DCGAN调试。得到垃圾
DCGAN debugging. Getting just garbage
简介：
我正在尝试让 CDCGAN（条件深度卷积生成对抗网络）在 MNIST 数据集上工作，考虑到我使用的库 (PyTorch) 在其网站上有教程，这应该相当容易。
但我似乎无法让它工作，它只会产生垃圾或模型崩溃或两者兼而有之。
我试过的：
- 制作模型条件半监督学习
- 使用批量规范
- 在生成器和鉴别器 input/output 层之外的每一层上使用 dropout
- 标签平滑以对抗过度自信
- 向图像添加噪声（我猜你称之为实例噪声）以获得更好的数据分布
- 使用 leaky relu 避免梯度消失
- 使用回放缓冲区来防止遗忘所学内容和过度拟合
- 使用超参数
- 将其与 PyTorch 教程中的模型进行比较
- basically what I did besides some things like Embedding layer ect.
我的模型生成的图像：
超参数：
batch_size=50, learning_rate_discrimiantor=0.0001, learning_rate_generator=0.0003, shuffle=True, ndf=64, ngf=64, droupout=0.5
batch_size=50, learning_rate_discriminator=0.0003, learning_rate_generator=0.0003, shuffle=True, ndf=64, ngf=64, dropout=0
生成的图像 Pytorch tutorial Model：
Code for the pytorch tutorial dcgan model
作为比较，这里是来自 pytorch turoial 的 DCGAN 的图像：
我的代码：
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms, datasets
import torch.nn.functional as F
from torch import optim as optim
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import os
import time
class Discriminator(torch.nn.Module):
def __init__(self, ndf=16, dropout_value=0.5): # ndf feature map discriminator
super().__init__()
self.ndf = ndf
self.droupout_value = dropout_value
self.condi = nn.Sequential(
nn.Linear(in_features=10, out_features=64 * 64)
)
self.hidden0 = nn.Sequential(
nn.Conv2d(in_channels=2, out_channels=self.ndf, kernel_size=4, stride=2, padding=1, bias=False),
nn.LeakyReLU(0.2),
)
self.hidden1 = nn.Sequential(
nn.Conv2d(in_channels=self.ndf, out_channels=self.ndf * 2, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ndf * 2),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden2 = nn.Sequential(
nn.Conv2d(in_channels=self.ndf * 2, out_channels=self.ndf * 4, kernel_size=4, stride=2, padding=1, bias=False),
#nn.BatchNorm2d(self.ndf * 4),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden3 = nn.Sequential(
nn.Conv2d(in_channels=self.ndf * 4, out_channels=self.ndf * 8, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ndf * 8),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.out = nn.Sequential(
nn.Conv2d(in_channels=self.ndf * 8, out_channels=1, kernel_size=4, stride=1, padding=0, bias=False),
torch.nn.Sigmoid()
)
def forward(self, x, y):
y = self.condi(y.view(-1, 10))
y = y.view(-1, 1, 64, 64)
x = torch.cat((x, y), dim=1)
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.hidden3(x)
x = self.out(x)
return x
class Generator(torch.nn.Module):
def __init__(self, n_features=100, ngf=16, c_channels=1, dropout_value=0.5): # ngf feature map of generator
super().__init__()
self.ngf = ngf
self.n_features = n_features
self.c_channels = c_channels
self.droupout_value = dropout_value
self.hidden0 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.n_features + 10, out_channels=self.ngf * 8,
kernel_size=4, stride=1, padding=0, bias=False),
nn.BatchNorm2d(self.ngf * 8),
nn.LeakyReLU(0.2)
)
self.hidden1 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.ngf * 8, out_channels=self.ngf * 4,
kernel_size=4, stride=2, padding=1, bias=False),
#nn.BatchNorm2d(self.ngf * 4),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden2 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.ngf * 4, out_channels=self.ngf * 2,
kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ngf * 2),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden3 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.ngf * 2, out_channels=self.ngf,
kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ngf),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.out = nn.Sequential(
# "out_channels=1" because gray scale
nn.ConvTranspose2d(in_channels=self.ngf, out_channels=1, kernel_size=4,
stride=2, padding=1, bias=False),
nn.Tanh()
)
def forward(self, x, y):
x_cond = torch.cat((x, y), dim=1) # Combine flatten image with conditional input (class labels)
x = self.hidden0(x_cond) # Image goes into a "ConvTranspose2d" layer
x = self.hidden1(x)
x = self.hidden2(x)
x = self.hidden3(x)
x = self.out(x)
return x
class Logger:
def __init__(self, model_name, model1, model2, m1_optimizer, m2_optimizer, model_parameter, train_loader):
self.out_dir = "data"
self.model_name = model_name
self.train_loader = train_loader
self.model1 = model1
self.model2 = model2
self.model_parameter = model_parameter
self.m1_optimizer = m1_optimizer
self.m2_optimizer = m2_optimizer
# Exclude Epochs of the model name. This make sense e.g. when we stop a training progress and continue later on.
self.experiment_name = '_'.join("{!s}={!r}".format(k, v) for (k, v) in model_parameter.items())\
.replace("Epochs" + "=" + str(model_parameter["Epochs"]), "")
self.d_error = 0
self.g_error = 0
self.tb = SummaryWriter(log_dir=str(self.out_dir + "/log/" + self.model_name + "/runs/" + self.experiment_name))
self.path_image = os.path.join(os.getcwd(), f'{self.out_dir}/log/{self.model_name}/images/{self.experiment_name}')
self.path_model = os.path.join(os.getcwd(), f'{self.out_dir}/log/{self.model_name}/model/{self.experiment_name}')
try:
os.makedirs(self.path_image)
except Exception as e:
print("WARNING: ", str(e))
try:
os.makedirs(self.path_model)
except Exception as e:
print("WARNING: ", str(e))
def log_graph(self, model1_input, model2_input, model1_label, model2_label):
self.tb.add_graph(self.model1, input_to_model=(model1_input, model1_label))
self.tb.add_graph(self.model2, input_to_model=(model2_input, model2_label))
def log(self, num_epoch, d_error, g_error):
self.d_error = d_error
self.g_error = g_error
self.tb.add_scalar("Discriminator Train Error", self.d_error, num_epoch)
self.tb.add_scalar("Generator Train Error", self.g_error, num_epoch)
def log_image(self, images, epoch, batch_num):
grid = torchvision.utils.make_grid(images)
torchvision.utils.save_image(grid, f'{self.path_image}\Epoch_{epoch}_batch_{batch_num}.png')
self.tb.add_image("Generator Image", grid)
def log_histogramm(self):
for name, param in self.model2.named_parameters():
self.tb.add_histogram(name, param, self.model_parameter["Epochs"])
self.tb.add_histogram(f'gen_{name}.grad', param.grad, self.model_parameter["Epochs"])
for name, param in self.model1.named_parameters():
self.tb.add_histogram(name, param, self.model_parameter["Epochs"])
self.tb.add_histogram(f'dis_{name}.grad', param.grad, self.model_parameter["Epochs"])
def log_model(self, num_epoch):
torch.save({
"epoch": num_epoch,
"model_generator_state_dict": self.model1.state_dict(),
"model_discriminator_state_dict": self.model2.state_dict(),
"optimizer_generator_state_dict": self.m1_optimizer.state_dict(),
"optimizer_discriminator_state_dict": self.m2_optimizer.state_dict(),
}, str(self.path_model + f'\{time.time()}_epoch{num_epoch}.pth'))
def close(self, logger, images, num_epoch, d_error, g_error):
logger.log_model(num_epoch)
logger.log_histogramm()
logger.log(num_epoch, d_error, g_error)
self.tb.close()
def display_stats(self, epoch, batch_num, dis_error, gen_error):
print(f'Epoch: [{epoch}/{self.model_parameter["Epochs"]}] '
f'Batch: [{batch_num}/{len(self.train_loader)}] '
f'Loss_D: {dis_error.data.cpu()}, '
f'Loss_G: {gen_error.data.cpu()}')
def get_MNIST_dataset(num_workers_loader, model_parameter, out_dir="data"):
compose = transforms.Compose([
transforms.Resize((64, 64)),
transforms.CenterCrop((64, 64)),
transforms.ToTensor(),
torchvision.transforms.Normalize(mean=[0.5], std=[0.5])
])
dataset = datasets.MNIST(
root=out_dir,
train=True,
download=True,
transform=compose
)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=model_parameter["batch_size"],
num_workers=num_workers_loader,
shuffle=model_parameter["shuffle"])
return dataset, train_loader
def train_discriminator(p_optimizer, p_noise, p_images, p_fake_target, p_real_target, p_images_labels, p_fake_labels, device):
p_optimizer.zero_grad()
# 1.1 Train on real data
pred_dis_real = discriminator(p_images, p_images_labels)
error_real = loss(pred_dis_real, p_real_target)
error_real.backward()
# 1.2 Train on fake data
fake_data = generator(p_noise, p_fake_labels).detach()
fake_data = add_noise_to_image(fake_data, device)
pred_dis_fake = discriminator(fake_data, p_fake_labels)
error_fake = loss(pred_dis_fake, p_fake_target)
error_fake.backward()
p_optimizer.step()
return error_fake + error_real
def train_generator(p_optimizer, p_noise, p_real_target, p_fake_labels, device):
p_optimizer.zero_grad()
fake_images = generator(p_noise, p_fake_labels)
fake_images = add_noise_to_image(fake_images, device)
pred_dis_fake = discriminator(fake_images, p_fake_labels)
error_fake = loss(pred_dis_fake, p_real_target) # because
"""
We use "p_real_target" instead of "p_fake_target" because we want to
maximize that the discriminator is wrong.
"""
error_fake.backward()
p_optimizer.step()
return fake_images, pred_dis_fake, error_fake
# TODO change to a Truncated normal distribution
def get_noise(batch_size, n_features=100):
return torch.FloatTensor(batch_size, n_features, 1, 1).uniform_(-1, 1)
# We flip label of real and fate data. Better gradient flow I have told
def get_real_data_target(batch_size):
return torch.FloatTensor(batch_size, 1, 1, 1).uniform_(0.0, 0.2)
def get_fake_data_target(batch_size):
return torch.FloatTensor(batch_size, 1, 1, 1).uniform_(0.8, 1.1)
def image_to_vector(images):
return torch.flatten(images, start_dim=1, end_dim=-1)
def vector_to_image(images):
return images.view(images.size(0), 1, 28, 28)
def get_rand_labels(batch_size):
return torch.randint(low=0, high=9, size=(batch_size,))
def load_model(model_load_path):
if model_load_path:
checkpoint = torch.load(model_load_path)
discriminator.load_state_dict(checkpoint["model_discriminator_state_dict"])
generator.load_state_dict(checkpoint["model_generator_state_dict"])
dis_opti.load_state_dict(checkpoint["optimizer_discriminator_state_dict"])
gen_opti.load_state_dict(checkpoint["optimizer_generator_state_dict"])
return checkpoint["epoch"]
else:
return 0
def init_model_optimizer(model_parameter, device):
# Initialize the Models
discriminator = Discriminator(ndf=model_parameter["ndf"], dropout_value=model_parameter["dropout"]).to(device)
generator = Generator(ngf=model_parameter["ngf"], dropout_value=model_parameter["dropout"]).to(device)
# train
dis_opti = optim.Adam(discriminator.parameters(), lr=model_parameter["learning_rate_dis"], betas=(0.5, 0.999))
gen_opti = optim.Adam(generator.parameters(), lr=model_parameter["learning_rate_gen"], betas=(0.5, 0.999))
return discriminator, generator, dis_opti, gen_opti
def get_hot_vector_encode(labels, device):
return torch.eye(10)[labels].view(-1, 10, 1, 1).to(device)
def add_noise_to_image(images, device, level_of_noise=0.1):
return images[0].to(device) + (level_of_noise) * torch.randn(images.shape).to(device)
if __name__ == "__main__":
# Hyperparameter
model_parameter = {
"batch_size": 500,
"learning_rate_dis": 0.0002,
"learning_rate_gen": 0.0002,
"shuffle": False,
"Epochs": 10,
"ndf": 64,
"ngf": 64,
"dropout": 0.5
}
# Parameter
r_frequent = 10 # How many samples we save for replay per batch (batch_size / r_frequent).
model_name = "CDCGAN" # The name of you model e.g. "Gan"
num_workers_loader = 1 # How many workers should load the data
sample_save_size = 16 # How many numbers your saved imaged should show
device = "cuda" # Which device should be used to train the neural network
model_load_path = "" # If set load model instead of training from new
num_epoch_log = 1 # How frequent you want to log/
torch.manual_seed(43) # Sets a seed for torch for reproducibility
dataset_train, train_loader = get_MNIST_dataset(num_workers_loader, model_parameter) # Get dataset
# Initialize the Models and optimizer
discriminator, generator, dis_opti, gen_opti = init_model_optimizer(model_parameter, device) # Init model/Optimizer
start_epoch = load_model(model_load_path) # when we want to load a model
# Init Logger
logger = Logger(model_name, generator, discriminator, gen_opti, dis_opti, model_parameter, train_loader)
loss = nn.BCELoss()
images, labels = next(iter(train_loader)) # For logging
# For testing
# pred = generator(get_noise(model_parameter["batch_size"]).to(device), get_hot_vector_encode(get_rand_labels(model_parameter["batch_size"]), device))
# dis = discriminator(images.to(device), get_hot_vector_encode(labels, device))
logger.log_graph(get_noise(model_parameter["batch_size"]).to(device), images.to(device),
get_hot_vector_encode(get_rand_labels(model_parameter["batch_size"]), device),
get_hot_vector_encode(labels, device))
# Array to store
exp_replay = torch.tensor([]).to(device)
for num_epoch in range(start_epoch, model_parameter["Epochs"]):
for batch_num, data_loader in enumerate(train_loader):
images, labels = data_loader
images = add_noise_to_image(images, device) # Add noise to the images
# 1. Train Discriminator
dis_error = train_discriminator(
dis_opti,
get_noise(model_parameter["batch_size"]).to(device),
images.to(device),
get_fake_data_target(model_parameter["batch_size"]).to(device),
get_real_data_target(model_parameter["batch_size"]).to(device),
get_hot_vector_encode(labels, device),
get_hot_vector_encode(
get_rand_labels(model_parameter["batch_size"]), device),
device
)
# 2. Train Generator
fake_image, pred_dis_fake, gen_error = train_generator(
gen_opti,
get_noise(model_parameter["batch_size"]).to(device),
get_real_data_target(model_parameter["batch_size"]).to(device),
get_hot_vector_encode(
get_rand_labels(model_parameter["batch_size"]),
device),
device
)
# Store a random point for experience replay
perm = torch.randperm(fake_image.size(0))
r_idx = perm[:max(1, int(model_parameter["batch_size"] / r_frequent))]
r_samples = add_noise_to_image(fake_image[r_idx], device)
exp_replay = torch.cat((exp_replay, r_samples), 0).detach()
if exp_replay.size(0) >= model_parameter["batch_size"]:
# Train on experienced data
dis_opti.zero_grad()
r_label = get_hot_vector_encode(torch.zeros(exp_replay.size(0)).numpy(), device)
pred_dis_real = discriminator(exp_replay, r_label)
error_real = loss(pred_dis_real, get_fake_data_target(exp_replay.size(0)).to(device))
error_real.backward()
dis_opti.step()
print(f'Epoch: [{num_epoch}/{model_parameter["Epochs"]}] '
f'Batch: Replay/Experience batch '
f'Loss_D: {error_real.data.cpu()}, '
)
exp_replay = torch.tensor([]).to(device)
logger.display_stats(epoch=num_epoch, batch_num=batch_num, dis_error=dis_error, gen_error=gen_error)
if batch_num % 100 == 0:
logger.log_image(fake_image[:sample_save_size], num_epoch, batch_num)
logger.log(num_epoch, dis_error, gen_error)
if num_epoch % num_epoch_log == 0:
logger.log_model(num_epoch)
logger.log_histogramm()
logger.close(logger, fake_image[:sample_save_size], num_epoch, dis_error, gen_error)
First link to my Code (Pastebin)
Second link to my Code (0bin)
结论：
自从我实现了所有这些被认为对GAN/DCGAN有益的东西（例如标签平滑）。
而且我的模型仍然比 PyTorch 的教程 DCGAN 表现更差我想我的代码中可能有一个错误，但我似乎找不到它。
再现性：
如果你安装了我导入的库，你应该可以只复制代码并运行它，如果你能找到任何东西，你可以自己寻找。
感谢任何反馈。
所以我刚才解决了这个问题，但是忘了post关于堆栈溢出的答案。所以我将简单地 post 我的代码在这里应该工作得很好。
一些免责声明：
- 我不太确定它是否有效，因为我一年前就这样做了
- 它适用于 128x128px 图像 MNIST
- 这不是普通的 GAN 我使用了各种优化技术
- 如果你想使用它你需要改变各种细节，比如训练数据集
资源：
``
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning import loggers
from numpy.random import choice
import os
from pathlib import Path
import shutil
from collections import OrderedDict
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
# randomly flip some labels
def noisy_labels(y, p_flip=0.05): # # flip labels with 5% probability
# determine the number of labels to flip
n_select = int(p_flip * y.shape[0])
# choose labels to flip
flip_ix = choice([i for i in range(y.shape[0])], size=n_select)
# invert the labels in place
y[flip_ix] = 1 - y[flip_ix]
return y
class AddGaussianNoise(object):
def __init__(self, mean=0.0, std=0.1):
self.std = std
self.mean = mean
def __call__(self, tensor):
tensor = tensor.cuda()
return tensor + (torch.randn(tensor.size()) * self.std + self.mean).cuda()
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
def resize2d(img, size):
return (F.adaptive_avg_pool2d(img, size).data).cuda()
def get_valid_labels(img):
return ((0.8 - 1.1) * torch.rand(img.shape[0], 1, 1, 1) + 1.1).cuda() # soft labels
def get_unvalid_labels(img):
return (noisy_labels((0.0 - 0.3) * torch.rand(img.shape[0], 1, 1, 1) + 0.3)).cuda() # soft labels
class Generator(pl.LightningModule):
def __init__(self, ngf, nc, latent_dim):
super(Generator, self).__init__()
self.ngf = ngf
self.latent_dim = latent_dim
self.nc = nc
self.fc0 = nn.Sequential(
# input is Z, going into a convolution
nn.utils.spectral_norm(nn.ConvTranspose2d(latent_dim, ngf * 16, 4, 1, 0, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 16)
)
self.fc1 = nn.Sequential(
# state size. (ngf*8) x 4 x 4
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 16, ngf * 8, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 8)
)
self.fc2 = nn.Sequential(
# state size. (ngf*4) x 8 x 8
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 4)
)
self.fc3 = nn.Sequential(
# state size. (ngf*2) x 16 x 16
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 2)
)
self.fc4 = nn.Sequential(
# state size. (ngf) x 32 x 32
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf)
)
self.fc5 = nn.Sequential(
# state size. (nc) x 64 x 64
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False)),
nn.Tanh()
)
# state size. (nc) x 128 x 128
# For Multi-Scale Gradient
# Converting the intermediate layers into images
self.fc0_r = nn.Conv2d(ngf * 16, self.nc, 1)
self.fc1_r = nn.Conv2d(ngf * 8, self.nc, 1)
self.fc2_r = nn.Conv2d(ngf * 4, self.nc, 1)
self.fc3_r = nn.Conv2d(ngf * 2, self.nc, 1)
self.fc4_r = nn.Conv2d(ngf, self.nc, 1)
def forward(self, input):
x_0 = self.fc0(input)
x_1 = self.fc1(x_0)
x_2 = self.fc2(x_1)
x_3 = self.fc3(x_2)
x_4 = self.fc4(x_3)
x_5 = self.fc5(x_4)
# For Multi-Scale Gradient
# Converting the intermediate layers into images
x_0_r = self.fc0_r(x_0)
x_1_r = self.fc1_r(x_1)
x_2_r = self.fc2_r(x_2)
x_3_r = self.fc3_r(x_3)
x_4_r = self.fc4_r(x_4)
return x_5, x_0_r, x_1_r, x_2_r, x_3_r, x_4_r
class Discriminator(pl.LightningModule):
def __init__(self, ndf, nc):
super(Discriminator, self).__init__()
self.nc = nc
self.ndf = ndf
self.fc0 = nn.Sequential(
# input is (nc) x 128 x 128
nn.utils.spectral_norm(nn.Conv2d(nc, ndf, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True)
)
self.fc1 = nn.Sequential(
# state size. (ndf) x 64 x 64
nn.utils.spectral_norm(nn.Conv2d(ndf + nc, ndf * 2, 4, 2, 1, bias=False)),
# "+ nc" because of multi scale gradient
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 2)
)
self.fc2 = nn.Sequential(
# state size. (ndf*2) x 32 x 32
nn.utils.spectral_norm(nn.Conv2d(ndf * 2 + nc, ndf * 4, 4, 2, 1, bias=False)),
# "+ nc" because of multi scale gradient
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 4)
)
self.fc3 = nn.Sequential(
# state size. (ndf*4) x 16 x 16e
nn.utils.spectral_norm(nn.Conv2d(ndf * 4 + nc, ndf * 8, 4, 2, 1, bias=False)),
# "+ nc" because of multi scale gradient
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 8),
)
self.fc4 = nn.Sequential(
# state size. (ndf*8) x 8 x 8
nn.utils.spectral_norm(nn.Conv2d(ndf * 8 + nc, ndf * 16, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 16)
)
self.fc5 = nn.Sequential(
# state size. (ndf*8) x 4 x 4
nn.utils.spectral_norm(nn.Conv2d(ndf * 16 + nc, 1, 4, 1, 0, bias=False)),
nn.Sigmoid()
)
# state size. 1 x 1 x 1
def forward(self, input, detach_or_not):
# When we train i ncombination with generator we use multi scale gradient.
x, x_0_r, x_1_r, x_2_r, x_3_r, x_4_r = input
if detach_or_not:
x = x.detach()
x_0 = self.fc0(x)
x_0 = torch.cat((x_0, x_4_r), dim=1) # Concat Multi-Scale Gradient
x_1 = self.fc1(x_0)
x_1 = torch.cat((x_1, x_3_r), dim=1) # Concat Multi-Scale Gradient
x_2 = self.fc2(x_1)
x_2 = torch.cat((x_2, x_2_r), dim=1) # Concat Multi-Scale Gradient
x_3 = self.fc3(x_2)
x_3 = torch.cat((x_3, x_1_r), dim=1) # Concat Multi-Scale Gradient
x_4 = self.fc4(x_3)
x_4 = torch.cat((x_4, x_0_r), dim=1) # Concat Multi-Scale Gradient
x_5 = self.fc5(x_4)
return x_5
class DCGAN(pl.LightningModule):
def __init__(self, hparams, checkpoint_folder, experiment_name):
super().__init__()
self.hparams = hparams
self.checkpoint_folder = checkpoint_folder
self.experiment_name = experiment_name
# networks
self.generator = Generator(ngf=hparams.ngf, nc=hparams.nc, latent_dim=hparams.latent_dim)
self.discriminator = Discriminator(ndf=hparams.ndf, nc=hparams.nc)
self.generator.apply(weights_init)
self.discriminator.apply(weights_init)
# cache for generated images
self.generated_imgs = None
self.last_imgs = None
# For experience replay
self.exp_replay_dis = torch.tensor([])
def forward(self, z):
return self.generator(z)
def adversarial_loss(self, y_hat, y):
return F.binary_cross_entropy(y_hat, y)
def training_step(self, batch, batch_nb, optimizer_idx):
# For adding Instance noise for more visit: https://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/
std_gaussian = max(0, self.hparams.level_of_noise - (
(self.hparams.level_of_noise * 2) * (self.current_epoch / self.hparams.epochs)))
AddGaussianNoiseInst = AddGaussianNoise(std=std_gaussian) # the noise decays over time
imgs, _ = batch
imgs = AddGaussianNoiseInst(imgs) # Adding instance noise to real images
self.last_imgs = imgs
# train generator
if optimizer_idx == 0:
# sample noise
z = torch.randn(imgs.shape[0], self.hparams.latent_dim, 1, 1).cuda()
# generate images
self.generated_imgs = self(z)
# ground truth result (ie: all fake)
g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs, False), get_valid_labels(self.generated_imgs[0])) # adversarial loss is binary cross-entropy; [0] is the image of the last layer
tqdm_dict = {'g_loss': g_loss}
log = {'g_loss': g_loss, "std_gaussian": std_gaussian}
output = OrderedDict({
'loss': g_loss,
'progress_bar': tqdm_dict,
'log': log
})
return output
# train discriminator
if optimizer_idx == 1:
# Measure discriminator's ability to classify real from generated samples
# how well can it label as real?
real_loss = self.adversarial_loss(
self.discriminator([imgs, resize2d(imgs, 4), resize2d(imgs, 8), resize2d(imgs, 16), resize2d(imgs, 32), resize2d(imgs, 64)],
False), get_valid_labels(imgs))
fake_loss = self.adversarial_loss(self.discriminator(self.generated_imgs, True), get_unvalid_labels(
self.generated_imgs[0])) # how well can it label as fake?; [0] is the image of the last layer
# discriminator loss is the average of these
d_loss = (real_loss + fake_loss) / 2
tqdm_dict = {'d_loss': d_loss}
log = {'d_loss': d_loss, "std_gaussian": std_gaussian}
output = OrderedDict({
'loss': d_loss,
'progress_bar': tqdm_dict,
'log': log
})
return output
def configure_optimizers(self):
lr_gen = self.hparams.lr_gen
lr_dis = self.hparams.lr_dis
b1 = self.hparams.b1
b2 = self.hparams.b2
opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr_gen, betas=(b1, b2))
opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr_dis, betas=(b1, b2))
return [opt_g, opt_d], []
def backward(self, trainer, loss, optimizer, optimizer_idx: int) -> None:
loss.backward(retain_graph=True)
def train_dataloader(self):
# transform = transforms.Compose([transforms.Resize((self.hparams.image_size, self.hparams.image_size)),
# transforms.ToTensor(),
# transforms.Normalize([0.5], [0.5])])
# dataset = torchvision.datasets.MNIST(os.getcwd(), train=False, download=True, transform=transform)
# return DataLoader(dataset, batch_size=self.hparams.batch_size)
# transform = transforms.Compose([transforms.Resize((self.hparams.image_size, self.hparams.image_size)),
# transforms.ToTensor(),
# transforms.Normalize([0.5], [0.5])
# ])
# train_dataset = torchvision.datasets.ImageFolder(
# root="./drive/My Drive/datasets/flower_dataset/",
# # root="./drive/My Drive/datasets/ghibli_dataset_small_overfit/",
# transform=transform
# )
# return DataLoader(train_dataset, num_workers=self.hparams.num_workers, shuffle=True,
# batch_size=self.hparams.batch_size)
transform = transforms.Compose([transforms.Resize((self.hparams.image_size, self.hparams.image_size)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
train_dataset = torchvision.datasets.ImageFolder(
root="ghibli_dataset_small_overfit/",
transform=transform
)
return DataLoader(train_dataset, num_workers=self.hparams.num_workers, shuffle=True,
batch_size=self.hparams.batch_size)
def on_epoch_end(self):
z = torch.randn(4, self.hparams.latent_dim, 1, 1).cuda()
# match gpu device (or keep as cpu)
if self.on_gpu:
z = z.cuda(self.last_imgs.device.index)
# log sampled images
sample_imgs = self.generator(z)[0]
torchvision.utils.save_image(sample_imgs, f'generated_images_epoch{self.current_epoch}.png')
# save model
if self.current_epoch % self.hparams.save_model_every_epoch == 0:
trainer.save_checkpoint(
self.checkpoint_folder + "/" + self.experiment_name + "_epoch_" + str(self.current_epoch) + ".ckpt")
from argparse import Namespace
args = {
'batch_size': 128, # batch size
'lr_gen': 0.0003, # TTUR;learnin rate of both networks; tested value: 0.0002
'lr_dis': 0.0003, # TTUR;learnin rate of both networks; tested value: 0.0002
'b1': 0.5, # Momentum for adam; tested value(dcgan paper): 0.5
'b2': 0.999, # Momentum for adam; tested value(dcgan paper): 0.999
'latent_dim': 256, # tested value which worked(in V4_1): 100
'nc': 3, # number of color channels
'ndf': 8, # number of discriminator features
'ngf': 8, # number of generator features
'epochs': 4, # the maxima lamount of epochs the algorith should run
'save_model_every_epoch': 1, # how often we save our model
'image_size': 128, # size of the image
'num_workers': 3,
'level_of_noise': 0.1, # how much instance noise we introduce(std; tested value: 0.15 and 0.1
'experience_save_per_batch': 1, # this value should be very low; tested value which works: 1
'experience_batch_size': 50 # this value shouldnt be too high; tested value which works: 50
}
hparams = Namespace(**args)
# Parameters
experiment_name = "DCGAN_6_2_MNIST_128px"
dataset_name = "mnist"
checkpoint_folder = "DCGAN/"
tags = ["DCGAN", "128x128"]
dirpath = Path(checkpoint_folder)
# defining net
net = DCGAN(hparams, checkpoint_folder, experiment_name)
torch.autograd.set_detect_anomaly(True)
trainer = pl.Trainer( # resume_from_checkpoint="DCGAN_V4_2_GHIBLI_epoch_999.ckpt",
max_epochs=args["epochs"],
gpus=1
)
trainer.fit(net)
``
简介：
我正在尝试让 CDCGAN（条件深度卷积生成对抗网络）在 MNIST 数据集上工作，考虑到我使用的库 (PyTorch) 在其网站上有教程，这应该相当容易。
但我似乎无法让它工作，它只会产生垃圾或模型崩溃或两者兼而有之。
我试过的：
- 制作模型条件半监督学习
- 使用批量规范
- 在生成器和鉴别器 input/output 层之外的每一层上使用 dropout
- 标签平滑以对抗过度自信
- 向图像添加噪声（我猜你称之为实例噪声）以获得更好的数据分布
- 使用 leaky relu 避免梯度消失
- 使用回放缓冲区来防止遗忘所学内容和过度拟合
- 使用超参数
- 将其与 PyTorch 教程中的模型进行比较
- basically what I did besides some things like Embedding layer ect.
我的模型生成的图像：
超参数：
batch_size=50, learning_rate_discrimiantor=0.0001, learning_rate_generator=0.0003, shuffle=True, ndf=64, ngf=64, droupout=0.5
batch_size=50, learning_rate_discriminator=0.0003, learning_rate_generator=0.0003, shuffle=True, ndf=64, ngf=64, dropout=0
生成的图像 Pytorch tutorial Model：
Code for the pytorch tutorial dcgan model
作为比较，这里是来自 pytorch turoial 的 DCGAN 的图像：
我的代码：
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms, datasets
import torch.nn.functional as F
from torch import optim as optim
from torch.utils.tensorboard import SummaryWriter
import numpy as np
import os
import time
class Discriminator(torch.nn.Module):
def __init__(self, ndf=16, dropout_value=0.5): # ndf feature map discriminator
super().__init__()
self.ndf = ndf
self.droupout_value = dropout_value
self.condi = nn.Sequential(
nn.Linear(in_features=10, out_features=64 * 64)
)
self.hidden0 = nn.Sequential(
nn.Conv2d(in_channels=2, out_channels=self.ndf, kernel_size=4, stride=2, padding=1, bias=False),
nn.LeakyReLU(0.2),
)
self.hidden1 = nn.Sequential(
nn.Conv2d(in_channels=self.ndf, out_channels=self.ndf * 2, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ndf * 2),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden2 = nn.Sequential(
nn.Conv2d(in_channels=self.ndf * 2, out_channels=self.ndf * 4, kernel_size=4, stride=2, padding=1, bias=False),
#nn.BatchNorm2d(self.ndf * 4),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden3 = nn.Sequential(
nn.Conv2d(in_channels=self.ndf * 4, out_channels=self.ndf * 8, kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ndf * 8),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.out = nn.Sequential(
nn.Conv2d(in_channels=self.ndf * 8, out_channels=1, kernel_size=4, stride=1, padding=0, bias=False),
torch.nn.Sigmoid()
)
def forward(self, x, y):
y = self.condi(y.view(-1, 10))
y = y.view(-1, 1, 64, 64)
x = torch.cat((x, y), dim=1)
x = self.hidden0(x)
x = self.hidden1(x)
x = self.hidden2(x)
x = self.hidden3(x)
x = self.out(x)
return x
class Generator(torch.nn.Module):
def __init__(self, n_features=100, ngf=16, c_channels=1, dropout_value=0.5): # ngf feature map of generator
super().__init__()
self.ngf = ngf
self.n_features = n_features
self.c_channels = c_channels
self.droupout_value = dropout_value
self.hidden0 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.n_features + 10, out_channels=self.ngf * 8,
kernel_size=4, stride=1, padding=0, bias=False),
nn.BatchNorm2d(self.ngf * 8),
nn.LeakyReLU(0.2)
)
self.hidden1 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.ngf * 8, out_channels=self.ngf * 4,
kernel_size=4, stride=2, padding=1, bias=False),
#nn.BatchNorm2d(self.ngf * 4),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden2 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.ngf * 4, out_channels=self.ngf * 2,
kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ngf * 2),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.hidden3 = nn.Sequential(
nn.ConvTranspose2d(in_channels=self.ngf * 2, out_channels=self.ngf,
kernel_size=4, stride=2, padding=1, bias=False),
nn.BatchNorm2d(self.ngf),
nn.LeakyReLU(0.2),
nn.Dropout(self.droupout_value)
)
self.out = nn.Sequential(
# "out_channels=1" because gray scale
nn.ConvTranspose2d(in_channels=self.ngf, out_channels=1, kernel_size=4,
stride=2, padding=1, bias=False),
nn.Tanh()
)
def forward(self, x, y):
x_cond = torch.cat((x, y), dim=1) # Combine flatten image with conditional input (class labels)
x = self.hidden0(x_cond) # Image goes into a "ConvTranspose2d" layer
x = self.hidden1(x)
x = self.hidden2(x)
x = self.hidden3(x)
x = self.out(x)
return x
class Logger:
def __init__(self, model_name, model1, model2, m1_optimizer, m2_optimizer, model_parameter, train_loader):
self.out_dir = "data"
self.model_name = model_name
self.train_loader = train_loader
self.model1 = model1
self.model2 = model2
self.model_parameter = model_parameter
self.m1_optimizer = m1_optimizer
self.m2_optimizer = m2_optimizer
# Exclude Epochs of the model name. This make sense e.g. when we stop a training progress and continue later on.
self.experiment_name = '_'.join("{!s}={!r}".format(k, v) for (k, v) in model_parameter.items())\
.replace("Epochs" + "=" + str(model_parameter["Epochs"]), "")
self.d_error = 0
self.g_error = 0
self.tb = SummaryWriter(log_dir=str(self.out_dir + "/log/" + self.model_name + "/runs/" + self.experiment_name))
self.path_image = os.path.join(os.getcwd(), f'{self.out_dir}/log/{self.model_name}/images/{self.experiment_name}')
self.path_model = os.path.join(os.getcwd(), f'{self.out_dir}/log/{self.model_name}/model/{self.experiment_name}')
try:
os.makedirs(self.path_image)
except Exception as e:
print("WARNING: ", str(e))
try:
os.makedirs(self.path_model)
except Exception as e:
print("WARNING: ", str(e))
def log_graph(self, model1_input, model2_input, model1_label, model2_label):
self.tb.add_graph(self.model1, input_to_model=(model1_input, model1_label))
self.tb.add_graph(self.model2, input_to_model=(model2_input, model2_label))
def log(self, num_epoch, d_error, g_error):
self.d_error = d_error
self.g_error = g_error
self.tb.add_scalar("Discriminator Train Error", self.d_error, num_epoch)
self.tb.add_scalar("Generator Train Error", self.g_error, num_epoch)
def log_image(self, images, epoch, batch_num):
grid = torchvision.utils.make_grid(images)
torchvision.utils.save_image(grid, f'{self.path_image}\Epoch_{epoch}_batch_{batch_num}.png')
self.tb.add_image("Generator Image", grid)
def log_histogramm(self):
for name, param in self.model2.named_parameters():
self.tb.add_histogram(name, param, self.model_parameter["Epochs"])
self.tb.add_histogram(f'gen_{name}.grad', param.grad, self.model_parameter["Epochs"])
for name, param in self.model1.named_parameters():
self.tb.add_histogram(name, param, self.model_parameter["Epochs"])
self.tb.add_histogram(f'dis_{name}.grad', param.grad, self.model_parameter["Epochs"])
def log_model(self, num_epoch):
torch.save({
"epoch": num_epoch,
"model_generator_state_dict": self.model1.state_dict(),
"model_discriminator_state_dict": self.model2.state_dict(),
"optimizer_generator_state_dict": self.m1_optimizer.state_dict(),
"optimizer_discriminator_state_dict": self.m2_optimizer.state_dict(),
}, str(self.path_model + f'\{time.time()}_epoch{num_epoch}.pth'))
def close(self, logger, images, num_epoch, d_error, g_error):
logger.log_model(num_epoch)
logger.log_histogramm()
logger.log(num_epoch, d_error, g_error)
self.tb.close()
def display_stats(self, epoch, batch_num, dis_error, gen_error):
print(f'Epoch: [{epoch}/{self.model_parameter["Epochs"]}] '
f'Batch: [{batch_num}/{len(self.train_loader)}] '
f'Loss_D: {dis_error.data.cpu()}, '
f'Loss_G: {gen_error.data.cpu()}')
def get_MNIST_dataset(num_workers_loader, model_parameter, out_dir="data"):
compose = transforms.Compose([
transforms.Resize((64, 64)),
transforms.CenterCrop((64, 64)),
transforms.ToTensor(),
torchvision.transforms.Normalize(mean=[0.5], std=[0.5])
])
dataset = datasets.MNIST(
root=out_dir,
train=True,
download=True,
transform=compose
)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=model_parameter["batch_size"],
num_workers=num_workers_loader,
shuffle=model_parameter["shuffle"])
return dataset, train_loader
def train_discriminator(p_optimizer, p_noise, p_images, p_fake_target, p_real_target, p_images_labels, p_fake_labels, device):
p_optimizer.zero_grad()
# 1.1 Train on real data
pred_dis_real = discriminator(p_images, p_images_labels)
error_real = loss(pred_dis_real, p_real_target)
error_real.backward()
# 1.2 Train on fake data
fake_data = generator(p_noise, p_fake_labels).detach()
fake_data = add_noise_to_image(fake_data, device)
pred_dis_fake = discriminator(fake_data, p_fake_labels)
error_fake = loss(pred_dis_fake, p_fake_target)
error_fake.backward()
p_optimizer.step()
return error_fake + error_real
def train_generator(p_optimizer, p_noise, p_real_target, p_fake_labels, device):
p_optimizer.zero_grad()
fake_images = generator(p_noise, p_fake_labels)
fake_images = add_noise_to_image(fake_images, device)
pred_dis_fake = discriminator(fake_images, p_fake_labels)
error_fake = loss(pred_dis_fake, p_real_target) # because
"""
We use "p_real_target" instead of "p_fake_target" because we want to
maximize that the discriminator is wrong.
"""
error_fake.backward()
p_optimizer.step()
return fake_images, pred_dis_fake, error_fake
# TODO change to a Truncated normal distribution
def get_noise(batch_size, n_features=100):
return torch.FloatTensor(batch_size, n_features, 1, 1).uniform_(-1, 1)
# We flip label of real and fate data. Better gradient flow I have told
def get_real_data_target(batch_size):
return torch.FloatTensor(batch_size, 1, 1, 1).uniform_(0.0, 0.2)
def get_fake_data_target(batch_size):
return torch.FloatTensor(batch_size, 1, 1, 1).uniform_(0.8, 1.1)
def image_to_vector(images):
return torch.flatten(images, start_dim=1, end_dim=-1)
def vector_to_image(images):
return images.view(images.size(0), 1, 28, 28)
def get_rand_labels(batch_size):
return torch.randint(low=0, high=9, size=(batch_size,))
def load_model(model_load_path):
if model_load_path:
checkpoint = torch.load(model_load_path)
discriminator.load_state_dict(checkpoint["model_discriminator_state_dict"])
generator.load_state_dict(checkpoint["model_generator_state_dict"])
dis_opti.load_state_dict(checkpoint["optimizer_discriminator_state_dict"])
gen_opti.load_state_dict(checkpoint["optimizer_generator_state_dict"])
return checkpoint["epoch"]
else:
return 0
def init_model_optimizer(model_parameter, device):
# Initialize the Models
discriminator = Discriminator(ndf=model_parameter["ndf"], dropout_value=model_parameter["dropout"]).to(device)
generator = Generator(ngf=model_parameter["ngf"], dropout_value=model_parameter["dropout"]).to(device)
# train
dis_opti = optim.Adam(discriminator.parameters(), lr=model_parameter["learning_rate_dis"], betas=(0.5, 0.999))
gen_opti = optim.Adam(generator.parameters(), lr=model_parameter["learning_rate_gen"], betas=(0.5, 0.999))
return discriminator, generator, dis_opti, gen_opti
def get_hot_vector_encode(labels, device):
return torch.eye(10)[labels].view(-1, 10, 1, 1).to(device)
def add_noise_to_image(images, device, level_of_noise=0.1):
return images[0].to(device) + (level_of_noise) * torch.randn(images.shape).to(device)
if __name__ == "__main__":
# Hyperparameter
model_parameter = {
"batch_size": 500,
"learning_rate_dis": 0.0002,
"learning_rate_gen": 0.0002,
"shuffle": False,
"Epochs": 10,
"ndf": 64,
"ngf": 64,
"dropout": 0.5
}
# Parameter
r_frequent = 10 # How many samples we save for replay per batch (batch_size / r_frequent).
model_name = "CDCGAN" # The name of you model e.g. "Gan"
num_workers_loader = 1 # How many workers should load the data
sample_save_size = 16 # How many numbers your saved imaged should show
device = "cuda" # Which device should be used to train the neural network
model_load_path = "" # If set load model instead of training from new
num_epoch_log = 1 # How frequent you want to log/
torch.manual_seed(43) # Sets a seed for torch for reproducibility
dataset_train, train_loader = get_MNIST_dataset(num_workers_loader, model_parameter) # Get dataset
# Initialize the Models and optimizer
discriminator, generator, dis_opti, gen_opti = init_model_optimizer(model_parameter, device) # Init model/Optimizer
start_epoch = load_model(model_load_path) # when we want to load a model
# Init Logger
logger = Logger(model_name, generator, discriminator, gen_opti, dis_opti, model_parameter, train_loader)
loss = nn.BCELoss()
images, labels = next(iter(train_loader)) # For logging
# For testing
# pred = generator(get_noise(model_parameter["batch_size"]).to(device), get_hot_vector_encode(get_rand_labels(model_parameter["batch_size"]), device))
# dis = discriminator(images.to(device), get_hot_vector_encode(labels, device))
logger.log_graph(get_noise(model_parameter["batch_size"]).to(device), images.to(device),
get_hot_vector_encode(get_rand_labels(model_parameter["batch_size"]), device),
get_hot_vector_encode(labels, device))
# Array to store
exp_replay = torch.tensor([]).to(device)
for num_epoch in range(start_epoch, model_parameter["Epochs"]):
for batch_num, data_loader in enumerate(train_loader):
images, labels = data_loader
images = add_noise_to_image(images, device) # Add noise to the images
# 1. Train Discriminator
dis_error = train_discriminator(
dis_opti,
get_noise(model_parameter["batch_size"]).to(device),
images.to(device),
get_fake_data_target(model_parameter["batch_size"]).to(device),
get_real_data_target(model_parameter["batch_size"]).to(device),
get_hot_vector_encode(labels, device),
get_hot_vector_encode(
get_rand_labels(model_parameter["batch_size"]), device),
device
)
# 2. Train Generator
fake_image, pred_dis_fake, gen_error = train_generator(
gen_opti,
get_noise(model_parameter["batch_size"]).to(device),
get_real_data_target(model_parameter["batch_size"]).to(device),
get_hot_vector_encode(
get_rand_labels(model_parameter["batch_size"]),
device),
device
)
# Store a random point for experience replay
perm = torch.randperm(fake_image.size(0))
r_idx = perm[:max(1, int(model_parameter["batch_size"] / r_frequent))]
r_samples = add_noise_to_image(fake_image[r_idx], device)
exp_replay = torch.cat((exp_replay, r_samples), 0).detach()
if exp_replay.size(0) >= model_parameter["batch_size"]:
# Train on experienced data
dis_opti.zero_grad()
r_label = get_hot_vector_encode(torch.zeros(exp_replay.size(0)).numpy(), device)
pred_dis_real = discriminator(exp_replay, r_label)
error_real = loss(pred_dis_real, get_fake_data_target(exp_replay.size(0)).to(device))
error_real.backward()
dis_opti.step()
print(f'Epoch: [{num_epoch}/{model_parameter["Epochs"]}] '
f'Batch: Replay/Experience batch '
f'Loss_D: {error_real.data.cpu()}, '
)
exp_replay = torch.tensor([]).to(device)
logger.display_stats(epoch=num_epoch, batch_num=batch_num, dis_error=dis_error, gen_error=gen_error)
if batch_num % 100 == 0:
logger.log_image(fake_image[:sample_save_size], num_epoch, batch_num)
logger.log(num_epoch, dis_error, gen_error)
if num_epoch % num_epoch_log == 0:
logger.log_model(num_epoch)
logger.log_histogramm()
logger.close(logger, fake_image[:sample_save_size], num_epoch, dis_error, gen_error)
First link to my Code (Pastebin)
Second link to my Code (0bin)
结论：
自从我实现了所有这些被认为对GAN/DCGAN有益的东西（例如标签平滑）。
而且我的模型仍然比 PyTorch 的教程 DCGAN 表现更差我想我的代码中可能有一个错误，但我似乎找不到它。
再现性：
如果你安装了我导入的库，你应该可以只复制代码并运行它，如果你能找到任何东西，你可以自己寻找。
感谢任何反馈。
所以我刚才解决了这个问题，但是忘了post关于堆栈溢出的答案。所以我将简单地 post 我的代码在这里应该工作得很好。 一些免责声明：
- 我不太确定它是否有效，因为我一年前就这样做了
- 它适用于 128x128px 图像 MNIST
- 这不是普通的 GAN 我使用了各种优化技术
- 如果你想使用它你需要改变各种细节，比如训练数据集
资源：
``
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning import loggers
from numpy.random import choice
import os
from pathlib import Path
import shutil
from collections import OrderedDict
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
# randomly flip some labels
def noisy_labels(y, p_flip=0.05): # # flip labels with 5% probability
# determine the number of labels to flip
n_select = int(p_flip * y.shape[0])
# choose labels to flip
flip_ix = choice([i for i in range(y.shape[0])], size=n_select)
# invert the labels in place
y[flip_ix] = 1 - y[flip_ix]
return y
class AddGaussianNoise(object):
def __init__(self, mean=0.0, std=0.1):
self.std = std
self.mean = mean
def __call__(self, tensor):
tensor = tensor.cuda()
return tensor + (torch.randn(tensor.size()) * self.std + self.mean).cuda()
def __repr__(self):
return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
def resize2d(img, size):
return (F.adaptive_avg_pool2d(img, size).data).cuda()
def get_valid_labels(img):
return ((0.8 - 1.1) * torch.rand(img.shape[0], 1, 1, 1) + 1.1).cuda() # soft labels
def get_unvalid_labels(img):
return (noisy_labels((0.0 - 0.3) * torch.rand(img.shape[0], 1, 1, 1) + 0.3)).cuda() # soft labels
class Generator(pl.LightningModule):
def __init__(self, ngf, nc, latent_dim):
super(Generator, self).__init__()
self.ngf = ngf
self.latent_dim = latent_dim
self.nc = nc
self.fc0 = nn.Sequential(
# input is Z, going into a convolution
nn.utils.spectral_norm(nn.ConvTranspose2d(latent_dim, ngf * 16, 4, 1, 0, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 16)
)
self.fc1 = nn.Sequential(
# state size. (ngf*8) x 4 x 4
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 16, ngf * 8, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 8)
)
self.fc2 = nn.Sequential(
# state size. (ngf*4) x 8 x 8
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 4)
)
self.fc3 = nn.Sequential(
# state size. (ngf*2) x 16 x 16
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf * 2)
)
self.fc4 = nn.Sequential(
# state size. (ngf) x 32 x 32
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ngf)
)
self.fc5 = nn.Sequential(
# state size. (nc) x 64 x 64
nn.utils.spectral_norm(nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False)),
nn.Tanh()
)
# state size. (nc) x 128 x 128
# For Multi-Scale Gradient
# Converting the intermediate layers into images
self.fc0_r = nn.Conv2d(ngf * 16, self.nc, 1)
self.fc1_r = nn.Conv2d(ngf * 8, self.nc, 1)
self.fc2_r = nn.Conv2d(ngf * 4, self.nc, 1)
self.fc3_r = nn.Conv2d(ngf * 2, self.nc, 1)
self.fc4_r = nn.Conv2d(ngf, self.nc, 1)
def forward(self, input):
x_0 = self.fc0(input)
x_1 = self.fc1(x_0)
x_2 = self.fc2(x_1)
x_3 = self.fc3(x_2)
x_4 = self.fc4(x_3)
x_5 = self.fc5(x_4)
# For Multi-Scale Gradient
# Converting the intermediate layers into images
x_0_r = self.fc0_r(x_0)
x_1_r = self.fc1_r(x_1)
x_2_r = self.fc2_r(x_2)
x_3_r = self.fc3_r(x_3)
x_4_r = self.fc4_r(x_4)
return x_5, x_0_r, x_1_r, x_2_r, x_3_r, x_4_r
class Discriminator(pl.LightningModule):
def __init__(self, ndf, nc):
super(Discriminator, self).__init__()
self.nc = nc
self.ndf = ndf
self.fc0 = nn.Sequential(
# input is (nc) x 128 x 128
nn.utils.spectral_norm(nn.Conv2d(nc, ndf, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True)
)
self.fc1 = nn.Sequential(
# state size. (ndf) x 64 x 64
nn.utils.spectral_norm(nn.Conv2d(ndf + nc, ndf * 2, 4, 2, 1, bias=False)),
# "+ nc" because of multi scale gradient
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 2)
)
self.fc2 = nn.Sequential(
# state size. (ndf*2) x 32 x 32
nn.utils.spectral_norm(nn.Conv2d(ndf * 2 + nc, ndf * 4, 4, 2, 1, bias=False)),
# "+ nc" because of multi scale gradient
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 4)
)
self.fc3 = nn.Sequential(
# state size. (ndf*4) x 16 x 16e
nn.utils.spectral_norm(nn.Conv2d(ndf * 4 + nc, ndf * 8, 4, 2, 1, bias=False)),
# "+ nc" because of multi scale gradient
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 8),
)
self.fc4 = nn.Sequential(
# state size. (ndf*8) x 8 x 8
nn.utils.spectral_norm(nn.Conv2d(ndf * 8 + nc, ndf * 16, 4, 2, 1, bias=False)),
nn.LeakyReLU(0.2, inplace=True),
nn.BatchNorm2d(ndf * 16)
)
self.fc5 = nn.Sequential(
# state size. (ndf*8) x 4 x 4
nn.utils.spectral_norm(nn.Conv2d(ndf * 16 + nc, 1, 4, 1, 0, bias=False)),
nn.Sigmoid()
)
# state size. 1 x 1 x 1
def forward(self, input, detach_or_not):
# When we train i ncombination with generator we use multi scale gradient.
x, x_0_r, x_1_r, x_2_r, x_3_r, x_4_r = input
if detach_or_not:
x = x.detach()
x_0 = self.fc0(x)
x_0 = torch.cat((x_0, x_4_r), dim=1) # Concat Multi-Scale Gradient
x_1 = self.fc1(x_0)
x_1 = torch.cat((x_1, x_3_r), dim=1) # Concat Multi-Scale Gradient
x_2 = self.fc2(x_1)
x_2 = torch.cat((x_2, x_2_r), dim=1) # Concat Multi-Scale Gradient
x_3 = self.fc3(x_2)
x_3 = torch.cat((x_3, x_1_r), dim=1) # Concat Multi-Scale Gradient
x_4 = self.fc4(x_3)
x_4 = torch.cat((x_4, x_0_r), dim=1) # Concat Multi-Scale Gradient
x_5 = self.fc5(x_4)
return x_5
class DCGAN(pl.LightningModule):
def __init__(self, hparams, checkpoint_folder, experiment_name):
super().__init__()
self.hparams = hparams
self.checkpoint_folder = checkpoint_folder
self.experiment_name = experiment_name
# networks
self.generator = Generator(ngf=hparams.ngf, nc=hparams.nc, latent_dim=hparams.latent_dim)
self.discriminator = Discriminator(ndf=hparams.ndf, nc=hparams.nc)
self.generator.apply(weights_init)
self.discriminator.apply(weights_init)
# cache for generated images
self.generated_imgs = None
self.last_imgs = None
# For experience replay
self.exp_replay_dis = torch.tensor([])
def forward(self, z):
return self.generator(z)
def adversarial_loss(self, y_hat, y):
return F.binary_cross_entropy(y_hat, y)
def training_step(self, batch, batch_nb, optimizer_idx):
# For adding Instance noise for more visit: https://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/
std_gaussian = max(0, self.hparams.level_of_noise - (
(self.hparams.level_of_noise * 2) * (self.current_epoch / self.hparams.epochs)))
AddGaussianNoiseInst = AddGaussianNoise(std=std_gaussian) # the noise decays over time
imgs, _ = batch
imgs = AddGaussianNoiseInst(imgs) # Adding instance noise to real images
self.last_imgs = imgs
# train generator
if optimizer_idx == 0:
# sample noise
z = torch.randn(imgs.shape[0], self.hparams.latent_dim, 1, 1).cuda()
# generate images
self.generated_imgs = self(z)
# ground truth result (ie: all fake)
g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs, False), get_valid_labels(self.generated_imgs[0])) # adversarial loss is binary cross-entropy; [0] is the image of the last layer
tqdm_dict = {'g_loss': g_loss}
log = {'g_loss': g_loss, "std_gaussian": std_gaussian}
output = OrderedDict({
'loss': g_loss,
'progress_bar': tqdm_dict,
'log': log
})
return output
# train discriminator
if optimizer_idx == 1:
# Measure discriminator's ability to classify real from generated samples
# how well can it label as real?
real_loss = self.adversarial_loss(
self.discriminator([imgs, resize2d(imgs, 4), resize2d(imgs, 8), resize2d(imgs, 16), resize2d(imgs, 32), resize2d(imgs, 64)],
False), get_valid_labels(imgs))
fake_loss = self.adversarial_loss(self.discriminator(self.generated_imgs, True), get_unvalid_labels(
self.generated_imgs[0])) # how well can it label as fake?; [0] is the image of the last layer
# discriminator loss is the average of these
d_loss = (real_loss + fake_loss) / 2
tqdm_dict = {'d_loss': d_loss}
log = {'d_loss': d_loss, "std_gaussian": std_gaussian}
output = OrderedDict({
'loss': d_loss,
'progress_bar': tqdm_dict,
'log': log
})
return output
def configure_optimizers(self):
lr_gen = self.hparams.lr_gen
lr_dis = self.hparams.lr_dis
b1 = self.hparams.b1
b2 = self.hparams.b2
opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr_gen, betas=(b1, b2))
opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr_dis, betas=(b1, b2))
return [opt_g, opt_d], []
def backward(self, trainer, loss, optimizer, optimizer_idx: int) -> None:
loss.backward(retain_graph=True)
def train_dataloader(self):
# transform = transforms.Compose([transforms.Resize((self.hparams.image_size, self.hparams.image_size)),
# transforms.ToTensor(),
# transforms.Normalize([0.5], [0.5])])
# dataset = torchvision.datasets.MNIST(os.getcwd(), train=False, download=True, transform=transform)
# return DataLoader(dataset, batch_size=self.hparams.batch_size)
# transform = transforms.Compose([transforms.Resize((self.hparams.image_size, self.hparams.image_size)),
# transforms.ToTensor(),
# transforms.Normalize([0.5], [0.5])
# ])
# train_dataset = torchvision.datasets.ImageFolder(
# root="./drive/My Drive/datasets/flower_dataset/",
# # root="./drive/My Drive/datasets/ghibli_dataset_small_overfit/",
# transform=transform
# )
# return DataLoader(train_dataset, num_workers=self.hparams.num_workers, shuffle=True,
# batch_size=self.hparams.batch_size)
transform = transforms.Compose([transforms.Resize((self.hparams.image_size, self.hparams.image_size)),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
train_dataset = torchvision.datasets.ImageFolder(
root="ghibli_dataset_small_overfit/",
transform=transform
)
return DataLoader(train_dataset, num_workers=self.hparams.num_workers, shuffle=True,
batch_size=self.hparams.batch_size)
def on_epoch_end(self):
z = torch.randn(4, self.hparams.latent_dim, 1, 1).cuda()
# match gpu device (or keep as cpu)
if self.on_gpu:
z = z.cuda(self.last_imgs.device.index)
# log sampled images
sample_imgs = self.generator(z)[0]
torchvision.utils.save_image(sample_imgs, f'generated_images_epoch{self.current_epoch}.png')
# save model
if self.current_epoch % self.hparams.save_model_every_epoch == 0:
trainer.save_checkpoint(
self.checkpoint_folder + "/" + self.experiment_name + "_epoch_" + str(self.current_epoch) + ".ckpt")
from argparse import Namespace
args = {
'batch_size': 128, # batch size
'lr_gen': 0.0003, # TTUR;learnin rate of both networks; tested value: 0.0002
'lr_dis': 0.0003, # TTUR;learnin rate of both networks; tested value: 0.0002
'b1': 0.5, # Momentum for adam; tested value(dcgan paper): 0.5
'b2': 0.999, # Momentum for adam; tested value(dcgan paper): 0.999
'latent_dim': 256, # tested value which worked(in V4_1): 100
'nc': 3, # number of color channels
'ndf': 8, # number of discriminator features
'ngf': 8, # number of generator features
'epochs': 4, # the maxima lamount of epochs the algorith should run
'save_model_every_epoch': 1, # how often we save our model
'image_size': 128, # size of the image
'num_workers': 3,
'level_of_noise': 0.1, # how much instance noise we introduce(std; tested value: 0.15 and 0.1
'experience_save_per_batch': 1, # this value should be very low; tested value which works: 1
'experience_batch_size': 50 # this value shouldnt be too high; tested value which works: 50
}
hparams = Namespace(**args)
# Parameters
experiment_name = "DCGAN_6_2_MNIST_128px"
dataset_name = "mnist"
checkpoint_folder = "DCGAN/"
tags = ["DCGAN", "128x128"]
dirpath = Path(checkpoint_folder)
# defining net
net = DCGAN(hparams, checkpoint_folder, experiment_name)
torch.autograd.set_detect_anomaly(True)
trainer = pl.Trainer( # resume_from_checkpoint="DCGAN_V4_2_GHIBLI_epoch_999.ckpt",
max_epochs=args["epochs"],
gpus=1
)
trainer.fit(net)
``