renaming and comments

src/common/layers.py

@@ -67,7 +67,6 @@ class UpscaleBlock(nn.Module):
         x = x.reshape(b, c, h*self.upscale_factor, w*self.upscale_factor) 
         return x  
 
-# https://github.com/kornia/kornia/blob/2c084f8dc108b3f0f3c8983ac3f25bf88638d01a/kornia/color/ycbcr.py#L105
 class RgbToYcbcr(nn.Module):
     r"""Convert an image from RGB to YCbCr.
 
@@ -84,6 +83,8 @@ class RgbToYcbcr(nn.Module):
         >>> input = torch.rand(2, 3, 4, 5)
         >>> ycbcr = RgbToYcbcr()
         >>> output = ycbcr(input)  # 2x3x4x5
+
+    https://github.com/kornia/kornia/blob/2c084f8dc108b3f0f3c8983ac3f25bf88638d01a/kornia/color/ycbcr.py#L105
     """
 
     def forward(self, image):
@@ -113,6 +114,8 @@ class YcbcrToRgb(nn.Module):
         >>> input = torch.rand(2, 3, 4, 5)
         >>> rgb = YcbcrToRgb()
         >>> output = rgb(input)  # 2x3x4x5
+    
+    https://github.com/kornia/kornia/blob/2c084f8dc108b3f0f3c8983ac3f25bf88638d01a/kornia/color/ycbcr.py#L127
     """
 
     def forward(self, image):

src/common/lut.py

@@ -83,7 +83,7 @@ def transfer_2x2_input_SxS_output(block, quantization_interval=16, batch_size=2*
 def forward_2x2_input_SxS_output(index, lut):
     b,c,hs,ws = index.shape
     scale = lut.shape[-1]
-    index = F.pad(input=index, pad=[0,1,0,1], mode='replicate') #?
+    index = F.pad(input=index, pad=[0,1,0,1], mode='replicate') 
     out = select_index_4dlut_tetrahedral(
         ixA = index, 
         ixB = torch.roll(index, shifts=[0, -1], dims=[2,3]), 
@@ -168,7 +168,7 @@ def select_index_1dlut_linear(ixA, lut):
     out = out.reshape((b,c,h,w))
     return out
 
-def select_index_4dlut_tetrahedral(ixA, ixB, ixC, ixD, lut): #self, weight, upscale, mode, img_in, bd):
+def select_index_4dlut_tetrahedral(ixA, ixB, ixC, ixD, lut):
     lut = torch.clamp(lut, 0, 255)
     dimA, dimB, dimC, dimD = lut.shape[:4]
     q = 256/(dimA-1)
@@ -284,7 +284,5 @@ def select_index_4dlut_tetrahedral(ixA, ixB, ixC, ixD, lut): #self, weight, upsc
     i24 = i = torch.all(torch.cat([~(fab), ~(fac), ~(fbc), ~i21[:, None], ~i22[:, None], ~i23[:, None]], dim=1), dim=1); out[i] = (q - fd[i]) * p0000[i] + (fd[i] - fc[i]) * p0001[i] + (fc[i] - fb[i]) * p0011[i] + (fb[i] - fa[i]) * p0111[i] + (fa[i]) * p1111[i]
 
     out = out.reshape((img_a1.shape[0], img_a1.shape[1], img_a1.shape[2], img_a1.shape[3], upscale, upscale))
-    # out = out.permute(0, 1, 2, 4, 3, 5).reshape((img_a1.shape[0], img_a1.shape[1], img_a1.shape[2] * upscale, img_a1.shape[3] * upscale))
     out = out / q
-    # print(out.shape)
     return out

src/models/rclut.py

@@ -19,7 +19,7 @@ class RCLutCentered_3x3(nn.Module):
         self.rc_conv_luts = nn.Parameter(torch.randint(0, 255, size=(3, 3, 256//quantization_interval+1)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         rc_conv_luts, dense_conv_lut
     ):   
         scale = int(dense_conv_lut.shape[-1])
@@ -60,7 +60,7 @@ class RCLutCentered_7x7(nn.Module):
         self.rc_conv_luts = nn.Parameter(torch.randint(0, 255, size=(7, 7, 256//quantization_interval+1)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         rc_conv_luts, dense_conv_lut
     ):   
         scale = int(dense_conv_lut.shape[-1])
@@ -99,7 +99,7 @@ class RCLutRot90_3x3(nn.Module):
         self.rc_conv_luts = nn.Parameter(torch.randint(0, 255, size=(3, 3, 256//quantization_interval+1)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         rc_conv_luts, dense_conv_lut
     ):   
         scale = int(dense_conv_lut.shape[-1])
@@ -143,7 +143,7 @@ class RCLutRot90_7x7(nn.Module):
         self.rc_conv_luts = nn.Parameter(torch.randint(0, 255, size=(7, 7, 256//quantization_interval+1)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         rc_conv_luts, dense_conv_lut
     ):   
         scale = int(dense_conv_lut.shape[-1])
@@ -192,7 +192,7 @@ class RCLutx1(nn.Module):
         self.dense_conv_lut_7x7 = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         rc_conv_luts_3x3, dense_conv_lut_3x3,
         rc_conv_luts_5x5, dense_conv_lut_5x5,
         rc_conv_luts_7x7, dense_conv_lut_7x7
@@ -279,7 +279,7 @@ class RCLutx2(nn.Module):
         self.s2_dense_conv_lut_7x7 = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3,
         s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5,
         s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7,
@@ -404,7 +404,7 @@ class RCLutx2Centered(nn.Module):
         self.s2_dense_conv_lut_7x7 = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3,
         s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5,
         s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7,

src/models/rcnet.py

@@ -68,7 +68,7 @@ class RCNetCentered_3x3(nn.Module):
         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_3x3.init_from_lut(rc_conv_luts, dense_conv_lut)
+        lut_model = rclut.RCLutCentered_3x3.init_from_numpy(rc_conv_luts, dense_conv_lut)
         return lut_model
 
 class RCNetCentered_7x7(nn.Module):
@@ -91,7 +91,7 @@ class RCNetCentered_7x7(nn.Module):
         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)
+        lut_model = rclut.RCLutCentered_7x7.init_from_numpy(rc_conv_luts, dense_conv_lut)
         return lut_model
 
 
@@ -149,7 +149,7 @@ class RCNetRot90_3x3(nn.Module):
         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.RCLutRot90_3x3.init_from_lut(rc_conv_luts, dense_conv_lut)
+        lut_model = rclut.RCLutRot90_3x3.init_from_numpy(rc_conv_luts, dense_conv_lut)
         return lut_model
 
     def forward(self, x):
@@ -177,7 +177,7 @@ class RCNetRot90_7x7(nn.Module):
         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.RCLutRot90_7x7.init_from_lut(rc_conv_luts, dense_conv_lut)
+        lut_model = rclut.RCLutRot90_7x7.init_from_numpy(rc_conv_luts, dense_conv_lut)
         return lut_model
 
     def forward(self, x):
@@ -212,7 +212,7 @@ class RCNetx1(nn.Module):
         rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage1_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
         dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage1_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
         
-        lut_model = rclut.RCLutx1.init_from_lut(
+        lut_model = rclut.RCLutx1.init_from_numpy(
             rc_conv_luts_3x3=rc_conv_luts_3x3, dense_conv_lut_3x3=dense_conv_lut_3x3,
             rc_conv_luts_5x5=rc_conv_luts_5x5, dense_conv_lut_5x5=dense_conv_lut_5x5,
             rc_conv_luts_7x7=rc_conv_luts_7x7, dense_conv_lut_7x7=dense_conv_lut_7x7
@@ -265,7 +265,7 @@ class RCNetx2(nn.Module):
         s2_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage2_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
         s2_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage2_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
         
-        lut_model = rclut.RCLutx2.init_from_lut(
+        lut_model = rclut.RCLutx2.init_from_numpy(
             s1_rc_conv_luts_3x3=s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3=s1_dense_conv_lut_3x3,
             s1_rc_conv_luts_5x5=s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5=s1_dense_conv_lut_5x5,
             s1_rc_conv_luts_7x7=s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7=s1_dense_conv_lut_7x7,
@@ -327,7 +327,7 @@ class RCNetx2Centered(nn.Module):
         s2_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage2_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
         s2_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage2_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
         
-        lut_model = rclut.RCLutx2Centered.init_from_lut(
+        lut_model = rclut.RCLutx2Centered.init_from_numpy(
             s1_rc_conv_luts_3x3=s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3=s1_dense_conv_lut_3x3,
             s1_rc_conv_luts_5x5=s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5=s1_dense_conv_lut_5x5,
             s1_rc_conv_luts_7x7=s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7=s1_dense_conv_lut_7x7,
@@ -430,7 +430,7 @@ class RCNetx2Unlutable(nn.Module):
         s2_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage2_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
         s2_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage2_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
         
-        lut_model = rclut.RCLutx2.init_from_lut(
+        lut_model = rclut.RCLutx2.init_from_numpy(
             s1_rc_conv_luts_3x3=s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3=s1_dense_conv_lut_3x3,
             s1_rc_conv_luts_5x5=s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5=s1_dense_conv_lut_5x5,
             s1_rc_conv_luts_7x7=s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7=s1_dense_conv_lut_7x7,
@@ -535,7 +535,7 @@ class RCNetx2CenteredUnlutable(nn.Module):
         s2_rc_conv_luts_7x7 = lut.transfer_rc_conv(self.stage2_7x7.rc_conv, quantization_interval=quantization_interval).reshape(7,7,-1)
         s2_dense_conv_lut_7x7 = lut.transfer_2x2_input_SxS_output(self.stage2_7x7.dense_conv_block, quantization_interval=quantization_interval, batch_size=batch_size)
         
-        lut_model = rclut.RCLutx2Centered.init_from_lut(
+        lut_model = rclut.RCLutx2Centered.init_from_numpy(
             s1_rc_conv_luts_3x3=s1_rc_conv_luts_3x3, s1_dense_conv_lut_3x3=s1_dense_conv_lut_3x3,
             s1_rc_conv_luts_5x5=s1_rc_conv_luts_5x5, s1_dense_conv_lut_5x5=s1_dense_conv_lut_5x5,
             s1_rc_conv_luts_7x7=s1_rc_conv_luts_7x7, s1_dense_conv_lut_7x7=s1_dense_conv_lut_7x7,

src/models/sdylut.py

@@ -23,7 +23,7 @@ class SDYLutx1(nn.Module):
         self.stageY = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         stageS, stageD, stageY
     ):   
         scale = int(stageS.shape[-1])
@@ -89,7 +89,7 @@ class SDYLutx2(nn.Module):
         self.stage2_Y = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y
     ):   
         scale = int(stage2_S.shape[-1])
@@ -182,7 +182,7 @@ class SDYLutCenteredx1(nn.Module):
         self.stageY = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         stageS, stageD, stageY
     ):   
         scale = int(stageS.shape[-1])

src/models/sdynet.py

@@ -41,7 +41,7 @@ class SDYNetx1(nn.Module):
         stageS = lut.transfer_2x2_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
         stageD = lut.transfer_2x2_input_SxS_output(self.stage1_D, quantization_interval=quantization_interval, batch_size=batch_size)
         stageY = lut.transfer_2x2_input_SxS_output(self.stage1_Y, quantization_interval=quantization_interval, batch_size=batch_size)
-        lut_model = sdylut.SDYLutx1.init_from_lut(stageS, stageD, stageY)
+        lut_model = sdylut.SDYLutx1.init_from_numpy(stageS, stageD, stageY)
         return lut_model
 
 class SDYNetx2(nn.Module):
@@ -93,5 +93,5 @@ class SDYNetx2(nn.Module):
         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.SDYLutx2.init_from_lut(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
+        lut_model = sdylut.SDYLutx2.init_from_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
         return lut_model

src/models/srlut.py

@@ -18,7 +18,7 @@ class SRLut(nn.Module):
         self.stage_lut = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         stage_lut
     ):   
         scale = int(stage_lut.shape[-1])
@@ -51,7 +51,7 @@ class SRLutR90(nn.Module):
         self.stage_lut = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy(
         stage_lut
     ):   
         scale = int(stage_lut.shape[-1])
@@ -91,7 +91,7 @@ class SRLutR90Y(nn.Module):
         self.ycbcr_to_rgb = layers.YcbcrToRgb()
 
     @staticmethod
-    def init_from_lut(
+    def init_from_numpy( 
         stage_lut
     ):   
         scale = int(stage_lut.shape[-1])

src/models/srnet.py

@@ -31,7 +31,7 @@ class SRNet(nn.Module):
 
     def get_lut_model(self, quantization_interval=16, batch_size=2**10):
         stage_lut = lut.transfer_2x2_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
-        lut_model = srlut.SRLut.init_from_lut(stage_lut)
+        lut_model = srlut.SRLut.init_from_numpy(stage_lut)
         return lut_model
 
 class SRNetR90(nn.Module):
@@ -62,7 +62,7 @@ class SRNetR90(nn.Module):
 
     def get_lut_model(self, quantization_interval=16, batch_size=2**10):
         stage_lut = lut.transfer_2x2_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
-        lut_model = srlut.SRLutR90.init_from_lut(stage_lut)
+        lut_model = srlut.SRLutR90.init_from_numpy(stage_lut)
         return lut_model
 
 class SRNetR90Y(nn.Module):
@@ -101,5 +101,5 @@ class SRNetR90Y(nn.Module):
 
     def get_lut_model(self, quantization_interval=16, batch_size=2**10):
         stage_lut = lut.transfer_2x2_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
-        lut_model = srlut.SRLutR90Y.init_from_lut(stage_lut)
+        lut_model = srlut.SRLutR90Y.init_from_numpy(stage_lut)
         return lut_model