Corrupted features in srgan

codes/models/SRGAN_model.py

@@ -20,6 +20,48 @@ import os
 logger = logging.getLogger('base')
 
 
+class GaussianBlur(nn.Module):
+    def __init__(self):
+        super(GaussianBlur, self).__init__()
+
+        # Set these to whatever you want for your gaussian filter
+        kernel_size = 3
+        sigma = 2
+
+        # Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
+        x_cord = torch.arange(kernel_size)
+        x_grid = x_cord.repeat(kernel_size).view(kernel_size, kernel_size)
+        y_grid = x_grid.t()
+        xy_grid = torch.stack([x_grid, y_grid], dim=-1)
+
+        mean = (kernel_size - 1) / 2.
+        variance = sigma ** 2.
+
+        # Calculate the 2-dimensional gaussian kernel which is
+        # the product of two gaussian distributions for two different
+        # variables (in this case called x and y)
+        gaussian_kernel = (1. / (2. * 3.1415926 * variance)) * \
+                          torch.exp(
+                              -torch.sum((xy_grid - mean) ** 2., dim=-1) / \
+                              (2 * variance)
+                          )
+        # Make sure sum of values in gaussian kernel equals 1.
+        gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
+
+        # Reshape to 2d depthwise convolutional weight
+        gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
+        gaussian_kernel = gaussian_kernel.repeat(3, 1, 1, 1)
+
+        self.gaussian_filter = nn.Conv2d(in_channels=3, out_channels=3,
+                                    kernel_size=kernel_size, groups=3, bias=False)
+
+        self.gaussian_filter.weight.data = gaussian_kernel
+        self.gaussian_filter.weight.requires_grad = False
+
+    def forward(self, x):
+        return self.gaussian_filter(x)
+
+
 class SRGANModel(BaseModel):
     def __init__(self, opt):
         super(SRGANModel, self).__init__(opt)
@@ -132,6 +174,9 @@ class SRGANModel(BaseModel):
                 logger.info('Remove feature loss.')
                 self.cri_fea = None
             if self.cri_fea:  # load VGG perceptual loss
+                self.use_corrupted_feature_input = train_opt['corrupted_feature_input'] if 'corrupted_feature_input' in train_opt.keys() else False
+                if self.use_corrupted_feature_input:
+                    self.feature_corruptor = GaussianBlur()
                 self.netF = networks.define_F(use_bn=False).to(self.device)
                 self.lr_netF = None
                 if 'lr_fea_path' in train_opt.keys():
@@ -385,7 +430,6 @@ class SRGANModel(BaseModel):
             print("Misc setup %f" % (time() - _t,))
             _t = time()
 
-        if step >= self.init_iters:
         self.optimizer_G.zero_grad()
         self.fake_GenOut = []
         self.fea_GenOut = []
@@ -451,8 +495,14 @@ class SRGANModel(BaseModel):
                 if self.cri_fea and not using_gan_img and fea_w > 0:  # feature loss
                     if self.lr_netF is not None:
                         real_fea = self.lr_netF(var_L, interpolate_factor=self.opt['scale'])
+                    elif self.use_corrupted_feature_input:
+                        cor_Pix = F.interpolate(self.feature_corruptor(pix), size=var_L.shape[2:])
+                        real_fea = self.netF(cor_Pix).detach()
                     else:
                         real_fea = self.netF(pix).detach()
+                    if self.use_corrupted_feature_input:
+                        fake_fea = self.netF(F.interpolate(self.feature_corruptor(fea_GenOut), size=var_L.shape[2:]))
+                    else:
                         fake_fea = self.netF(fea_GenOut)
                     l_g_fea = fea_w * self.cri_fea(fake_fea, real_fea)
                     l_g_fea_log = l_g_fea / fea_w