import torch import torch.nn as nn import torch.nn.init as init import torch.nn.functional as F try: from .arch_util import LayerNorm2d except: from arch_util import LayerNorm2d class SimpleGate(nn.Module): def forward(self, x): x1, x2 = x.chunk(2, dim=1) return x1 * x2 class Adapter(nn.Module): def __init__(self, c, ffn_channel = None): super().__init__() if ffn_channel: ffn_channel = 2 else: ffn_channel = c self.conv1 = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True) self.conv2 = nn.Conv2d(in_channels=ffn_channel, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True) self.depthwise = nn.Conv2d(in_channels=c, out_channels=ffn_channel, kernel_size=3, padding=1, stride=1, groups=c, bias=True, dilation=1) def forward(self, input): x = self.conv1(input) + self.depthwise(input) x = self.conv2(x) return x class FreMLP(nn.Module): def __init__(self, nc, expand = 2): super(FreMLP, self).__init__() self.process1 = nn.Sequential( nn.Conv2d(nc, expand * nc, 1, 1, 0), nn.LeakyReLU(0.1, inplace=True), nn.Conv2d(expand * nc, nc, 1, 1, 0)) def forward(self, x): _, _, H, W = x.shape x_freq = torch.fft.rfft2(x, norm='backward') mag = torch.abs(x_freq) pha = torch.angle(x_freq) mag = self.process1(mag) 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 Branch(nn.Module): ''' Branch that lasts lonly the dilated convolutions ''' def __init__(self, c, DW_Expand, dilation = 1): super().__init__() self.dw_channel = DW_Expand * c self.branch = nn.Sequential( nn.Conv2d(in_channels=self.dw_channel, out_channels=self.dw_channel, kernel_size=3, padding=dilation, stride=1, groups=self.dw_channel, bias=True, dilation = dilation) # the dconv ) def forward(self, input): return self.branch(input) class DBlock(nn.Module): ''' Change this block using Branch ''' def __init__(self, c, DW_Expand=2, FFN_Expand=2, dilations = [1], extra_depth_wise = False): super().__init__() #we define the 2 branches self.dw_channel = DW_Expand * c self.conv1 = nn.Conv2d(in_channels=c, out_channels=self.dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1) self.extra_conv = nn.Conv2d(self.dw_channel, self.dw_channel, kernel_size=3, padding=1, stride=1, groups=c, bias=True, dilation=1) if extra_depth_wise else nn.Identity() #optional extra dw self.branches = nn.ModuleList() for dilation in dilations: self.branches.append(Branch(self.dw_channel, DW_Expand = 1, dilation = dilation)) 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.sg2 = 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) 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.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) # self.adapter = Adapter(c, ffn_channel=None) # self.use_adapters = False # def set_use_adapters(self, use_adapters): # self.use_adapters = use_adapters def forward(self, inp, adapter = None): y = inp x = self.norm1(inp) # x = self.conv1(self.extra_conv(x)) x = self.extra_conv(self.conv1(x)) 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 #second step x = self.conv4(self.norm2(y)) # size [B, 2*C, H, W] x = self.sg2(x) # size [B, C, H, W] x = self.conv5(x) # size [B, C, H, W] x = y + x * self.gamma # if self.use_adapters: # return self.adapter(x) # else: return x class EBlock(nn.Module): ''' Change this block using Branch ''' def __init__(self, c, DW_Expand=2, dilations = [1], extra_depth_wise = False): super().__init__() #we define the 2 branches self.dw_channel = DW_Expand * c self.extra_conv = nn.Conv2d(c, c, kernel_size=3, padding=1, stride=1, groups=c, bias=True, dilation=1) if extra_depth_wise else nn.Identity() #optional extra dw self.conv1 = nn.Conv2d(in_channels=c, out_channels=self.dw_channel, kernel_size=1, padding=0, stride=1, groups=1, bias=True, dilation = 1) self.branches = nn.ModuleList() for dilation in dilations: self.branches.append(Branch(c, DW_Expand, dilation = dilation)) 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) # second step self.norm1 = LayerNorm2d(c) self.norm2 = LayerNorm2d(c) self.freq = FreMLP(nc = c, expand=2) 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) # self.adapter = Adapter(c, ffn_channel=None) # self.use_adapters = False # def set_use_adapters(self, use_adapters): # self.use_adapters = use_adapters def forward(self, inp): y = inp x = self.norm1(inp) x = self.conv1(self.extra_conv(x)) 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 #second step x_step2 = self.norm2(y) # size [B, 2*C, H, W] x_freq = self.freq(x_step2) # size [B, C, H, W] x = y * x_freq x = y + x * self.gamma # if self.use_adapters: # return self.adapter(x) # else: return x #---------------------------------------------------------------------------------------------- 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 = True # 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(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=False) output = net(torch.randn((4, 3, 256, 256))) # print('Values of EBlock:') print(macs, params) channels = 128 resol = 32 ksize = 5 # net = FAC(channels=channels, ksize=ksize) # inp_shape = (channels, resol, resol) # macs, params = get_model_complexity_info(net, inp_shape, verbose=False, print_per_layer_stat=True) # print('Values of FAC:') # print(macs, params)