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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| try: | |
| from .arch_util import LayerNorm2d | |
| from .local_arch import Local_Base | |
| except: | |
| from arch_util import LayerNorm2d | |
| from local_arch import Local_Base | |
| class SimpleGate(nn.Module): | |
| def forward(self, x): | |
| x1, x2 = x.chunk(2, dim=1) | |
| return x1 * x2 | |
| class NAFBlock(nn.Module): | |
| def __init__(self, c, DW_Expand=2, FFN_Expand=2, drop_out_rate=0.): | |
| super().__init__() | |
| dw_channel = c * DW_Expand | |
| self.conv1 = nn.Conv2d(in_channels=c, out_channels=dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True) | |
| self.conv2 = nn.Conv2d(in_channels=dw_channel, out_channels=dw_channel, kernel_size=3, padding=1, stride=1, groups=dw_channel, | |
| bias=True) # the dconv | |
| self.conv3 = nn.Conv2d(in_channels=dw_channel // 2, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True) | |
| # Simplified Channel Attention | |
| self.sca = nn.Sequential( | |
| nn.AdaptiveAvgPool2d(1), | |
| nn.Conv2d(in_channels=dw_channel // 2, out_channels=dw_channel // 2, kernel_size=1, padding=0, stride=1, | |
| groups=1, bias=True), | |
| ) | |
| # SimpleGate | |
| self.sg = SimpleGate() | |
| ffn_channel = FFN_Expand * c | |
| self.conv4 = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True) | |
| self.conv5 = nn.Conv2d(in_channels=ffn_channel // 2, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True) | |
| self.norm1 = LayerNorm2d(c) | |
| self.norm2 = LayerNorm2d(c) | |
| self.dropout1 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity() | |
| self.dropout2 = nn.Dropout(drop_out_rate) if drop_out_rate > 0. else nn.Identity() | |
| self.beta = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True) | |
| self.gamma = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True) | |
| def forward(self, inp): | |
| x = inp # size [B, C, H, W] | |
| x = self.norm1(x) # size [B, C, H, W] | |
| x = self.conv1(x) # size [B, 2*C, H, W] | |
| x = self.conv2(x) # size [B, 2*C, H, W] | |
| x = self.sg(x) # size [B, C, H, W] | |
| x = x * self.sca(x) # size [B, C, H, W] | |
| x = self.conv3(x) # size [B, C, H, W] | |
| x = self.dropout1(x) | |
| y = inp + x * self.beta # size [B, C, H, W] | |
| x = self.conv4(self.norm2(y)) # size [B, 2*C, H, W] | |
| x = self.sg(x) # size [B, C, H, W] | |
| x = self.conv5(x) # size [B, C, H, W] | |
| x = self.dropout2(x) | |
| return y + x * self.gamma | |
| class NAFNet(nn.Module): | |
| def __init__(self, img_channel=3, width=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[]): | |
| super().__init__() | |
| self.intro = nn.Conv2d(in_channels=img_channel, out_channels=width, kernel_size=3, padding=1, stride=1, groups=1, | |
| bias=True) | |
| self.ending = nn.Conv2d(in_channels=width, out_channels=img_channel, kernel_size=3, padding=1, stride=1, groups=1, | |
| bias=True) | |
| self.encoders = nn.ModuleList() | |
| self.decoders = nn.ModuleList() | |
| self.middle_blks = nn.ModuleList() | |
| self.ups = nn.ModuleList() | |
| self.downs = nn.ModuleList() | |
| chan = width | |
| for num in enc_blk_nums: | |
| self.encoders.append( | |
| nn.Sequential( | |
| *[NAFBlock(chan) for _ in range(num)] | |
| ) | |
| ) | |
| self.downs.append( | |
| nn.Conv2d(chan, 2*chan, 2, 2) | |
| ) | |
| chan = chan * 2 | |
| self.middle_blks = \ | |
| nn.Sequential( | |
| *[NAFBlock(chan) for _ in range(middle_blk_num)] | |
| ) | |
| for num in dec_blk_nums: | |
| self.ups.append( | |
| nn.Sequential( | |
| nn.Conv2d(chan, chan * 2, 1, bias=False), | |
| nn.PixelShuffle(2) | |
| ) | |
| ) | |
| chan = chan // 2 | |
| self.decoders.append( | |
| nn.Sequential( | |
| *[NAFBlock(chan) for _ in range(num)] | |
| ) | |
| ) | |
| self.padder_size = 2 ** len(self.encoders) | |
| def forward(self, inp): | |
| B, C, H, W = inp.shape | |
| inp = self.check_image_size(inp) | |
| x = self.intro(inp) | |
| encs = [] | |
| for encoder, down in zip(self.encoders, self.downs): | |
| x = encoder(x) | |
| encs.append(x) | |
| x = down(x) | |
| x = self.middle_blks(x) | |
| for decoder, up, enc_skip in zip(self.decoders, self.ups, encs[::-1]): | |
| x = up(x) | |
| x = x + enc_skip | |
| x = decoder(x) | |
| x = self.ending(x) | |
| x = x + inp | |
| return x[:, :, :H, :W] | |
| def check_image_size(self, x): | |
| _, _, h, w = x.size() | |
| mod_pad_h = (self.padder_size - h % self.padder_size) % self.padder_size | |
| mod_pad_w = (self.padder_size - w % self.padder_size) % self.padder_size | |
| x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), value = 0) | |
| return x | |
| class NAFNetLocal(Local_Base, NAFNet): | |
| def __init__(self, *args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs): | |
| Local_Base.__init__(self) | |
| NAFNet.__init__(self, *args, **kwargs) | |
| N, C, H, W = train_size | |
| base_size = (int(H * 1.5), int(W * 1.5)) | |
| self.eval() | |
| with torch.no_grad(): | |
| self.convert(base_size=base_size, train_size=train_size, fast_imp=fast_imp) | |
| class FreBlock(nn.Module): | |
| def __init__(self, nc): | |
| super(FreBlock, self).__init__() | |
| self.fpre = nn.Conv2d(nc, nc, 1, 1, 0) | |
| self.process1 = nn.Sequential( | |
| nn.Conv2d(nc, nc, 1, 1, 0), | |
| nn.LeakyReLU(0.1, inplace=True), | |
| nn.Conv2d(nc, nc, 1, 1, 0)) | |
| self.process2 = nn.Sequential( | |
| nn.Conv2d(nc, nc, 1, 1, 0), | |
| nn.LeakyReLU(0.1, inplace=True), | |
| nn.Conv2d(nc, nc, 1, 1, 0)) | |
| def forward(self, x): | |
| _, _, H, W = x.shape | |
| x_freq = torch.fft.rfft2(self.fpre(x), norm='backward') | |
| mag = torch.abs(x_freq) | |
| pha = torch.angle(x_freq) | |
| mag = self.process1(mag) | |
| pha = self.process2(pha) | |
| real = mag * torch.cos(pha) | |
| imag = mag * torch.sin(pha) | |
| x_out = torch.complex(real, imag) | |
| x_out = torch.fft.irfft2(x_out, s=(H, W), norm='backward') | |
| return x_out+x | |
| # class FPA(nn.Module): | |
| # def __init__(self,nc): | |
| # super(FPA, self).__init__() | |
| # self.process_mag = nn.Sequential( | |
| # nn.Conv2d(nc, nc, 1, 1, 0), | |
| # nn.LeakyReLU(0.1, inplace=True), | |
| # nn.Conv2d(nc, nc, 1, 1, 0), | |
| # nn.LeakyReLU(0.1, inplace=True), | |
| # nn.Conv2d(nc, nc, 1, 1, 0)) | |
| # self.process_pha = nn.Sequential( | |
| # nn.Conv2d(nc, nc, 1, 1, 0), | |
| # nn.LeakyReLU(0.1, inplace=True), | |
| # nn.Conv2d(nc, nc, 1, 1, 0), | |
| # nn.LeakyReLU(0.1, inplace=True), | |
| # nn.Conv2d(nc, nc, 1, 1, 0)) | |
| # def forward(self, input): | |
| # _, _, H, W = input.shape | |
| # x_freq = torch.fft.rfft2(input, norm='backward') | |
| # mag = torch.abs(x_freq) | |
| # pha = torch.angle(x_freq) | |
| # mag = mag + self.process_mag(mag) | |
| # pha = pha + self.process_pha(pha) | |
| # real = mag * torch.cos(pha) | |
| # imag = mag * torch.sin(pha) | |
| # x_out = torch.complex(real, imag) | |
| # x_out = torch.fft.irfft2(x_out, s=(H, W), norm='backward') | |
| # return x_out | |
| # class FBlock(nn.Module): | |
| # def __init__(self, c, DW_Expand=2, FFN_Expand=2, dilations = [1], extra_depth_wise = False): | |
| # super(FBlock, self).__init__() | |
| # self.branches = nn.ModuleList() | |
| # for dilation in dilations: | |
| # self.branches.append(Branch_v2(c, DW_Expand, dilation = dilation, extra_depth_wise=extra_depth_wise)) | |
| # assert len(dilations) == len(self.branches) | |
| # self.dw_channel = DW_Expand * c | |
| # self.sca = nn.Sequential( | |
| # nn.AdaptiveAvgPool2d(1), | |
| # nn.Conv2d(in_channels=self.dw_channel // 2, out_channels=self.dw_channel // 2, kernel_size=1, padding=0, stride=1, | |
| # groups=1, bias=True, dilation = 1), | |
| # ) | |
| # self.sg1 = SimpleGate() | |
| # self.conv3 = nn.Conv2d(in_channels=self.dw_channel // 2, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1) | |
| # self.norm1 = LayerNorm2d(c) | |
| # self.norm2 = LayerNorm2d(c) | |
| # ffn_channel = FFN_Expand * c | |
| # self.conv_fpr_intro = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1) | |
| # self.fpa = FPA(nc = ffn_channel) | |
| # self.conv_fpr_out = nn.Conv2d(in_channels=ffn_channel, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1) | |
| # self.gamma = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True) | |
| # self.beta = nn.Parameter(torch.zeros((1, c, 1, 1)), requires_grad=True) | |
| def forward(self, inp): | |
| y = inp | |
| x = self.norm1(inp) | |
| z=0 | |
| for branch in self.branches: | |
| z += branch(x) | |
| z = self.sg1(z) | |
| x = self.sca(z) * z | |
| x = self.conv3(x) | |
| y = inp + self.beta * x | |
| #Frequency pixel residue | |
| x = self.conv_fpr_intro(self.norm2(y)) # size [B, C, H, W] | |
| x = self.fpa(x) # size [B, C, H, W] | |
| x = self.conv_fpr_out(x) | |
| return y + x * self.gamma | |
| if __name__ == '__main__': | |
| img_channel = 3 | |
| width = 32 | |
| enc_blks = [1, 2, 3] | |
| middle_blk_num = 3 | |
| dec_blks = [3, 1, 1] | |
| dilations = [1, 4, 9] | |
| extra_depth_wise = False | |
| # net = NAFNet(img_channel=img_channel, width=width, middle_blk_num=middle_blk_num, | |
| # enc_blk_nums=enc_blks, dec_blk_nums=dec_blks) | |
| net = EBlock_v2(c = img_channel, | |
| dilations = dilations, | |
| extra_depth_wise=extra_depth_wise) | |
| inp_shape = (3, 256, 256) | |
| from ptflops import get_model_complexity_info | |
| macs, params = get_model_complexity_info(net, inp_shape, verbose=False, print_per_layer_stat=True) | |
| print(macs, params) |