Baseline arch
mayorx
parent
a61021bf5e
commit
e563857437
|
|
@ -0,0 +1,202 @@
|
|||
# ------------------------------------------------------------------------
|
||||
# Copyright (c) 2022 megvii-model. All Rights Reserved.
|
||||
# ------------------------------------------------------------------------
|
||||
|
||||
'''
|
||||
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 BaselineBlock(nn.Module):
|
||||
def __init__(self, c, DW_Expand=1, 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, out_channels=c, kernel_size=1, padding=0, stride=1, groups=1, bias=True)
|
||||
|
||||
# Channel Attention
|
||||
self.se = nn.Sequential(
|
||||
nn.AdaptiveAvgPool2d(1),
|
||||
nn.Conv2d(in_channels=dw_channel, out_channels=dw_channel // 2, kernel_size=1, padding=0, stride=1,
|
||||
groups=1, bias=True),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(in_channels=dw_channel // 2, out_channels=dw_channel, kernel_size=1, padding=0, stride=1,
|
||||
groups=1, bias=True),
|
||||
nn.Sigmoid()
|
||||
)
|
||||
|
||||
# GELU
|
||||
self.gelu = nn.GELU()
|
||||
|
||||
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, 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.gelu(x)
|
||||
x = x * self.se(x)
|
||||
x = self.conv3(x)
|
||||
|
||||
x = self.dropout1(x)
|
||||
|
||||
y = inp + x * self.beta
|
||||
|
||||
x = self.conv4(self.norm2(y))
|
||||
x = self.gelu(x)
|
||||
x = self.conv5(x)
|
||||
|
||||
x = self.dropout2(x)
|
||||
|
||||
return y + x * self.gamma
|
||||
|
||||
|
||||
class Baseline(nn.Module):
|
||||
|
||||
def __init__(self, img_channel=3, width=16, middle_blk_num=1, enc_blk_nums=[], dec_blk_nums=[], dw_expand=1, ffn_expand=2):
|
||||
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(
|
||||
*[BaselineBlock(chan, dw_expand, ffn_expand) for _ in range(num)]
|
||||
)
|
||||
)
|
||||
self.downs.append(
|
||||
nn.Conv2d(chan, 2*chan, 2, 2)
|
||||
)
|
||||
chan = chan * 2
|
||||
|
||||
self.middle_blks = \
|
||||
nn.Sequential(
|
||||
*[BaselineBlock(chan, dw_expand, ffn_expand) 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(
|
||||
*[BaselineBlock(chan, dw_expand, ffn_expand) 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 BaselineLocal(Local_Base, Baseline):
|
||||
def __init__(self, *args, train_size=(1, 3, 256, 256), fast_imp=False, **kwargs):
|
||||
Local_Base.__init__(self)
|
||||
Baseline.__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
|
||||
|
||||
dw_expand = 1
|
||||
ffn_expand = 2
|
||||
|
||||
# enc_blks = [2, 2, 4, 8]
|
||||
# middle_blk_num = 12
|
||||
# dec_blks = [2, 2, 2, 2]
|
||||
|
||||
enc_blks = [1, 1, 1, 28]
|
||||
middle_blk_num = 1
|
||||
dec_blks = [1, 1, 1, 1]
|
||||
|
||||
net = Baseline(img_channel=img_channel, width=width, middle_blk_num=middle_blk_num,
|
||||
enc_blk_nums=enc_blks, dec_blk_nums=dec_blks, dw_expand=dw_expand, ffn_expand=ffn_expand)
|
||||
|
||||
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)
|
||||
Loading…
Reference in New Issue