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)