XHChen0528
240 lines
9.2 KiB
Python
240 lines
9.2 KiB
Python
#!/usr/bin/env python3
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# -*- coding:utf-8 -*-
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#############################################################
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# File: Generator_Invobn_config1.py
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# Created Date: Saturday February 26th 2022
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# Author: Chen Xuanhong
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# Email: chenxuanhongzju@outlook.com
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# Last Modified: Sunday, 27th February 2022 7:50:18 pm
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# Modified By: Chen Xuanhong
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# Copyright (c) 2022 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 components.LSTU import LSTU
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# from components.DeConv_Invo import DeConv
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class InstanceNorm(nn.Module):
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def __init__(self, epsilon=1e-8):
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"""
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@notice: avoid in-place ops.
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https://discuss.pytorch.org/t/encounter-the-runtimeerror-one-of-the-variables-needed-for-gradient-computation-has-been-modified-by-an-inplace-operation/836/3
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"""
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super(InstanceNorm, self).__init__()
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self.epsilon = epsilon
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def forward(self, x):
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x = x - torch.mean(x, (2, 3), True)
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tmp = torch.mul(x, x) # or x ** 2
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tmp = torch.rsqrt(torch.mean(tmp, (2, 3), True) + self.epsilon)
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return x * tmp
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class ApplyStyle(nn.Module):
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"""
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@ref: https://github.com/lernapparat/lernapparat/blob/master/style_gan/pytorch_style_gan.ipynb
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"""
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def __init__(self, latent_size, channels):
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super(ApplyStyle, self).__init__()
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self.linear = nn.Linear(latent_size, channels * 2)
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def forward(self, x, latent):
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style = self.linear(latent) # style => [batch_size, n_channels*2]
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shape = [-1, 2, x.size(1), 1, 1]
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style = style.view(shape) # [batch_size, 2, n_channels, ...]
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#x = x * (style[:, 0] + 1.) + style[:, 1]
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x = x * (style[:, 0] * 1 + 1.) + style[:, 1] * 1
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return x
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class ResnetBlock_Adain(nn.Module):
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def __init__(self,
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dim,
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latent_size,
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padding_type,
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activation=nn.ReLU(True),
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res_mode="depthwise"):
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super(ResnetBlock_Adain, self).__init__()
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p = 0
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conv1 = []
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if padding_type == 'reflect':
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conv1 += [nn.ReflectionPad2d(1)]
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elif padding_type == 'replicate':
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conv1 += [nn.ReplicationPad2d(1)]
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elif padding_type == 'zero':
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p = 1
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else:
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raise NotImplementedError('padding [%s] is not implemented' % padding_type)
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if res_mode.lower() == "conv":
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conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
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elif res_mode.lower() == "depthwise":
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conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
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nn.Conv2d(dim, dim, kernel_size=1),
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InstanceNorm()]
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elif res_mode.lower() == "depthwise_eca":
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conv1 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
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nn.Conv2d(dim, dim, kernel_size=1),
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InstanceNorm()]
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self.conv1 = nn.Sequential(*conv1)
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self.style1 = ApplyStyle(latent_size, dim)
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self.act1 = activation
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p = 0
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conv2 = []
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if padding_type == 'reflect':
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conv2 += [nn.ReflectionPad2d(1)]
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elif padding_type == 'replicate':
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conv2 += [nn.ReplicationPad2d(1)]
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elif padding_type == 'zero':
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p = 1
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else:
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raise NotImplementedError('padding [%s] is not implemented' % padding_type)
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if res_mode.lower() == "conv":
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conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p), InstanceNorm()]
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elif res_mode.lower() == "depthwise":
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conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
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nn.Conv2d(dim, dim, kernel_size=1),
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InstanceNorm()]
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elif res_mode.lower() == "depthwise_eca":
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conv2 += [nn.Conv2d(dim, dim, kernel_size=3, padding=p,groups=dim, bias=False),
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nn.Conv2d(dim, dim, kernel_size=1),
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InstanceNorm()]
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self.conv2 = nn.Sequential(*conv2)
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self.style2 = ApplyStyle(latent_size, dim)
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def forward(self, x, dlatents_in_slice):
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y = self.conv1(x)
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y = self.style1(y, dlatents_in_slice)
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y = self.act1(y)
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y = self.conv2(y)
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y = self.style2(y, dlatents_in_slice)
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out = x + y
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return out
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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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id_dim = kwargs["id_dim"]
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k_size = kwargs["g_kernel_size"]
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res_num = kwargs["res_num"]
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in_channel = kwargs["in_channel"]
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up_mode = kwargs["up_mode"]
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aggregator = kwargs["aggregator"]
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res_mode = kwargs["res_mode"]
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padding_size= int((k_size -1)/2)
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padding_type= 'reflect'
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activation = nn.ReLU(True)
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from components.DeConv_Depthwise import DeConv
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# self.first_layer = nn.Sequential(nn.ReflectionPad2d(3), nn.Conv2d(3, 64, kernel_size=7, padding=0, bias=False),
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# nn.BatchNorm2d(64), activation)
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self.first_layer = nn.Sequential(nn.ReflectionPad2d(1),
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nn.Conv2d(3, in_channel, kernel_size=3, padding=0, bias=False),
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nn.BatchNorm2d(in_channel),
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activation)
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# self.first_layer = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
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# nn.BatchNorm2d(64), activation)
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### downsample
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self.down1 = nn.Sequential(
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nn.Conv2d(in_channel, in_channel*2, stride=2, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(in_channel*2),
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activation)
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self.down2 = nn.Sequential(
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nn.Conv2d(in_channel*2, in_channel*4, stride=2, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(in_channel*4),
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activation)
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# self.lstu = LSTU(in_channel*4,in_channel*4,in_channel*8,4)
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self.down3 = nn.Sequential(
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nn.Conv2d(in_channel*4, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(in_channel*8),
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activation)
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self.down4 = nn.Sequential(
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nn.Conv2d(in_channel*8, in_channel*8, stride=2, kernel_size=3, padding=1, bias=False),
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nn.BatchNorm2d(in_channel*8),
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activation)
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### resnet blocks
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BN = []
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for i in range(res_num):
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BN += [
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ResnetBlock_Adain(in_channel*8, latent_size=id_dim,
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padding_type=padding_type, activation=activation, res_mode=res_mode)]
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self.BottleNeck = nn.Sequential(*BN)
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self.up4 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(in_channel*8, in_channel*8, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(in_channel*8),
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activation
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)
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self.up3 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(in_channel*8, in_channel*4, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(in_channel*4),
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activation
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)
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self.up2 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(in_channel*4, in_channel*2, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(in_channel*2),
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activation
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)
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self.up1 = nn.Sequential(
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nn.Upsample(scale_factor=2, mode='bilinear'),
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nn.Conv2d(in_channel*2, in_channel, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(in_channel),
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activation
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)
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# self.last_layer = nn.Sequential(nn.Conv2d(64, 3, kernel_size=3, padding=1))
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self.last_layer = nn.Sequential(nn.ReflectionPad2d(1),
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nn.Conv2d(in_channel, 3, kernel_size=3, padding=0))
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# self.last_layer = nn.Sequential(nn.ReflectionPad2d(3),
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# nn.Conv2d(64, 3, kernel_size=7, padding=0))
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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, img, id):
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res = self.first_layer(img)
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res = self.down1(res)
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res = self.down2(res)
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res = self.down3(res)
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res = self.down4(res)
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for i in range(len(self.BottleNeck)):
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res = self.BottleNeck[i](res, id)
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# skip = self.lstu(res1, res)
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res = self.up4(res)
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res = self.up3(res)
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res = self.up2(res) # + skip
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res = self.up1(res)
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res = self.last_layer(res)
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return res |