| """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)) |
| 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)) |
|
|
| |
| 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 |
|
|
