First commit

models/network_feature.py

+import torch
+import torch.nn as nn
+import torchvision
+
+
+"""
+# --------------------------------------------
+# VGG Feature Extractor
+# --------------------------------------------
+"""
+
+# --------------------------------------------
+# VGG features
+# Assume input range is [0, 1]
+# --------------------------------------------
+class VGGFeatureExtractor(nn.Module):
+    def __init__(self,
+                 feature_layer=34,
+                 use_bn=False,
+                 use_input_norm=True,
+                 device=torch.device('cpu')):
+        super(VGGFeatureExtractor, self).__init__()
+        if use_bn:
+            model = torchvision.models.vgg19_bn(pretrained=True)
+        else:
+            model = torchvision.models.vgg19(pretrained=True)
+        self.use_input_norm = use_input_norm
+        if self.use_input_norm:
+            mean = torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1).to(device)
+            # [0.485-1, 0.456-1, 0.406-1] if input in range [-1,1]
+            std = torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1).to(device)
+            # [0.229*2, 0.224*2, 0.225*2] if input in range [-1,1]
+            self.register_buffer('mean', mean)
+            self.register_buffer('std', std)
+        self.features = nn.Sequential(*list(model.features.children())[:(feature_layer + 1)])
+        # No need to BP to variable
+        for k, v in self.features.named_parameters():
+            v.requires_grad = False
+
+    def forward(self, x):
+        if self.use_input_norm:
+            x = (x - self.mean) / self.std
+        output = self.features(x)
+        return output
+
+

models/op/__init__.py

+from .fused_act import FusedLeakyReLU, fused_leaky_relu
+from .upfirdn2d import upfirdn2d