debug and demo

channel.py

@@ -5,9 +5,13 @@ import numpy as np
 
 def channel(channel_type='AWGN', snr=20):
     def AWGN_channel(z_hat: torch.Tensor):
-        k = np.prod(z_hat.size()[1:])
-        sig_pwr = torch.sum(torch.abs(z_hat).square(), dim=(1, 2, 3), keepdim=True)
-        noi_pwr = sig_pwr / (k * 10 ** (snr / 10))
+        if z_hat.dim() == 4:
+            k = np.prod(z_hat.size()[1:])
+            sig_pwr = torch.sum(torch.abs(z_hat).square(), dim=(1, 2, 3), keepdim=True)/k
+        elif z_hat.dim() == 3:
+            k = np.prod(z_hat.size())
+            sig_pwr = torch.sum(torch.abs(z_hat).square())/k
+        noi_pwr = sig_pwr / ( 10 ** (snr / 10))
         noise = torch.randn_like(z_hat) * torch.sqrt(noi_pwr)
         return z_hat + noise
 

eval.py

@@ -21,8 +21,8 @@ def config_parser():
 
 def main():
     args = config_parser()
-    transform = transforms.Compose([transforms.ToTensor(), ])
-
+    transform = transforms.Compose([transforms.ToTensor()])
+    args.saved = './saved/model_cifar10_0.33_19.00_40.pth' # to be deleted
     test_image = Image.open(args.test_image)
     test_image.load()
     test_image = transform(test_image)

model.py

@@ -61,7 +61,7 @@ class _TransConvWithPReLU(nn.Module):
 
 
 class _Encoder(nn.Module):
-    def __init__(self, c=1, is_temp=False):
+    def __init__(self, c=1, is_temp=False,P=1):
         super(_Encoder, self).__init__()
         self.is_temp = is_temp
         # self.imgae_normalization = _image_normalization(norm_type='nomalization')
@@ -71,7 +71,7 @@ class _Encoder(nn.Module):
                                     kernel_size=5, padding=2)  # padding size could be changed here
         self.conv4 = _ConvWithPReLU(in_channels=32, out_channels=32, kernel_size=5, padding=2)
         self.conv5 = _ConvWithPReLU(in_channels=32, out_channels=c, kernel_size=5, padding=2)
-        self.norm = self._normlizationLayer()
+        self.norm = self._normlizationLayer(P=P)
 
     @staticmethod
     def _normlizationLayer(P=1):
@@ -88,6 +88,8 @@ class _Encoder(nn.Module):
             z_temp = z_hat.reshape(batch_size, 1, 1, -1)
             z_trans = z_hat.reshape(batch_size, 1, -1, 1)
             tensor = torch.sqrt(P * k) * z_hat / torch.sqrt((z_temp @ z_trans))
+            if batch_size == 1:
+                return tensor.squeeze(0)
             return tensor
         return _inner