# ------------------------------------------------------------------------
# Copyright (c) 2022 megvii-model. All Rights Reserved.
# ------------------------------------------------------------------------
# Source: https://github.com/megvii-research/NAFNet
'''
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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from models.nafnet_utils import Local_Base, LayerNorm2d
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)
# 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
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)
def create_nafnet(input_channels = 3, width = 32, enc_blks = [2, 2, 4, 8], middle_blk_num = 12, dec_blks = [2, 2, 2, 2]):
"""
Create Nafnet model
https://github.com/megvii-research/NAFNet/blob/main/options/test/SIDD/NAFNet-width32.yml
"""
net = NAFNet(img_channel=input_channels, 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)
return net