diff --git a/app.py b/app.py index ccaf80a3c5d29334ef322fca30a1e113a16a6802..163456b5e37b4a8937ce9fd303b6dfcafc874e24 100644 --- a/app.py +++ b/app.py @@ -167,15 +167,15 @@ class Tester(TesterBase): sampled_code = tex_seg[:, tex_idx, :] rec_tex = sampled_code.view(1, -1, 1, 1).repeat(1, 1, tex_size, tex_size) - sine_wave = self.model.get_sine_wave(rec_tex, 'rec') + sine_wave = self.model.get_sine_wave(rec_tex, 'rec')[:1] H = tex_size // 8; W = tex_size // 8 noise = torch.randn(B, self.model.sine_wave_dim, H, W).to(tex_code.device) dec_input = torch.cat((sine_wave, noise), dim = 1) - weight = self.model.ChannelWeight(rec_tex) - weight = F.adaptive_avg_pool2d(weight, output_size = (1)).view(weight.shape[0], -1, 1, 1) - weight = torch.sigmoid(weight) - dec_input *= weight + #weight = self.model.ChannelWeight(rec_tex) + #weight = F.adaptive_avg_pool2d(weight, output_size = (1)).view(weight.shape[0], -1, 1, 1) + #weight = torch.sigmoid(weight) + #dec_input *= weight rep_rec = self.model.G(dec_input, rec_tex) rep_rec = (rep_rec + 1) / 2.0 @@ -256,6 +256,7 @@ class Tester(TesterBase): given_mask = torch.from_numpy(given_mask) H, W = given_mask.shape[0], given_mask.shape[1] given_mask = label2one_hot_torch(given_mask.view(1, 1, H, W), C = (given_mask.max()+1)) + given_mask = F.interpolate(given_mask, size = (512, 512), mode = 'bilinear', align_corners = False) mask_img_list = [] for i in range(given_mask.shape[1]): part_img = self.to_pil(given_mask[0, i]) @@ -291,22 +292,26 @@ class Tester(TesterBase): #tex_size = (tex_size // 8) * 8 with torch.no_grad(): edited = self.model_forward_editing(self.data, self.slic, options=options, given_mask=given_mask, return_type = 'edited') - col1, col2, col3, col4 = st.columns([1, 1, 4, 1]) + col1, col2, col3 = st.columns([1, 1, 1]) with col1: - st.markdown("") - - with col2: st.markdown("Input image") img = F.interpolate(self.data, size = edited.shape[-2:], mode = 'bilinear', align_corners = False) st.image(self.to_pil((img + 1) / 2.0)) print(img.shape, edited.shape) + with col2: + st.markdown("Given mask") + seg = Image.open(mask_path).convert("L") + seg = np.asarray(seg) + seg = torch.from_numpy(seg).view(1, 1, seg.shape[0], seg.shape[1]) + color_vq = self.draw_color_seg(seg) + color_vq = F.interpolate(color_vq, size = (512, 512), mode = 'bilinear', align_corners = False) + st.image(self.to_pil(color_vq)) + with col3: st.markdown("Synthesized texture image") st.image(self.to_pil(edited)) - with col4: - st.markdown("") st.markdown('

You can choose another image from the examplar images on the top and start again!

', unsafe_allow_html=True) def model_forward_editing(self, rgb_img, slic, epoch = 1000, test_time = False, @@ -334,23 +339,17 @@ class Tester(TesterBase): tex_seg = poolfeat(conv_feats, softmax, avg = True) seg = label2one_hot_torch(torch.argmax(softmax, dim = 1).unsqueeze(1), C = softmax.shape[1]) - given_mask = F.interpolate(given_mask, size = (512, 512), mode = 'bilinear', align_corners = False) rec_tex = torch.zeros((1, tex_seg.shape[-1], 512, 512)) for i in range(given_mask.shape[1]): label = options[i] code = tex_seg[0, label, :].view(1, -1, 1, 1).repeat(1, 1, 512, 512) rec_tex += code * given_mask[:, i:i+1] tex_size = 512 - sine_wave = self.model.get_sine_wave(rec_tex, 'rec') + sine_wave = self.model.get_sine_wave(rec_tex, 'rec')[:1] H = tex_size // 8; W = tex_size // 8 noise = torch.randn(B, self.model.sine_wave_dim, H, W).to(tex_code.device) dec_input = torch.cat((sine_wave, noise), dim = 1) - weight = self.model.ChannelWeight(rec_tex) - weight = F.adaptive_avg_pool2d(weight, output_size = (1)).view(weight.shape[0], -1, 1, 1) - weight = torch.sigmoid(weight) - dec_input *= weight - rep_rec = self.model.G(dec_input, rec_tex) rep_rec = (rep_rec + 1) / 2.0 return rep_rec @@ -359,6 +358,7 @@ class Tester(TesterBase): rgb_img = Image.open(data_path) crop_size = self.args.crop_size i = 40; j = 40; h = crop_size; w = crop_size + rgb_img = transforms.Resize(size=320)(rgb_img) rgb_img = TF.crop(rgb_img, i, j, h, w) # compute superpixel diff --git a/data/images/108073.jpg b/data/images/108073.jpg deleted file mode 100755 index b4a23e5c18c7120d2cfc11996e2ce87fa6f04389..0000000000000000000000000000000000000000 Binary files a/data/images/108073.jpg and /dev/null differ diff --git a/data/test_images/130014.jpg b/data/images/130014.jpg similarity index 100% rename from data/test_images/130014.jpg rename to data/images/130014.jpg diff --git a/data/images/134008.jpg b/data/images/134008.jpg deleted file mode 100755 index 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b/models/week0417/__pycache__/taming_blocks.cpython-38.pyc differ diff --git a/models/week0417/meanshift_utils.py b/models/week0417/meanshift_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8d7d131565d770429215cbf02e2ff14be5c98947 --- /dev/null +++ b/models/week0417/meanshift_utils.py @@ -0,0 +1,62 @@ +import torch +import numpy as np + +def pairwise_distances(x, y): + #Input: x is a Nxd matrix + # y is an optional Mxd matirx + #Output: dist is a NxM matrix where dist[i,j] is the square norm between x[i,:] and y[j,:] + # if y is not given then use 'y=x'. + #i.e. dist[i,j] = ||x[i,:]-y[j,:]||^2 + x_norm = (x ** 2).sum(1).view(-1, 1) + y_t = torch.transpose(y, 0, 1) + y_norm = (y ** 2).sum(1).view(1, -1) + dist = x_norm + y_norm - 2.0 * torch.mm(x, y_t) + return torch.clamp(dist, 0.0, np.inf) + +def meanshift_cluster(pts, bandwidth, weights = None, meanshift_step = 15, step_size = 0.3): + """ + meanshift written in pytorch + :param pts: input points + :param weights: weight per point during clustering + :return: clustered points + """ + pts_steps = [] + for i in range(meanshift_step): + Y = pairwise_distances(pts, pts) + K = torch.nn.functional.relu(bandwidth ** 2 - Y) + if weights is not None: + K = K * weights + P = torch.nn.functional.normalize(K, p=1, dim=0, eps=1e-10) + P = P.transpose(0, 1) + pts = step_size * (torch.matmul(P, pts) - pts) + pts + pts_steps.append(pts) + return pts_steps + +def distance(a,b): + return torch.sqrt(((a-b)**2).sum()) + +def meanshift_assign(points, bandwidth): + cluster_ids = [] + cluster_idx = 0 + cluster_centers = [] + + for i, point in enumerate(points): + if(len(cluster_ids) == 0): + cluster_ids.append(cluster_idx) + cluster_centers.append(point) + cluster_idx += 1 + else: + # assign to nearest cluster + #for j,center in enumerate(cluster_centers): + # dist = distance(point, center) + # if(dist < bandwidth): + # cluster_ids.append(j) + cdist = torch.cdist(point.unsqueeze(0), torch.stack(cluster_centers), p = 2) + nearest_idx = torch.argmin(cdist, dim = 1) + if cdist[0, nearest_idx] < bandwidth: + cluster_ids.append(nearest_idx) + else: + cluster_ids.append(cluster_idx) + cluster_centers.append(point) + cluster_idx += 1 + return cluster_ids, cluster_centers diff --git a/models/week0417/model.py b/models/week0417/model.py index 23c2c8045979894495158fb5406cbfd240094c79..1d2cb81f1024b31e3213b4d4365ddaf0a63d2e51 100644 --- a/models/week0417/model.py +++ b/models/week0417/model.py @@ -7,21 +7,26 @@ 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 -from .loss import styleLossMaskv3 + +import sys +sys.path.append('models/third_party/cython') +from connectivity import enforce_connectivity class GCN(nn.Module): - def __init__(self, n_cluster, temperature = 1, add_self_loops = True, hidden_dim = 256): + def __init__(self, n_cluster, temperature = 1, 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.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 @@ -65,10 +70,7 @@ class GCN(nn.Module): # 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) @@ -137,10 +139,7 @@ class AE(nn.Module): # 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.G = SPADEGenerator(args.spatial_code_dim + 32, args.hidden_dim) self.add_module( "ToTexCode", @@ -150,40 +149,61 @@ class AE(nn.Module): 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.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 - 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') + 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 @@ -192,13 +212,170 @@ class AE(nn.Module): 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]) + + # 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): - return + #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 diff --git a/models/week0417/model_bk.py b/models/week0417/model_bk.py new file mode 100644 index 0000000000000000000000000000000000000000..23c2c8045979894495158fb5406cbfd240094c79 --- /dev/null +++ b/models/week0417/model_bk.py @@ -0,0 +1,204 @@ +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 diff --git a/swapae/models/__pycache__/__init__.cpython-38.pyc b/swapae/models/__pycache__/__init__.cpython-38.pyc index d42006bbc27b9832db384c377317d69d81d1c8c1..7d30740f836ae226da82fec297f08d974a30fa98 100644 Binary files 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