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| # ------------------------------------------------------------------------ | |
| # Copyright (c) 2022 megvii-model. All Rights Reserved. | |
| # ------------------------------------------------------------------------ | |
| # Source: https://github.com/megvii-research/NAFNet | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from .nafnet_utils import Local_Base, LayerNorm2d | |
| class SimpleGate(nn.Module): | |
| """ | |
| A simple gating mechanism that splits the input tensor into two halves | |
| along the channel dimension and performs element-wise multiplication. | |
| """ | |
| def forward(self, x): | |
| x1, x2 = x.chunk(2, dim=1) | |
| return x1 * x2 | |
| class NAFBlock(nn.Module): | |
| """ | |
| NAFNet Block. This block is the main building component of NAFNet. | |
| It consists of a main branch with LayerNorm, Depth-wise convolution, | |
| Simplified Channel Attention (SCA), and a skip connection. It also | |
| has a Feed-Forward Network (FFN) branch. | |
| Args: | |
| c (int): Number of input and output channels. | |
| DW_Expand (int): Expansion factor for the depth-wise convolution. | |
| FFN_Expand (int): Expansion factor for the FFN. | |
| drop_out_rate (float): Dropout rate. | |
| """ | |
| def __init__(self, c, DW_Expand=2, FFN_Expand=2, drop_out_rate=0.): | |
| super().__init__() | |
| dw_channel = c * DW_Expand | |
| # Main Branch: Depth-wise convolution and Simplified Channel Attention | |
| 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) | |
| # Simplified Channel Attention (SCA) | |
| 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), | |
| ) | |
| # SimpleGating | |
| self.sg = SimpleGate() | |
| # FFN Branch | |
| 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) | |
| # Layer Normalization | |
| self.norm1 = LayerNorm2d(c) | |
| self.norm2 = LayerNorm2d(c) | |
| # Dropout | |
| 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() | |
| # Learnable parameters for scaling skip connections | |
| 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 | |
| # Main Branch | |
| x = self.norm1(x) | |
| x = self.conv1(x) | |
| x = self.conv2(x) | |
| x = self.sg(x) # Gating | |
| x = x * self.sca(x) # Apply Simplified Channel Attention | |
| x = self.conv3(x) | |
| x = self.dropout1(x) | |
| # First skip connection | |
| y = inp + x * self.beta | |
| # FFN Branch | |
| x = self.conv4(self.norm2(y)) | |
| x = self.sg(x) # Gating | |
| x = self.conv5(x) | |
| x = self.dropout2(x) | |
| # Second skip connection | |
| return y + x * self.gamma | |
| class NAFNet(nn.Module): | |
| """ | |
| NAFNet (Non-linear Activation Free Network) for image restoration. | |
| This model uses a U-Net like architecture with NAFBlocks. | |
| Args: | |
| img_channel (int): Number of input image channels. | |
| width (int): Base width of the network. | |
| middle_blk_num (int): Number of NAFBlocks in the bottleneck. | |
| enc_blk_nums (list[int]): Number of NAFBlocks in each encoder stage. | |
| dec_blk_nums (list[int]): Number of NAFBlocks in each decoder stage. | |
| """ | |
| def __init__(self, img_channel=3, width=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[]): | |
| super().__init__() | |
| # Initial and final convolutions | |
| 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) | |
| # U-Net components | |
| self.encoders = nn.ModuleList() | |
| self.decoders = nn.ModuleList() | |
| self.middle_blks = nn.ModuleList() | |
| self.ups = nn.ModuleList() | |
| self.downs = nn.ModuleList() | |
| chan = width | |
| # Encoder stages | |
| 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 | |
| # Bottleneck | |
| self.middle_blks = \ | |
| nn.Sequential( | |
| *[NAFBlock(chan) for _ in range(middle_blk_num)] | |
| ) | |
| # Decoder stages | |
| 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 | |
| # Pad input to be divisible by the downsampling factor | |
| inp = self.check_image_size(inp) | |
| # Initial convolution | |
| x = self.intro(inp) | |
| # Encoder path | |
| encs = [] | |
| for encoder, down in zip(self.encoders, self.downs): | |
| x = encoder(x) | |
| encs.append(x) | |
| x = down(x) | |
| # Bottleneck | |
| x = self.middle_blks(x) | |
| # Decoder path with skip connections | |
| for decoder, up, enc_skip in zip(self.decoders, self.ups, encs[::-1]): | |
| x = up(x) | |
| x = x + enc_skip # Skip connection | |
| x = decoder(x) | |
| # Final convolution and residual connection | |
| x = self.ending(x) | |
| x = x + inp | |
| # Crop to original size | |
| return x[:, :, :H, :W] | |
| def check_image_size(self, x): | |
| """ | |
| Pads the input image so its height and width are divisible by the | |
| total downsampling factor. | |
| """ | |
| _, _, 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 | |
| # Pad the right and bottom sides of the image | |
| x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h)) | |
| return x | |
| class NAFNetLocal(Local_Base, NAFNet): | |
| """ | |
| A local version of NAFNet that adapts the model for inference on | |
| arbitrary-sized images by replacing pooling layers. This is useful | |
| when the training and testing resolutions differ. | |
| Args: | |
| train_size (tuple): The size of the training images (N, C, H, W). | |
| fast_imp (bool): Whether to use a faster, non-equivalent implementation | |
| for the replaced pooling layers. | |
| """ | |
| 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 for adaptive pooling layers, typically larger than train size | |
| base_size = (int(H * 1.5), int(W * 1.5)) | |
| self.eval() | |
| with torch.no_grad(): | |
| # Convert the model to use custom pooling layers | |
| self.convert(base_size=base_size, train_size=train_size, fast_imp=fast_imp) | |
| def create_nafnet(input_channels = 3, width = 32, enc_blks = [2, 2, 4, 8], middle_blk_num = 12, dec_blks = [2, 2, 2, 2]): | |
| """ | |
| Creates a NAFNet model with specific architecture parameters. | |
| Default parameters are based on the NAFNet-width32 configuration for SIDD. | |
| Reference: https://github.com/megvii-research/NAFNet/blob/main/options/test/SIDD/NAFNet-width32.yml | |
| Args: | |
| input_channels (int): Number of input image channels. | |
| width (int): Base width of the network. | |
| enc_blks (list[int]): Number of NAFBlocks in each encoder stage. | |
| middle_blk_num (int): Number of NAFBlocks in the bottleneck. | |
| dec_blks (list[int]): Number of NAFBlocks in each decoder stage. | |
| Returns: | |
| nn.Module: The created NAFNet model. | |
| """ | |
| net = NAFNet(img_channel=input_channels, width=width, middle_blk_num=middle_blk_num, | |
| enc_blk_nums=enc_blks, dec_blk_nums=dec_blks) | |
| return net |