10 KiB
10 KiB
训练生成对抗网络
生成对抗网络(Generative Adversarial Network, GAN)可以用来生成图像视频等数据。这篇教程将带你一步步用 MMEngine 训练 GAN ！
我们可以通过以下步骤来训练一个生成对抗网络。
构建数据加载器
构建数据集
接下来, 我们为 MNIST 数据集构建一个数据集类 MNISTDataset, 继承自数据集基类 BaseDataset, 并且重载数据集基类的 load_data_list 函数, 保证返回值为 list[dict]，其中每个 dict 代表一个数据样本。更多关于 MMEngine 中数据集的用法，可以参考数据集教程。
import numpy as np
from mmcv.transforms import to_tensor
from torch.utils.data import random_split
from torchvision.datasets import MNIST
from mmengine.dataset import BaseDataset
class MNISTDataset(BaseDataset):
def __init__(self, data_root, pipeline, test_mode=False):
# 下载 MNIST 数据集
if test_mode:
mnist_full = MNIST(data_root, train=True, download=True)
self.mnist_dataset, _ = random_split(mnist_full, [55000, 5000])
else:
self.mnist_dataset = MNIST(data_root, train=False, download=True)
super().__init__(
data_root=data_root, pipeline=pipeline, test_mode=test_mode)
@staticmethod
def totensor(img):
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
img = np.ascontiguousarray(img.transpose(2, 0, 1))
return to_tensor(img)
def load_data_list(self):
return [
dict(inputs=self.totensor(np.array(x[0]))) for x in self.mnist_dataset
]
dataset = MNISTDataset("./data", [])
使用 Runner 中的函数 build_dataloader 来构建数据加载器。
import os
import torch
from mmengine.runner import Runner
NUM_WORKERS = int(os.cpu_count() / 2)
BATCH_SIZE = 256 if torch.cuda.is_available() else 64
train_dataloader = dict(
batch_size=BATCH_SIZE,
num_workers=NUM_WORKERS,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
dataset=dataset)
train_dataloader = Runner.build_dataloader(train_dataloader)
构建生成器网络和判别器网络
下面的代码构建并实例化了一个生成器(Generator)和一个判别器(Discriminator)。
import torch.nn as nn
class Generator(nn.Module):
def __init__(self, noise_size, img_shape):
super().__init__()
self.img_shape = img_shape
self.noise_size = noise_size
def block(in_feat, out_feat, normalize=True):
layers = [nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*block(noise_size, 128, normalize=False),
*block(128, 256),
*block(256, 512),
*block(512, 1024),
nn.Linear(1024, int(np.prod(img_shape))),
nn.Tanh(),
)
def forward(self, z):
img = self.model(z)
img = img.view(img.size(0), *self.img_shape)
return img
class Discriminator(nn.Module):
def __init__(self, img_shape):
super().__init__()
self.model = nn.Sequential(
nn.Linear(int(np.prod(img_shape)), 512),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(512, 256),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(256, 1),
nn.Sigmoid(),
)
def forward(self, img):
img_flat = img.view(img.size(0), -1)
validity = self.model(img_flat)
return validity
generator = Generator(100, (1, 28, 28))
discriminator = Discriminator((1, 28, 28))
构建一个生成对抗网络模型
在使用 MMEngine 时，我们用 ImgDataPreprocessor 来对数据进行归一化和颜色通道的转换。
from mmengine.model import ImgDataPreprocessor
data_preprocessor = ImgDataPreprocessor(mean=([127.5]), std=([127.5]))
下面的代码实现了基础 GAN 的算法。使用 MMEngine 实现算法类，需要继承 BaseModel 基类，在 train_step 中实现训练过程。GAN 需要交替训练生成器和判别器，分别由 train_discriminator 和 train_generator 实现，并实现 disc_loss 和 gen_loss 计算判别器损失函数和生成器损失函数。 关于 BaseModel 的更多信息，请参考模型教程.
import torch.nn.functional as F
from mmengine.model import BaseModel
class GAN(BaseModel):
def __init__(self, generator, discriminator, noise_size,
data_preprocessor):
super().__init__(data_preprocessor=data_preprocessor)
assert generator.noise_size == noise_size
self.generator = generator
self.discriminator = discriminator
self.noise_size = noise_size
def train_step(self, data, optim_wrapper):
# 获取数据和数据预处理
inputs_dict = self.data_preprocessor(data, True)
# 训练判别器
disc_optimizer_wrapper = optim_wrapper['discriminator']
with disc_optimizer_wrapper.optim_context(self.discriminator):
log_vars = self.train_discriminator(inputs_dict,
disc_optimizer_wrapper)
# 训练生成器
set_requires_grad(self.discriminator, False)
gen_optimizer_wrapper = optim_wrapper['generator']
with gen_optimizer_wrapper.optim_context(self.generator):
log_vars_gen = self.train_generator(inputs_dict,
gen_optimizer_wrapper)
set_requires_grad(self.discriminator, True)
log_vars.update(log_vars_gen)
return log_vars
def forward(self, batch_inputs, data_samples=None, mode=None):
return self.generator(batch_inputs)
def disc_loss(self, disc_pred_fake, disc_pred_real):
losses_dict = dict()
losses_dict['loss_disc_fake'] = F.binary_cross_entropy(
disc_pred_fake, 0. * torch.ones_like(disc_pred_fake))
losses_dict['loss_disc_real'] = F.binary_cross_entropy(
disc_pred_real, 1. * torch.ones_like(disc_pred_real))
loss, log_var = self.parse_losses(losses_dict)
return loss, log_var
def gen_loss(self, disc_pred_fake):
losses_dict = dict()
losses_dict['loss_gen'] = F.binary_cross_entropy(
disc_pred_fake, 1. * torch.ones_like(disc_pred_fake))
loss, log_var = self.parse_losses(losses_dict)
return loss, log_var
def train_discriminator(self, inputs, optimizer_wrapper):
real_imgs = inputs['inputs']
z = torch.randn(
(real_imgs.shape[0], self.noise_size)).type_as(real_imgs)
with torch.no_grad():
fake_imgs = self.generator(z)
disc_pred_fake = self.discriminator(fake_imgs)
disc_pred_real = self.discriminator(real_imgs)
parsed_losses, log_vars = self.disc_loss(disc_pred_fake,
disc_pred_real)
optimizer_wrapper.update_params(parsed_losses)
return log_vars
def train_generator(self, inputs, optimizer_wrapper):
real_imgs = inputs['inputs']
z = torch.randn(real_imgs.shape[0], self.noise_size).type_as(real_imgs)
fake_imgs = self.generator(z)
disc_pred_fake = self.discriminator(fake_imgs)
parsed_loss, log_vars = self.gen_loss(disc_pred_fake)
optimizer_wrapper.update_params(parsed_loss)
return log_vars
其中一个函数 set_requires_grad 用来锁定训练生成器时判别器的权重。
def set_requires_grad(nets, requires_grad=False):
"""Set requires_grad for all the networks.
Args:
nets (nn.Module | list[nn.Module]): A list of networks or a single
network.
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
model = GAN(generator, discriminator, 100, data_preprocessor)
构建优化器
MMEngine 使用 OptimWrapper 来封装优化器，对于多个优化器的情况，使用 OptimWrapperDict 对 OptimWrapper 再进行一次封装。 关于优化器的更多信息，请参考优化器教程.
from mmengine.optim import OptimWrapper, OptimWrapperDict
opt_g = torch.optim.Adam(generator.parameters(), lr=0.0001, betas=(0.5, 0.999))
opt_g_wrapper = OptimWrapper(opt_g)
opt_d = torch.optim.Adam(
discriminator.parameters(), lr=0.0001, betas=(0.5, 0.999))
opt_d_wrapper = OptimWrapper(opt_d)
opt_wrapper_dict = OptimWrapperDict(
generator=opt_g_wrapper, discriminator=opt_d_wrapper)
使用执行器进行训练
下面的代码演示了如何使用 Runner 进行模型训练。关于 Runner 的更多信息，请参考执行器教程。
train_cfg = dict(by_epoch=True, max_epochs=220)
runner = Runner(
model,
work_dir='runs/gan/',
train_dataloader=train_dataloader,
train_cfg=train_cfg,
optim_wrapper=opt_wrapper_dict)
runner.train()
到这里，我们就完成了一个 GAN 的训练，通过下面的代码可以查看刚才训练的 GAN 生成的结果。
z = torch.randn(64, 100).cuda()
img = model(z)
from torchvision.utils import save_image
save_image(img, "result.png", normalize=True)
如果你想了解更多如何使用 MMEngine 实现 GAN 和生成模型，我们强烈建议你使用同样基于 MMEngine 开发的生成框架 MMGen。