chenxuanhong
144 lines
5.3 KiB
Python
144 lines
5.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: FastNST_Liif.py
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# Created Date: Thursday October 14th 2021
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Tuesday, 19th October 2021 2:39:09 am
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# Modified By: Chen Xuanhong
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# Copyright (c) 2021 Shanghai Jiao Tong University
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#############################################################
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import torch
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from torch import nn
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from torch.nn import init
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from torch.nn import functional as F
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from components.ResBlock import ResBlock
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from components.DeConv import DeConv
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from components.Liif import LIIF
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class ImageLN(nn.Module):
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def __init__(self, dim) -> None:
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super().__init__()
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self.layer = nn.LayerNorm(dim)
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def forward(self, x):
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y = self.layer(x.permute(0,2,3,1)).permute(0,3,1,2)
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return y
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class Generator(nn.Module):
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def __init__(
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self,
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**kwargs
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):
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super().__init__()
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chn = kwargs["g_conv_dim"]
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k_size = kwargs["g_kernel_size"]
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res_num = kwargs["res_num"]
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class_num = kwargs["n_class"]
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window_size = kwargs["window_size"]
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image_size = kwargs["image_size"]
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batch_size = kwargs["batch_size"]
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# mlp_in_dim = kwargs["mlp_in_dim"]
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# mlp_out_dim = kwargs["mlp_out_dim"]
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mlp_hidden_list = kwargs["mlp_hidden_list"]
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padding_size = int((k_size -1)/2)
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self.resblock_list = []
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embed_dim = 96
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window_size = 8
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num_heads = 8
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mlp_ratio = 2.
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norm_layer = nn.LayerNorm
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qk_scale = None
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qkv_bias = True
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self.patch_norm = True
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self.lnnorm = norm_layer(embed_dim)
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self.encoder = nn.Sequential(
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nn.Conv2d(in_channels = 3 , out_channels = chn , kernel_size=k_size, stride=1, padding=1, bias= False),
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nn.InstanceNorm2d(chn),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels = chn , out_channels = chn*2, kernel_size=k_size, stride=2, padding=1,bias =False), #
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nn.InstanceNorm2d(chn * 2),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels = chn*2, out_channels = chn*4, kernel_size=k_size, stride=2, padding=1,bias =False),
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nn.InstanceNorm2d(chn * 4),
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nn.LeakyReLU(),
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nn.Conv2d(in_channels = chn*4 , out_channels = chn * 4, kernel_size=k_size, stride=2, padding=1,bias =False),
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ImageLN(chn * 4),
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nn.LeakyReLU(),
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)
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for _ in range(res_num):
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self.resblock_list += [ResBlock(chn * 4,k_size),]
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self.resblocks = nn.Sequential(*self.resblock_list)
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# self.encoder2 = nn.Sequential(
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# nn.Conv2d(in_channels = chn*4 , out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
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# ImageLN(chn * 8),
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# nn.LeakyReLU(),
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# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
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# ImageLN(chn * 8),
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# nn.LeakyReLU(),
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# nn.Conv2d(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size, stride=2, padding=1,bias =False),
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# ImageLN(chn * 8),
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# nn.LeakyReLU()
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# )
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self.decoder = nn.Sequential(
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# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
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# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
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# nn.LeakyReLU(),
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# DeConv(in_channels = chn * 8, out_channels = chn * 8, kernel_size=k_size),
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# nn.InstanceNorm2d(chn * 8, affine=True, momentum=0),
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# nn.LeakyReLU(),
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DeConv(in_channels = chn * 4, out_channels = chn *2, kernel_size=k_size),
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nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
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nn.LeakyReLU(),
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DeConv(in_channels = chn * 2, out_channels = chn, kernel_size=k_size),
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# nn.InstanceNorm2d(chn * 2, affine=True, momentum=0),
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nn.InstanceNorm2d(chn),
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nn.LeakyReLU()
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# DeConv(in_channels = chn *2, out_channels = chn, kernel_size=k_size),
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# nn.InstanceNorm2d(chn),
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# nn.LeakyReLU(),
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# nn.Conv2d(in_channels = chn, out_channels =3, kernel_size=k_size, stride=1, padding=1,bias =True)
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)
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self.upsample = LIIF(chn, 3, mlp_hidden_list)
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self.upsample.gen_coord((batch_size, \
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chn,image_size//2,image_size//2),(image_size,image_size))
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# self.__weights_init__()
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# def __weights_init__(self):
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# for layer in self.encoder:
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# if isinstance(layer,nn.Conv2d):
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# nn.init.xavier_uniform_(layer.weight)
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# for layer in self.encoder2:
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# if isinstance(layer,nn.Conv2d):
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# nn.init.xavier_uniform_(layer.weight)
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def forward(self, input):
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x2 = self.encoder(input)
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x2 = self.resblocks(x2)
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out = self.decoder(x2)
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out = self.upsample(out)
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return out
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if __name__ == '__main__':
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upscale = 4
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window_size = 8
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height = 1024
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width = 1024
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model = Generator()
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print(model)
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x = torch.randn((1, 3, height, width))
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x = model(x)
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print(x.shape) |