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Refactor DenseConvUpscaleBlock, explicit window_size for PercievePattern

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Vladimir Protsenko 6 months ago
parent b1f2f6d76b
commit 6240dd05fc
4 changed files with 95 additions and 107 deletions
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43
src/common/layers.py
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@ -4,9 +4,11 @@ import torch.nn.functional as F
import numpy as np
class PercievePattern():
def __init__(self, receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]]):
def __init__(self, receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=2):
assert window_size >= (np.max(receptive_field_idxes)+1)
self.receptive_field_idxes = np.array(receptive_field_idxes)
self.window_size = np.max(self.receptive_field_idxes) + 1
self.window_size = window_size
self.center = center
self.receptive_field_idxes = [
self.receptive_field_idxes[0,0]*self.window_size + self.receptive_field_idxes[0,1],
self.receptive_field_idxes[1,0]*self.window_size + self.receptive_field_idxes[1,1],
@ -16,7 +18,8 @@ class PercievePattern():
def __call__(self, x):
b,c,h,w = x.shape
x = F.pad(x, pad=[0,self.window_size-1,0,self.window_size-1], mode='replicate')
x = F.pad(x, pad=[self.center[0], self.window_size-self.center[0]-1,
self.center[1], self.window_size-self.center[1]-1], mode='replicate')
x = F.unfold(input=x, kernel_size=self.window_size)
x = torch.stack([
x[:,self.receptive_field_idxes[0],:],
@ -25,4 +28,36 @@ class PercievePattern():
x[:,self.receptive_field_idxes[3],:]
], 2)
x = x.reshape(x.shape[0]*x.shape[1], 1, 2, 2)
return x
return x
# Huang G. et al. Densely connected convolutional networks //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2017. – С. 4700-4708.
# https://ar5iv.labs.arxiv.org/html/1608.06993
# https://github.com/andreasveit/densenet-pytorch/blob/63152f4a40644b62717749536ed2e011c6e4d9ab/densenet.py#L40
class DenseConvUpscaleBlock(nn.Module):
def __init__(self, hidden_dim = 32, layers_count=5, upscale_factor=1):
super(DenseConvUpscaleBlock, self).__init__()
assert layers_count > 0
self.upscale_factor = upscale_factor
self.hidden_dim = hidden_dim
self.percieve = nn.Conv2d(1, hidden_dim, kernel_size=(2, 2), padding='valid', stride=1, dilation=1, bias=True)
self.convs = []
for i in range(layers_count):
self.convs.append(nn.Conv2d(in_channels = (i+1)*hidden_dim, out_channels = hidden_dim, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True))
self.convs = nn.ModuleList(self.convs)
for name, p in self.named_parameters():
if "weight" in name: nn.init.kaiming_normal_(p)
if "bias" in name: nn.init.constant_(p, 0)
self.project_channels = nn.Conv2d(in_channels = (layers_count+1)*hidden_dim, out_channels = upscale_factor * upscale_factor, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True)
self.shuffle = nn.PixelShuffle(upscale_factor)
def forward(self, x):
x = (x-127.5)/127.5
x = torch.relu(self.percieve(x))
for conv in self.convs:
x = torch.cat([x, torch.relu(conv(x))], dim=1)
x = self.shuffle(self.project_channels(x))
x = torch.tanh(x)
x = round_func(x*127.5 + 127.5)
return x

33
src/models/rcnet.py
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@ -6,38 +6,7 @@ from common.utils import round_func
from pathlib import Path
from common import lut
from . import rclut
# Huang G. et al. Densely connected convolutional networks //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2017. – С. 4700-4708.
# https://ar5iv.labs.arxiv.org/html/1608.06993
# https://github.com/andreasveit/densenet-pytorch/blob/63152f4a40644b62717749536ed2e011c6e4d9ab/densenet.py#L40
class DenseConvUpscaleBlock(nn.Module):
def __init__(self, hidden_dim = 32, layers_count=5, upscale_factor=1):
super(DenseConvUpscaleBlock, self).__init__()
assert layers_count > 0
self.upscale_factor = upscale_factor
self.percieve = nn.Conv2d(1, hidden_dim, kernel_size=(2, 2), padding='valid', stride=1, dilation=1, bias=True)
self.convs = []
for i in range(layers_count):
self.convs.append(nn.Conv2d(in_channels = (i+1)*hidden_dim, out_channels = hidden_dim, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True))
self.convs = nn.ModuleList(self.convs)
for name, p in self.named_parameters():
if "weight" in name: nn.init.kaiming_normal_(p)
if "bias" in name: nn.init.constant_(p, 0)
self.project_channels = nn.Conv2d(in_channels = (layers_count+1)*hidden_dim, out_channels = upscale_factor * upscale_factor, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True)
self.shuffle = nn.PixelShuffle(upscale_factor)
def forward(self, x):
x = (x-127.5)/127.5
x = torch.relu(self.percieve(x))
for conv in self.convs:
x = torch.cat([x, torch.relu(conv(x))], dim=1)
x = self.shuffle(self.project_channels(x))
x = torch.tanh(x)
x = round_func(x*127.5 + 127.5)
return x
from common.layers import DenseConvUpscaleBlock
class ReconstructedConvCentered(nn.Module):
def __init__(self, hidden_dim, window_size=7):

93
src/models/sdynet.py
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@ -4,49 +4,17 @@ import torch.nn.functional as F
import numpy as np
from common.utils import round_func
from common import lut
from common.layers import PercievePattern
from common.layers import PercievePattern, DenseConvUpscaleBlock
from pathlib import Path
from . import sdylut
# Huang G. et al. Densely connected convolutional networks //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2017. – С. 4700-4708.
# https://ar5iv.labs.arxiv.org/html/1608.06993
# https://github.com/andreasveit/densenet-pytorch/blob/63152f4a40644b62717749536ed2e011c6e4d9ab/densenet.py#L40
class DenseConvUpscaleBlock(nn.Module):
def __init__(self, hidden_dim = 32, layers_count=5, upscale_factor=1):
super(DenseConvUpscaleBlock, self).__init__()
assert layers_count > 0
self.upscale_factor = upscale_factor
self.hidden_dim = hidden_dim
self.percieve = nn.Conv2d(1, hidden_dim, kernel_size=(2, 2), padding='valid', stride=1, dilation=1, bias=True)
self.convs = []
for i in range(layers_count):
self.convs.append(nn.Conv2d(in_channels = (i+1)*hidden_dim, out_channels = hidden_dim, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True))
self.convs = nn.ModuleList(self.convs)
for name, p in self.named_parameters():
if "weight" in name: nn.init.kaiming_normal_(p)
if "bias" in name: nn.init.constant_(p, 0)
self.project_channels = nn.Conv2d(in_channels = (layers_count+1)*hidden_dim, out_channels = upscale_factor * upscale_factor, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True)
self.shuffle = nn.PixelShuffle(upscale_factor)
def forward(self, x):
x = (x-127.5)/127.5
x = torch.relu(self.percieve(x))
for conv in self.convs:
x = torch.cat([x, torch.relu(conv(x))], dim=1)
x = self.shuffle(self.project_channels(x))
x = torch.tanh(x)
x = round_func(x*127.5 + 127.5)
return x
class SDYNetx1(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):
super(SDYNetx1, self).__init__()
self.scale = scale
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]])
self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]])
self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]])
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=3)
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 = 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)
@ -90,9 +58,9 @@ 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 = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]])
self._extract_pattern_D = PercievePattern(receptive_field_idxes=[[0,0],[2,0],[0,2],[2,2]])
self._extract_pattern_Y = PercievePattern(receptive_field_idxes=[[0,0],[1,1],[1,2],[2,1]])
self._extract_pattern_S = PercievePattern(receptive_field_idxes=[[0,0],[0,1],[1,0],[1,1]], center=[0,0], window_size=3)
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_1 = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stageD_1 = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
self.stageY_1 = DenseConvUpscaleBlock(hidden_dim=hidden_dim, layers_count=layers_count, upscale_factor=1)
@ -159,4 +127,51 @@ class SDYNetx2(nn.Module):
stageD_2 = lut.transfer_2x2_input_SxS_output(self.stageD_2, quantization_interval=quantization_interval, batch_size=batch_size)
stageY_2 = lut.transfer_2x2_input_SxS_output(self.stageY_2, quantization_interval=quantization_interval, batch_size=batch_size)
lut_model = sdylut.SDYLutx2.init_from_lut(stageS_1, stageD_1, stageY_1, stageS_2, stageD_2, stageY_2)
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

33
src/models/srnet.py
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@ -6,38 +6,7 @@ from common.utils import round_func
from common import lut
from pathlib import Path
from .srlut import SRLut, SRLutRot90
# Huang G. et al. Densely connected convolutional networks //Proceedings of the IEEE conference on computer vision and pattern recognition. – 2017. – С. 4700-4708.
# https://ar5iv.labs.arxiv.org/html/1608.06993
# https://github.com/andreasveit/densenet-pytorch/blob/63152f4a40644b62717749536ed2e011c6e4d9ab/densenet.py#L40
class DenseConvUpscaleBlock(nn.Module):
def __init__(self, hidden_dim = 32, layers_count=5, upscale_factor=1):
super(DenseConvUpscaleBlock, self).__init__()
assert layers_count > 0
self.upscale_factor = upscale_factor
self.percieve = nn.Conv2d(1, hidden_dim, kernel_size=(2, 2), padding='valid', stride=1, dilation=1, bias=True)
self.convs = []
for i in range(layers_count):
self.convs.append(nn.Conv2d(in_channels = (i+1)*hidden_dim, out_channels = hidden_dim, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True))
self.convs = nn.ModuleList(self.convs)
for name, p in self.named_parameters():
if "weight" in name: nn.init.kaiming_normal_(p)
if "bias" in name: nn.init.constant_(p, 0)
self.project_channels = nn.Conv2d(in_channels = (layers_count+1)*hidden_dim, out_channels = upscale_factor * upscale_factor, kernel_size = 1, stride=1, padding=0, dilation=1, bias=True)
self.shuffle = nn.PixelShuffle(upscale_factor)
def forward(self, x):
x = (x-127.5)/127.5
x = torch.relu(self.percieve(x))
for conv in self.convs:
x = torch.cat([x, torch.relu(conv(x))], dim=1)
x = self.shuffle(self.project_channels(x))
x = torch.tanh(x)
x = round_func(x*127.5 + 127.5)
return x
from common.layers import DenseConvUpscaleBlock
class SRNet(nn.Module):
def __init__(self, hidden_dim = 64, layers_count = 4, scale = 4):

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