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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 .loss import styleLossMaskv3 | |
| from .nnutils import SPADEResnetBlock, get_edges, initWave | |
| from libs.nnutils import poolfeat, upfeat | |
| from libs.utils import label2one_hot_torch | |
| from .meanshift_utils import meanshift_cluster, meanshift_assign | |
| from swapae.models.networks.stylegan2_layers import ConvLayer | |
| from torch_geometric.nn import GCNConv | |
| from torch_geometric.utils import softmax | |
| class GCN(nn.Module): | |
| def __init__(self, n_cluster, temperature = 1, hidden_dim = 256): | |
| super().__init__() | |
| self.gcnconv1 = GCNConv(hidden_dim, hidden_dim, add_self_loops = True) | |
| self.gcnconv2 = GCNConv(hidden_dim, hidden_dim, add_self_loops = True) | |
| 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) | |
| feat = upfeat(pool_feat, 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) | |
| self.G = SPADEGenerator(args.spatial_code_dim + 32, 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, 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.style_loss = styleLossMaskv3(device = args.device) | |
| self.sine_wave_dim = args.spatial_code_dim | |
| self.noise_dim = 32 | |
| self.spatial_code_dim = args.spatial_code_dim | |
| # inpainting network | |
| if args.spatial_code_dim > 0: | |
| self.learnedWN = Waver(args.hidden_dim, zPeriodic = args.spatial_code_dim) | |
| self.add_module( | |
| "Amplitude", | |
| nn.Sequential( | |
| nn.Conv2d(args.hidden_dim, args.hidden_dim//2, 1, 1, 0), | |
| nn.Conv2d(args.hidden_dim//2, args.hidden_dim//4, 1, 1, 0), | |
| nn.Conv2d(args.hidden_dim//4, args.spatial_code_dim, 1, 1, 0) | |
| ) | |
| ) | |
| self.bandwidth = 3.0 | |
| def sample_patch_from_mask(self, mask, patch_num = 10, patch_size = 64): | |
| """ | |
| - Sample `patch_num` patches of size `patch_size*patch_size` w.r.t given mask | |
| """ | |
| nonzeros = torch.nonzero(mask.view(-1)).squeeze() | |
| n = len(nonzeros) | |
| xys = [] | |
| imgH, imgW = mask.shape | |
| half_patch = patch_size // 2 | |
| iter_num = 0 | |
| while len(xys) < patch_num: | |
| id = (torch.ones(n)*1.0/n).multinomial(num_samples=1, replacement=False) | |
| rx = nonzeros[id] // imgW | |
| ry = nonzeros[id] % imgW | |
| top = max(0, rx - half_patch) | |
| bot = min(imgH, rx + half_patch) | |
| left = max(0, ry - half_patch) | |
| right = min(imgW, ry + half_patch) | |
| patch_mask = mask[top:bot, left:right] | |
| if torch.sum(patch_mask) / (patch_size ** 2) > 0.5 or iter_num > 20: | |
| xys.append([top, bot, left, right]) | |
| iter_num += 1 | |
| return xys | |
| def get_sine_wave(self, GL, offset_mode = 'rec'): | |
| imgH, imgW = GL.shape[-2]//8, GL.shape[-1] // 8 | |
| GL = F.interpolate(GL, size = (imgH, imgW), mode = 'nearest') | |
| xv, yv = np.meshgrid(np.arange(imgH), np.arange(imgW),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 | |
| # random offset | |
| roffset = torch.zeros((GL.shape[0], self.sine_wave_dim, 1, 1)).to(GL.device).uniform_(-1, 1) * 6.28 | |
| roffset = roffset.repeat(1, 1, imgH, imgW) | |
| rwave = torch.sin(raw[:, ::2] + raw[:, 1::2] + roffset) | |
| # zero offset | |
| zwave = torch.sin(raw[:, ::2] + raw[:, 1::2]) | |
| A = self.Amplitude(GL) | |
| A = torch.sigmoid(A) | |
| wave = torch.cat((zwave, rwave)) * A.repeat(2, 1, 1, 1) | |
| return wave | |
| def syn_tex(self, tex_code, mask, imgH, imgW, offset_mode = 'rec', tex_idx = None): | |
| # synthesize all textures | |
| # spatial: B x 256 x 14 x 14 | |
| # tex_code: B x N x 256 | |
| B, N, _ = tex_code.shape | |
| H = imgH // 8 | |
| W = imgW // 8 | |
| # randomly sample a texture and synthesize it | |
| # throw away small texture segments | |
| areas = torch.sum(mask, dim=(2, 3)) | |
| valid_idxs = torch.nonzero(areas[0] / (imgH * imgW) > 0.01).squeeze(-1) | |
| if tex_idx is None or tex_idx >= tex_code.shape[1]: | |
| tex_idx = valid_idxs[torch.multinomial(areas[0, valid_idxs], 1).squeeze()] | |
| else: | |
| sorted_list = torch.argsort(areas, dim = 1, descending = True) | |
| tex_idx = sorted_list[0, tex_idx] | |
| sampled_code = tex_code[:, tex_idx, :] | |
| rec_tex = sampled_code.view(1, -1, 1, 1).repeat(1, 1, imgH, imgW) | |
| # Decoder: Spatial & Texture code -> Image | |
| if self.noise_dim == 0: | |
| dec_input = self.get_sine_wave(rec_tex, offset_mode) | |
| elif self.spatial_code_dim == 0: | |
| dec_input = torch.randn(rec_tex.shape[0], self.noise_dim, H, W).to(tex_code.device) | |
| else: | |
| sine_wave = self.get_sine_wave(rec_tex, offset_mode) | |
| noise = torch.randn(sine_wave.shape[0], self.noise_dim, H, W).to(tex_code.device) | |
| dec_input = torch.cat((sine_wave, noise), dim = 1) | |
| tex_syn = self.G(dec_input, rec_tex.repeat(dec_input.shape[0], 1, 1, 1)) | |
| return tex_syn, tex_idx | |
| def sample_tex_patches(self, tex_idx, rgb_img, rep_rec, mask, patch_num = 10): | |
| patches = [] | |
| masks = [] | |
| patch_masks = [] | |
| # sample patches from input image and reconstruction | |
| for i in range(rgb_img.shape[0]): | |
| # WARNING: : This only works for batch_size = 1 for now | |
| maski = mask[i, tex_idx] | |
| masks.append(maski.unsqueeze(0)) | |
| xys = self.sample_patch_from_mask(maski, patch_num = patch_num, patch_size = self.patch_size) | |
| # sample 10 patches from input image & reconstruction w.r.t group mask | |
| for k in range(patch_num): | |
| top, bot, left, right = xys[k] | |
| patch_ = rgb_img[i, :, top:bot, left:right] | |
| patch_mask_ = maski[top:bot, left:right] | |
| # In case the patch is on the boundary and smaller than patch_size | |
| # We put the patch at some random place of a black image | |
| h, w = patch_.shape[-2:] | |
| x = 0; y = 0 | |
| if h < self.patch_size: | |
| x = np.random.randint(0, self.patch_size - h) | |
| if w < self.patch_size: | |
| y = np.random.randint(0, self.patch_size - w) | |
| patch = torch.zeros(1, 3, self.patch_size, self.patch_size).to(patch_.device) | |
| patch_mask = torch.zeros(1, 1, self.patch_size, self.patch_size).to(patch_.device) | |
| patch[:, :, x:x+h, y:y+w] = patch_ | |
| patch_mask[:, :, x:x+h, y:y+w] = patch_mask_ | |
| patches.append(patch) | |
| patch_masks.append(patch_mask) | |
| patches = torch.cat(patches) | |
| masks = torch.stack(masks) | |
| patch_masks = torch.cat(patch_masks) | |
| # sample patches from synthesized texture | |
| tex_patch_size = self.patch_size | |
| rep_patches = [] | |
| for k in range(patch_num): | |
| i, j, h, w = transforms.RandomCrop.get_params(rep_rec, output_size=(tex_patch_size, tex_patch_size)) | |
| rep_rec_patch = TF.crop(rep_rec, i, j, h, w) | |
| rep_patches.append(rep_rec_patch) | |
| rep_patches = torch.stack(rep_patches, dim = 1) | |
| rep_patches = rep_patches.view(-1, 3, tex_patch_size, tex_patch_size) | |
| return masks, patch_masks, patches, rep_patches | |
| def forward(self, rgb_img, slic, epoch = 0, test_time = False, test = False, tex_idx = None): | |
| #self.patch_size = np.random.randint(64, 160) | |
| B, _, imgH, imgW = rgb_img.shape | |
| outputs = {} | |
| rec_feat_list = [] | |
| seg_map = [torch.argmax(slic.cpu(), dim = 1)] | |
| # Encoder: img (B, 3, H, W) -> feature (B, C, imgH//8, imgW//8) | |
| conv_feat, layer_feats = self.enc(rgb_img) | |
| B, C, H, W = conv_feat.shape | |
| # Texture code for each superpixel | |
| tex_code = self.ToTexCode(conv_feat) | |
| code = F.interpolate(tex_code, size = (imgH, imgW), mode = 'bilinear', align_corners = False) | |
| pool_code = poolfeat(code, slic, avg = True) | |
| if epoch >= self.add_gcn_epoch: | |
| prop_code, sp_assign, conv_feats = self.gcn(pool_code, slic, (self.add_clustering_epoch <= epoch)) | |
| softmax = F.softmax(sp_assign * self.gcn.temperature, dim = 1) | |
| rec_feat_list.append(prop_code) | |
| seg_map = [torch.argmax(sp_assign.cpu(), dim = 1)] | |
| else: | |
| rec_code = upfeat(pool_code, slic) | |
| rec_feat_list.append(rec_code) | |
| softmax = slic | |
| # Texture synthesis | |
| if epoch >= self.add_texture_epoch: | |
| sp_feat = poolfeat(conv_feats, slic, avg = True).squeeze(0) | |
| pts = meanshift_cluster(sp_feat, self.bandwidth, meanshift_step = 15)[-1] | |
| with torch.no_grad(): | |
| sp_assign, _ = meanshift_assign(pts, self.bandwidth) | |
| sp_assign = torch.tensor(sp_assign).unsqueeze(-1).to(slic.device).float() | |
| sp_assign = upfeat(sp_assign, slic) | |
| seg = label2one_hot_torch(sp_assign, C = sp_assign.max().long() + 1) | |
| seg_map = [torch.argmax(seg.cpu(), dim = 1)] | |
| # texture code for each connected group | |
| tex_seg = poolfeat(conv_feats, seg, avg = True) | |
| if test: | |
| rep_rec, tex_idx = self.syn_tex(tex_seg, seg, 564, 564, tex_idx = tex_idx) | |
| #rep_rec, tex_idx = self.syn_tex(tex_seg, seg, 1024, 1024, tex_idx = tex_idx) | |
| else: | |
| rep_rec, tex_idx = self.syn_tex(tex_seg, seg, imgH, imgW, tex_idx = tex_idx) | |
| rep_rec = (rep_rec + 1) / 2.0 | |
| rgb_img = (rgb_img + 1) / 2.0 | |
| # sample patches from input image, reconstruction & synthesized texture | |
| # zero offset | |
| zmasks, zpatch_masks, zpatches, zrep_patches = self.sample_tex_patches(tex_idx, rgb_img, rep_rec[:1], seg) | |
| # random offset | |
| rmasks, rpatch_masks, rpatches, rrep_patches = self.sample_tex_patches(tex_idx, rgb_img, rep_rec[1:], seg) | |
| masks = torch.cat((zmasks, rmasks)) | |
| patch_masks = torch.cat((zpatch_masks, rpatch_masks)) | |
| patches = torch.cat((zpatches, rpatches)) | |
| rep_patches = torch.cat((zrep_patches, rrep_patches)) | |
| # Gram matrix matching loss between: | |
| # - patches from synthesized texture v.s. patches from input image | |
| # - patches from reconstruction v.s. patches from input image | |
| outputs['style_loss'] = self.style_loss.forward_patch_img(rep_patches, rgb_img.repeat(2, 1, 1, 1), masks) | |
| outputs['rep_rec'] = rep_rec | |
| outputs['masks'] = masks | |
| outputs['patches'] = patches.view(-1, 3, self.patch_size, self.patch_size) | |
| outputs['patch_masks'] = patch_masks | |
| outputs['rep_patches'] = rep_patches * patch_masks + patches * (1 - patch_masks) | |
| outputs['gt'] = rgb_img | |
| bp_tex = rep_rec[:1, :, :imgH, :imgW] * masks[:1] + rgb_img * (1 - masks[:1]) | |
| outputs['rec'] = bp_tex | |
| outputs['HA'] = torch.cat(seg_map) | |
| return outputs | |