possible solution for rcconv

src/models/rcnet.py

@@ -327,7 +327,6 @@ class RCNetx2(nn.Module):
         output = output.view(b, c, h*self.scale, w*self.scale)
         return output
 
-
 class RCNetx2Centered(nn.Module):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(RCNetx2Centered, self).__init__()
@@ -390,7 +389,6 @@ class RCNetx2Centered(nn.Module):
         output = output.view(b, c, h*self.scale, w*self.scale)
         return output
 
-
 class ReconstructedConvRot90Unlutable(nn.Module):
     def __init__(self, hidden_dim,  window_size=7):
         super(ReconstructedConvRot90Unlutable, self).__init__()
@@ -546,3 +544,70 @@ class RCNetx2CenteredUnlutable(nn.Module):
         output /= 3*4
         output = output.view(b, c, h*self.scale, w*self.scale)
         return output
+
+
+
+class ReconstructedConvCenteredv2(nn.Module):
+    def __init__(self, hidden_dim,  window_size=7):
+        super(ReconstructedConvCenteredv2, self).__init__()
+        self.window_size = window_size
+        self.projection1 = torch.nn.Parameter(torch.rand((window_size**2, hidden_dim))/window_size)
+        self.projection2 = torch.nn.Parameter(torch.rand((window_size**2, hidden_dim))/window_size)
+
+    def pixel_wise_forward(self, x):
+        x = (x-127.5)/127.5
+        out = torch.einsum('bwk,wh,wh -> bwk', x, self.projection1, self.projection2) 
+        out = torch.tanh(out)
+        out = out*127.5 + 127.5
+        return out 
+
+    def forward(self, x):
+        original_shape = x.shape  
+        x = F.pad(x, pad=[self.window_size//2]*4, mode='replicate')
+        x = F.unfold(x, self.window_size)
+        x = self.pixel_wise_forward(x)     
+        x = (x.mean(1) - (x.max().values - x.min().values))/2
+        x = x.reshape(*original_shape)    
+        x = round_func(x) 
+        return x
+
+    def __repr__(self):
+        return f"{self.__class__.__name__} projection1: {self.projection1.shape} projection2: {self.projection2.shape}"
+
+class RCBlockCenteredv2(nn.Module):
+    def __init__(self, hidden_dim = 32, window_size=3, dense_conv_layer_count=4, upscale_factor=4):
+        super(RCBlockCenteredv2, self).__init__()   
+        self.window_size = window_size    
+        self.rc_conv = ReconstructedConvCenteredv2(hidden_dim=hidden_dim, window_size=window_size)
+        self.dense_conv_block = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=dense_conv_layer_count, upscale_factor=upscale_factor)
+
+    def forward(self, x):
+        b,c,hs,ws = x.shape
+        x = self.rc_conv(x)
+        x = F.pad(x, pad=[0,1,0,1], mode='replicate') 
+        x = self.dense_conv_block(x)
+        return x
+
+
+class RCNetCentered_7x7v2(nn.Module):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(RCNetCentered_7x7v2, self).__init__()
+        self.hidden_dim = hidden_dim
+        self.layers_count = layers_count
+        self.scale = scale       
+        window_size = 7
+        self.stage = RCBlockCenteredv2(hidden_dim=hidden_dim, dense_conv_layer_count=layers_count, upscale_factor=scale, window_size=window_size)
+            
+    def forward(self, x):
+        b,c,h,w = x.shape
+        x = x.view(b*c, 1, h, w)
+        x = self.stage(x)
+        x = x.view(b, c, h*self.scale, w*self.scale)
+        return x
+
+    def get_lut_model(self, quantization_interval=16, batch_size=2**10):
+        window_size = self.stage.rc_conv.window_size
+        rc_conv_luts = lut.transfer_rc_conv(self.stage.rc_conv, quantization_interval=quantization_interval).reshape(window_size,window_size,-1)
+        dense_conv_lut = lut.transfer_2x2_input_SxS_output(self.stage.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
+        lut_model = rclut.RCLutCentered_7x7.init_from_lut(rc_conv_luts, dense_conv_lut)
+        return lut_model