Spaces:
Runtime error
Runtime error
File size: 9,012 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 |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from .taming_blocks import Encoder
from .nnutils import SPADEResnetBlock, get_edges, initWave
from libs.nnutils import poolfeat, upfeat
from libs.utils import label2one_hot_torch
from swapae.models.networks.stylegan2_layers import ConvLayer
from torch_geometric.nn import GCNConv
from torch_geometric.utils import softmax
from .loss import styleLossMaskv3
class GCN(nn.Module):
def __init__(self, n_cluster, temperature = 1, add_self_loops = True, hidden_dim = 256):
super().__init__()
self.gcnconv1 = GCNConv(hidden_dim, hidden_dim, add_self_loops = add_self_loops)
self.gcnconv2 = GCNConv(hidden_dim, hidden_dim, add_self_loops = add_self_loops)
self.pool1 = nn.Sequential(nn.Conv2d(hidden_dim, n_cluster, 3, 1, 1))
self.temperature = temperature
def compute_edge_score_softmax(self, raw_edge_score, edge_index, num_nodes):
return softmax(raw_edge_score, edge_index[1], num_nodes=num_nodes)
def compute_edge_weight(self, node_feature, edge_index):
src_feat = torch.gather(node_feature, 0, edge_index[0].unsqueeze(1).repeat(1, node_feature.shape[1]))
tgt_feat = torch.gather(node_feature, 0, edge_index[1].unsqueeze(1).repeat(1, node_feature.shape[1]))
raw_edge_weight = nn.CosineSimilarity(dim=1, eps=1e-6)(src_feat, tgt_feat)
edge_weight = self.compute_edge_score_softmax(raw_edge_weight, edge_index, node_feature.shape[0])
return raw_edge_weight.squeeze(), edge_weight.squeeze()
def forward(self, sp_code, slic, clustering = False):
edges, aff = get_edges(torch.argmax(slic, dim = 1).unsqueeze(1), sp_code.shape[1])
prop_code = []
sp_assign = []
edge_weights = []
conv_feats = []
for i in range(sp_code.shape[0]):
# compute edge weight
edge_index = edges[i]
raw_edge_weight, edge_weight = self.compute_edge_weight(sp_code[i], edge_index)
feat = self.gcnconv1(sp_code[i], edge_index, edge_weight = edge_weight)
raw_edge_weight, edge_weight = self.compute_edge_weight(feat, edge_index)
edge_weights.append(raw_edge_weight)
feat = F.leaky_relu(feat, 0.2)
feat = self.gcnconv2(feat, edge_index, edge_weight = edge_weight)
# maybe clustering
conv_feat = upfeat(feat, slic[i:i+1])
conv_feats.append(conv_feat)
if not clustering:
feat = conv_feat
pred_mask = slic[i:i+1]
else:
pred_mask = self.pool1(conv_feat)
# enforce pixels belong to the same superpixel to have same grouping label
pred_mask = upfeat(poolfeat(pred_mask, slic[i:i+1]), slic[i:i+1])
s_ = F.softmax(pred_mask * self.temperature, dim = 1)
# compute texture code w.r.t grouping
pool_feat = poolfeat(conv_feat, s_, avg = True)
# hard upsampling
#hard_s_ = label2one_hot_torch(torch.argmax(s_, dim = 1).unsqueeze(1), C = s_.shape[1])
feat = upfeat(pool_feat, s_)
#feat = upfeat(pool_feat, hard_s_)
prop_code.append(feat)
sp_assign.append(pred_mask)
prop_code = torch.cat(prop_code)
conv_feats = torch.cat(conv_feats)
return prop_code, torch.cat(sp_assign), conv_feats
class SPADEGenerator(nn.Module):
def __init__(self, in_dim, hidden_dim):
super().__init__()
nf = hidden_dim // 16
self.head_0 = SPADEResnetBlock(in_dim, 16 * nf)
self.G_middle_0 = SPADEResnetBlock(16 * nf, 16 * nf)
self.G_middle_1 = SPADEResnetBlock(16 * nf, 16 * nf)
self.up_0 = SPADEResnetBlock(16 * nf, 8 * nf)
self.up_1 = SPADEResnetBlock(8 * nf, 4 * nf)
self.up_2 = SPADEResnetBlock(4 * nf, 2 * nf)
self.up_3 = SPADEResnetBlock(2 * nf, 1 * nf)
final_nc = nf
self.conv_img = nn.Conv2d(final_nc, 3, 3, padding=1)
self.up = nn.Upsample(scale_factor=2)
def forward(self, sine_wave, texon):
x = self.head_0(sine_wave, texon)
x = self.up(x)
x = self.G_middle_0(x, texon)
x = self.G_middle_1(x, texon)
x = self.up(x)
x = self.up_0(x, texon)
x = self.up(x)
x = self.up_1(x, texon)
#x = self.up(x)
x = self.up_2(x, texon)
#x = self.up(x)
x = self.up_3(x, texon)
x = self.conv_img(F.leaky_relu(x, 2e-1))
return x
class Waver(nn.Module):
def __init__(self, tex_code_dim, zPeriodic):
super(Waver, self).__init__()
K = tex_code_dim
layers = [nn.Conv2d(tex_code_dim, K, 1)]
layers += [nn.ReLU(True)]
layers += [nn.Conv2d(K, 2 * zPeriodic, 1)]
self.learnedWN = nn.Sequential(*layers)
self.waveNumbers = initWave(zPeriodic)
def forward(self, GLZ=None):
return (self.waveNumbers.to(GLZ.device) + self.learnedWN(GLZ))
class AE(nn.Module):
def __init__(self, args, **ignore_kwargs):
super(AE, self).__init__()
# encoder & decoder
self.enc = Encoder(ch=64, out_ch=3, ch_mult=[1,2,4,8], num_res_blocks=1, attn_resolutions=[],
in_channels=3, resolution=args.crop_size, z_channels=args.hidden_dim, double_z=False)
if args.dec_input_mode == 'sine_wave_noise':
self.G = SPADEGenerator(args.spatial_code_dim * 2, args.hidden_dim)
else:
self.G = SPADEGenerator(args.spatial_code_dim, args.hidden_dim)
self.add_module(
"ToTexCode",
nn.Sequential(
ConvLayer(args.hidden_dim, args.hidden_dim, kernel_size=3, activate=True, bias=True),
ConvLayer(args.hidden_dim, args.tex_code_dim, kernel_size=3, activate=True, bias=True),
ConvLayer(args.tex_code_dim, args.hidden_dim, kernel_size=1, activate=False, bias=False)
)
)
self.gcn = GCN(n_cluster = args.n_cluster, temperature = args.temperature, add_self_loops = (args.add_self_loops == 1), hidden_dim = args.hidden_dim)
self.add_gcn_epoch = args.add_gcn_epoch
self.add_clustering_epoch = args.add_clustering_epoch
self.add_texture_epoch = args.add_texture_epoch
self.patch_size = args.patch_size
self.sine_wave_dim = args.spatial_code_dim
# inpainting network
self.learnedWN = Waver(args.hidden_dim, zPeriodic = args.spatial_code_dim)
self.dec_input_mode = args.dec_input_mode
self.style_loss = styleLossMaskv3(device = args.device)
if args.sine_weight:
if args.dec_input_mode == 'sine_wave_noise':
self.add_module(
"ChannelWeight",
nn.Sequential(
ConvLayer(args.hidden_dim, args.hidden_dim//2, kernel_size=3, activate=True, bias=True, downsample=True),
ConvLayer(args.hidden_dim//2, args.hidden_dim//4, kernel_size=3, activate=True, bias=True, downsample=True),
ConvLayer(args.hidden_dim//4, args.spatial_code_dim*2, kernel_size=1, activate=False, bias=False, downsample=True)))
else:
self.add_module(
"ChannelWeight",
nn.Sequential(
ConvLayer(args.hidden_dim, args.hidden_dim//2, kernel_size=3, activate=True, bias=True, downsample=True),
ConvLayer(args.hidden_dim//2, args.hidden_dim//4, kernel_size=3, activate=True, bias=True, downsample=True),
ConvLayer(args.hidden_dim//4, args.spatial_code_dim, kernel_size=1, activate=False, bias=False, downsample=True)))
def get_sine_wave(self, GL, offset_mode = 'random'):
img_size = GL.shape[-1] // 8
GL = F.interpolate(GL, size = (img_size, img_size), mode = 'nearest')
xv, yv = np.meshgrid(np.arange(img_size), np.arange(img_size),indexing='ij')
c = torch.FloatTensor(np.concatenate([xv[np.newaxis], yv[np.newaxis]], 0)[np.newaxis])
c = c.to(GL.device)
# c: 1, 2, 28, 28
c = c.repeat(GL.shape[0], self.sine_wave_dim, 1, 1)
# c: 1, 64, 28, 28
period = self.learnedWN(GL)
# period: 1, 64, 28, 28
raw = period * c
if offset_mode == 'random':
offset = torch.zeros((GL.shape[0], self.sine_wave_dim, 1, 1)).to(GL.device).uniform_(-1, 1) * 6.28
offset = offset.repeat(1, 1, img_size, img_size)
wave = torch.sin(raw[:, ::2] + raw[:, 1::2] + offset)
elif offset_mode == 'rec':
wave = torch.sin(raw[:, ::2] + raw[:, 1::2])
return wave
def forward(self, rgb_img, slic, epoch = 0, test_time = False, test = False, tex_idx = None):
return
|