Spaces:
Runtime error
Runtime error
File size: 10,104 Bytes
1b2a9b1 |
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 |
from collections import OrderedDict
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import swapae.util as util
from swapae.models.networks import BaseNetwork
from swapae.models.networks.stylegan2_layers import ConvLayer, ResBlock, EqualLinear
class BasePatchDiscriminator(BaseNetwork):
@staticmethod
def modify_commandline_options(parser, is_train):
parser.add_argument("--netPatchD_scale_capacity", default=4.0, type=float)
parser.add_argument("--netPatchD_max_nc", default=256 + 128, type=int)
parser.add_argument("--patch_size", default=128, type=int)
parser.add_argument("--max_num_tiles", default=8, type=int)
parser.add_argument("--patch_random_transformation",
type=util.str2bool, nargs='?', const=True, default=False)
return parser
def __init__(self, opt):
super().__init__(opt)
#self.visdom = util.Visualizer(opt)
def needs_regularization(self):
return False
def extract_features(self, patches):
raise NotImplementedError()
def discriminate_features(self, feature1, feature2):
raise NotImplementedError()
def apply_random_transformation(self, patches):
B, ntiles, C, H, W = patches.size()
patches = patches.view(B * ntiles, C, H, W)
before = patches
transformer = util.RandomSpatialTransformer(self.opt, B * ntiles)
patches = transformer.forward_transform(patches, (self.opt.patch_size, self.opt.patch_size))
#self.visdom.display_current_results({'before': before,
# 'after': patches}, 0, save_result=False)
return patches.view(B, ntiles, C, H, W)
def sample_patches_old(self, img, indices):
B, C, H, W = img.size()
s = self.opt.patch_size
if H % s > 0 or W % s > 0:
y_offset = torch.randint(H % s, (), device=img.device)
x_offset = torch.randint(W % s, (), device=img.device)
img = img[:, :,
y_offset:y_offset + s * (H // s),
x_offset:x_offset + s * (W // s)]
img = img.view(B, C, H//s, s, W//s, s)
ntiles = (H // s) * (W // s)
tiles = img.permute(0, 2, 4, 1, 3, 5).reshape(B, ntiles, C, s, s)
if indices is None:
indices = torch.randperm(ntiles, device=img.device)[:self.opt.max_num_tiles]
return self.apply_random_transformation(tiles[:, indices]), indices
else:
return self.apply_random_transformation(tiles[:, indices])
def forward(self, real, fake, fake_only=False):
assert real is not None
real_patches, patch_ids = self.sample_patches(real, None)
if fake is None:
real_patches.requires_grad_()
real_feat = self.extract_features(real_patches)
bs = real.size(0)
if fake is None or not fake_only:
pred_real = self.discriminate_features(
real_feat,
torch.roll(real_feat, 1, 1))
pred_real = pred_real.view(bs, -1)
if fake is not None:
fake_patches = self.sample_patches(fake, patch_ids)
#self.visualizer.display_current_results({'real_A': real_patches[0],
# 'real_B': torch.roll(fake_patches, 1, 1)[0]}, 0, False, max_num_images=16)
fake_feat = self.extract_features(fake_patches)
pred_fake = self.discriminate_features(
real_feat,
torch.roll(fake_feat, 1, 1))
pred_fake = pred_fake.view(bs, -1)
if fake is None:
return pred_real, real_patches
elif fake_only:
return pred_fake
else:
return pred_real, pred_fake
class StyleGAN2PatchDiscriminator(BasePatchDiscriminator):
@staticmethod
def modify_commandline_options(parser, is_train):
BasePatchDiscriminator.modify_commandline_options(parser, is_train)
return parser
def __init__(self, opt):
super().__init__(opt)
channel_multiplier = self.opt.netPatchD_scale_capacity
size = self.opt.patch_size
channels = {
4: min(self.opt.netPatchD_max_nc, int(256 * channel_multiplier)),
8: min(self.opt.netPatchD_max_nc, int(128 * channel_multiplier)),
16: min(self.opt.netPatchD_max_nc, int(64 * channel_multiplier)),
32: int(32 * channel_multiplier),
64: int(16 * channel_multiplier),
128: int(8 * channel_multiplier),
256: int(4 * channel_multiplier),
}
log_size = int(math.ceil(math.log(size, 2)))
in_channel = channels[2 ** log_size]
blur_kernel = [1, 3, 3, 1] if self.opt.use_antialias else [1]
convs = [('0', ConvLayer(3, in_channel, 3))]
for i in range(log_size, 2, -1):
out_channel = channels[2 ** (i - 1)]
layer_name = str(7 - i) if i <= 6 else "%dx%d" % (2 ** i, 2 ** i)
convs.append((layer_name, ResBlock(in_channel, out_channel, blur_kernel)))
in_channel = out_channel
convs.append(('5', ResBlock(in_channel, self.opt.netPatchD_max_nc * 2, downsample=False)))
convs.append(('6', ConvLayer(self.opt.netPatchD_max_nc * 2, self.opt.netPatchD_max_nc, 3, pad=0)))
self.convs = nn.Sequential(OrderedDict(convs))
out_dim = 1
pairlinear1 = EqualLinear(channels[4] * 2 * 2, 2048, activation='fused_lrelu')
pairlinear2 = EqualLinear(2048, 2048, activation='fused_lrelu')
pairlinear3 = EqualLinear(2048, 1024, activation='fused_lrelu')
pairlinear4 = EqualLinear(1024, out_dim)
self.pairlinear = nn.Sequential(pairlinear1, pairlinear2, pairlinear3, pairlinear4)
def extract_features(self, patches, aggregate=False):
if patches.ndim == 5:
B, T, C, H, W = patches.size()
flattened_patches = patches.flatten(0, 1)
else:
B, C, H, W = patches.size()
T = patches.size(1)
flattened_patches = patches
features = self.convs(flattened_patches)
features = features.view(B, T, features.size(1), features.size(2), features.size(3))
if aggregate:
features = features.mean(1, keepdim=True).expand(-1, T, -1, -1, -1)
return features.flatten(0, 1)
def extract_layerwise_features(self, image):
feats = [image]
for m in self.convs:
feats.append(m(feats[-1]))
return feats
def discriminate_features(self, feature1):
feature1 = feature1.flatten(1)
#feature2 = feature2.flatten(1)
#out = self.pairlinear(torch.cat([feature1, feature2], dim=1))
out = self.pairlinear(feature1)
return out
"""
def discriminate_features(self, feature1, feature2):
feature1 = feature1.flatten(1)
feature2 = feature2.flatten(1)
out = self.pairlinear(torch.cat([feature1, feature2], dim=1))
return out
"""
class StyleGAN2COGANPatchDiscriminator(BasePatchDiscriminator):
@staticmethod
def modify_commandline_options(parser, is_train):
BasePatchDiscriminator.modify_commandline_options(parser, is_train)
return parser
def __init__(self, opt):
super().__init__(opt)
channel_multiplier = self.opt.netPatchD_scale_capacity
size = self.opt.patch_size
channels = {
4: min(self.opt.netPatchD_max_nc, int(256 * channel_multiplier)),
8: min(self.opt.netPatchD_max_nc, int(128 * channel_multiplier)),
16: min(self.opt.netPatchD_max_nc, int(64 * channel_multiplier)),
32: int(32 * channel_multiplier),
64: int(16 * channel_multiplier),
128: int(8 * channel_multiplier),
256: int(4 * channel_multiplier),
}
log_size = int(math.ceil(math.log(size, 2)))
in_channel = channels[2 ** log_size]
blur_kernel = [1, 3, 3, 1] if self.opt.use_antialias else [1]
convs = [('0', ConvLayer(3, in_channel, 3))]
for i in range(log_size, 2, -1):
out_channel = channels[2 ** (i - 1)]
layer_name = str(7 - i) if i <= 6 else "%dx%d" % (2 ** i, 2 ** i)
convs.append((layer_name, ResBlock(in_channel, out_channel, blur_kernel)))
in_channel = out_channel
convs.append(('5', ResBlock(in_channel, self.opt.netPatchD_max_nc * 2, downsample=False)))
convs.append(('6', ConvLayer(self.opt.netPatchD_max_nc * 2, self.opt.netPatchD_max_nc, 3, pad=0)))
self.convs = nn.Sequential(OrderedDict(convs))
out_dim = 1
pairlinear1 = EqualLinear(channels[4] * 2 * 2 * 2, 2048, activation='fused_lrelu')
pairlinear2 = EqualLinear(2048, 2048, activation='fused_lrelu')
pairlinear3 = EqualLinear(2048, 1024, activation='fused_lrelu')
pairlinear4 = EqualLinear(1024, out_dim)
self.pairlinear = nn.Sequential(pairlinear1, pairlinear2, pairlinear3, pairlinear4)
def extract_features(self, patches, aggregate=False):
if patches.ndim == 5:
B, T, C, H, W = patches.size()
flattened_patches = patches.flatten(0, 1)
else:
B, C, H, W = patches.size()
T = patches.size(1)
flattened_patches = patches
features = self.convs(flattened_patches)
features = features.view(B, T, features.size(1), features.size(2), features.size(3))
if aggregate:
features = features.mean(1, keepdim=True).expand(-1, T, -1, -1, -1)
return features.flatten(0, 1)
def extract_layerwise_features(self, image):
feats = [image]
for m in self.convs:
feats.append(m(feats[-1]))
return feats
def discriminate_features(self, feature1, feature2):
feature1 = feature1.flatten(1)
feature2 = feature2.flatten(1)
out = self.pairlinear(torch.cat([feature1, feature2], dim=1))
return out
|