DCGAN 调试。得到只是垃圾
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【中文标题】DCGAN 调试。得到只是垃圾【英文标题】：DCGAN debugging. Getting just garbage 【发布时间】：2020-06-10 18:32:10 【问题描述】：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，考虑到我正在使用的库 (PyTorch) 在其网站上提供了教程，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。
我尝试了什么：
制作模型条件半监督学习 使用批量规范 在生成器和判别器的输入/输出层之外的每一层都使用 dropout 标签平滑以对抗过度自信 为图像添加噪声（我猜你称之为实例噪声）以获得更好的数据分布 使用leaky relu 来避免梯度消失 使用回放缓冲区来防止忘记学习内容和过度拟合 玩转超参数 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
图片Pytorch tutorial Model生成：
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()_epochnum_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：
自从我实施了所有这些被认为对 GAN/DCGAN 有益的事情（例如标签平滑）。 而且我的模型的性能仍然比 PyTorch 的教程 DCGAN 差，我想我的代码中可能有错误，但我似乎找不到它。
重现性：
如果你安装了我导入的库，你应该可以复制代码并运行它，如果你能找到任何东西，你可以自己寻找。
感谢任何反馈。
【问题讨论】：
您同时重新实现代码并添加到模型中？您应该从教程中重新实现完全相同版本的 GAN，然后对其进行测试，然后如果它有效，则可以使您的标签平滑添加 @TiagoMartinsPeres 不完全正确。我希望也许有经验的人会识别图片中的模式以及相应的错误或建议如何调试它，因为它相对地很难调试神经网络，我不知道如何。 @ThomaS 我在那工作。 这个问题不是更适合数据科学堆栈交换吗？ 您的结果看起来不错。我的意思是他们是数字-like，这是我所期望的。 Pytorch 版本只是看起来训练时间更长。正如@ThomaS 指出的那样，这可能是由于模型的变化。 Pytorch 版本将针对对他们有用的内容进行优化，任何偏离都会使结果恶化。 【参考方案1】：所以我前一阵子解决了这个问题，但忘记在堆栈溢出上发布答案。所以我将在这里简单地发布我的代码，它应该可以很好地工作。 一些免责声明：
我不太确定它是否有效，因为我在一年前这样做了 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，你需要更改各种细节，例如训练数据集资源：
Multi-Scale Gradients Instance Noise Various tricks I used More tricks``
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_epochself.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)
``
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