updates

src/common/data.py

@@ -13,6 +13,8 @@ from common.utils import pil2numpy
 
 image_extensions = ['.jpg', '.png']
 def load_images_cached(images_dir_path, color_model, reset_cache):
+    if not Path(images_dir_path).exists():
+        raise Exception(f"{images_dir_path} does not exist.")
     image_paths = sorted([f for f in Path(images_dir_path).glob("*") if f.suffix.lower() in image_extensions])
     cache_path = Path(images_dir_path).parent / f"{Path(images_dir_path).stem}_{color_model}_cache.npy"
     cache_path = cache_path.resolve()

src/common/layers.py

@@ -527,3 +527,34 @@ class ComplexGaborLayer2D(nn.Module):
         gauss_term = torch.exp(-self.scale_0*self.scale_0*arg)
                 
         return freq_term*gauss_term
+
+
+class UpscaleBlockGabor(nn.Module):
+    def __init__(self, in_features=4, hidden_dim = 32, layers_count=4, upscale_factor=1, input_max_value=255, output_max_value=255):
+        super(UpscaleBlockGabor, self).__init__()   
+        assert layers_count > 0     
+        self.upscale_factor = upscale_factor 
+        self.hidden_dim = hidden_dim
+        self.embed = ComplexGaborLayer2D(in_features=in_features, out_features=hidden_dim, is_first=True)
+        
+        self.linear_projections = []
+        for i in range(layers_count):
+            self.linear_projections.append(ComplexGaborLayer2D(in_features=hidden_dim, out_features=hidden_dim, is_first=False))
+        self.linear_projections = nn.ModuleList(self.linear_projections)          
+
+        self.project_channels = nn.Linear(in_features=hidden_dim, out_features=upscale_factor * upscale_factor, bias=True)
+
+        self.in_bias = self.in_scale = input_max_value/2
+        self.out_bias = self.out_scale = output_max_value/2
+        self.layer_norm = nn.LayerNorm(hidden_dim) # To avoid gradient vanishing caused by tanh
+    
+    def forward(self, x):
+        x = (x-self.in_bias)/self.in_scale
+        x = self.embed(x)
+        for linear_projection in self.linear_projections:
+            x = linear_projection(x)
+        x = x.real
+        x = self.project_channels(x)
+        x = torch.tanh(x)         
+        x = x*self.out_scale + self.out_bias
+        return x  

src/common/losses.py

@@ -401,7 +401,7 @@ class SSIM(torch.nn.Module):
         size_average: bool = True,
         win_size: int = 11,
         win_sigma: float = 1.5,
-        channel: int = 3,
+        # channel: int = 3,
         spatial_dims: int = 2,
         K: Union[Tuple[float, float], List[float]] = (0.01, 0.03),
         nonnegative_ssim: bool = False,
@@ -419,13 +419,16 @@ class SSIM(torch.nn.Module):
 
         super(SSIM, self).__init__()
         self.win_size = win_size
-        self.win = _fspecial_gauss_1d(win_size, win_sigma).repeat([channel, 1] + [1] * spatial_dims)
+        self.win_sigma = win_sigma
+        self.win = None
         self.size_average = size_average
         self.data_range = data_range
         self.K = K
         self.nonnegative_ssim = nonnegative_ssim
 
     def forward(self, X: Tensor, Y: Tensor) -> Tensor:
+        if self.win is None:
+            self.win = _fspecial_gauss_1d(self.win_size, self.win_sigma).repeat([X.shape[1], 1] + [1] * len(X.shape[2:]))
         return ssim(
             X,
             Y,
@@ -507,7 +510,6 @@ def get_outnorm(x:torch.Tensor, out_norm:str='') -> torch.Tensor:
 
     return norm
 
-
 class CharbonnierLoss(nn.Module):
     """Charbonnier Loss (L1)"""
     def __init__(self, eps=1e-6, out_norm:str='bci'):
@@ -519,3 +521,21 @@ class CharbonnierLoss(nn.Module):
         norm = get_outnorm(x, self.out_norm)
         loss = torch.sum(torch.sqrt((x - y).pow(2) + self.eps**2))
         return loss*norm
+
+
+def huber(input, target, delta=0.01, reduce=True):
+    abs_error = torch.abs(input - target)
+    quadratic = torch.clamp(abs_error, max=delta)
+
+    # The following expression is the same in value as
+    # tf.maximum(abs_error - delta, 0), but importantly the gradient for the
+    # expression when abs_error == delta is 0 (for tf.maximum it would be 1).
+    # This is necessary to avoid doubling the gradient, since there is already a
+    # nonzero contribution to the gradient from the quadratic term.
+    linear = (abs_error - quadratic)
+    losses = 0.5 * torch.pow(quadratic, 2) + delta * linear
+    
+    if reduce:
+        return torch.mean(losses)
+    else:
+        return losses

src/models/sdynet.py

@@ -64,15 +64,15 @@ class SDYNetx2(SRNetBase):
         b,c,h,w = x.shape
         x = x.reshape(b*c, 1, h, w)
         output = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
-        output += self.forward_stage(x, 1, self._extract_pattern_S, self.stage1_S)
-        output += self.forward_stage(x, 1, self._extract_pattern_D, self.stage1_D)
-        output += self.forward_stage(x, 1, self._extract_pattern_Y, self.stage1_Y)
+        output += self.forward_stage(x, self._extract_pattern_S, self.stage1_S)
+        output += self.forward_stage(x, self._extract_pattern_D, self.stage1_D)
+        output += self.forward_stage(x, self._extract_pattern_Y, self.stage1_Y)
         output /= 3
         x = output
         output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
-        output += self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage2_S)
-        output += self.forward_stage(x, self.scale, self._extract_pattern_D, self.stage2_D)
-        output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage2_Y)
+        output += self.forward_stage(x, self._extract_pattern_S, self.stage2_S)
+        output += self.forward_stage(x, self._extract_pattern_D, self.stage2_D)
+        output += self.forward_stage(x, self._extract_pattern_Y, self.stage2_Y)
         output /= 3
         x = output
         x = x.reshape(b, c, h*self.scale, w*self.scale)
@@ -260,17 +260,17 @@ class SDYNetR90x2(SRNetBase):
         output_1 = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
         for rotations_count in range(4):
             rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1])
-            output_1 += torch.rot90(self.forward_stage(rotated, 1, self._extract_pattern_S, self.stage1_S), k=-rotations_count, dims=[-2, -1])
-            output_1 += torch.rot90(self.forward_stage(rotated, 1, self._extract_pattern_D, self.stage1_D), k=-rotations_count, dims=[-2, -1])
-            output_1 += torch.rot90(self.forward_stage(rotated, 1, self._extract_pattern_Y, self.stage1_Y), k=-rotations_count, dims=[-2, -1])              
+            output_1 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1_S), k=-rotations_count, dims=[-2, -1])
+            output_1 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_D, self.stage1_D), k=-rotations_count, dims=[-2, -1])
+            output_1 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_Y, self.stage1_Y), k=-rotations_count, dims=[-2, -1])          
         output_1 /= 4*3
         x = output_1
         output_2 = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
         for rotations_count in range(4):
             rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1])
-            output_2 += torch.rot90(self.forward_stage(rotated, self.scale, self._extract_pattern_S, self.stage2_S), k=-rotations_count, dims=[-2, -1])
-            output_2 += torch.rot90(self.forward_stage(rotated, self.scale, self._extract_pattern_D, self.stage2_D), k=-rotations_count, dims=[-2, -1])
-            output_2 += torch.rot90(self.forward_stage(rotated, self.scale, self._extract_pattern_Y, self.stage2_Y), k=-rotations_count, dims=[-2, -1])
+            output_2 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage2_S), k=-rotations_count, dims=[-2, -1])
+            output_2 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_D, self.stage2_D), k=-rotations_count, dims=[-2, -1])
+            output_2 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_Y, self.stage2_Y), k=-rotations_count, dims=[-2, -1])
         output_2 /= 4*3
         x = output_2
         x = x.view(b, c, h*self.scale, w*self.scale)
@@ -291,6 +291,59 @@ class SDYNetR90x2(SRNetBase):
             return F.mse_loss(pred/255, target/255)
         return loss_fn
 
+
+class SDYNetR90x2Inv(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SDYNetR90x2Inv, self).__init__()
+        self.scale = scale   
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_D = layers.PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_Y = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]], center=[0,0], window_size=3)
+        self.stage1_S = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
+        self.stage1_D = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
+        self.stage1_Y = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
+        self.stage2_S = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage2_D = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage2_Y = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.view(b*c, 1, h, w)
+        output_1 = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
+        for rotations_count in range(4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1])
+            output_1 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1_S), k=-rotations_count, dims=[-2, -1])
+            output_1 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_D, self.stage1_D), k=-rotations_count, dims=[-2, -1])
+            output_1 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_Y, self.stage1_Y), k=-rotations_count, dims=[-2, -1])              
+        output_1 /= 4*3
+        x = output_1
+        output_2 = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
+        for rotations_count in range(4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1])
+            output_2 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage2_S), k=-rotations_count, dims=[-2, -1])
+            output_2 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_D, self.stage2_D), k=-rotations_count, dims=[-2, -1])
+            output_2 += torch.rot90(self.forward_stage(rotated, self._extract_pattern_Y, self.stage2_Y), k=-rotations_count, dims=[-2, -1])
+        output_2 /= 4*3
+        x = output_2
+        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):
+        stage1_S = lut.transfer_2x2_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
+        stage1_D = lut.transfer_2x2_input_SxS_output(self.stage1_D, quantization_interval=quantization_interval, batch_size=batch_size)
+        stage1_Y = lut.transfer_2x2_input_SxS_output(self.stage1_Y, quantization_interval=quantization_interval, batch_size=batch_size)
+        stage2_S = lut.transfer_2x2_input_SxS_output(self.stage2_S, quantization_interval=quantization_interval, batch_size=batch_size)
+        stage2_D = lut.transfer_2x2_input_SxS_output(self.stage2_D, quantization_interval=quantization_interval, batch_size=batch_size)
+        stage2_Y = lut.transfer_2x2_input_SxS_output(self.stage2_Y, quantization_interval=quantization_interval, batch_size=batch_size)
+        lut_model = sdylut.SDYLutR90x2.init_from_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
+        return lut_model
+
+    def get_loss_fn(self):
+        def loss_fn(pred, target):
+            return F.mse_loss(pred/255, target/255)
+        return loss_fn
+
+
 class SDYEHONetR90x1(SRNetBase):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
         super(SDYEHONetR90x1, self).__init__()
@@ -793,3 +846,294 @@ class SDYMixNetx1v7(SRNetBase):
             # return F.mse_loss(pred/255, target/255)# + ssim_loss
             return ssim_loss(pred, target) + l1_loss(pred, target)
         return loss_fn
+        
+
+class SDYMixNetx1v8(SRNetBase):
+    """
+    22			
+    12	23	32	21
+    11	13	33	31
+    10	14	34	30
+    01	03	43	41
+    00	04	44	40
+    """
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SDYMixNetx1v8, self).__init__()
+        self.scale = scale   
+
+        self._extract_pattern_1 = layers.PercievePattern(receptive_field_idxes= [[3,3]], center=[3,3], window_size=7)
+        self._extract_pattern_2 = layers.PercievePattern(receptive_field_idxes= [[2,3],[3,4],[4,3],[3,2]], center=[3,3], window_size=7)
+        self._extract_pattern_3 = layers.PercievePattern(receptive_field_idxes= [[2,2],[2,4],[4,4],[4,2]], center=[3,3], window_size=7)
+        self._extract_pattern_4 = layers.PercievePattern(receptive_field_idxes= [[1,2],[1,4],[5,4],[5,2]], center=[3,3], window_size=7)
+        self._extract_pattern_5 = layers.PercievePattern(receptive_field_idxes= [[1,1],[1,5],[5,5],[5,1]], center=[3,3], window_size=7)
+        self._extract_pattern_6 = layers.PercievePattern(receptive_field_idxes= [[2,1],[2,5],[4,5],[4,1]], center=[3,3], window_size=7)
+        self._extract_pattern_7 = layers.PercievePattern(receptive_field_idxes= [[3,1],[1,3],[3,5],[5,3]], center=[3,3], window_size=7)
+        self._extract_pattern_8 = layers.PercievePattern(receptive_field_idxes= [[0,0],[0,6],[6,6],[6,0]], center=[3,3], window_size=7)
+        self._extract_pattern_9 = layers.PercievePattern(receptive_field_idxes= [[0,3],[3,6],[6,3],[3,0]], center=[3,3], window_size=7)
+        self._extract_pattern_10 = layers.PercievePattern(receptive_field_idxes=[[2,0],[1,0],[0,1],[0,2]], center=[3,3], window_size=7)
+        self._extract_pattern_11 = layers.PercievePattern(receptive_field_idxes=[[0,4],[0,5],[1,6],[2,6]], center=[3,3], window_size=7)
+        self._extract_pattern_12 = layers.PercievePattern(receptive_field_idxes=[[4,6],[5,6],[6,5],[6,4]], center=[3,3], window_size=7)
+        self._extract_pattern_13 = layers.PercievePattern(receptive_field_idxes=[[6,2],[6,1],[5,0],[4,0]], center=[3,3], window_size=7)
+        
+        self.stage1_1 =  layers.UpscaleBlock(in_features=1, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_2 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_3 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_4 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_5 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_6 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_7 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_8 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_9 =  layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_10 = layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_11 = layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_12 = layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        self.stage1_13 = layers.UpscaleBlock(in_features=4, hidden_dim=32, layers_count=2, upscale_factor=1)
+        
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=13)
+        self.stage1_Mix = layers.UpscaleBlock(in_features=13, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_1, self.stage1_1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_2, self.stage1_2)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_3, self.stage1_3)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_4, self.stage1_4)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_5, self.stage1_5)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_6, self.stage1_6)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_7, self.stage1_7)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_8, self.stage1_8)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_9, self.stage1_9)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_10, self.stage1_10)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_11, self.stage1_11)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_12, self.stage1_12)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_13, self.stage1_13)], dim=1)
+        output = self.forward_stage(output, self._extract_pattern_mix, self.stage1_Mix)
+        x = output
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            # return F.mse_loss(pred/255, target/255)# + ssim_loss
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SDYMixNetx1v9(SRNetBase):
+    """
+    22			
+    12	23	32	21
+    11	13	33	31
+    10	14	34	30
+    01	03	43	41
+    00	04	44	40
+    """
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SDYMixNetx1v9, self).__init__()
+        self.scale = scale   
+
+        self._extract_pattern_1 = layers.PercievePattern(receptive_field_idxes= [[3,3]], center=[3,3], window_size=7)
+        self._extract_pattern_2 = layers.PercievePattern(receptive_field_idxes= [[2,3],[3,4],[4,3],[3,2]], center=[3,3], window_size=7)
+        self._extract_pattern_3 = layers.PercievePattern(receptive_field_idxes= [[2,2],[2,4],[4,4],[4,2]], center=[3,3], window_size=7)
+        self._extract_pattern_4 = layers.PercievePattern(receptive_field_idxes= [[1,2],[1,4],[5,4],[5,2]], center=[3,3], window_size=7)
+        self._extract_pattern_5 = layers.PercievePattern(receptive_field_idxes= [[1,1],[1,5],[5,5],[5,1]], center=[3,3], window_size=7)
+        self._extract_pattern_6 = layers.PercievePattern(receptive_field_idxes= [[2,1],[2,5],[4,5],[4,1]], center=[3,3], window_size=7)
+        self._extract_pattern_7 = layers.PercievePattern(receptive_field_idxes= [[3,1],[1,3],[3,5],[5,3]], center=[3,3], window_size=7)
+        self._extract_pattern_8 = layers.PercievePattern(receptive_field_idxes= [[0,0],[0,6],[6,6],[6,0]], center=[3,3], window_size=7)
+        self._extract_pattern_9 = layers.PercievePattern(receptive_field_idxes= [[0,3],[3,6],[6,3],[3,0]], center=[3,3], window_size=7)
+        self._extract_pattern_10 = layers.PercievePattern(receptive_field_idxes=[[2,0],[1,0],[0,1],[0,2]], center=[3,3], window_size=7)
+        self._extract_pattern_11 = layers.PercievePattern(receptive_field_idxes=[[0,4],[0,5],[1,6],[2,6]], center=[3,3], window_size=7)
+        self._extract_pattern_12 = layers.PercievePattern(receptive_field_idxes=[[4,6],[5,6],[6,5],[6,4]], center=[3,3], window_size=7)
+        self._extract_pattern_13 = layers.PercievePattern(receptive_field_idxes=[[6,2],[6,1],[5,0],[4,0]], center=[3,3], window_size=7)
+        
+        self.stage1_1 =  layers.UpscaleBlock(in_features=1, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_2 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_3 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_4 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_5 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_6 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_7 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_8 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_9 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_10 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_11 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_12 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_13 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=13)
+        self.stage1_Mix = layers.UpscaleBlockChebyKAN(in_features=13, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_1, self.stage1_1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_2, self.stage1_2)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_3, self.stage1_3)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_4, self.stage1_4)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_5, self.stage1_5)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_6, self.stage1_6)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_7, self.stage1_7)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_8, self.stage1_8)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_9, self.stage1_9)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_10, self.stage1_10)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_11, self.stage1_11)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_12, self.stage1_12)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_13, self.stage1_13)], dim=1)
+        x = self.forward_stage(output, self._extract_pattern_mix, self.stage1_Mix)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            # return F.mse_loss(pred/255, target/255)# + ssim_loss
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+
+class SDYMixNetx1v10(SRNetBase):
+    """
+    22			
+    12	23	32	21
+    11	13	33	31
+    10	14	34	30
+    01	03	43	41
+    00	04	44	40
+    """
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SDYMixNetx1v10, self).__init__()
+        self.scale = scale   
+
+        self._extract_pattern_1 = layers.PercievePattern(receptive_field_idxes= [[3,3]], center=[3,3], window_size=7)
+        self._extract_pattern_2 = layers.PercievePattern(receptive_field_idxes= [[2,3],[3,4],[4,3],[3,2]], center=[3,3], window_size=7)
+        self._extract_pattern_3 = layers.PercievePattern(receptive_field_idxes= [[2,2],[2,4],[4,4],[4,2]], center=[3,3], window_size=7)
+        self._extract_pattern_4 = layers.PercievePattern(receptive_field_idxes= [[1,2],[1,4],[5,4],[5,2]], center=[3,3], window_size=7)
+        self._extract_pattern_5 = layers.PercievePattern(receptive_field_idxes= [[1,1],[1,5],[5,5],[5,1]], center=[3,3], window_size=7)
+        self._extract_pattern_6 = layers.PercievePattern(receptive_field_idxes= [[2,1],[2,5],[4,5],[4,1]], center=[3,3], window_size=7)
+        self._extract_pattern_7 = layers.PercievePattern(receptive_field_idxes= [[3,1],[1,3],[3,5],[5,3]], center=[3,3], window_size=7)
+        self._extract_pattern_8 = layers.PercievePattern(receptive_field_idxes= [[0,0],[0,6],[6,6],[6,0]], center=[3,3], window_size=7)
+        self._extract_pattern_9 = layers.PercievePattern(receptive_field_idxes= [[0,3],[3,6],[6,3],[3,0]], center=[3,3], window_size=7)
+        self._extract_pattern_10 = layers.PercievePattern(receptive_field_idxes=[[2,0],[1,0],[0,1],[0,2]], center=[3,3], window_size=7)
+        self._extract_pattern_11 = layers.PercievePattern(receptive_field_idxes=[[0,4],[0,5],[1,6],[2,6]], center=[3,3], window_size=7)
+        self._extract_pattern_12 = layers.PercievePattern(receptive_field_idxes=[[4,6],[5,6],[6,5],[6,4]], center=[3,3], window_size=7)
+        self._extract_pattern_13 = layers.PercievePattern(receptive_field_idxes=[[6,2],[6,1],[5,0],[4,0]], center=[3,3], window_size=7)
+        
+        self.stage1_1 =  layers.UpscaleBlock(in_features=1, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_2 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_3 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_4 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_5 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_6 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_7 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_8 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_9 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_10 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_11 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_12 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        self.stage1_13 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=1)
+        
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=13)
+        self.stage1_Mix = layers.UpscaleBlockGabor(in_features=13, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_1, self.stage1_1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_2, self.stage1_2)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_3, self.stage1_3)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_4, self.stage1_4)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_5, self.stage1_5)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_6, self.stage1_6)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_7, self.stage1_7)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_8, self.stage1_8)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_9, self.stage1_9)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_10, self.stage1_10)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_11, self.stage1_11)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_12, self.stage1_12)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_13, self.stage1_13)], dim=1)
+        x = self.forward_stage(output, self._extract_pattern_mix, self.stage1_Mix)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            # return F.mse_loss(pred/255, target/255)# + ssim_loss
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SDYMixNetx1v11(SRNetBase):
+    """
+    22			
+    12	23	32	21
+    11	13	33	31
+    10	14	34	30
+    01	03	43	41
+    00	04	44	40
+    """
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SDYMixNetx1v11, self).__init__()
+        self.scale = scale   
+
+        self._extract_pattern_1 = layers.PercievePattern(receptive_field_idxes= [[3,3]], center=[3,3], window_size=7)
+        self._extract_pattern_2 = layers.PercievePattern(receptive_field_idxes= [[2,3],[3,4],[4,3],[3,2]], center=[3,3], window_size=7)
+        self._extract_pattern_3 = layers.PercievePattern(receptive_field_idxes= [[2,2],[2,4],[4,4],[4,2]], center=[3,3], window_size=7)
+        self._extract_pattern_4 = layers.PercievePattern(receptive_field_idxes= [[1,2],[1,4],[5,4],[5,2]], center=[3,3], window_size=7)
+        self._extract_pattern_5 = layers.PercievePattern(receptive_field_idxes= [[1,1],[1,5],[5,5],[5,1]], center=[3,3], window_size=7)
+        self._extract_pattern_6 = layers.PercievePattern(receptive_field_idxes= [[2,1],[2,5],[4,5],[4,1]], center=[3,3], window_size=7)
+        self._extract_pattern_7 = layers.PercievePattern(receptive_field_idxes= [[3,1],[1,3],[3,5],[5,3]], center=[3,3], window_size=7)
+        self._extract_pattern_8 = layers.PercievePattern(receptive_field_idxes= [[0,0],[0,6],[6,6],[6,0]], center=[3,3], window_size=7)
+        self._extract_pattern_9 = layers.PercievePattern(receptive_field_idxes= [[0,3],[3,6],[6,3],[3,0]], center=[3,3], window_size=7)
+        self._extract_pattern_10 = layers.PercievePattern(receptive_field_idxes=[[2,0],[1,0],[0,1],[0,2]], center=[3,3], window_size=7)
+        self._extract_pattern_11 = layers.PercievePattern(receptive_field_idxes=[[0,4],[0,5],[1,6],[2,6]], center=[3,3], window_size=7)
+        self._extract_pattern_12 = layers.PercievePattern(receptive_field_idxes=[[4,6],[5,6],[6,5],[6,4]], center=[3,3], window_size=7)
+        self._extract_pattern_13 = layers.PercievePattern(receptive_field_idxes=[[6,2],[6,1],[5,0],[4,0]], center=[3,3], window_size=7)
+        
+        self.stage1_1 =  layers.UpscaleBlock(in_features=1, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_2 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_3 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_4 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_5 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_6 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_7 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_8 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_9 =  layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_10 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_11 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_12 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        self.stage1_13 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=1, upscale_factor=scale)
+        
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=13)
+        self.stage1_Mix = layers.UpscaleBlockGabor(in_features=13, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_1, self.stage1_1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_2, self.stage1_2)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_3, self.stage1_3)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_4, self.stage1_4)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_5, self.stage1_5)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_6, self.stage1_6)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_7, self.stage1_7)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_8, self.stage1_8)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_9, self.stage1_9)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_10, self.stage1_10)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_11, self.stage1_11)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_12, self.stage1_12)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_13, self.stage1_13)], dim=1)
+        x = self.forward_stage(output, self._extract_pattern_mix, self.stage1_Mix)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            # return F.mse_loss(pred/255, target/255)# + ssim_loss
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn

src/models/srnet.py

@@ -33,6 +33,510 @@ class SRNet(SRNetBase):
         lut_model = srlut.SRLut.init_from_numpy(stage_lut)
         return lut_model
 
+class SRNetMixer(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixer, self).__init__()
+        self.scale = scale 
+        self.stage1_1 = layers.UpscaleBlock(in_features=1, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_3 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_4 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+
+        self.stage1_5 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_6 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_7 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_8 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+
+        self.stage1_9 = layers.UpscaleBlock(in_features=1, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_10 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_11 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage1_12 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+
+        self.stage1_13 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+
+
+        self._extract_pattern_S1 = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1)
+        self._extract_pattern_S2 = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,2],[2,0],[2,2]], center=[1,1], window_size=3)
+        self._extract_pattern_S3 = layers.PercievePattern(receptive_field_idxes=[[0,1],[1,2],[2,1],[1,0]], center=[1,1], window_size=3)
+        self._extract_pattern_S4 = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,4],[4,4],[4,0]], center=[1,1], window_size=5)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output =                    self.forward_stage(x, self._extract_pattern_S1, self.stage1_1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_S2, self.stage1_2)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_S3, self.stage1_3)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_S4, self.stage1_4)], dim=1)
+        x = output
+        output =                    self.forward_stage(x, self._extract_pattern_mix, self.stage1_5)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_mix, self.stage1_6)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_mix, self.stage1_7)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_mix, self.stage1_8)], dim=1)
+        x = output
+        output =                    self.forward_stage(x, self._extract_pattern_S1, self.stage1_9)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_S2, self.stage1_10)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_S3, self.stage1_11)], dim=1)
+        output = torch.cat([output, self.forward_stage(x, self._extract_pattern_S4, self.stage1_12)], dim=1)
+        x = output
+        x =                         self.forward_stage(x, self._extract_pattern_mix, self.stage1_13)
+
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SRNetMixerR90(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90, self).__init__()
+        self.scale = scale 
+        self.stage1 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1) for _ in range(4)])
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1[rotations_count]), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+
+class SRNetMixerR90v2(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v2, self).__init__()
+        self.scale = scale 
+        self.stage1 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale) for _ in range(4)])
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1[rotations_count]), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SRNetMixerR90v3(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v3, self).__init__()
+        self.scale = scale 
+        self.stage1 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale//2) for _ in range(4)])
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale//2)
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1[rotations_count]), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SRNetMixerR90v5(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v5, self).__init__()
+        self.scale = scale 
+        self.stage1 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale) for _ in range(4)])
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage2 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1) for _ in range(4)])
+        self.stage_mix2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1[rotations_count]), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage2[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage2[rotations_count]), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix2)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+
+class SRNetMixerR90v9(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v9, self).__init__()
+        self.scale = scale 
+        self.stage1 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+        self.stage_mix2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage2)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage2), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix2)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+
+class SRNetMixerR90v10(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v10, self).__init__()
+        self.scale = scale 
+        self.stage1S = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage1D = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage1Y = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage2S = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+        self.stage_mix2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_D = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,2],[2,0],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_Y = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,2],[2,1],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+
+    def stage(self, x, pattern, stage_net, mix_pattern, stage_mix_net):
+        tmp_output = self.forward_stage(x, pattern, stage_net)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, pattern, stage_net), k=-rotations_count, dims=[2, 3])], dim=1)
+        output = self.forward_stage(tmp_output, mix_pattern, stage_mix_net)
+        return output
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)        
+        output = self.stage(x, self._extract_pattern_S, self.stage1S, self._extract_pattern_mix, self.stage_mix)
+        output += self.stage(x, self._extract_pattern_D, self.stage1D, self._extract_pattern_mix, self.stage_mix)
+        output += self.stage(x, self._extract_pattern_Y, self.stage1Y, self._extract_pattern_mix, self.stage_mix)
+        x = output / 3
+        x = x.clamp(0, 255)
+        x = self.stage(x, self._extract_pattern_S, self.stage2S, self._extract_pattern_mix, self.stage_mix2)
+        x = x.clamp(0, 255)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SRNetMixerR90v10Lsb(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v10Lsb, self).__init__()
+        self.scale = scale 
+        self.stage1S = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage1D = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage1Y = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage2S = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+        self.stage_mix2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+
+        self.stage1S_lsb = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=15, output_max_value=15)
+        self.stage1D_lsb = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=15, output_max_value=15)
+        self.stage1Y_lsb = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=15, output_max_value=15)
+        self.stage_mix_lsb = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1, input_max_value=15, output_max_value=255)
+        self.stage2S_lsb = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1, input_max_value=15, output_max_value=15)
+        self.stage_mix2_lsb = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1, input_max_value=15, output_max_value=255)
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_D = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,2],[2,0],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_Y = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,2],[2,1],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+
+    def stage(self, x, pattern, stage_net, mix_pattern, stage_mix_net):
+        tmp_output = self.forward_stage(x, pattern, stage_net)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, pattern, stage_net), k=-rotations_count, dims=[2, 3])], dim=1)
+        output = self.forward_stage(tmp_output, mix_pattern, stage_mix_net)
+        return output
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        lsb = x % 16
+        msb = x - lsb
+        output_msb = self.stage(msb, self._extract_pattern_S, self.stage1S, self._extract_pattern_mix, self.stage_mix)
+        output_msb += self.stage(msb, self._extract_pattern_D, self.stage1D, self._extract_pattern_mix, self.stage_mix)
+        output_msb += self.stage(msb, self._extract_pattern_Y, self.stage1Y, self._extract_pattern_mix, self.stage_mix)
+        output_lsb = self.stage(lsb, self._extract_pattern_S, self.stage1S_lsb, self._extract_pattern_mix, self.stage_mix_lsb)
+        output_lsb += self.stage(lsb, self._extract_pattern_D, self.stage1D_lsb, self._extract_pattern_mix, self.stage_mix_lsb)
+        output_lsb += self.stage(lsb, self._extract_pattern_Y, self.stage1Y_lsb, self._extract_pattern_mix, self.stage_mix_lsb)
+        x = output_msb / 3 + output_lsb / 3
+        x = x.clamp(0, 255)
+        lsb = x % 16
+        msb = x - lsb
+        output_msb = self.stage(msb, self._extract_pattern_S, self.stage2S, self._extract_pattern_mix, self.stage_mix2)
+        output_lsb = self.stage(lsb, self._extract_pattern_S, self.stage2S_lsb, self._extract_pattern_mix, self.stage_mix2_lsb)
+        x = output_msb + output_lsb
+        x = x.clamp(0, 255)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn        
+
+class SRNetMixerR90v4(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v4, self).__init__()
+        self.scale = scale 
+        self.stage1 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale) for _ in range(4)])
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1[rotations_count]), k=-rotations_count, dims=[2, 3])
+        x = output
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SRNetMixerR90v6(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v6, self).__init__()
+        self.scale = scale 
+        self.stage1 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale) for _ in range(4)])
+        self.stage2 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1) for _ in range(4)])
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1[rotations_count]), k=-rotations_count, dims=[2, 3])
+        x = output
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage2[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage2[rotations_count]), k=-rotations_count, dims=[2, 3])
+        x = output
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(channel=1, data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn
+
+class SRNetMixerR90v7(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v7, self).__init__()
+        self.scale = scale 
+        self.stage1 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale) for _ in range(4)])
+        self.stage2 = nn.ModuleList([layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1) for _ in range(4)])
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage1[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1[rotations_count]), k=-rotations_count, dims=[2, 3])
+        x = output
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage2[0])
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output += torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage2[rotations_count]), k=-rotations_count, dims=[2, 3])
+        x = output
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+class HDBNetv4(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4, rotations = 4):
+        super(HDBNetv4, self).__init__()
+        assert scale == 4
+        self.scale = scale 
+        self.rotations = rotations
+        self.stage1_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=255, output_max_value=255)
+        self.stage1_3D = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=255, output_max_value=255)
+        self.stage1_3B = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=255, output_max_value=255)
+        self.stage1_2H = layers.UpscaleBlock(in_features=2, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=15, output_max_value=255)
+        self.stage1_2D = layers.UpscaleBlock(in_features=2, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale, input_max_value=15, output_max_value=255)
+
+        self.stage2_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1, input_max_value=255, output_max_value=255)
+        self.stage2_3D = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1, input_max_value=255, output_max_value=255)
+        self.stage2_3B = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1, input_max_value=255, output_max_value=255)
+        self.stage2_2H = layers.UpscaleBlock(in_features=2, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1, input_max_value=15, output_max_value=255)
+        self.stage2_2D = layers.UpscaleBlock(in_features=2, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1, input_max_value=15, output_max_value=255)
+
+        self._extract_pattern_3H = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[0,2]], center=[0,0], window_size=3)
+        self._extract_pattern_3D = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,1],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_3B = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,2],[2,1]], center=[0,0], window_size=3)
+        self._extract_pattern_2H = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1]], center=[0,0], window_size=2)
+        self._extract_pattern_2D = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,1]], center=[0,0], window_size=2)        
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        upsampled = nn.Upsample(scale_factor=self.scale, mode='nearest')(x)
+        lsb = x % 16
+        msb = x - lsb     
+        output_msb = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
+        output_lsb = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
+        for rotations_count in range(self.rotations):
+           rotated_msb = torch.rot90(msb, k=rotations_count, dims=[2, 3])
+           rotated_lsb = torch.rot90(lsb, k=rotations_count, dims=[2, 3])
+           output_msbt = self.forward_stage(rotated_msb, self._extract_pattern_3H, self.stage1_3H) + \
+                         self.forward_stage(rotated_msb, self._extract_pattern_3D, self.stage1_3D) + \
+                         self.forward_stage(rotated_msb, self._extract_pattern_3B, self.stage1_3B)
+           output_lsbt = self.forward_stage(rotated_lsb, self._extract_pattern_2H, self.stage1_2H) + \
+                         self.forward_stage(rotated_lsb, self._extract_pattern_2D, self.stage1_2D)
+           if not config is None and config.current_iter % config.display_step == 0:
+                config.writer.add_histogram('s1_output_lsb', output_lsb.detach().cpu().numpy(), config.current_iter)
+                config.writer.add_histogram('s1_output_msb', output_msb.detach().cpu().numpy(), config.current_iter) 
+           output_msb += torch.rot90(output_msbt, k=-rotations_count, dims=[2, 3])
+           output_lsb += torch.rot90(output_lsbt, k=-rotations_count, dims=[2, 3])
+        output_msb /= self.rotations*3
+        output_lsb /= self.rotations*2
+        output = output_msb + output_lsb
+        x = output.clamp(0, 255)
+        lsb = x % 16
+        msb = x - lsb
+        output_msb = torch.zeros([b*c, 1, h*self.scale, w*self.scale,], dtype=x.dtype, device=x.device)
+        output_lsb = torch.zeros([b*c, 1, h*self.scale, w*self.scale,], dtype=x.dtype, device=x.device)
+        for rotations_count in range(self.rotations):
+           rotated_msb = torch.rot90(msb, k=rotations_count, dims=[2, 3])
+           rotated_lsb = torch.rot90(lsb, k=rotations_count, dims=[2, 3])
+           output_msbt = self.forward_stage(rotated_msb, self._extract_pattern_3H, self.stage2_3H) + \
+                         self.forward_stage(rotated_msb, self._extract_pattern_3D, self.stage2_3D) + \
+                         self.forward_stage(rotated_msb, self._extract_pattern_3B, self.stage2_3B)
+           output_lsbt = self.forward_stage(rotated_lsb, self._extract_pattern_2H, self.stage2_2H) + \
+                         self.forward_stage(rotated_lsb, self._extract_pattern_2D, self.stage2_2D)
+           if not config is None and config.current_iter % config.display_step == 0:
+                config.writer.add_histogram('s2_output_lsb', output_lsb.detach().cpu().numpy(), config.current_iter)
+                config.writer.add_histogram('s2_output_msb', output_msb.detach().cpu().numpy(), config.current_iter) 
+           output_msb += torch.rot90(output_msbt, k=-rotations_count, dims=[2, 3])
+           output_lsb += torch.rot90(output_lsbt, k=-rotations_count, dims=[2, 3])
+        output_msb /= self.rotations*3
+        output_lsb /= self.rotations*2
+        output_msb -= 127.5
+        output_msb -= 127.5
+        output = output_msb + output_lsb + upsampled
+        x = output.clamp(0, 255)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+class SRNetGabor(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetGabor, self).__init__()
+        self.scale = scale 
+        self.stage1_S = layers.UpscaleBlockGabor(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+            
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+        x = self.forward_stage(x, self._extract_pattern_S, self.stage1_S)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+
+    def get_lut_model(self, quantization_interval=16, batch_size=2**10):
+        stage_lut = lut.transfer_4_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
+        lut_model = srlut.SRLut.init_from_numpy(stage_lut)
+        return lut_model
 
 class SRNetChebyKan(SRNetBase):
     def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
@@ -627,3 +1131,103 @@ class SRMsbLsb4R90Net(SRNetBase):
 
     def get_lut_model(self, quantization_interval=16, batch_size=2**10):
         raise NotImplementedError
+
+
+
+class SRNetMixerR90v11(SRNetBase):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SRNetMixerR90v11, self).__init__()
+        self.scale = scale 
+        self.stage1S = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage1D = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage1Y = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage2S = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+
+        self.stage_mix = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
+        self.stage_mix2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+        self.stage_mix3 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=2, upscale_factor=1)
+
+        self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
+        self._extract_pattern_D = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,2],[2,0],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_Y = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,2],[2,1],[2,2]], center=[0,0], window_size=3)
+        self._extract_pattern_mix = layers.PercievePattern(receptive_field_idxes=[[0,0]], center=[0,0], window_size=1, channels=4)
+
+        self._extract_pattern_1 = layers.PercievePattern(receptive_field_idxes=[[3,0],[3,1],[3,2],[3,3]], center=[0,0], window_size=4)
+        self._extract_pattern_2 = layers.PercievePattern(receptive_field_idxes=[[0,3],[1,3],[2,3],[3,3]], center=[0,0], window_size=4)
+        self._extract_pattern_3 = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,1],[2,2],[3,2]], center=[0,0], window_size=4)
+        self._extract_pattern_4 = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,4],[4,0],[4,4]], center=[0,0], window_size=5)
+
+        self.stage_pattern_1 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage_pattern_2 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage_pattern_3 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+        self.stage_pattern_4 = layers.UpscaleBlock(in_features=4, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=self.scale)
+
+
+    def forward(self, x, config=None):
+        b,c,h,w = x.shape
+        x = x.reshape(b*c, 1, h, w)
+
+        tmp_output = self.forward_stage(x, self._extract_pattern_S, self.stage1S)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage1S), k=-rotations_count, dims=[2, 3])], dim=1)
+        output = self.forward_stage(tmp_output, self._extract_pattern_mix, self.stage_mix)
+
+        tmp_output = self.forward_stage(x, self._extract_pattern_D, self.stage1D)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_D, self.stage1D), k=-rotations_count, dims=[2, 3])], dim=1)
+        output += self.forward_stage(tmp_output, self._extract_pattern_mix, self.stage_mix)
+
+        tmp_output = self.forward_stage(x, self._extract_pattern_Y, self.stage1Y)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_Y, self.stage1Y), k=-rotations_count, dims=[2, 3])], dim=1)
+        output += self.forward_stage(tmp_output, self._extract_pattern_mix, self.stage_mix)
+        
+        ########################################
+        tmp_output = self.forward_stage(x, self._extract_pattern_1, self.stage_pattern_1)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_1, self.stage_pattern_1), k=-rotations_count, dims=[2, 3])], dim=1)
+        output += self.forward_stage(tmp_output, self._extract_pattern_mix, self.stage_mix2)
+
+        tmp_output = self.forward_stage(x, self._extract_pattern_2, self.stage_pattern_2)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_2, self.stage_pattern_2), k=-rotations_count, dims=[2, 3])], dim=1)
+        output += self.forward_stage(tmp_output, self._extract_pattern_mix, self.stage_mix2)
+
+        tmp_output = self.forward_stage(x, self._extract_pattern_3, self.stage_pattern_3)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_3, self.stage_pattern_3), k=-rotations_count, dims=[2, 3])], dim=1)
+        output += self.forward_stage(tmp_output, self._extract_pattern_mix, self.stage_mix2)
+
+        tmp_output = self.forward_stage(x, self._extract_pattern_4, self.stage_pattern_4)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            tmp_output = torch.cat([tmp_output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_4, self.stage_pattern_4), k=-rotations_count, dims=[2, 3])], dim=1)
+        output += self.forward_stage(tmp_output, self._extract_pattern_mix, self.stage_mix2)
+        
+        output /= 7
+        x = output
+        x = x.clamp(0, 255)
+        
+        output = self.forward_stage(x, self._extract_pattern_S, self.stage2S)
+        for rotations_count in range(1,4):
+            rotated = torch.rot90(x, k=rotations_count, dims=[2, 3])
+            output = torch.cat([output, torch.rot90(self.forward_stage(rotated, self._extract_pattern_S, self.stage2S), k=-rotations_count, dims=[2, 3])], dim=1)
+        x = output
+        x = self.forward_stage(x, self._extract_pattern_mix, self.stage_mix3)
+        
+        x = x.clamp(0, 255)
+        x = x.reshape(b, c, h*self.scale, w*self.scale)
+        return x
+    
+    def get_loss_fn(self):
+        ssim_loss = losses.SSIM(data_range=255)
+        l1_loss = losses.CharbonnierLoss()
+        def loss_fn(pred, target):
+            return ssim_loss(pred, target) + l1_loss(pred, target)
+        return loss_fn