diff options
Diffstat (limited to 'r_facelib/parsing/parsenet.py')
| -rw-r--r-- | r_facelib/parsing/parsenet.py | 194 |
1 files changed, 194 insertions, 0 deletions
diff --git a/r_facelib/parsing/parsenet.py b/r_facelib/parsing/parsenet.py new file mode 100644 index 0000000..2e80921 --- /dev/null +++ b/r_facelib/parsing/parsenet.py @@ -0,0 +1,194 @@ +"""Modified from https://github.com/chaofengc/PSFRGAN
+"""
+import numpy as np
+import torch.nn as nn
+from torch.nn import functional as F
+
+
+class NormLayer(nn.Module):
+ """Normalization Layers.
+
+ Args:
+ channels: input channels, for batch norm and instance norm.
+ input_size: input shape without batch size, for layer norm.
+ """
+
+ def __init__(self, channels, normalize_shape=None, norm_type='bn'):
+ super(NormLayer, self).__init__()
+ norm_type = norm_type.lower()
+ self.norm_type = norm_type
+ if norm_type == 'bn':
+ self.norm = nn.BatchNorm2d(channels, affine=True)
+ elif norm_type == 'in':
+ self.norm = nn.InstanceNorm2d(channels, affine=False)
+ elif norm_type == 'gn':
+ self.norm = nn.GroupNorm(32, channels, affine=True)
+ elif norm_type == 'pixel':
+ self.norm = lambda x: F.normalize(x, p=2, dim=1)
+ elif norm_type == 'layer':
+ self.norm = nn.LayerNorm(normalize_shape)
+ elif norm_type == 'none':
+ self.norm = lambda x: x * 1.0
+ else:
+ assert 1 == 0, f'Norm type {norm_type} not support.'
+
+ def forward(self, x, ref=None):
+ if self.norm_type == 'spade':
+ return self.norm(x, ref)
+ else:
+ return self.norm(x)
+
+
+class ReluLayer(nn.Module):
+ """Relu Layer.
+
+ Args:
+ relu type: type of relu layer, candidates are
+ - ReLU
+ - LeakyReLU: default relu slope 0.2
+ - PRelu
+ - SELU
+ - none: direct pass
+ """
+
+ def __init__(self, channels, relu_type='relu'):
+ super(ReluLayer, self).__init__()
+ relu_type = relu_type.lower()
+ if relu_type == 'relu':
+ self.func = nn.ReLU(True)
+ elif relu_type == 'leakyrelu':
+ self.func = nn.LeakyReLU(0.2, inplace=True)
+ elif relu_type == 'prelu':
+ self.func = nn.PReLU(channels)
+ elif relu_type == 'selu':
+ self.func = nn.SELU(True)
+ elif relu_type == 'none':
+ self.func = lambda x: x * 1.0
+ else:
+ assert 1 == 0, f'Relu type {relu_type} not support.'
+
+ def forward(self, x):
+ return self.func(x)
+
+
+class ConvLayer(nn.Module):
+
+ def __init__(self,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ scale='none',
+ norm_type='none',
+ relu_type='none',
+ use_pad=True,
+ bias=True):
+ super(ConvLayer, self).__init__()
+ self.use_pad = use_pad
+ self.norm_type = norm_type
+ if norm_type in ['bn']:
+ bias = False
+
+ stride = 2 if scale == 'down' else 1
+
+ self.scale_func = lambda x: x
+ if scale == 'up':
+ self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest')
+
+ self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2)))
+ self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias)
+
+ self.relu = ReluLayer(out_channels, relu_type)
+ self.norm = NormLayer(out_channels, norm_type=norm_type)
+
+ def forward(self, x):
+ out = self.scale_func(x)
+ if self.use_pad:
+ out = self.reflection_pad(out)
+ out = self.conv2d(out)
+ out = self.norm(out)
+ out = self.relu(out)
+ return out
+
+
+class ResidualBlock(nn.Module):
+ """
+ Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html
+ """
+
+ def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'):
+ super(ResidualBlock, self).__init__()
+
+ if scale == 'none' and c_in == c_out:
+ self.shortcut_func = lambda x: x
+ else:
+ self.shortcut_func = ConvLayer(c_in, c_out, 3, scale)
+
+ scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']}
+ scale_conf = scale_config_dict[scale]
+
+ self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type)
+ self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none')
+
+ def forward(self, x):
+ identity = self.shortcut_func(x)
+
+ res = self.conv1(x)
+ res = self.conv2(res)
+ return identity + res
+
+
+class ParseNet(nn.Module):
+
+ def __init__(self,
+ in_size=128,
+ out_size=128,
+ min_feat_size=32,
+ base_ch=64,
+ parsing_ch=19,
+ res_depth=10,
+ relu_type='LeakyReLU',
+ norm_type='bn',
+ ch_range=[32, 256]):
+ super().__init__()
+ self.res_depth = res_depth
+ act_args = {'norm_type': norm_type, 'relu_type': relu_type}
+ min_ch, max_ch = ch_range
+
+ ch_clip = lambda x: max(min_ch, min(x, max_ch)) # noqa: E731
+ min_feat_size = min(in_size, min_feat_size)
+
+ down_steps = int(np.log2(in_size // min_feat_size))
+ up_steps = int(np.log2(out_size // min_feat_size))
+
+ # =============== define encoder-body-decoder ====================
+ self.encoder = []
+ self.encoder.append(ConvLayer(3, base_ch, 3, 1))
+ head_ch = base_ch
+ for i in range(down_steps):
+ cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
+ self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
+ head_ch = head_ch * 2
+
+ self.body = []
+ for i in range(res_depth):
+ self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
+
+ self.decoder = []
+ for i in range(up_steps):
+ cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
+ self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
+ head_ch = head_ch // 2
+
+ self.encoder = nn.Sequential(*self.encoder)
+ self.body = nn.Sequential(*self.body)
+ self.decoder = nn.Sequential(*self.decoder)
+ self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
+ self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
+
+ def forward(self, x):
+ feat = self.encoder(x)
+ x = feat + self.body(feat)
+ x = self.decoder(x)
+ out_img = self.out_img_conv(x)
+ out_mask = self.out_mask_conv(x)
+ return out_mask, out_img
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