Upload StyleMix.py

StyleMix.py

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+import torch
+from torch import nn
+import torch.optim as optim
+import torch.nn.functional as F
+from torch.utils.data.dataloader import DataLoader
+from torchvision import transforms
+from torchvision import utils as vutils
+
+from models import Generator
+from utils import copy_G_params, load_params
+
+
+
+def get_early_features(net, noise):
+    with torch.no_grad():
+        feat_4 = net._init(noise)
+        feat_8 = net._upsample_8(feat_4)
+        feat_16 = net._upsample_16(feat_8)
+        feat_32 = net._upsample_32(feat_16)
+        feat_64 = net._upsample_64(feat_32)
+    return feat_8, feat_16, feat_32, feat_64
+
+def get_late_features(net, feat_64, feat_8, feat_16, feat_32):
+    with torch.no_grad():
+        feat_128 = net._upsample_128(feat_64)
+        feat_128 = net._sle_128(feat_8, feat_128)
+
+        feat_256 = net._upsample_256(feat_128)
+        feat_256 = net._sle_256(feat_16, feat_256)
+
+        feat_512 = net._upsample_512(feat_256)
+        feat_512 = net._sle_512(feat_32, feat_512)
+
+        feat_1024 = net._upsample_1024(feat_512)
+
+    return net._out_1024(feat_1024)
+
+def style_mix(model_name_or_path, bs, device):
+    _in_channels = 256
+    im_size = 1024
+
+    netG = Generator(in_channels=_in_channels, out_channels=3)
+    netG = netG.from_pretrained(model_name_or_path, in_channels=256, out_channels=3)
+    _ = netG.to(device)
+    _ = netG.eval()
+
+    avg_param_G = copy_G_params(netG)
+    load_params(netG, avg_param_G)
+
+    noise_a = torch.randn(bs, 256, 1, 1, device=device).to(device)
+    noise_b = torch.randn(bs, 256, 1, 1, device=device).to(device)
+
+    feat_8_a, feat_16_a, feat_32_a, feat_64_a = get_early_features(netG, noise_a)
+    feat_8_b, feat_16_b, feat_32_b, feat_64_b = get_early_features(netG, noise_b)
+
+    images_b = get_late_features(netG, feat_64_b, feat_8_b, feat_16_b, feat_32_b)
+    images_a = get_late_features(netG, feat_64_a, feat_8_a, feat_16_a, feat_32_a)
+
+    imgs = [ torch.ones(1, 3, im_size, im_size) ]
+
+    imgs.append(images_b.cpu())
+    for i in range(bs):
+        imgs.append(images_a[i].unsqueeze(0).cpu())
+        gimgs = get_late_features(netG, feat_64_a[i].unsqueeze(0).repeat(bs, 1, 1, 1), feat_8_b, feat_16_b, feat_32_b)
+        imgs.append(gimgs.cpu())
+
+    imgs = torch.cat(imgs)
+    # vutils.save_image(imgs.add(1).mul(0.5), 'style_mix/style_mix_2.jpg', nrow=bs+1)
+
+    return imgs