update

src/models/__init__.py

@@ -2,6 +2,7 @@ from . import rcnet
 from . import rclut 
 from . import srnet
 from . import srlut
+from . import sdynet
 import torch
 import numpy as np 
 from pathlib import Path
@@ -16,6 +17,7 @@ AVAILABLE_MODELS = {
     'RCNetx1': rcnet.RCNetx1, 'RCLutx1': rclut.RCLutx1,
     'RCNetx2': rcnet.RCNetx2, 'RCLutx2': rclut.RCLutx2,
     'RCNetx2Centered': rcnet.RCNetx2Centered, 'RCLutx2Centered': rclut.RCLutx2Centered,
+    'SDYNetx1': sdynet.SDYNetx1
 }
 
 def SaveCheckpoint(model, path):

src/models/munet.py

@@ -1,65 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-from common.utils import round_func
-from common import lut
-from pathlib import Path
-# from .mulut import MuLutx1, MuLutx2
-
-# Huang G. et al. Densely connected convolutional networks //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2017. – С. 4700-4708.
-# https://ar5iv.labs.arxiv.org/html/1608.06993
-# https://github.com/andreasveit/densenet-pytorch/blob/63152f4a40644b62717749536ed2e011c6e4d9ab/densenet.py#L40
-class DenseConvUpscaleBlock(nn.Module):
-    def __init__(self, hidden_dim = 32, layers_count=5, upscale_factor=1):
-        super(DenseConvUpscaleBlock, self).__init__()   
-        assert layers_count > 0     
-        self.upscale_factor = upscale_factor 
-
-        self.percieve = nn.Conv2d(1, hidden_dim, kernel_size=(2, 2), padding='valid',  stride=1, dilation=1, bias=True)
-        self.convs = []
-        for i in range(layers_count):
-            self.convs.append(nn.Conv2d(in_channels = (i+1)*hidden_dim, out_channels = hidden_dim, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True))
-        self.convs = nn.ModuleList(self.convs)          
-
-        for name, p in self.named_parameters():
-            if "weight" in name: nn.init.kaiming_normal_(p)
-            if "bias" in name: nn.init.constant_(p, 0) 
-
-        self.project_channels = nn.Conv2d(in_channels = (layers_count+1)*hidden_dim, out_channels = upscale_factor * upscale_factor, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True) 
-        self.shuffle = nn.PixelShuffle(upscale_factor)
-    
-    def forward(self, x):
-        x = (x-127.5)/127.5
-        x = torch.relu(self.percieve(x))
-        for conv in self.convs:
-            x = torch.cat([x, torch.relu(conv(x))], dim=1)
-        x = self.shuffle(self.project_channels(x))
-        x = torch.tanh(x)         
-        x = round_func(x*127.5 + 127.5)
-        return x
-
-class MuNetx1(nn.Module):
-    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
-        super(SRNet2x2, self).__init__()
-        self.scale = scale       
-        self.stage = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
-            
-    def forward(self, x):
-        b,c,h,w = x.shape
-        x = x.view(b*c, 1, h, w)
-        output = 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, 3])
-            rotated_padded = F.pad(rotated, pad=[0,1,0,1], mode='replicate')
-            rotated_prediction = self.stage(rotated_padded)
-            unrotated_prediction = torch.rot90(rotated_prediction, k=-rotations_count, dims=[2, 3])
-            output += unrotated_prediction
-        output /= 4
-        output = output.view(b, c, h*self.scale, w*self.scale)
-        return output
-
-    def get_lut_model(self, quantization_interval=16, batch_size=2**10):
-        stage_lut = lut.transfer_2x2_input_SxS_output(self.stage, quantization_interval=quantization_interval, batch_size=batch_size)
-        lut_model = MuLutx1.init_from_lut(stage_lut)
-        return lut_model

src/models/rcnet.py

@@ -48,7 +48,7 @@ class ReconstructedConvCentered(nn.Module):
 
     def pixel_wise_forward(self, x):
         x = (x-127.5)/127.5
-        out = torch.einsum('bik,ij,ij -> bik', x, self.projection1, self.projection2) 
+        out = torch.einsum('bwk,wh,wh -> bwk', x, self.projection1, self.projection2) 
         out = torch.tanh(out)
         out = out*127.5 + 127.5
         return out 

src/models/sdynet.py

@@ -0,0 +1,108 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from common.utils import round_func
+from common import lut
+from pathlib import Path
+from . import sdylut
+
+# Huang G. et al. Densely connected convolutional networks //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2017. – С. 4700-4708.
+# https://ar5iv.labs.arxiv.org/html/1608.06993
+# https://github.com/andreasveit/densenet-pytorch/blob/63152f4a40644b62717749536ed2e011c6e4d9ab/densenet.py#L40
+class DenseConvUpscaleBlock(nn.Module):
+    def __init__(self, hidden_dim = 32, layers_count=5, upscale_factor=1):
+        super(DenseConvUpscaleBlock, self).__init__()   
+        assert layers_count > 0     
+        self.upscale_factor = upscale_factor 
+        self.hidden_dim = hidden_dim
+        self.percieve = nn.Conv2d(1, hidden_dim, kernel_size=(2, 2), padding='valid',  stride=1, dilation=1, bias=True)
+        self.convs = []
+        for i in range(layers_count):
+            self.convs.append(nn.Conv2d(in_channels = (i+1)*hidden_dim, out_channels = hidden_dim, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True))
+        self.convs = nn.ModuleList(self.convs)          
+
+        for name, p in self.named_parameters():
+            if "weight" in name: nn.init.kaiming_normal_(p)
+            if "bias" in name: nn.init.constant_(p, 0) 
+
+        self.project_channels = nn.Conv2d(in_channels = (layers_count+1)*hidden_dim, out_channels = upscale_factor * upscale_factor, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True) 
+        self.shuffle = nn.PixelShuffle(upscale_factor)
+    
+    def forward(self, x):
+        x = (x-127.5)/127.5    
+        x = torch.relu(self.percieve(x))
+        for conv in self.convs:
+            x = torch.cat([x, torch.relu(conv(x))], dim=1)
+        x = self.shuffle(self.project_channels(x))
+        x = torch.tanh(x)         
+        x = round_func(x*127.5 + 127.5)
+        return x
+
+class PercievePattern():
+    def __init__(self, receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]]):
+        self.receptive_field_idxes = np.array(receptive_field_idxes)
+        self.window_size = np.max(self.receptive_field_idxes) + 1
+        self.receptive_field_idxes = [
+            self.receptive_field_idxes[0,0]*self.window_size + self.receptive_field_idxes[0,1],
+            self.receptive_field_idxes[1,0]*self.window_size + self.receptive_field_idxes[1,1],
+            self.receptive_field_idxes[2,0]*self.window_size + self.receptive_field_idxes[2,1],
+            self.receptive_field_idxes[3,0]*self.window_size + self.receptive_field_idxes[3,1],
+        ]
+
+    def __call__(self, x):
+        b,c,h,w = x.shape
+        x = F.pad(x, pad=[0,self.window_size-1,0,self.window_size-1], mode='replicate')
+        x = F.unfold(input=x, kernel_size=self.window_size)
+        x = torch.stack([
+            x[:,self.receptive_field_idxes[0],:],
+            x[:,self.receptive_field_idxes[1],:],
+            x[:,self.receptive_field_idxes[2],:],
+            x[:,self.receptive_field_idxes[3],:]
+        ], 2)
+        x = x.reshape(x.shape[0]*x.shape[1], 1, 2, 2)
+        return x
+    
+
+class SDYNetx1(nn.Module):
+    def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
+        super(SDYNetx1, self).__init__()
+        self.scale = scale   
+        self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]])
+        self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]])
+        self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]])
+        self.stageS = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
+        self.stageD = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
+        self.stageY = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
+
+    def forward(self, x):
+        b,c,h,w = x.shape
+        x = x.view(b*c, 1, h, w)
+        output = 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])
+            rb,rc,rh,rw = rotated.shape
+
+            s = self.stageS(self._extract_pattern_S(rotated))
+            s = s.view(rb*rc, 1, rh, rw, self.scale, self.scale).permute(0,1,2,4,3,5).reshape(rb*rc, 1, rh*self.scale, rw*self.scale) 
+            s = torch.rot90(s, k=-rotations_count, dims=[-2, -1])            
+            output += s
+
+            d = self.stageD(self._extract_pattern_D(rotated))            
+            d = d.view(rb*rc, 1, rh, rw, self.scale, self.scale).permute(0,1,2,4,3,5).reshape(rb*rc, 1, rh*self.scale, rw*self.scale) 
+            d = torch.rot90(d, k=-rotations_count, dims=[-2, -1])
+            output += d
+
+            y = self.stageY(self._extract_pattern_Y(rotated))            
+            y = y.view(rb*rc, 1, rh, rw, self.scale, self.scale).permute(0,1,2,4,3,5).reshape(rb*rc, 1, rh*self.scale, rw*self.scale) 
+            y = torch.rot90(y, k=-rotations_count, dims=[-2, -1])
+            output += y
+                       
+        output /= 4*3
+        output = output.view(b, c, h*self.scale, w*self.scale)
+        return output
+
+    def get_lut_model(self, quantization_interval=16, batch_size=2**10):
+        stage_lut = lut.transfer_2x2_input_SxS_output(self.stage, quantization_interval=quantization_interval, batch_size=batch_size)
+        lut_model = sdylut.SDYLutx1.init_from_lut(stage_lut)
+        return lut_model