| |
| |
| |
|
|
| ''' |
| Simple Baselines for Image Restoration |
| |
| @article{chen2022simple, |
| title={Simple Baselines for Image Restoration}, |
| author={Chen, Liangyu and Chu, Xiaojie and Zhang, Xiangyu and Sun, Jian}, |
| journal={arXiv preprint arXiv:2204.04676}, |
| year={2022} |
| } |
| ''' |
|
|
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from basicsr.models.archs.arch_util import LayerNorm2d |
| from basicsr.models.archs.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) |
| self.conv3 = nn.Conv2d(in_channels=dw_channel // 2, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True) |
| |
| |
| 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), |
| ) |
|
|
| |
| 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 |
|
|
| x = self.norm1(x) |
|
|
| x = self.conv1(x) |
| x = self.conv2(x) |
| x = self.sg(x) |
| x = x * self.sca(x) |
| x = self.conv3(x) |
|
|
| x = self.dropout1(x) |
|
|
| y = inp + x * self.beta |
|
|
| x = self.conv4(self.norm2(y)) |
| x = self.sg(x) |
| x = self.conv5(x) |
|
|
| 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)) |
| 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) |
|
|
|
|
| if __name__ == '__main__': |
| img_channel = 3 |
| width = 32 |
|
|
| |
| |
| |
|
|
| enc_blks = [1, 1, 1, 28] |
| middle_blk_num = 1 |
| dec_blks = [1, 1, 1, 1] |
| |
| net = NAFNet(img_channel=img_channel, width=width, middle_blk_num=middle_blk_num, |
| enc_blk_nums=enc_blks, dec_blk_nums=dec_blks) |
|
|
|
|
| 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) |
|
|
| params = float(params[:-3]) |
| macs = float(macs[:-4]) |
|
|
| print(macs, params) |
|
|
