File size: 9,321 Bytes
7c98460 |
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 |
import torch
import torch.nn as nn
from typing import Any, Tuple, Union
from utils import (
ImageType,
crop_image_part,
)
from layers import (
SpectralConv2d,
InitLayer,
SLEBlock,
UpsampleBlockT1,
UpsampleBlockT2,
DownsampleBlockT1,
DownsampleBlockT2,
Decoder,
)
from huggan.pytorch.huggan_mixin import HugGANModelHubMixin
class Generator(nn.Module, HugGANModelHubMixin):
def __init__(self, in_channels: int,
out_channels: int):
super().__init__()
self._channels = {
4: 1024,
8: 512,
16: 256,
32: 128,
64: 128,
128: 64,
256: 32,
512: 16,
1024: 8,
}
self._init = InitLayer(
in_channels=in_channels,
out_channels=self._channels[4],
)
self._upsample_8 = UpsampleBlockT2(in_channels=self._channels[4], out_channels=self._channels[8] )
self._upsample_16 = UpsampleBlockT1(in_channels=self._channels[8], out_channels=self._channels[16] )
self._upsample_32 = UpsampleBlockT2(in_channels=self._channels[16], out_channels=self._channels[32] )
self._upsample_64 = UpsampleBlockT1(in_channels=self._channels[32], out_channels=self._channels[64] )
self._upsample_128 = UpsampleBlockT2(in_channels=self._channels[64], out_channels=self._channels[128] )
self._upsample_256 = UpsampleBlockT1(in_channels=self._channels[128], out_channels=self._channels[256] )
self._upsample_512 = UpsampleBlockT2(in_channels=self._channels[256], out_channels=self._channels[512] )
self._upsample_1024 = UpsampleBlockT1(in_channels=self._channels[512], out_channels=self._channels[1024])
self._sle_64 = SLEBlock(in_channels=self._channels[4], out_channels=self._channels[64] )
self._sle_128 = SLEBlock(in_channels=self._channels[8], out_channels=self._channels[128])
self._sle_256 = SLEBlock(in_channels=self._channels[16], out_channels=self._channels[256])
self._sle_512 = SLEBlock(in_channels=self._channels[32], out_channels=self._channels[512])
self._out_128 = nn.Sequential(
SpectralConv2d(
in_channels=self._channels[128],
out_channels=out_channels,
kernel_size=1,
stride=1,
padding='same',
bias=False,
),
nn.Tanh(),
)
self._out_1024 = nn.Sequential(
SpectralConv2d(
in_channels=self._channels[1024],
out_channels=out_channels,
kernel_size=3,
stride=1,
padding='same',
bias=False,
),
nn.Tanh(),
)
def forward(self, input: torch.Tensor) -> \
Tuple[torch.Tensor, torch.Tensor]:
size_4 = self._init(input)
size_8 = self._upsample_8(size_4)
size_16 = self._upsample_16(size_8)
size_32 = self._upsample_32(size_16)
size_64 = self._sle_64 (size_4, self._upsample_64 (size_32) )
size_128 = self._sle_128(size_8, self._upsample_128(size_64) )
size_256 = self._sle_256(size_16, self._upsample_256(size_128))
size_512 = self._sle_512(size_32, self._upsample_512(size_256))
size_1024 = self._upsample_1024(size_512)
out_128 = self._out_128 (size_128)
out_1024 = self._out_1024(size_1024)
return out_1024, out_128
class Discriminrator(nn.Module, HugGANModelHubMixin):
def __init__(self, in_channels: int):
super().__init__()
self._channels = {
4: 1024,
8: 512,
16: 256,
32: 128,
64: 128,
128: 64,
256: 32,
512: 16,
1024: 8,
}
self._init = nn.Sequential(
SpectralConv2d(
in_channels=in_channels,
out_channels=self._channels[1024],
kernel_size=4,
stride=2,
padding=1,
bias=False,
),
nn.LeakyReLU(negative_slope=0.2),
SpectralConv2d(
in_channels=self._channels[1024],
out_channels=self._channels[512],
kernel_size=4,
stride=2,
padding=1,
bias=False,
),
nn.BatchNorm2d(num_features=self._channels[512]),
nn.LeakyReLU(negative_slope=0.2),
)
self._downsample_256 = DownsampleBlockT2(in_channels=self._channels[512], out_channels=self._channels[256])
self._downsample_128 = DownsampleBlockT2(in_channels=self._channels[256], out_channels=self._channels[128])
self._downsample_64 = DownsampleBlockT2(in_channels=self._channels[128], out_channels=self._channels[64] )
self._downsample_32 = DownsampleBlockT2(in_channels=self._channels[64], out_channels=self._channels[32] )
self._downsample_16 = DownsampleBlockT2(in_channels=self._channels[32], out_channels=self._channels[16] )
self._sle_64 = SLEBlock(in_channels=self._channels[512], out_channels=self._channels[64])
self._sle_32 = SLEBlock(in_channels=self._channels[256], out_channels=self._channels[32])
self._sle_16 = SLEBlock(in_channels=self._channels[128], out_channels=self._channels[16])
self._small_track = nn.Sequential(
SpectralConv2d(
in_channels=in_channels,
out_channels=self._channels[256],
kernel_size=4,
stride=2,
padding=1,
bias=False,
),
nn.LeakyReLU(negative_slope=0.2),
DownsampleBlockT1(in_channels=self._channels[256], out_channels=self._channels[128]),
DownsampleBlockT1(in_channels=self._channels[128], out_channels=self._channels[64] ),
DownsampleBlockT1(in_channels=self._channels[64], out_channels=self._channels[32] ),
)
self._features_large = nn.Sequential(
SpectralConv2d(
in_channels=self._channels[16] ,
out_channels=self._channels[8],
kernel_size=1,
stride=1,
padding=0,
bias=False,
),
nn.BatchNorm2d(num_features=self._channels[8]),
nn.LeakyReLU(negative_slope=0.2),
SpectralConv2d(
in_channels=self._channels[8],
out_channels=1,
kernel_size=4,
stride=1,
padding=0,
bias=False,
)
)
self._features_small = nn.Sequential(
SpectralConv2d(
in_channels=self._channels[32],
out_channels=1,
kernel_size=4,
stride=1,
padding=0,
bias=False,
),
)
self._decoder_large = Decoder(in_channels=self._channels[16], out_channels=3)
self._decoder_small = Decoder(in_channels=self._channels[32], out_channels=3)
self._decoder_piece = Decoder(in_channels=self._channels[32], out_channels=3)
def forward(self, images_1024: torch.Tensor,
images_128: torch.Tensor,
image_type: ImageType) -> \
Union[
torch.Tensor,
Tuple[torch.Tensor, Tuple[Any, Any, Any]]
]:
# large track
down_512 = self._init(images_1024)
down_256 = self._downsample_256(down_512)
down_128 = self._downsample_128(down_256)
down_64 = self._downsample_64(down_128)
down_64 = self._sle_64(down_512, down_64)
down_32 = self._downsample_32(down_64)
down_32 = self._sle_32(down_256, down_32)
down_16 = self._downsample_16(down_32)
down_16 = self._sle_16(down_128, down_16)
# small track
down_small = self._small_track(images_128)
# features
features_large = self._features_large(down_16).view(-1)
features_small = self._features_small(down_small).view(-1)
features = torch.cat([features_large, features_small], dim=0)
# decoder
if image_type != ImageType.FAKE:
dec_large = self._decoder_large(down_16)
dec_small = self._decoder_small(down_small)
dec_piece = self._decoder_piece(crop_image_part(down_32, image_type))
return features, (dec_large, dec_small, dec_piece)
return features
|