diff --git a/src/common/layers.py b/src/common/layers.py index 4c2886f..f1bc405 100644 --- a/src/common/layers.py +++ b/src/common/layers.py @@ -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 \ No newline at end of file + 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 \ No newline at end of file diff --git a/src/models/rcnet.py b/src/models/rcnet.py index 4fbc181..e036fd6 100644 --- a/src/models/rcnet.py +++ b/src/models/rcnet.py @@ -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): diff --git a/src/models/sdynet.py b/src/models/sdynet.py index be37422..19a7aba 100644 --- a/src/models/sdynet.py +++ b/src/models/sdynet.py @@ -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 \ No newline at end of file diff --git a/src/models/srnet.py b/src/models/srnet.py index 9501e1a..cd30d8c 100644 --- a/src/models/srnet.py +++ b/src/models/srnet.py @@ -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):