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https://github.com/iperov/DeepFaceLive
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iperov
parent
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1 changed files with 34 additions and 31 deletions
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@ -44,6 +44,7 @@ class ConvNormActivation(nn.Sequential):
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if activation_layer is not None:
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layers.append(activation_layer())
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super().__init__(*layers)
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self.out_ch = out_ch
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class InvertedResidual(nn.Module):
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@ -52,7 +53,6 @@ class InvertedResidual(nn.Module):
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norm_layer = None,):
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super().__init__()
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in_ch = _make_divisible(in_ch * width_mult, 8)
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mid_ch = _make_divisible(mid_ch * width_mult, 8)
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out_ch = _make_divisible(out_ch * width_mult, 8)
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self._is_res_connect = stride == 1 and in_ch == out_ch
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@ -82,31 +82,31 @@ class InvertedResidual(nn.Module):
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class FaceAlignerNet(nn.Module):
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def __init__(self):
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super().__init__()
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norm_layer = partial(nn.BatchNorm2d, eps=0.001, momentum=0.01)
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self.c0 = ConvNormActivation(3, 16, kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=nn.Hardswish)
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self.c1 = c1 = InvertedResidual ( 16, 16, 16, 3, 1, use_se=False, hs_act=False, norm_layer=norm_layer)
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self.c2 = c2 = InvertedResidual ( 16, 64, 24, 3, 2, use_se=False, hs_act=False, norm_layer=norm_layer)
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self.c3 = c3 = InvertedResidual ( 24, 72, 24, 3, 1, use_se=False, hs_act=False, norm_layer=norm_layer)
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self.c4 = c4 = InvertedResidual ( 24, 72, 40, 5, 2, use_se=True, hs_act=False, norm_layer=norm_layer)
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self.c5 = c5 = InvertedResidual ( 40, 120, 40, 5, 1, use_se=True, hs_act=False, norm_layer=norm_layer)
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self.c6 = c6 = InvertedResidual ( 40, 120, 40, 5, 1, use_se=True, hs_act=False, norm_layer=norm_layer)
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self.c7 = c7 = InvertedResidual ( 40, 240, 80, 3, 2, use_se=False, hs_act=True, norm_layer=norm_layer)
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self.c8 = c8 = InvertedResidual ( 80, 200, 80, 3, 1, use_se=False, hs_act=True, norm_layer=norm_layer)
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self.c9 = c9 = InvertedResidual ( 80, 184, 80, 3, 1, use_se=False, hs_act=True, norm_layer=norm_layer)
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self.c10 = c10 = InvertedResidual( 80, 184, 80, 3, 1, use_se=False, hs_act=True, norm_layer=norm_layer)
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self.c11 = c11 = InvertedResidual( 80, 480, 112, 3, 1, use_se=True, hs_act=True, norm_layer=norm_layer)
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self.c12 = c12 = InvertedResidual( 112, 672, 112, 3, 1, use_se=True, hs_act=True, norm_layer=norm_layer)
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self.c13 = c13 = InvertedResidual( 112, 672, 160, 5, 2, use_se=True, hs_act=True, norm_layer=norm_layer)
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self.c14 = c14 = InvertedResidual( 160, 960, 160, 5, 1, use_se=True, hs_act=True, norm_layer=norm_layer)
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self.c15 = c15 = InvertedResidual( 160, 960, 160, 5, 1, use_se=True, hs_act=True, norm_layer=norm_layer)
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conv_out_ch = sum([c1.out_ch, c3.out_ch, c6.out_ch, c10.out_ch, c13.out_ch, c15.out_ch])
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#fc_in_ch = _make_divisible(conv_out_ch // 2, 8)
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self.fc1 = nn.Linear(conv_out_ch, conv_out_ch )
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self.fc2 = nn.Linear(conv_out_ch, 4 )
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width_mult = 1.66
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self.c0 = c0 = ConvNormActivation(3, _make_divisible(16 * width_mult, 8), kernel_size=3, stride=2, norm_layer=norm_layer, activation_layer=nn.Hardswish)
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self.c1 = c1 = InvertedResidual ( c0.out_ch, 16, 16, 3, 1, use_se=False, hs_act=False, norm_layer=norm_layer, width_mult=width_mult)
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self.c2 = c2 = InvertedResidual ( c1.out_ch, 64, 24, 3, 2, use_se=False, hs_act=False, norm_layer=norm_layer, width_mult=width_mult)
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self.c3 = c3 = InvertedResidual ( c2.out_ch, 72, 24, 3, 1, use_se=False, hs_act=False, norm_layer=norm_layer, width_mult=width_mult)
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self.c4 = c4 = InvertedResidual ( c3.out_ch, 72, 40, 5, 2, use_se=True, hs_act=False, norm_layer=norm_layer, width_mult=width_mult)
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self.c5 = c5 = InvertedResidual ( c4.out_ch, 120, 40, 5, 1, use_se=True, hs_act=False, norm_layer=norm_layer, width_mult=width_mult)
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self.c6 = c6 = InvertedResidual ( c5.out_ch, 120, 40, 5, 1, use_se=True, hs_act=False, norm_layer=norm_layer, width_mult=width_mult)
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self.c7 = c7 = InvertedResidual ( c6.out_ch, 240, 80, 3, 2, use_se=False, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c8 = c8 = InvertedResidual ( c7.out_ch, 200, 80, 3, 1, use_se=False, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c9 = c9 = InvertedResidual ( c8.out_ch, 184, 80, 3, 1, use_se=False, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c10 = c10 = InvertedResidual ( c9.out_ch, 184, 80, 3, 1, use_se=False, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c11 = c11 = InvertedResidual ( c10.out_ch, 480, 112, 3, 1, use_se=True, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c12 = c12 = InvertedResidual ( c11.out_ch, 672, 112, 3, 1, use_se=True, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c13 = c13 = InvertedResidual ( c12.out_ch, 672, 160, 5, 2, use_se=True, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c14 = c14 = InvertedResidual ( c13.out_ch, 960, 160, 5, 1, use_se=True, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c15 = c15 = InvertedResidual ( c14.out_ch, 960, 160, 5, 1, use_se=True, hs_act=True, norm_layer=norm_layer, width_mult=width_mult)
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self.c16 = c16 = ConvNormActivation(c15.out_ch, _make_divisible(6*160*width_mult, 8), kernel_size=1, norm_layer=norm_layer, activation_layer=nn.Hardswish)
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self.fc1 = nn.Linear(c16.out_ch, _make_divisible(c16.out_ch*1.33, 8))
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self.fc1_act = nn.Hardswish()
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self.fc2 = nn.Linear(self.fc1.out_features, 4)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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@ -124,24 +124,27 @@ class FaceAlignerNet(nn.Module):
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x = inp
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x = self.c0(x)
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x = x1 = self.c1(x)
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x = self.c1(x)
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x = self.c2(x)
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x = x3 = self.c3(x)
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x = self.c3(x)
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x = self.c4(x)
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x = self.c5(x)
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x = x6 = self.c6(x)
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x = self.c6(x)
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x = self.c7(x)
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x = self.c8(x)
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x = self.c9(x)
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x = x10 = self.c10(x)
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x = self.c10(x)
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x = self.c11(x)
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x = self.c12(x)
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x = x13 = self.c13(x)
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x = self.c13(x)
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x = self.c14(x)
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x = x15 = self.c15(x)
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x = self.c15(x)
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x = self.c16(x)
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x = torch.cat( [x1.mean((-2,-1)), x3.mean((-2,-1)), x6.mean((-2,-1)), x10.mean((-2,-1)), x13.mean((-2,-1)), x15.mean((-2,-1))], -1 )
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x = x.mean((-2,-1))
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x = self.fc1(x)
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x = self.fc1_act(x)
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x = self.fc2(x)
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scale_t, angle_t, tx_t, ty_t = torch.split(x, 1, -1)
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