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migrate sdynet to linear backbone

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vlpr 6 months ago
parent 6169d3daac
commit 62f0e6324a
1 changed files with 45 additions and 126 deletions
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src/models/sdynet.py
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@ -4,7 +4,7 @@ import torch.nn.functional as F
import numpy as np import numpy as np
from common.utils import round_func from common.utils import round_func
from common import lut from common import lut
from common.layers import PercievePattern, DenseConvUpscaleBlock from common import layers
from pathlib import Path from pathlib import Path
from . import sdylut from . import sdylut
@ -12,12 +12,12 @@ 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
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=3) s_pattern = [[0,0],[0,1],[1,0],[1,1]]
self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]], center=[0,0], window_size=3) d_pattern = [[0,0],[2,0],[0,2],[2,2]]
self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]], center=[0,0], window_size=3) y_pattern = [[0,0],[1,1],[1,2],[2,1]]
self.stageS = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage1_S = layers.UpscaleBlock(receptive_field_idxes=s_pattern, center=[0,0], window_size=2, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stageD = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage1_D = layers.UpscaleBlock(receptive_field_idxes=d_pattern, center=[0,0], window_size=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stageY = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage1_Y = layers.UpscaleBlock(receptive_field_idxes=y_pattern, center=[0,0], window_size=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
def forward(self, x): def forward(self, x):
b,c,h,w = x.shape b,c,h,w = x.shape
@ -25,99 +25,65 @@ class SDYNetx1(nn.Module):
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)
for rotations_count in range(4): for rotations_count in range(4):
rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1]) rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1])
rb,rc,rh,rw = rotated.shape rb,rc,rh,rw = rotated.shape
output += torch.rot90(self.stage1_S(rotated), k=-rotations_count, dims=[-2, -1])
s = self.stageS(self._extract_pattern_S(rotated)) output += torch.rot90(self.stage1_D(rotated), k=-rotations_count, dims=[-2, -1])
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) output += torch.rot90(self.stage1_Y(rotated), k=-rotations_count, dims=[-2, -1])
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 /= 4*3
output = output.view(b, c, h*self.scale, w*self.scale) x = output
return output x = round_func(x)
x = x.view(b, c, h*self.scale, w*self.scale)
return x
def get_lut_model(self, quantization_interval=16, batch_size=2**10): def get_lut_model(self, quantization_interval=16, batch_size=2**10):
stageS = lut.transfer_2x2_input_SxS_output(self.stageS, quantization_interval=quantization_interval, batch_size=batch_size) 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.stageD, 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.stageY, 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_lut(stageS, stageD, stageY)
return lut_model return lut_model
class SDYNetx2(nn.Module): class SDYNetx2(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(SDYNetx2, self).__init__() super(SDYNetx2, self).__init__()
self.scale = scale self.scale = scale
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=3) s_pattern = [[0,0],[0,1],[1,0],[1,1]]
self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]], center=[0,0], window_size=3) d_pattern = [[0,0],[2,0],[0,2],[2,2]]
self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]], center=[0,0], window_size=3) y_pattern = [[0,0],[1,1],[1,2],[2,1]]
self.stage1_S = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1) self.stage1_S = layers.UpscaleBlock(receptive_field_idxes=s_pattern, center=[0,0], window_size=2, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stage1_D = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1) self.stage1_D = layers.UpscaleBlock(receptive_field_idxes=d_pattern, center=[0,0], window_size=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stage1_Y = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1) self.stage1_Y = layers.UpscaleBlock(receptive_field_idxes=y_pattern, center=[0,0], window_size=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stage2_S = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage2_S = layers.UpscaleBlock(receptive_field_idxes=s_pattern, center=[0,0], window_size=2, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
self.stage2_D = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage2_D = layers.UpscaleBlock(receptive_field_idxes=d_pattern, center=[0,0], window_size=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
self.stage2_Y = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale) self.stage2_Y = layers.UpscaleBlock(receptive_field_idxes=y_pattern, center=[0,0], window_size=3, hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=scale)
def forward(self, x): def forward(self, x):
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)
output_1 = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device) output_1 = torch.zeros([b*c, 1, h, w], dtype=x.dtype, device=x.device)
for rotations_count in range(4): output_1 += self.stage1_S(x)
output_1 += self.stage1_D(x)
output_1 += self.stage1_Y(x)
for rotations_count in range(1,4):
rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1]) rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1])
rb,rc,rh,rw = rotated.shape output_1 += torch.rot90(self.stage1_S(rotated), k=-rotations_count, dims=[-2, -1])
output_1 += torch.rot90(self.stage1_D(rotated), k=-rotations_count, dims=[-2, -1])
s = self.stage1_S(self._extract_pattern_S(rotated)) output_1 += torch.rot90(self.stage1_Y(rotated), k=-rotations_count, dims=[-2, -1])
s = s.view(rb*rc, 1, rh, rw, 1, 1).permute(0,1,2,4,3,5).reshape(rb*rc, 1, rh, rw)
s = torch.rot90(s, k=-rotations_count, dims=[-2, -1])
output_1 += s
d = self.stage1_D(self._extract_pattern_D(rotated))
d = d.view(rb*rc, 1, rh, rw, 1, 1).permute(0,1,2,4,3,5).reshape(rb*rc, 1, rh, rw)
d = torch.rot90(d, k=-rotations_count, dims=[-2, -1])
output_1 += d
y = self.stage1_Y(self._extract_pattern_Y(rotated))
y = y.view(rb*rc, 1, rh, rw, 1, 1).permute(0,1,2,4,3,5).reshape(rb*rc, 1, rh, rw)
y = torch.rot90(y, k=-rotations_count, dims=[-2, -1])
output_1 += y
output_1 /= 4*3 output_1 /= 4*3
output_1 = output_1.view(b*c, 1, h, w) 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)
for rotations_count in range(4): output_2 += self.stage2_S(x)
output_2 += self.stage2_D(x)
output_2 += self.stage2_Y(x)
for rotations_count in range(1,4):
rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1]) rotated = torch.rot90(x, k=rotations_count, dims=[-2, -1])
rb,rc,rh,rw = rotated.shape output_2 += torch.rot90(self.stage2_S(rotated), k=-rotations_count, dims=[-2, -1])
output_2 += torch.rot90(self.stage2_D(rotated), k=-rotations_count, dims=[-2, -1])
s = self.stage2_S(self._extract_pattern_S(rotated)) output_2 += torch.rot90(self.stage2_Y(rotated), k=-rotations_count, dims=[-2, -1])
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_2 += s
d = self.stage2_D(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_2 += d
y = self.stage2_Y(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_2 += y
output_2 /= 4*3 output_2 /= 4*3
output_2 = output_2.view(b, c, h*self.scale, w*self.scale) x = round_func(output_2)
return output_2 x = x.view(b, c, h*self.scale, w*self.scale)
return x
def get_lut_model(self, quantization_interval=16, batch_size=2**10): 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_S = lut.transfer_2x2_input_SxS_output(self.stage1_S, quantization_interval=quantization_interval, batch_size=batch_size)
@ -127,51 +93,4 @@ class SDYNetx2(nn.Module):
stage2_D = lut.transfer_2x2_input_SxS_output(self.stage2_D, 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) 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_lut(stage1_S, stage1_D, stage1_Y, stage2_S, stage2_D, stage2_Y)
return lut_model
class SDYNetCenteredx1(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetCenteredx1, self).__init__()
self.scale = scale
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[1,1], window_size=3)
self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]], center=[1,1], window_size=3)
self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]], center=[1,1], window_size=3)
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):
stageS = lut.transfer_2x2_input_SxS_output(self.stageS, quantization_interval=quantization_interval, batch_size=batch_size)
stageD = lut.transfer_2x2_input_SxS_output(self.stageD, quantization_interval=quantization_interval, batch_size=batch_size)
stageY = lut.transfer_2x2_input_SxS_output(self.stageY, quantization_interval=quantization_interval, batch_size=batch_size)
lut_model = sdylut.SDYLutCenteredx1.init_from_lut(stageS, stageD, stageY)
return lut_model return lut_model
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