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Source code for mmedit.models.editors.nafnet.nafnet_net

# Copyright (c) 2022 megvii-model. All Rights Reserved.
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.model import BaseModule

from mmedit.registry import MODELS
from .naf_avgpool2d import Local_Base
from .naf_layerNorm2d import LayerNorm2d


@MODELS.register_module()
[docs]class NAFNet(BaseModule): """NAFNet. The original version of NAFNet in "Simple Baseline for Image Restoration". Args: img_channels (int): Channel number of inputs. mid_channels (int): Channel number of intermediate features. middle_blk_num (int): Number of middle blocks. enc_blk_nums (List of int): Number of blocks for each encoder. dec_blk_nums (List of int): Number of blocks for each decoder. """ def __init__(self, img_channel=3, mid_channels=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[]): super().__init__() self.intro = nn.Conv2d( in_channels=img_channel, out_channels=mid_channels, kernel_size=3, padding=1, stride=1, groups=1, bias=True) self.ending = nn.Conv2d( in_channels=mid_channels, 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 = mid_channels 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)
[docs] def forward(self, inp): """Forward function. args: inp: input tensor image with (B, C, H, W) shape """ 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]
[docs] def check_image_size(self, x): """Check image size and pad images so that it has enough dimension do downsample. args: x: input tensor image with (B, C, H, W) shape. """ _, _, 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
@MODELS.register_module()
[docs]class NAFNetLocal(Local_Base, NAFNet): """The original version of NAFNetLocal in "Simple Baseline for Image Restoration". NAFNetLocal uses local average pooling modules than NAFNet. Args: img_channels (int): Channel number of inputs. mid_channels (int): Channel number of intermediate features. middle_blk_num (int): Number of middle blocks. enc_blk_nums (List of int): Number of blocks for each encoder. dec_blk_nums (List of int): Number of blocks for each decoder. """ 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)
# Components for NAFNet
[docs]class NAFBlock(BaseModule): """NAFNet's Block in paper. Simple gate will shrink the channel to a half. To keep the number of channels, it expands the channels first. Args: in_channels (int): number of channels DW_Expand (int): channel expansion factor for part 1 FFN_Expand (int): channel expansion factor for part 2 drop_out_rate (float): drop out ratio """ def __init__(self, in_channels, DW_Expand=2, FFN_Expand=2, drop_out_rate=0.): super().__init__() # Part 1 dw_channel = in_channels * DW_Expand self.conv1 = nn.Conv2d( in_channels=in_channels, 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) # 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), ) self.conv3 = nn.Conv2d( in_channels=dw_channel // 2, out_channels=in_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=True) # Part 2 ffn_channel = FFN_Expand * in_channels self.conv4 = nn.Conv2d( in_channels=in_channels, 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=in_channels, kernel_size=1, padding=0, stride=1, groups=1, bias=True) # Simple Gate self.sg = SimpleGate() # Layer Normalization self.norm1 = LayerNorm2d(in_channels) self.norm2 = LayerNorm2d(in_channels) # 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() # Feature weight ratio self.beta = nn.Parameter( torch.zeros((1, in_channels, 1, 1)), requires_grad=True) self.gamma = nn.Parameter( torch.zeros((1, in_channels, 1, 1)), requires_grad=True)
[docs] def forward(self, inp): """Forward Function. Args: inp: input tensor image """ x = inp # part 1 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 # part 2 x = self.norm2(y) x = self.conv4(x) x = self.sg(x) x = self.conv5(x) x = self.dropout2(x) out = y + x * self.gamma return out
[docs]class SimpleGate(BaseModule): """The Simple Gate in "Simple Baseline for Image Restoration". Args: x: input tensor feature map with (B, 2 * C, H, W) Return: x1 * x2 (where x1, x2 are two separate parts by simple split x to [B, C, H, W]) """
[docs] def forward(self, x): x1, x2 = x.chunk(2, dim=1) return x1 * x2
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