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hdblut complete

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protsenkovi 5 months ago
parent 6103a929c7
commit ca53d81a48
3 changed files with 85 additions and 359 deletions
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2
src/models/__init__.py
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@ -29,7 +29,7 @@ AVAILABLE_MODELS = {
'SRNetY': srnet.SRNetY,
'SRLutY': srlut.SRLutY,
'HDBNet': hdbnet.HDBNet,
'HDBLNet': hdbnet.HDBLNet,
'HDBLut': hdblut.HDBLut,
# 'RCNetCentered_3x3': rcnet.RCNetCentered_3x3, 'RCLutCentered_3x3': rclut.RCLutCentered_3x3,
# 'RCNetCentered_7x7': rcnet.RCNetCentered_7x7, 'RCLutCentered_7x7': rclut.RCLutCentered_7x7,
# 'RCNetRot90_3x3': rcnet.RCNetRot90_3x3, 'RCLutRot90_3x3': rclut.RCLutRot90_3x3,

222
src/models/hdblut.py
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@ -14,19 +14,47 @@ class HDBLut(nn.Module):
scale
):
super(HDBLut, self).__init__()
assert scale == 4
self.scale = scale
self.quantization_interval = quantization_interval
self.stage_lut = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
self.stage1_3H = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage1_3D = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage1_3B = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage1_2H = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage1_2D = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage2_3H = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage2_3D = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage2_3B = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage2_2H = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
self.stage2_2D = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (2,2)).type(torch.float32))
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)
@staticmethod
def init_from_numpy(
stage_lut
stage1_3H, stage1_3D, stage1_3B, stage1_2H, stage1_2D,
stage2_3H, stage2_3D, stage2_3B, stage2_2H, stage2_2D
):
scale = int(stage_lut.shape[-1])
quantization_interval = 256//(stage_lut.shape[0]-1)
lut_model = HDBLut(quantization_interval=quantization_interval, scale=scale)
lut_model.stage_lut = nn.Parameter(torch.tensor(stage_lut).type(torch.float32))
lut_model.stage1_3H = nn.Parameter(torch.tensor(stage1_3H).type(torch.float32))
lut_model.stage1_3D = nn.Parameter(torch.tensor(stage1_3D).type(torch.float32))
lut_model.stage1_3B = nn.Parameter(torch.tensor(stage1_3B).type(torch.float32))
lut_model.stage1_2H = nn.Parameter(torch.tensor(stage1_2H).type(torch.float32))
lut_model.stage1_2D = nn.Parameter(torch.tensor(stage1_2D).type(torch.float32))
lut_model.stage2_3H = nn.Parameter(torch.tensor(stage2_3H).type(torch.float32))
lut_model.stage2_3D = nn.Parameter(torch.tensor(stage2_3D).type(torch.float32))
lut_model.stage2_3B = nn.Parameter(torch.tensor(stage2_3B).type(torch.float32))
lut_model.stage2_2H = nn.Parameter(torch.tensor(stage2_2H).type(torch.float32))
lut_model.stage2_2D = nn.Parameter(torch.tensor(stage2_2D).type(torch.float32))
return lut_model
def forward_stage(self, x, scale, percieve_pattern, lut):
@ -41,161 +69,41 @@ class HDBLut(nn.Module):
def forward(self, x):
b,c,h,w = x.shape
x = x.reshape(b*c, 1, h, w).type(torch.float32)
x = self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage_lut)
x = x.reshape(b*c, 1, h, w)
lsb = x % 16
msb = x - lsb
output_msb = torch.zeros([b*c, 1, h*2, w*2], dtype=x.dtype, device=x.device)
output_lsb = torch.zeros([b*c, 1, h*2, w*2], dtype=x.dtype, device=x.device)
for rotations_count in range(4):
rotated_msb = torch.rot90(msb, k=rotations_count, dims=[2, 3])
rotated_lsb = torch.rot90(lsb, k=rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3H, self.stage1_3H), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3D, self.stage1_3D), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3B, self.stage1_3B), k=-rotations_count, dims=[2, 3])
output_lsb += torch.rot90(self.forward_stage(rotated_lsb, 2, self._extract_pattern_2H, self.stage1_2H), k=-rotations_count, dims=[2, 3])
output_lsb += torch.rot90(self.forward_stage(rotated_lsb, 2, self._extract_pattern_2D, self.stage1_2D), k=-rotations_count, dims=[2, 3])
output_msb /= 4*3
output_lsb /= 4*2
output_msb = output_msb + output_lsb
x = output_msb
lsb = x % 16
msb = x - lsb
output_msb = torch.zeros([b*c, 1, h*4, w*4], dtype=x.dtype, device=x.device)
output_lsb = torch.zeros([b*c, 1, h*4, w*4], dtype=x.dtype, device=x.device)
for rotations_count in range(4):
rotated_msb = torch.rot90(msb, k=rotations_count, dims=[2, 3])
rotated_lsb = torch.rot90(lsb, k=rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3H, self.stage2_3H), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3D, self.stage2_3D), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3B, self.stage2_3B), k=-rotations_count, dims=[2, 3])
output_lsb += torch.rot90(self.forward_stage(rotated_lsb, 2, self._extract_pattern_2H, self.stage2_2H), k=-rotations_count, dims=[2, 3])
output_lsb += torch.rot90(self.forward_stage(rotated_lsb, 2, self._extract_pattern_2D, self.stage2_2D), k=-rotations_count, dims=[2, 3])
output_msb /= 4*3
output_lsb /= 4*2
output_msb = output_msb + output_lsb
x = output_msb
x = x.reshape(b, c, h*self.scale, w*self.scale)
return x
def __repr__(self):
return f"{self.__class__.__name__}\n lut size: {self.stage_lut.shape}"
# class SRLutY(nn.Module):
# def __init__(
# self,
# quantization_interval,
# scale
# ):
# super(SRLutY, self).__init__()
# self.scale = scale
# self.quantization_interval = quantization_interval
# self.stage_lut = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
# self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
# self.rgb_to_ycbcr = layers.RgbToYcbcr()
# self.ycbcr_to_rgb = layers.YcbcrToRgb()
# @staticmethod
# def init_from_numpy(
# stage_lut
# ):
# scale = int(stage_lut.shape[-1])
# quantization_interval = 256//(stage_lut.shape[0]-1)
# lut_model = SRLutY(quantization_interval=quantization_interval, scale=scale)
# lut_model.stage_lut = nn.Parameter(torch.tensor(stage_lut).type(torch.float32))
# return lut_model
# def forward_stage(self, x, scale, percieve_pattern, lut):
# b,c,h,w = x.shape
# x = percieve_pattern(x)
# x = select_index_4dlut_tetrahedral(index=x, lut=lut)
# x = round_func(x)
# x = x.reshape(b, c, h, w, scale, scale)
# x = x.permute(0,1,2,4,3,5)
# x = x.reshape(b, c, h*scale, w*scale)
# return x
# def forward(self, x):
# b,c,h,w = x.shape
# x = self.rgb_to_ycbcr(x)
# y = x[:,0:1,:,:]
# cbcr = x[:,1:,:,:]
# cbcr_scaled = F.interpolate(cbcr, size=[h*self.scale, w*self.scale], mode='bilinear')
# output = self.forward_stage(y, self.scale, self._extract_pattern_S, self.stage_lut)
# output = torch.cat([output, cbcr_scaled], dim=1)
# output = self.ycbcr_to_rgb(output).clamp(0, 255)
# return output
# def __repr__(self):
# return f"{self.__class__.__name__}\n lut size: {self.stage_lut.shape}"
# class SRLutR90(nn.Module):
# def __init__(
# self,
# quantization_interval,
# scale
# ):
# super(SRLutR90, self).__init__()
# self.scale = scale
# self.quantization_interval = quantization_interval
# self.stage_lut = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
# self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
# @staticmethod
# def init_from_numpy(
# stage_lut
# ):
# scale = int(stage_lut.shape[-1])
# quantization_interval = 256//(stage_lut.shape[0]-1)
# lut_model = SRLutR90(quantization_interval=quantization_interval, scale=scale)
# lut_model.stage_lut = nn.Parameter(torch.tensor(stage_lut).type(torch.float32))
# return lut_model
# def forward_stage(self, x, scale, percieve_pattern, lut):
# b,c,h,w = x.shape
# x = percieve_pattern(x)
# x = select_index_4dlut_tetrahedral(index=x, lut=lut)
# x = round_func(x)
# x = x.reshape(b, c, h, w, scale, scale)
# x = x.permute(0,1,2,4,3,5)
# x = x.reshape(b, c, h*scale, w*scale)
# return x
# def forward(self, x):
# b,c,h,w = x.shape
# x = x.reshape(b*c, 1, h, w)
# output = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=torch.float32, device=x.device)
# output += self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage_lut)
# 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.scale, self._extract_pattern_S, self.stage_lut), k=-rotations_count, dims=[2, 3])
# output /= 4
# output = output.reshape(b, c, h*self.scale, w*self.scale)
# return output
# def __repr__(self):
# return f"{self.__class__.__name__}\n lut size: {self.stage_lut.shape}"
# class SRLutR90Y(nn.Module):
# def __init__(
# self,
# quantization_interval,
# scale
# ):
# super(SRLutR90Y, self).__init__()
# self.scale = scale
# self.quantization_interval = quantization_interval
# self.stage_lut = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
# self._extract_pattern_S = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
# self.rgb_to_ycbcr = layers.RgbToYcbcr()
# self.ycbcr_to_rgb = layers.YcbcrToRgb()
# @staticmethod
# def init_from_numpy(
# stage_lut
# ):
# scale = int(stage_lut.shape[-1])
# quantization_interval = 256//(stage_lut.shape[0]-1)
# lut_model = SRLutR90Y(quantization_interval=quantization_interval, scale=scale)
# lut_model.stage_lut = nn.Parameter(torch.tensor(stage_lut).type(torch.float32))
# return lut_model
# def forward_stage(self, x, scale, percieve_pattern, lut):
# b,c,h,w = x.shape
# x = percieve_pattern(x)
# x = select_index_4dlut_tetrahedral(index=x, lut=lut)
# x = round_func(x)
# x = x.reshape(b, c, h, w, scale, scale)
# x = x.permute(0,1,2,4,3,5)
# x = x.reshape(b, c, h*scale, w*scale)
# return x
# def forward(self, x):
# b,c,h,w = x.shape
# x = self.rgb_to_ycbcr(x)
# y = x[:,0:1,:,:]
# cbcr = x[:,1:,:,:]
# cbcr_scaled = F.interpolate(cbcr, size=[h*self.scale, w*self.scale], mode='bilinear')
# output = torch.zeros([b, 1, h*self.scale, w*self.scale], dtype=torch.float32, device=x.device)
# output += self.forward_stage(y, self.scale, self._extract_pattern_S, self.stage_lut)
# for rotations_count in range(1,4):
# rotated = torch.rot90(y, k=rotations_count, dims=[2, 3])
# output += torch.rot90(self.forward_stage(rotated, self.scale, self._extract_pattern_S, self.stage_lut), k=-rotations_count, dims=[2, 3])
# output /= 4
# output = torch.cat([output, cbcr_scaled], dim=1)
# output = self.ycbcr_to_rgb(output).clamp(0, 255)
# return output
# def __repr__(self):
# return f"{self.__class__.__name__}\n lut size: {self.stage_lut.shape}"

216
src/models/hdbnet.py
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@ -11,6 +11,7 @@ from common import layers
class HDBNet(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(HDBNet, self).__init__()
assert scale == 4
self.scale = scale
self.stage1_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage1_3D = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
@ -79,203 +80,20 @@ class HDBNet(nn.Module):
return x
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_numpy(stage_lut)
stage1_3H = lut.transfer_2x2_input_SxS_output(self.stage1_3H, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_3D = lut.transfer_2x2_input_SxS_output(self.stage1_3D, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_3B = lut.transfer_2x2_input_SxS_output(self.stage1_3B, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_2H = lut.transfer_2x2_input_SxS_output(self.stage1_2H, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_2D = lut.transfer_2x2_input_SxS_output(self.stage1_2D, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_3H = lut.transfer_2x2_input_SxS_output(self.stage2_3H, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_3D = lut.transfer_2x2_input_SxS_output(self.stage2_3D, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_3B = lut.transfer_2x2_input_SxS_output(self.stage2_3B, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_2H = lut.transfer_2x2_input_SxS_output(self.stage2_2H, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_2D = lut.transfer_2x2_input_SxS_output(self.stage2_2D, quantization_interval=quantization_interval, batch_size=batch_size)
lut_model = hdblut.HDBLut.init_from_numpy(
stage1_3H, stage1_3D, stage1_3B, stage1_2H, stage1_2D,
stage2_3H, stage2_3D, stage2_3B, stage2_2H, stage2_2D
)
return lut_model
class HDBLNet(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(HDBLNet, self).__init__()
self.scale = scale
self.stage1_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage1_3D = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage1_3B = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage1_3L = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage2_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage2_3D = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage2_3B = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
self.stage2_3L = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2)
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_3L = layers.PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,1]], center=[0,0], window_size=3)
def forward_stage(self, x, scale, percieve_pattern, stage):
b,c,h,w = x.shape
x = percieve_pattern(x)
x = stage(x)
x = round_func(x)
x = x.reshape(b, c, h, w, scale, scale)
x = x.permute(0,1,2,4,3,5)
x = x.reshape(b, c, h*scale, w*scale)
return x
def forward(self, x):
b,c,h,w = x.shape
x = x.reshape(b*c, 1, h, w)
lsb = x % 16
msb = x - lsb
output_msb = torch.zeros([b*c, 1, h*2, w*2], dtype=x.dtype, device=x.device)
output_lsb = torch.zeros([b*c, 1, h*2, w*2], dtype=x.dtype, device=x.device)
for rotations_count in range(4):
rotated_msb = torch.rot90(msb, k=rotations_count, dims=[2, 3])
rotated_lsb = torch.rot90(lsb, k=rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3H, self.stage1_3H), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3D, self.stage1_3D), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3B, self.stage1_3B), k=-rotations_count, dims=[2, 3])
output_lsb += torch.rot90(self.forward_stage(rotated_lsb, 2, self._extract_pattern_3L, self.stage1_3L), k=-rotations_count, dims=[2, 3])
output_msb /= 4*3
output_lsb /= 4
output_msb = output_msb + output_lsb
x = output_msb
lsb = x % 16
msb = x - lsb
output_msb = torch.zeros([b*c, 1, h*4, w*4], dtype=x.dtype, device=x.device)
output_lsb = torch.zeros([b*c, 1, h*4, w*4], dtype=x.dtype, device=x.device)
for rotations_count in range(4):
rotated_msb = torch.rot90(msb, k=rotations_count, dims=[2, 3])
rotated_lsb = torch.rot90(lsb, k=rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3H, self.stage2_3H), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3D, self.stage2_3D), k=-rotations_count, dims=[2, 3])
output_msb += torch.rot90(self.forward_stage(rotated_msb, 2, self._extract_pattern_3B, self.stage2_3B), k=-rotations_count, dims=[2, 3])
output_lsb += torch.rot90(self.forward_stage(rotated_lsb, 2, self._extract_pattern_3L, self.stage2_3L), k=-rotations_count, dims=[2, 3])
output_msb /= 4*3
output_lsb /= 4
output_msb = output_msb + output_lsb
x = output_msb
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_2x2_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 SRNetY(nn.Module):
# def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
# super(SRNetY, self).__init__()
# self.scale = scale
# self.stage1_S = layers.UpscaleBlock(
# 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.rgb_to_ycbcr = layers.RgbToYcbcr()
# self.ycbcr_to_rgb = layers.YcbcrToRgb()
# def forward_stage(self, x, scale, percieve_pattern, stage):
# b,c,h,w = x.shape
# x = percieve_pattern(x)
# x = stage(x)
# x = round_func(x)
# x = x.reshape(b, c, h, w, scale, scale)
# x = x.permute(0,1,2,4,3,5)
# x = x.reshape(b, c, h*scale, w*scale)
# return x
# def forward(self, x):
# b,c,h,w = x.shape
# x = self.rgb_to_ycbcr(x)
# y = x[:,0:1,:,:]
# cbcr = x[:,1:,:,:]
# cbcr_scaled = F.interpolate(cbcr, size=[h*self.scale, w*self.scale], mode='bilinear')
# x = y.view(b, 1, h, w)
# output = self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage1_S)
# output = torch.cat([output, cbcr_scaled], dim=1)
# output = self.ycbcr_to_rgb(output).clamp(0, 255)
# return output
# 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.SRLutY.init_from_numpy(stage_lut)
# return lut_model
# class SRNetR90(nn.Module):
# def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
# super(SRNetR90, self).__init__()
# self.scale = scale
# self.stage1_S = layers.UpscaleBlock(
# 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_stage(self, x, scale, percieve_pattern, stage):
# b,c,h,w = x.shape
# x = percieve_pattern(x)
# x = stage(x)
# x = round_func(x)
# x = x.reshape(b, c, h, w, scale, scale)
# x = x.permute(0,1,2,4,3,5)
# x = x.reshape(b, c, h*scale, w*scale)
# return x
# def forward(self, x):
# b,c,h,w = x.shape
# x = x.reshape(b*c, 1, h, w)
# 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.stage1_S)
# 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.scale, self._extract_pattern_S, self.stage1_S), k=-rotations_count, dims=[2, 3])
# output /= 4
# output = output.reshape(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.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
# lut_model = srlut.SRLutR90.init_from_numpy(stage_lut)
# return lut_model
# class SRNetR90Y(nn.Module):
# def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
# super(SRNetR90Y, self).__init__()
# self.scale = scale
# s_pattern=[[0,0],[0,1],[1,0],[1,1]]
# self.stage1_S = layers.UpscaleBlock(
# 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.rgb_to_ycbcr = layers.RgbToYcbcr()
# self.ycbcr_to_rgb = layers.YcbcrToRgb()
# def forward_stage(self, x, scale, percieve_pattern, stage):
# b,c,h,w = x.shape
# x = percieve_pattern(x)
# x = stage(x)
# x = round_func(x)
# x = x.reshape(b, c, h, w, scale, scale)
# x = x.permute(0,1,2,4,3,5)
# x = x.reshape(b, c, h*scale, w*scale)
# return x
# def forward(self, x):
# b,c,h,w = x.shape
# x = self.rgb_to_ycbcr(x)
# y = x[:,0:1,:,:]
# cbcr = x[:,1:,:,:]
# cbcr_scaled = F.interpolate(cbcr, size=[h*self.scale, w*self.scale], mode='bilinear')
# x = y.view(b, 1, h, w)
# output = torch.zeros([b, 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.stage1_S)
# 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.scale, self._extract_pattern_S, self.stage1_S), k=-rotations_count, dims=[2, 3])
# output /= 4
# output = torch.cat([output, cbcr_scaled], dim=1)
# output = self.ycbcr_to_rgb(output).clamp(0, 255)
# return output
# 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_numpy(stage_lut)
# return lut_model
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