+ out = F.relu(out)
+ return out
+
+
+class FPN(nn.Module):
+
+ def __init__(self, in_channels_list, out_channels):
+ super(FPN, self).__init__()
+ leaky = 0
+ if (out_channels <= 64):
+ leaky = 0.1
+ self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky)
+ self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky)
+ self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky)
+
+ self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
+ self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
+
+ def forward(self, input):
+ # names = list(input.keys())
+ # input = list(input.values())
+
+ output1 = self.output1(input[0])
+ output2 = self.output2(input[1])
+ output3 = self.output3(input[2])
+
+ up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest')
+ output2 = output2 + up3
+ output2 = self.merge2(output2)
+
+ up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest')
+ output1 = output1 + up2
+ output1 = self.merge1(output1)
+
+ out = [output1, output2, output3]
+ return out
+
+
+class MobileNetV1(nn.Module):
+
+ def __init__(self):
+ super(MobileNetV1, self).__init__()
+ self.stage1 = nn.Sequential(
+ conv_bn(3, 8, 2, leaky=0.1), # 3
+ conv_dw(8, 16, 1), # 7
+ conv_dw(16, 32, 2), # 11
+ conv_dw(32, 32, 1), # 19
+ conv_dw(32, 64, 2), # 27
+ conv_dw(64, 64, 1), # 43
+ )
+ self.stage2 = nn.Sequential(
+ conv_dw(64, 128, 2), # 43 + 16 = 59
+ conv_dw(128, 128, 1), # 59 + 32 = 91
+ conv_dw(128, 128, 1), # 91 + 32 = 123
+ conv_dw(128, 128, 1), # 123 + 32 = 155
+ conv_dw(128, 128, 1), # 155 + 32 = 187
+ conv_dw(128, 128, 1), # 187 + 32 = 219
+ )
+ self.stage3 = nn.Sequential(
+ conv_dw(128, 256, 2), # 219 +3 2 = 241
+ conv_dw(256, 256, 1), # 241 + 64 = 301
+ )
+ self.avg = nn.AdaptiveAvgPool2d((1, 1))
+ self.fc = nn.Linear(256, 1000)
+
+ def forward(self, x):
+ x = self.stage1(x)
+ x = self.stage2(x)
+ x = self.stage3(x)
+ x = self.avg(x)
+ # x = self.model(x)
+ x = x.view(-1, 256)
+ x = self.fc(x)
+ return x
+
+
+class ClassHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(ClassHead, self).__init__()
+ self.num_anchors = num_anchors
+ self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 2)
+
+
+class BboxHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(BboxHead, self).__init__()
+ self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 4)
+
+
+class LandmarkHead(nn.Module):
+
+ def __init__(self, inchannels=512, num_anchors=3):
+ super(LandmarkHead, self).__init__()
+ self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0)
+
+ def forward(self, x):
+ out = self.conv1x1(x)
+ out = out.permute(0, 2, 3, 1).contiguous()
+
+ return out.view(out.shape[0], -1, 10)
+
+
+def make_class_head(fpn_num=3, inchannels=64, anchor_num=2):
+ classhead = nn.ModuleList()
+ for i in range(fpn_num):
+ classhead.append(ClassHead(inchannels, anchor_num))
+ return classhead
+
+
+def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2):
+ bboxhead = nn.ModuleList()
+ for i in range(fpn_num):
+ bboxhead.append(BboxHead(inchannels, anchor_num))
+ return bboxhead
+
+
+def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2):
+ landmarkhead = nn.ModuleList()
+ for i in range(fpn_num):
+ landmarkhead.append(LandmarkHead(inchannels, anchor_num))
+ return landmarkhead
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