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protsenkovi 5 months ago
parent e2db157055
commit 55adc280be
3 changed files with 109 additions and 61 deletions
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6
src/common/layers.py
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@ -361,7 +361,7 @@ class ChebyKANLayer(nn.Module):
""" """
https://github.com/SynodicMonth/ChebyKAN/blob/main/ChebyKANLayer.py https://github.com/SynodicMonth/ChebyKAN/blob/main/ChebyKANLayer.py
""" """
def __init__(self, in_features, out_features, degree=5): def __init__(self, in_features, out_features, degree=8):
super(ChebyKANLayer, self).__init__() super(ChebyKANLayer, self).__init__()
self.inputdim = in_features self.inputdim = in_features
self.outdim = out_features self.outdim = out_features
@ -396,7 +396,7 @@ class ChebyKANLayer(nn.Module):
class UpscaleBlockChebyKAN(nn.Module): class UpscaleBlockChebyKAN(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): def __init__(self, in_features=4, hidden_dim = 32, layers_count=4, upscale_factor=1, input_max_value=255, output_max_value=255, degree=8):
super(UpscaleBlockChebyKAN, self).__init__() super(UpscaleBlockChebyKAN, self).__init__()
assert layers_count > 0 assert layers_count > 0
self.upscale_factor = upscale_factor self.upscale_factor = upscale_factor
@ -405,7 +405,7 @@ class UpscaleBlockChebyKAN(nn.Module):
self.linear_projections = [] self.linear_projections = []
for i in range(layers_count): for i in range(layers_count):
self.linear_projections.append(ChebyKANLayer(in_features=hidden_dim, out_features=hidden_dim)) self.linear_projections.append(ChebyKANLayer(in_features=hidden_dim, out_features=hidden_dim, degree=degree))
self.linear_projections = nn.ModuleList(self.linear_projections) 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.project_channels = nn.Linear(in_features=hidden_dim, out_features=upscale_factor * upscale_factor, bias=True)

103
src/models/hdbnet.py
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@ -11,10 +11,11 @@ from itertools import cycle
from models.base import SRNetBase from models.base import SRNetBase
class HDBNet(SRNetBase): class HDBNet(SRNetBase):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4): def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4, rotations = 4):
super(HDBNet, self).__init__() super(HDBNet, self).__init__()
assert scale == 4 assert scale == 4
self.scale = scale self.scale = scale
self.rotations = rotations
self.stage1_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2, input_max_value=255, output_max_value=255) self.stage1_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2, 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=2, 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=2, 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=2, 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=2, input_max_value=255, output_max_value=255)
@ -31,9 +32,7 @@ class HDBNet(SRNetBase):
self._extract_pattern_3D = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,1],[2,2]], 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_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_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) self._extract_pattern_2D = layers.PercievePattern(receptive_field_idxes=[[0,0],[1,1]], center=[0,0], window_size=2)
self.rotations = 4
def forward_stage(self, x, scale, percieve_pattern, stage): def forward_stage(self, x, scale, percieve_pattern, stage):
b,c,h,w = x.shape b,c,h,w = x.shape
@ -113,6 +112,102 @@ class HDBNet(SRNetBase):
return F.mse_loss(pred/255, target/255) return F.mse_loss(pred/255, target/255)
return loss_fn return loss_fn
class HDBNetv2(SRNetBase):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4, rotations = 4):
super(HDBNetv2, 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=2, 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=2, 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=2, 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=2, 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=2, input_max_value=15, output_max_value=255)
self.stage2_3H = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2, input_max_value=255, output_max_value=255)
self.stage2_3D = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2, input_max_value=255, output_max_value=255)
self.stage2_3B = layers.UpscaleBlock(in_features=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2, input_max_value=255, output_max_value=255)
self.stage2_2H = layers.UpscaleBlock(in_features=2, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2, input_max_value=15, output_max_value=255)
self.stage2_2D = layers.UpscaleBlock(in_features=2, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=2, 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)
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(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, 2, self._extract_pattern_3H, self.stage1_3H) + \
self.forward_stage(rotated_msb, 2, self._extract_pattern_3D, self.stage1_3D) + \
self.forward_stage(rotated_msb, 2, self._extract_pattern_3B, self.stage1_3B)
output_lsbt = self.forward_stage(rotated_lsb, 2, self._extract_pattern_2H, self.stage1_2H) + \
self.forward_stage(rotated_lsb, 2, 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*2*2, w*2*2], dtype=x.dtype, device=x.device)
output_lsb = torch.zeros([b*c, 1, h*2*2, w*2*2], 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, 2, self._extract_pattern_3H, self.stage2_3H) + \
self.forward_stage(rotated_msb, 2, self._extract_pattern_3D, self.stage2_3D) + \
self.forward_stage(rotated_msb, 2, self._extract_pattern_3B, self.stage2_3B)
output_lsbt = self.forward_stage(rotated_lsb, 2, self._extract_pattern_2H, self.stage2_2H) + \
self.forward_stage(rotated_lsb, 2, 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 = output_msb + output_lsb
x = output.clamp(0, 255)
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_3H = lut.transfer_3_input_SxS_output(self.stage1_3H, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_3D = lut.transfer_3_input_SxS_output(self.stage1_3D, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_3B = lut.transfer_3_input_SxS_output(self.stage1_3B, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_2H = lut.transfer_2_input_SxS_output(self.stage1_2H, quantization_interval=quantization_interval, batch_size=batch_size)
stage1_2D = lut.transfer_2_input_SxS_output(self.stage1_2D, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_3H = lut.transfer_3_input_SxS_output(self.stage2_3H, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_3D = lut.transfer_3_input_SxS_output(self.stage2_3D, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_3B = lut.transfer_3_input_SxS_output(self.stage2_3B, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_2H = lut.transfer_2_input_SxS_output(self.stage2_2H, quantization_interval=quantization_interval, batch_size=batch_size)
stage2_2D = lut.transfer_2_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
def get_loss_fn(self):
def loss_fn(pred, target):
return F.mse_loss(pred/255, target/255)
return loss_fn
class HDBLNet(nn.Module): class HDBLNet(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4): def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):

61
src/models/sdynet.py
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@ -7,9 +7,9 @@ from common import lut
from common import layers from common import layers
from pathlib import Path from pathlib import Path
from . import sdylut from . import sdylut
from models.base import SRNetBase
class SDYNetx1(SRNetBase):
class SDYNetx1(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4): def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetx1, self).__init__() super(SDYNetx1, self).__init__()
self.scale = scale self.scale = scale
@ -20,16 +20,6 @@ class SDYNetx1(nn.Module):
self.stage1_D = 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.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, config=None): def forward(self, x, config=None):
b,c,h,w = x.shape b,c,h,w = x.shape
x = x.reshape(b*c, 1, h, w) x = x.reshape(b*c, 1, h, w)
@ -39,7 +29,6 @@ class SDYNetx1(nn.Module):
output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage1_Y) output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage1_Y)
output /= 3 output /= 3
x = output x = output
x = round_func(x)
x = x.reshape(b, c, h*self.scale, w*self.scale) x = x.reshape(b, c, h*self.scale, w*self.scale)
return x return x
@ -56,7 +45,7 @@ class SDYNetx1(nn.Module):
return loss_fn return loss_fn
class SDYNetx2(nn.Module): class SDYNetx2(SRNetBase):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4): def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetx2, self).__init__() super(SDYNetx2, self).__init__()
self.scale = scale self.scale = scale
@ -70,16 +59,6 @@ class SDYNetx2(nn.Module):
self.stage2_D = 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) 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, config=None): def forward(self, x, config=None):
b,c,h,w = x.shape b,c,h,w = x.shape
x = x.reshape(b*c, 1, h, w) x = x.reshape(b*c, 1, h, w)
@ -89,14 +68,12 @@ class SDYNetx2(nn.Module):
output += self.forward_stage(x, 1, self._extract_pattern_Y, self.stage1_Y) output += self.forward_stage(x, 1, self._extract_pattern_Y, self.stage1_Y)
output /= 3 output /= 3
x = output 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 = 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_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_D, self.stage2_D)
output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage2_Y) output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage2_Y)
output /= 3 output /= 3
x = output x = output
x = round_func(x)
x = x.reshape(b, c, h*self.scale, w*self.scale) x = x.reshape(b, c, h*self.scale, w*self.scale)
return x return x
@ -115,7 +92,7 @@ class SDYNetx2(nn.Module):
return F.mse_loss(pred/255, target/255) return F.mse_loss(pred/255, target/255)
return loss_fn return loss_fn
class SDYNetx3(nn.Module): class SDYNetx3(SRNetBase):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4): def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetx3, self).__init__() super(SDYNetx3, self).__init__()
self.scale = scale self.scale = scale
@ -132,16 +109,6 @@ class SDYNetx3(nn.Module):
self.stage3_D = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage3_D = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
self.stage3_Y = layers.UpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage3_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, config=None): def forward(self, x, config=None):
b,c,h,w = x.shape b,c,h,w = x.shape
x = x.reshape(b*c, 1, h, w) x = x.reshape(b*c, 1, h, w)
@ -151,21 +118,18 @@ class SDYNetx3(nn.Module):
output += self.forward_stage(x, 1, self._extract_pattern_Y, self.stage1_Y) output += self.forward_stage(x, 1, self._extract_pattern_Y, self.stage1_Y)
output /= 3 output /= 3
x = output x = output
x = round_func(x)
output = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device) 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.stage2_S) output += self.forward_stage(x, 1, self._extract_pattern_S, self.stage2_S)
output += self.forward_stage(x, 1, self._extract_pattern_D, self.stage2_D) output += self.forward_stage(x, 1, self._extract_pattern_D, self.stage2_D)
output += self.forward_stage(x, 1, self._extract_pattern_Y, self.stage2_Y) output += self.forward_stage(x, 1, self._extract_pattern_Y, self.stage2_Y)
output /= 3 output /= 3
x = output 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 = 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.stage3_S) output += self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage3_S)
output += self.forward_stage(x, self.scale, self._extract_pattern_D, self.stage3_D) output += self.forward_stage(x, self.scale, self._extract_pattern_D, self.stage3_D)
output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage3_Y) output += self.forward_stage(x, self.scale, self._extract_pattern_Y, self.stage3_Y)
output /= 3 output /= 3
x = output x = output
x = round_func(x)
x = x.reshape(b, c, h*self.scale, w*self.scale) x = x.reshape(b, c, h*self.scale, w*self.scale)
return x return x
@ -222,7 +186,6 @@ class SDYNetR90x1(nn.Module):
output += torch.rot90(self.forward_stage(rotated, self.scale, self._extract_pattern_Y, self.stage1_Y), k=-rotations_count, dims=[-2, -1]) output += torch.rot90(self.forward_stage(rotated, self.scale, self._extract_pattern_Y, self.stage1_Y), k=-rotations_count, dims=[-2, -1])
output /= 4*3 output /= 4*3
x = output x = output
x = round_func(x)
x = x.reshape(b, c, h*self.scale, w*self.scale) x = x.reshape(b, c, h*self.scale, w*self.scale)
return x return x
@ -238,7 +201,7 @@ class SDYNetR90x1(nn.Module):
return F.mse_loss(pred/255, target/255) return F.mse_loss(pred/255, target/255)
return loss_fn return loss_fn
class SDYNetR90x2(nn.Module): class SDYNetR90x2(SRNetBase):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4): def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetR90x2, self).__init__() super(SDYNetR90x2, self).__init__()
self.scale = scale self.scale = scale
@ -252,16 +215,6 @@ class SDYNetR90x2(nn.Module):
self.stage2_D = 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) 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, config=None): def forward(self, x, config=None):
b,c,h,w = x.shape b,c,h,w = x.shape
x = x.view(b*c, 1, h, w) x = x.view(b*c, 1, h, w)
@ -275,7 +228,7 @@ class SDYNetR90x2(nn.Module):
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_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, 1, self._extract_pattern_Y, self.stage1_Y), k=-rotations_count, dims=[-2, -1])
output_1 /= 4*3 output_1 /= 4*3
x = round_func(output_1) x = output_1
output_2 = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device) output_2 = torch.zeros([b*c, 1, h*self.scale, w*self.scale], dtype=x.dtype, device=x.device)
output_2 += self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage2_S) output_2 += self.forward_stage(x, self.scale, self._extract_pattern_S, self.stage2_S)
output_2 += self.forward_stage(x, self.scale, self._extract_pattern_D, self.stage2_D) output_2 += self.forward_stage(x, self.scale, self._extract_pattern_D, self.stage2_D)
@ -286,7 +239,7 @@ class SDYNetR90x2(nn.Module):
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_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.scale, self._extract_pattern_Y, self.stage2_Y), k=-rotations_count, dims=[-2, -1])
output_2 /= 4*3 output_2 /= 4*3
x = round_func(output_2) x = output_2
x = x.view(b, c, h*self.scale, w*self.scale) x = x.view(b, c, h*self.scale, w*self.scale)
return x return x

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