前言：
论文中直接提供了GitHub 的代码下载地址
GitHub - junyanz/pytorch-CycleGAN-and-pix2pix: Image-to-Image Translation in PyTorch
这里面简单的解读一下.
目录：
- 模型参数配置
2： 生成器模型
3： 鉴别器模型
4： network代码
5: cycleGAN代码
6: 训练和测试代码
一 参数配置模块
文件目录：options\train_options.py
参数配置是通过argparse 实现的，这样通过google colab 调试的时候
可以动态的设置超参数进行训练
动态设置超参数命令
|-------------------------------------------------------------------|
|python my_program.py-``-``gender male ``-``-``height ``1.75|
二 生成器模型
在network.py中实现,后面在代码示例中，我直接提供了一个入口可以Debug一下
Total params: 11,378,179
Trainable params: 11,378,179
Non-trainable params: 0
生成器主要由5个模块组成
2.1 输入层
2.2 下采样层
2.3 残差网络层，默认9个
2.4 上采样层
三 鉴别器模型
network.py中实现
Total params: 2,764,737
Trainable params: 2,764,737
Non-trainable params: 0
模型结构
主要定义了生成器，鉴别器的网络结构
define_G 生成器
define_D 鉴别器
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 13:48:33 2024
@author: chengxf2
"""
import torch
import torch.nn as nn
from torch.nn import init
import functools
from torch.optim import lr_scheduler
from torchsummary import summary
###############################################################################
# Helper Functions
###############################################################################
class Identity(nn.Module):
def forward(self, x):
return x
def init_weights(net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
print('initialize network with %s' % init_type)
net.apply(init_func) # apply the initialization function <init_func>
def get_norm_layer(norm_type='instance'):
"""Return a normalization layer
Parameters:
affine：
代表gamma，beta是否可学。如果设为True，代表两个参数是通过学习得到的；
如果设为False，代表两个参数是固定值，默认情况下，gamma是1，beta是0。
track_running_stats：
BatchNorm2d中存储的的均值和方差是否需要更新，若为True，表示需要更新；
反之不需要更新。更新公式参考momentum参数介绍 。
"""
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
elif norm_type == 'instance':
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
elif norm_type == 'none':
def norm_layer(x):
return Identity()
else:
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
return norm_layer
def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Return an initialized network.
"""
if len(gpu_ids) > 0:
assert(torch.cuda.is_available())
net.to(gpu_ids[0])
net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs
init_weights(net, init_type, init_gain=init_gain)
return net
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = 1
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
self.model = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
return self.model(input)
def define_D(input_nc, ndf, netD, n_layers_D=3, norm='batch', init_type='normal', init_gain=0.02, gpu_ids=[]):
"""鉴别器
Parameters:
input_nc (3) -- the number of channels in input images
ndf (64) -- the number of filters in the first conv layer
netD ('basic') -- the architecture's name: basic | n_layers | pixel
n_layers_D (3) -- the number of conv layers in the discriminator; effective when netD=='n_layers'
norm ('instance') -- the type of normalization layers used in the network.
init_type ('normal') -- the name of the initialization method.
init_gain (0.02) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Returns a discriminator
Our current implementation provides three types of discriminators:
[basic]: 'PatchGAN' classifier described in the original pix2pix paper.
It can classify whether 70×70 overlapping patches are real or fake.
Such a patch-level discriminator architecture has fewer parameters
than a full-image discriminator and can work on arbitrarily-sized images
in a fully convolutional fashion.
[n_layers]: With this mode, you can specify the number of conv layers in the discriminator
with the parameter <n_layers_D> (default=3 as used in [basic] (PatchGAN).)
[pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
It encourages greater color diversity but has no effect on spatial statistics.
The discriminator has been initialized by <init_net>. It uses Leakly RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netD == 'basic': # default PatchGAN classifier
net = NLayerDiscriminator(input_nc, ndf, n_layers=3, norm_layer=norm_layer)
elif netD == 'n_layers': # more options
net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer)
raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
return init_net(net, init_type, init_gain, gpu_ids)
def define_G(input_nc, output_nc, ngf, netG, norm='batch', use_dropout=False, init_type='normal', init_gain=0.02, gpu_ids=[]):
"""创建一个生成器
Parameters:
input_nc=3-- input image channels: 3 for RGB and 1 for grayscale
output_nc=3 -- output image channels: 3 for RGB and 1 for grayscale'
ngf =64 -- the number of filters in the last conv layer
netG='resnet_9blocks' -- the architecture's name: resnet_9blocks | resnet_6blocks | unet_256 | unet_128
norm='instance' -- the name of normalization layers used in the network: batch | instance | none
use_dropout =False -- if use dropout layers.
init_type ='normal' -- the name of our initialization method.
init_gain =0.02 -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Returns a generator
Our current implementation provides two types of generators:
U-Net: [unet_128] (for 128x128 input images) and [unet_256] (for 256x256 input images)
The original U-Net paper: https://arxiv.org/abs/1505.04597
Resnet-based generator: [resnet_6blocks] (with 6 Resnet blocks) and [resnet_9blocks] (with 9 Resnet blocks)
Resnet-based generator consists of several Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code from Justin Johnson's neural style transfer project (https://github.com/jcjohnson/fast-neural-style).
The generator has been initialized by <init_net>. It uses RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netG == 'resnet_9blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=9)
elif netG == 'resnet_6blocks':
net = ResnetGenerator(input_nc, output_nc, ngf, norm_layer=norm_layer, use_dropout=use_dropout, n_blocks=6)
else:
raise NotImplementedError('Generator model name [%s] is not recognized' % netG)
return init_net(net, init_type, init_gain, gpu_ids)
class ResnetGenerator(nn.Module):
"""Resnet-based generator that consists of Resnet blocks between a few downsampling/upsampling operations.
We adapt Torch code and idea from Justin Johnson's neural style transfer project(https://github.com/jcjohnson/fast-neural-style)
"""
def __init__(self, input_nc, output_nc, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False, n_blocks=6, padding_type='reflect'):
"""Construct a Resnet-based generator
Parameters:
input_nc (3) -- the number of channels in input images
output_nc (3) -- the number of channels in output images
ngf (64) -- the number of filters in the last conv layer
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers
n_blocks (int) -- the number of ResNet blocks
padding_type (str) -- the name of padding layer in conv layers: reflect | replicate | zero
"""
assert(n_blocks >= 0)
super(ResnetGenerator, self).__init__()
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
#镜像天聪,卷积
model = [nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, ngf, kernel_size=7, padding=0, bias=use_bias),
norm_layer(ngf),
nn.ReLU(True)]
n_downsampling = 2
for i in range(n_downsampling): # add downsampling layers
mult = 2 ** i
[]
model += [nn.Conv2d(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1, bias=use_bias),
norm_layer(ngf * mult * 2),
nn.ReLU(True)]
mult = 2 ** n_downsampling
for i in range(n_blocks): # add ResNet blocks
model += [ResnetBlock(ngf * mult, padding_type=padding_type, norm_layer=norm_layer, use_dropout=use_dropout, use_bias=use_bias)]
for i in range(n_downsampling): # add upsampling layers
mult = 2 ** (n_downsampling - i)
model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2),
kernel_size=3, stride=2,
padding=1, output_padding=1,
bias=use_bias),
norm_layer(int(ngf * mult / 2)),
nn.ReLU(True)]
model += [nn.ReflectionPad2d(3)]
model += [nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
model += [nn.Tanh()]
self.model = nn.Sequential(*model)
def forward(self, input):
"""Standard forward"""
return self.model(input)
class ResnetBlock(nn.Module):
"""Define a Resnet block"""
def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Initialize the Resnet block
A resnet block is a conv block with skip connections
We construct a conv block with build_conv_block function,
and implement skip connections in <forward> function.
Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf
"""
super(ResnetBlock, self).__init__()
self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)
def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):
"""Construct a convolutional block.
Parameters:
dim (int) -- the number of channels in the conv layer.
padding_type (str) -- the name of padding layer: reflect | replicate | zero
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
use_bias (bool) -- if the conv layer uses bias or not
Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
"""
conv_block = []
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == 'reflect':
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == 'replicate':
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == 'zero':
p = 1
else:
raise NotImplementedError('padding [%s] is not implemented' % padding_type)
conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]
return nn.Sequential(*conv_block)
def forward(self, x):
"""Forward function (with skip connections)"""
out = x + self.conv_block(x) # add skip connections
return out
if __name__ == '__main__':
input_nc =3
output_nc =3
ndf = 64
ngf= 64
netD ='basic'
netG = 'resnet_9blocks'
n_layers_D =3
norm ='instance'
init_type = 'normal'
init_gain = 0.02
gpu_ids = []
use_dropout=False
netG_A = define_G(input_nc, output_nc, ngf, netG, norm, use_dropout, init_type, init_gain, gpu_ids)
#print(netG_A)
summary(model=netG_A, input_size=(3,256,256),batch_size=1, device="cpu")
netD_A= define_D(input_nc,
ndf,
netD,
n_layers_D,
norm,
init_type,
init_gain,
gpu_ids)
summary(model=netD_A, input_size=(3,256,256),batch_size=1, device="cpu")
五cycleGAN 模型
cycle_gan_model.py
主要定义了 损失函数，训练方法
GAN 模型是先训练鉴别器D, 再训练生成器G
CycleGAN 是先训练生成器G, 再训练鉴别器D
3.1 cycle_gan_model.py
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 28 14:02:15 2024
@author: chengxf2
"""
import torch
import itertools
from . import networks
from . import ImagePool
from .base_model import BaseModel
class CycleGANModel():
"""
netG: 生成器
netD: 鉴别器
"""
def __init__(self,opt):
"""
初始化CycleGAN 模型
"""
BaseModel.__init__(self, opt)
#论文实例代码 code(vs. paper) G_A(G) G_B(F),D_A(D_Y),D_B(D_X),这里直接更改成论文的命名方式
self.netG_X = networks.define_G(opt.input_nc, opt.output_nc, opt.ngf, opt.netG, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
self.netG_Y = networks.define_G(opt.output_nc, opt.input_nc, opt.ngf, opt.netG, opt.norm,
not opt.no_dropout, opt.init_type, opt.init_gain, self.gpu_ids)
if self.isTrain: #定义鉴别器
self.netD_X = networks.define_D(opt.input_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
self.netD_Y = networks.define_D(opt.output_nc, opt.ndf, opt.netD,
opt.n_layers_D, opt.norm, opt.init_type, opt.init_gain, self.gpu_ids)
if self.isTrain:
if opt.lambda_identity > 0.0: # only works when input and output images have the same number of channels
assert(opt.input_nc == opt.output_nc)
self.fake_A_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images
self.fake_B_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images
# define loss functions
self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device) # define GAN loss.
self.criterionCycle = torch.nn.L1Loss()
self.criterionIdt = torch.nn.L1Loss()
# initialize optimizers; schedulers will be automatically created by function <BaseModel.setup>.
self.optimizer_G = torch.optim.Adam(itertools.chain(self.netG_X.parameters(), self.netG_Y.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_X.parameters(), self.netD_Y.parameters()), lr=opt.lr, betas=(opt.beta1, 0.999))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
def set_input(self):
"""
从数据加载器中解压缩输入数据，并执行必要的预处理步骤。
参数：
input（dict）：包括数据本身及其元数据信息
选项"direction"可用于交换域X和域Y。
"""
XtoY = self.opt.direction == 'XtoY'
self.real_X = input['X' if XtoY else 'Y'].to(self.device)
self.real_Y= input['Y' if XtoY else 'X'].to(self.device)
self.image_path = input['X_paths' if XtoY else 'Y_Paths']
def forward(self):
"""运行前向传播；由函数 <optimize_parameters> 和 <test> 调用"""
self.fake_Y = self.netG_X(self.real_X) #G_X(X)
self.rec_X = self.netG_Y(self.fake_Y) #G_Y(G_X(X)) 重构
self.fake_X = self.netG_Y(self.real_Y) #G_Y(Y)
self.rec_Y = self.netG_X(self.fake_Y) #G_X(fake_Y)
def optimize_parameters(self):
"""计算损失、梯度并更新网络权重；在每次训练迭代中调用"""
# forward
self.forward() # compute fake images and reconstruction images.
#先训练生成器 G_X and G_Y,这个跟GAN是相反的
self.set_requires_grad([self.netD_X, self.netD_Y], False) # Ds require no gradients when optimizing Gs
self.optimizer_G.zero_grad() # set G_A and G_B's gradients to zero
self.backward_G() # calculate gradients for G_A and G_B
self.optimizer_G.step() # update G_A and G_B's weights
# D_X and D_Y
self.set_requires_grad([self.netD_X, self.netD_Y], True)
self.optimizer_D.zero_grad() # set D_A and D_B's gradients to zero
self.backward_D_X() # calculate gradients for D_A
self.backward_D_Y() # calculate graidents for D_B
self.optimizer_D.step() # update D_A and D_B's weights
def backward_G(self):
"""Calculate the loss for generators G_X and G_Y"""
lambda_idt = self.opt.lambda_identity
lambda_X = self.opt.lambda_X
lambda_Y = self.opt.lambda_Y
# Identity loss
if lambda_idt > 0:
# identity if real_Y is fed:
self.idt_X = self.netG_X(self.real_Y)
self.loss_idt_X = self.criterionIdt(self.idt_X, self.real_Y) * lambda_X * lambda_idt
# identity if real_X is fed:
self.idt_Y = self.netG_Y(self.real_X)
self.loss_idt_Y = self.criterionIdt(self.idt_Y, self.real_X) * lambda_Y * lambda_idt
else:
self.loss_idt_X = 0
self.loss_idt_Y = 0
# GAN loss D_A(G_A(A))
self.loss_G_X = self.criterionGAN(self.netD_Y(self.fake_Y), True)
# GAN loss D_B(G_B(B))
self.loss_G_Y = self.criterionGAN(self.netD_X(self.fake_X), True)
# Forward cycle loss || G_B(G_A(A)) - A||
self.loss_cycle_X = self.criterionCycle(self.rec_X, self.real_X) * lambda_X
# Backward cycle loss || G_A(G_B(B)) - B||
self.loss_cycle_Y = self.criterionCycle(self.rec_Y, self.real_Y) * lambda_Y
# combined loss and calculate gradients
self.loss_G = self.loss_G_X + self.loss_G_Y + self.loss_cycle_X + self.loss_cycle_Y + self.loss_idt_X + self.loss_idt_Y
self.loss_G.backward()
def backward_D_Y(self):
"""Calculate GAN loss for discriminator D_X"""
fake_Y = self.fake_B_pool.query(self.fake_Y)
self.loss_D_Y = self.backward_D_basic(self.netD_Y, self.real_Y, fake_Y)
def backward_D_X(self):
"""Calculate GAN loss for discriminator D_B"""
fake_X = self.fake_A_pool.query(self.fake_X)
self.loss_D_X = self.backward_D_basic(self.netD_X, self.real_X, fake_X)
def backward_D_basic(self, netD, real, fake):
"""Calculate GAN loss for the discriminator,patchGAN
Parameters:
netD (network) -- the discriminator D
real (tensor array) -- real images
fake (tensor array) -- images generated by a generator
Return the discriminator loss.
We also call loss_D.backward() to calculate the gradients.
"""
# Real
pred_real = netD(real)
loss_D_real = self.criterionGAN(pred_real, True)
# Fake
pred_fake = netD(fake.detach())
loss_D_fake = self.criterionGAN(pred_fake, False)
# Combined loss and calculate gradients
loss_D = (loss_D_real + loss_D_fake) * 0.5
loss_D.backward()
return loss_D
3.2 base_model.py
基类
import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: initialize the class; first call BaseModel.__init__(self, opt).
-- <set_input>: unpack data from dataset and apply preprocessing.
-- <forward>: produce intermediate results.
-- <optimize_parameters>: calculate losses, gradients, and update network weights.
-- <modify_commandline_options>: (optionally) add model-specific options and set default options.
"""
def __init__(self, opt):
"""Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this function, you should first call <BaseModel.__init__(self, opt)>
Then, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
"""
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU
self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir
if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
torch.backends.cudnn.benchmark = True
self.loss_names = []
self.model_names = []
self.visual_names = []
self.optimizers = []
self.image_paths = []
self.metric = 0 # used for learning rate policy 'plateau'
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new model-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
return parser
@abstractmethod
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input (dict): includes the data itself and its metadata information.
"""
pass
@abstractmethod
def forward(self):
"""Run forward pass; called by both functions <optimize_parameters> and <test>."""
pass
@abstractmethod
def optimize_parameters(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
pass
def setup(self, opt):
"""Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
if self.isTrain:
self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
if not self.isTrain or opt.continue_train:
load_suffix = 'iter_%d' % opt.load_iter if opt.load_iter > 0 else opt.epoch
self.load_networks(load_suffix)
self.print_networks(opt.verbose)
def eval(self):
"""Make models eval mode during test time"""
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, 'net' + name)
net.eval()
def test(self):
"""Forward function used in test time.
This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
It also calls <compute_visuals> to produce additional visualization results
"""
with torch.no_grad():
self.forward()
self.compute_visuals()
def compute_visuals(self):
"""Calculate additional output images for visdom and HTML visualization"""
pass
def get_image_paths(self):
""" Return image paths that are used to load current data"""
return self.image_paths
def update_learning_rate(self):
"""Update learning rates for all the networks; called at the end of every epoch"""
old_lr = self.optimizers[0].param_groups[0]['lr']
for scheduler in self.schedulers:
if self.opt.lr_policy == 'plateau':
scheduler.step(self.metric)
else:
scheduler.step()
lr = self.optimizers[0].param_groups[0]['lr']
print('learning rate %.7f -> %.7f' % (old_lr, lr))
def get_current_visuals(self):
"""Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
visual_ret = OrderedDict()
for name in self.visual_names:
if isinstance(name, str):
visual_ret[name] = getattr(self, name)
return visual_ret
def get_current_losses(self):
"""Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
errors_ret = OrderedDict()
for name in self.loss_names:
if isinstance(name, str):
errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number
return errors_ret
def save_networks(self, epoch):
"""Save all the networks to the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
for name in self.model_names:
if isinstance(name, str):
save_filename = '%s_net_%s.pth' % (epoch, name)
save_path = os.path.join(self.save_dir, save_filename)
net = getattr(self, 'net' + name)
if len(self.gpu_ids) > 0 and torch.cuda.is_available():
torch.save(net.module.cpu().state_dict(), save_path)
net.cuda(self.gpu_ids[0])
else:
torch.save(net.cpu().state_dict(), save_path)
def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
"""Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
key = keys[i]
if i + 1 == len(keys): # at the end, pointing to a parameter/buffer
if module.__class__.__name__.startswith('InstanceNorm') and \
(key == 'running_mean' or key == 'running_var'):
if getattr(module, key) is None:
state_dict.pop('.'.join(keys))
if module.__class__.__name__.startswith('InstanceNorm') and \
(key == 'num_batches_tracked'):
state_dict.pop('.'.join(keys))
else:
self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
def load_networks(self, epoch):
"""Load all the networks from the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
for name in self.model_names:
if isinstance(name, str):
load_filename = '%s_net_%s.pth' % (epoch, name)
load_path = os.path.join(self.save_dir, load_filename)
net = getattr(self, 'net' + name)
if isinstance(net, torch.nn.DataParallel):
net = net.module
print('loading the model from %s' % load_path)
# if you are using PyTorch newer than 0.4 (e.g., built from
# GitHub source), you can remove str() on self.device
state_dict = torch.load(load_path, map_location=str(self.device))
if hasattr(state_dict, '_metadata'):
del state_dict._metadata
# patch InstanceNorm checkpoints prior to 0.4
for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop
self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
net.load_state_dict(state_dict)
def print_networks(self, verbose):
"""Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
"""
print('---------- Networks initialized -------------')
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, 'net' + name)
num_params = 0
for param in net.parameters():
num_params += param.numel()
if verbose:
print(net)
print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
print('-----------------------------------------------')
def set_requires_grad(self, nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
四 训练和测试
4.1 train.py
"""General-purpose training script for image-to-image translation.
This script works for various models (with option '--model': e.g., pix2pix, cyclegan, colorization) and
different datasets (with option '--dataset_mode': e.g., aligned, unaligned, single, colorization).
You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
It first creates model, dataset, and visualizer given the option.
It then does standard network training. During the training, it also visualize/save the images, print/save the loss plot, and save models.
The script supports continue/resume training. Use '--continue_train' to resume your previous training.
Example:
Train a CycleGAN model:
python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
Train a pix2pix model:
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
See options/base_options.py and options/train_options.py for more training options.
See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
"""
import time
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
print('The number of training images = %d' % dataset_size)
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
total_iters = 0 # the total number of training iterations
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
model.update_learning_rate() # update learning rates in the beginning of every epoch.
for i, data in enumerate(dataset): # inner loop within one epoch
iter_start_time = time.time() # timer for computation per iteration
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
total_iters += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data) # unpack data from dataset and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
save_result = total_iters % opt.update_html_freq == 0
model.compute_visuals()
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
losses = model.get_current_losses()
t_comp = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_losses(epoch, epoch_iter, losses, t_comp, t_data)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq == 0: # cache our latest model every <save_latest_freq> iterations
print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
model.save_networks(save_suffix)
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks('latest')
model.save_networks(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
4.2 test.py
测试代码
"""General-purpose test script for image-to-image translation.
Once you have trained your model with train.py, you can use this script to test the model.
It will load a saved model from '--checkpoints_dir' and save the results to '--results_dir'.
It first creates model and dataset given the option. It will hard-code some parameters.
It then runs inference for '--num_test' images and save results to an HTML file.
Example (You need to train models first or download pre-trained models from our website):
Test a CycleGAN model (both sides):
python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
Test a CycleGAN model (one side only):
python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
The option '--model test' is used for generating CycleGAN results only for one side.
This option will automatically set '--dataset_mode single', which only loads the images from one set.
On the contrary, using '--model cycle_gan' requires loading and generating results in both directions,
which is sometimes unnecessary. The results will be saved at ./results/.
Use '--results_dir <directory_path_to_save_result>' to specify the results directory.
Test a pix2pix model:
python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
See options/base_options.py and options/test_options.py for more test options.
See training and test tips at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md
See frequently asked questions at: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md
"""
import os
from options.test_options import TestOptions
from data import create_dataset
from models import create_model
from util.visualizer import save_images
from util import html
try:
import wandb
except ImportError:
print('Warning: wandb package cannot be found. The option "--use_wandb" will result in error.')
if __name__ == '__main__':
opt = TestOptions().parse() # get test options
# hard-code some parameters for test
opt.num_threads = 0 # test code only supports num_threads = 0
opt.batch_size = 1 # test code only supports batch_size = 1
opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
model = create_model(opt) # create a model given opt.model and other options
model.setup(opt) # regular setup: load and print networks; create schedulers
# initialize logger
if opt.use_wandb:
wandb_run = wandb.init(project=opt.wandb_project_name, name=opt.name, config=opt) if not wandb.run else wandb.run
wandb_run._label(repo='CycleGAN-and-pix2pix')
# create a website
web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch)) # define the website directory
if opt.load_iter > 0: # load_iter is 0 by default
web_dir = '{:s}_iter{:d}'.format(web_dir, opt.load_iter)
print('creating web directory', web_dir)
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
# test with eval mode. This only affects layers like batchnorm and dropout.
# For [pix2pix]: we use batchnorm and dropout in the original pix2pix. You can experiment it with and without eval() mode.
# For [CycleGAN]: It should not affect CycleGAN as CycleGAN uses instancenorm without dropout.
if opt.eval:
model.eval()
for i, data in enumerate(dataset):
if i >= opt.num_test: # only apply our model to opt.num_test images.
break
model.set_input(data) # unpack data from data loader
model.test() # run inference
visuals = model.get_current_visuals() # get image results
img_path = model.get_image_paths() # get image paths
if i % 5 == 0: # save images to an HTML file
print('processing (%04d)-th image... %s' % (i, img_path))
save_images(webpage, visuals, img_path, aspect_ratio=opt.aspect_ratio, width=opt.display_winsize, use_wandb=opt.use_wandb)
webpage.save() # save the HTML