File size: 8,933 Bytes
a64b7d4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from basicsr.utils.registry import ARCH_REGISTRY
from .hifacegan_util import BaseNetwork, LIPEncoder, SPADEResnetBlock, get_nonspade_norm_layer
class SPADEGenerator(BaseNetwork):
"""Generator with SPADEResBlock"""
def __init__(self,
num_in_ch=3,
num_feat=64,
use_vae=False,
z_dim=256,
crop_size=512,
norm_g='spectralspadesyncbatch3x3',
is_train=True,
init_train_phase=3): # progressive training disabled
super().__init__()
self.nf = num_feat
self.input_nc = num_in_ch
self.is_train = is_train
self.train_phase = init_train_phase
self.scale_ratio = 5 # hardcoded now
self.sw = crop_size // (2**self.scale_ratio)
self.sh = self.sw # 20210519: By default use square image, aspect_ratio = 1.0
if use_vae:
# In case of VAE, we will sample from random z vector
self.fc = nn.Linear(z_dim, 16 * self.nf * self.sw * self.sh)
else:
# Otherwise, we make the network deterministic by starting with
# downsampled segmentation map instead of random z
self.fc = nn.Conv2d(num_in_ch, 16 * self.nf, 3, padding=1)
self.head_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
self.g_middle_0 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
self.g_middle_1 = SPADEResnetBlock(16 * self.nf, 16 * self.nf, norm_g)
self.ups = nn.ModuleList([
SPADEResnetBlock(16 * self.nf, 8 * self.nf, norm_g),
SPADEResnetBlock(8 * self.nf, 4 * self.nf, norm_g),
SPADEResnetBlock(4 * self.nf, 2 * self.nf, norm_g),
SPADEResnetBlock(2 * self.nf, 1 * self.nf, norm_g)
])
self.to_rgbs = nn.ModuleList([
nn.Conv2d(8 * self.nf, 3, 3, padding=1),
nn.Conv2d(4 * self.nf, 3, 3, padding=1),
nn.Conv2d(2 * self.nf, 3, 3, padding=1),
nn.Conv2d(1 * self.nf, 3, 3, padding=1)
])
self.up = nn.Upsample(scale_factor=2)
def encode(self, input_tensor):
"""
Encode input_tensor into feature maps, can be overridden in derived classes
Default: nearest downsampling of 2**5 = 32 times
"""
h, w = input_tensor.size()[-2:]
sh, sw = h // 2**self.scale_ratio, w // 2**self.scale_ratio
x = F.interpolate(input_tensor, size=(sh, sw))
return self.fc(x)
def forward(self, x):
# In oroginal SPADE, seg means a segmentation map, but here we use x instead.
seg = x
x = self.encode(x)
x = self.head_0(x, seg)
x = self.up(x)
x = self.g_middle_0(x, seg)
x = self.g_middle_1(x, seg)
if self.is_train:
phase = self.train_phase + 1
else:
phase = len(self.to_rgbs)
for i in range(phase):
x = self.up(x)
x = self.ups[i](x, seg)
x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1))
x = torch.tanh(x)
return x
def mixed_guidance_forward(self, input_x, seg=None, n=0, mode='progressive'):
"""
A helper class for subspace visualization. Input and seg are different images.
For the first n levels (including encoder) we use input, for the rest we use seg.
If mode = 'progressive', the output's like: AAABBB
If mode = 'one_plug', the output's like: AAABAA
If mode = 'one_ablate', the output's like: BBBABB
"""
if seg is None:
return self.forward(input_x)
if self.is_train:
phase = self.train_phase + 1
else:
phase = len(self.to_rgbs)
if mode == 'progressive':
n = max(min(n, 4 + phase), 0)
guide_list = [input_x] * n + [seg] * (4 + phase - n)
elif mode == 'one_plug':
n = max(min(n, 4 + phase - 1), 0)
guide_list = [seg] * (4 + phase)
guide_list[n] = input_x
elif mode == 'one_ablate':
if n > 3 + phase:
return self.forward(input_x)
guide_list = [input_x] * (4 + phase)
guide_list[n] = seg
x = self.encode(guide_list[0])
x = self.head_0(x, guide_list[1])
x = self.up(x)
x = self.g_middle_0(x, guide_list[2])
x = self.g_middle_1(x, guide_list[3])
for i in range(phase):
x = self.up(x)
x = self.ups[i](x, guide_list[4 + i])
x = self.to_rgbs[phase - 1](F.leaky_relu(x, 2e-1))
x = torch.tanh(x)
return x
@ARCH_REGISTRY.register()
class HiFaceGAN(SPADEGenerator):
"""
HiFaceGAN: SPADEGenerator with a learnable feature encoder
Current encoder design: LIPEncoder
"""
def __init__(self,
num_in_ch=3,
num_feat=64,
use_vae=False,
z_dim=256,
crop_size=512,
norm_g='spectralspadesyncbatch3x3',
is_train=True,
init_train_phase=3):
super().__init__(num_in_ch, num_feat, use_vae, z_dim, crop_size, norm_g, is_train, init_train_phase)
self.lip_encoder = LIPEncoder(num_in_ch, num_feat, self.sw, self.sh, self.scale_ratio)
def encode(self, input_tensor):
return self.lip_encoder(input_tensor)
@ARCH_REGISTRY.register()
class HiFaceGANDiscriminator(BaseNetwork):
"""
Inspired by pix2pixHD multiscale discriminator.
Args:
num_in_ch (int): Channel number of inputs. Default: 3.
num_out_ch (int): Channel number of outputs. Default: 3.
conditional_d (bool): Whether use conditional discriminator.
Default: True.
num_d (int): Number of Multiscale discriminators. Default: 3.
n_layers_d (int): Number of downsample layers in each D. Default: 4.
num_feat (int): Channel number of base intermediate features.
Default: 64.
norm_d (str): String to determine normalization layers in D.
Choices: [spectral][instance/batch/syncbatch]
Default: 'spectralinstance'.
keep_features (bool): Keep intermediate features for matching loss, etc.
Default: True.
"""
def __init__(self,
num_in_ch=3,
num_out_ch=3,
conditional_d=True,
num_d=2,
n_layers_d=4,
num_feat=64,
norm_d='spectralinstance',
keep_features=True):
super().__init__()
self.num_d = num_d
input_nc = num_in_ch
if conditional_d:
input_nc += num_out_ch
for i in range(num_d):
subnet_d = NLayerDiscriminator(input_nc, n_layers_d, num_feat, norm_d, keep_features)
self.add_module(f'discriminator_{i}', subnet_d)
def downsample(self, x):
return F.avg_pool2d(x, kernel_size=3, stride=2, padding=[1, 1], count_include_pad=False)
# Returns list of lists of discriminator outputs.
# The final result is of size opt.num_d x opt.n_layers_D
def forward(self, x):
result = []
for _, _net_d in self.named_children():
out = _net_d(x)
result.append(out)
x = self.downsample(x)
return result
class NLayerDiscriminator(BaseNetwork):
"""Defines the PatchGAN discriminator with the specified arguments."""
def __init__(self, input_nc, n_layers_d, num_feat, norm_d, keep_features):
super().__init__()
kw = 4
padw = int(np.ceil((kw - 1.0) / 2))
nf = num_feat
self.keep_features = keep_features
norm_layer = get_nonspade_norm_layer(norm_d)
sequence = [[nn.Conv2d(input_nc, nf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, False)]]
for n in range(1, n_layers_d):
nf_prev = nf
nf = min(nf * 2, 512)
stride = 1 if n == n_layers_d - 1 else 2
sequence += [[
norm_layer(nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=stride, padding=padw)),
nn.LeakyReLU(0.2, False)
]]
sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
# We divide the layers into groups to extract intermediate layer outputs
for n in range(len(sequence)):
self.add_module('model' + str(n), nn.Sequential(*sequence[n]))
def forward(self, x):
results = [x]
for submodel in self.children():
intermediate_output = submodel(results[-1])
results.append(intermediate_output)
if self.keep_features:
return results[1:]
else:
return results[-1]
|