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no rot 2 stage sdy model

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main
protsenkovi 7 months ago
parent b641401ed7
commit ae8f7b6742
3 changed files with 232 additions and 7 deletions
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4
src/models/__init__.py
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@ -18,6 +18,10 @@ AVAILABLE_MODELS = {
'SDYLutx1': sdylut.SDYLutx1,
'SDYNetx2': sdynet.SDYNetx2,
'SDYLutx2': sdylut.SDYLutx2,
'SDYNetR90x1': sdynet.SDYNetR90x1,
'SDYLutR90x1': sdylut.SDYLutR90x1,
'SDYNetR90x2': sdynet.SDYNetR90x2,
'SDYLutR90x2': sdylut.SDYLutR90x2,
'SRNetY': srnet.SRNetY,
'SRLutY': srlut.SRLutY,
# 'RCNetCentered_3x3': rcnet.RCNetCentered_3x3, 'RCLutCentered_3x3': rclut.RCLutCentered_3x3,

131
src/models/sdylut.py
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@ -45,6 +45,131 @@ class SDYLutx1(nn.Module):
x = x.reshape(b, c, h*scale, w*scale)
return x
def forward(self, x):
b,c,h,w = x.shape
x = x.view(b*c, 1, h, w).type(torch.float32)
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.stageS)
output += self.forward_stage(x, self.scale, self._extract_pattern_D, self.stageD)
output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stageY)
output /= 3
output = round_func(output)
output = output.view(b, c, h*self.scale, w*self.scale)
return output
def __repr__(self):
return f"{self.__class__.__name__}" + \
f"\n stageS size: {self.stageS.shape}" + \
f"\n stageD size: {self.stageD.shape}" + \
f"\n stageY size: {self.stageY.shape}"
class SDYLutx2(nn.Module):
def __init__(
self,
quantization_interval,
scale
):
super(SDYLutx2, self).__init__()
self.scale = scale
self.quantization_interval = quantization_interval
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]], center=[0,0], window_size=3)
self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]], center=[0,0], window_size=3)
self.stage1_S = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self.stage1_D = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self.stage1_Y = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self.stage2_S = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self.stage2_D = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self.stage2_Y = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
@staticmethod
def init_from_numpy(
stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y
):
scale = int(stageS.shape[-1])
quantization_interval = 256//(stageS.shape[0]-1)
lut_model = SDYLutx2(quantization_interval=quantization_interval, scale=scale)
lut_model.stage1_S = nn.Parameter(torch.tensor(stage1_S).type(torch.float32))
lut_model.stage1_D = nn.Parameter(torch.tensor(stage1_D).type(torch.float32))
lut_model.stage1_Y = nn.Parameter(torch.tensor(stage1_Y).type(torch.float32))
lut_model.stage2_S = nn.Parameter(torch.tensor(stage2_S).type(torch.float32))
lut_model.stage2_D = nn.Parameter(torch.tensor(stage2_D).type(torch.float32))
lut_model.stage2_Y = nn.Parameter(torch.tensor(stage2_Y).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.view(b*c, 1, h, w).type(torch.float32)
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 /= 3
output = round_func(output)
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 /= 3
output = round_func(output)
output = output.view(b, c, h*self.scale, w*self.scale)
return output
def __repr__(self):
return f"{self.__class__.__name__}" + \
f"\n stageS size: {self.stageS.shape}" + \
f"\n stageD size: {self.stageD.shape}" + \
f"\n stageY size: {self.stageY.shape}"
class SDYLutR90x1(nn.Module):
def __init__(
self,
quantization_interval,
scale
):
super(SDYLutR90x1, self).__init__()
self.scale = scale
self.quantization_interval = quantization_interval
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]], center=[0,0], window_size=3)
self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]], center=[0,0], window_size=3)
self.stageS = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self.stageD = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
self.stageY = nn.Parameter(torch.randint(0, 255, size=(256//quantization_interval+1,)*4 + (scale,scale)).type(torch.float32))
@staticmethod
def init_from_numpy(
stageS, stageD, stageY
):
scale = int(stageS.shape[-1])
quantization_interval = 256//(stageS.shape[0]-1)
lut_model = SDYLutR90x1(quantization_interval=quantization_interval, scale=scale)
lut_model.stageS = nn.Parameter(torch.tensor(stageS).type(torch.float32))
lut_model.stageD = nn.Parameter(torch.tensor(stageD).type(torch.float32))
lut_model.stageY = nn.Parameter(torch.tensor(stageY).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.view(b*c, 1, h, w).type(torch.float32)
@ -70,13 +195,13 @@ class SDYLutx1(nn.Module):
class SDYLutx2(nn.Module):
class SDYLutR90x2(nn.Module):
def __init__(
self,
quantization_interval,
scale
):
super(SDYLutx2, self).__init__()
super(SDYLutR90x2, self).__init__()
self.scale = scale
self.quantization_interval = quantization_interval
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2)
@ -95,7 +220,7 @@ class SDYLutx2(nn.Module):
):
scale = int(stage2_S.shape[-1])
quantization_interval = 256//(stage2_S.shape[0]-1)
lut_model = SDYLutx2(quantization_interval=quantization_interval, scale=scale)
lut_model = SDYLutR90x2(quantization_interval=quantization_interval, scale=scale)
lut_model.stage1_S = nn.Parameter(torch.tensor(stage1_S).type(torch.float32))
lut_model.stage1_D = nn.Parameter(torch.tensor(stage1_D).type(torch.float32))
lut_model.stage1_Y = nn.Parameter(torch.tensor(stage1_Y).type(torch.float32))

104
src/models/sdynet.py
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@ -8,6 +8,7 @@ from common import layers
from pathlib import Path
from . import sdylut
class SDYNetx1(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetx1, self).__init__()
@ -29,6 +30,101 @@ class SDYNetx1(nn.Module):
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)
output += self.forward_stage(x, self.scale, self._extract_pattern_D, self.stage1_D)
output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage1_Y)
output /= 3
x = output
x = round_func(x)
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):
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_numpy(stageS, stageD, stageY)
return lut_model
class SDYNetx2(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetx2, 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=1)
self.stage1_D = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stage1_Y = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stage2_S = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
self.stage2_D = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
self.stage2_Y = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
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, 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 /= 3
x = output
x = round_func(x)
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 /= 3
x = output
x = round_func(x)
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):
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.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_D = lut.transfer_2x2_input_SxS_output(self.stage1_D, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_Y = lut.transfer_2x2_input_SxS_output(self.stage1_Y, quantization_interval=quantization_interval, batch_size=batch_size)
lut_model = sdylut.SDYLutx2.init_from_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
return lut_model
class SDYNetR90x1(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetR90x1, 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)
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)
@ -51,12 +147,12 @@ 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_numpy(stageS, stageD, stageY)
lut_model = sdylut.SDYLutR90x1.init_from_numpy(stageS, stageD, stageY)
return lut_model
class SDYNetx2(nn.Module):
class SDYNetR90x2(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetx2, self).__init__()
super(SDYNetR90x2, 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)
@ -113,5 +209,5 @@ 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_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
lut_model = sdylut.SDYLutR90x2.init_from_numpy(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
return lut_model
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