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| 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 | |