【MindSpore社区活动】在对抗中增强网络实践

[JeffDing]

GAN图像生成

数据集简介

MNIST手写数字数据集是NIST数据集的子集，共有70000张手写数字图片，包含60000张训练样本和10000张测试样本。数字图片为二进制文件，图片大小为28*28，单通道。图片已经预先进行了尺寸归一化和中心化处理。

数据集网站：http://yann.lecun.com/exdb/mnist/

本案例将使用MNIST手写数字数据集来训练一个生成式对抗网络，使用该网络模拟生成手写数字图片。

数据集下载

# 数据下载
from download import download
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip"
download(url, ".", kind="zip", replace=True)

数据加载

import numpy as np
import mindspore.dataset as ds

batch_size = 128
latent_size = 100  # 隐码的长度

train_dataset = ds.MnistDataset(dataset_dir='./MNIST_Data/train')
test_dataset = ds.MnistDataset(dataset_dir='./MNIST_Data/test')

def data_load(dataset):
    dataset1 = ds.GeneratorDataset(dataset, ["image", "label"], shuffle=True, python_multiprocessing=False)
    # 数据增强
    mnist_ds = dataset1.map(
        operations=lambda x: (x.astype("float32"), np.random.normal(size=latent_size).astype("float32")),
        output_columns=["image", "latent_code"])
    mnist_ds = mnist_ds.project(["image", "latent_code"])

    # 批量操作
    mnist_ds = mnist_ds.batch(batch_size, True)

    return mnist_ds

mnist_ds = data_load(train_dataset)

iter_size = mnist_ds.get_dataset_size()
print('Iter size: %d' % iter_size)

运行结果

Iter size: 468

数据集可视化

import matplotlib.pyplot as plt

data_iter = next(mnist_ds.create_dict_iterator(output_numpy=True))
figure = plt.figure(figsize=(3, 3))
cols, rows = 5, 5
for idx in range(1, cols * rows + 1):
    image = data_iter['image'][idx]
    figure.add_subplot(rows, cols, idx)
    plt.axis("off")
    plt.imshow(image.squeeze(), cmap="gray")
plt.show()

隐码构造

import random
import numpy as np
from mindspore import Tensor, dtype

# 利用随机种子创建一批隐码
np.random.seed(2323)
test_noise = Tensor(np.random.normal(size=(25, 100)), dtype.float32)
random.shuffle(test_noise)

模型构建

生成器

from mindspore import nn
import mindspore.ops as ops

img_size = 28  # 训练图像长（宽）

class Generator(nn.Cell):
    def __init__(self, latent_size, auto_prefix=True):
        super(Generator, self).__init__(auto_prefix=auto_prefix)
        self.model = nn.SequentialCell()
        # [N, 100] -> [N, 128]
        # 输入一个100维的0～1之间的高斯分布，然后通过第一层线性变换将其映射到256维
        self.model.append(nn.Dense(latent_size, 128))
        self.model.append(nn.ReLU())
        # [N, 128] -> [N, 256]
        self.model.append(nn.Dense(128, 256))
        self.model.append(nn.BatchNorm1d(256))
        self.model.append(nn.ReLU())
        # [N, 256] -> [N, 512]
        self.model.append(nn.Dense(256, 512))
        self.model.append(nn.BatchNorm1d(512))
        self.model.append(nn.ReLU())
        # [N, 512] -> [N, 1024]
        self.model.append(nn.Dense(512, 1024))
        self.model.append(nn.BatchNorm1d(1024))
        self.model.append(nn.ReLU())
        # [N, 1024] -> [N, 784]
        # 经过线性变换将其变成784维
        self.model.append(nn.Dense(1024, img_size * img_size))
        # 经过Tanh激活函数是希望生成的假的图片数据分布能够在-1～1之间
        self.model.append(nn.Tanh())

    def construct(self, x):
        img = self.model(x)
        return ops.reshape(img, (-1, 1, 28, 28))

net_g = Generator(latent_size)
net_g.update_parameters_name('generator')

判别器

 # 判别器
class Discriminator(nn.Cell):
    def __init__(self, auto_prefix=True):
        super().__init__(auto_prefix=auto_prefix)
        self.model = nn.SequentialCell()
        # [N, 784] -> [N, 512]
        self.model.append(nn.Dense(img_size * img_size, 512))  # 输入特征数为784，输出为512
        self.model.append(nn.LeakyReLU())  # 默认斜率为0.2的非线性映射激活函数
        # [N, 512] -> [N, 256]
        self.model.append(nn.Dense(512, 256))  # 进行一个线性映射
        self.model.append(nn.LeakyReLU())
        # [N, 256] -> [N, 1]
        self.model.append(nn.Dense(256, 1))
        self.model.append(nn.Sigmoid())  # 二分类激活函数，将实数映射到[0,1]

    def construct(self, x):
        x_flat = ops.reshape(x, (-1, img_size * img_size))
        return self.model(x_flat)

net_d = Discriminator()
net_d.update_parameters_name('discriminator')

损失函数和优化器

lr = 0.0002  # 学习率

# 损失函数
adversarial_loss = nn.BCELoss(reduction='mean')

# 优化器
optimizer_d = nn.Adam(net_d.trainable_params(), learning_rate=lr, beta1=0.5, beta2=0.999)
optimizer_g = nn.Adam(net_g.trainable_params(), learning_rate=lr, beta1=0.5, beta2=0.999)
optimizer_g.update_parameters_name('optim_g')
optimizer_d.update_parameters_name('optim_d')

模型训练

import os
import time
import matplotlib.pyplot as plt
import mindspore as ms
from mindspore import Tensor, save_checkpoint

total_epoch = 200  # 训练周期数
batch_size = 128  # 用于训练的训练集批量大小

# 加载预训练模型的参数
pred_trained = False
pred_trained_g = './result/checkpoints/Generator99.ckpt'
pred_trained_d = './result/checkpoints/Discriminator99.ckpt'

checkpoints_path = "./result/checkpoints"  # 结果保存路径
image_path = "./result/images"  # 测试结果保存路径

# 生成器计算损失过程
def generator_forward(test_noises):
    fake_data = net_g(test_noises)
    fake_out = net_d(fake_data)
    loss_g = adversarial_loss(fake_out, ops.ones_like(fake_out))
    return loss_g

# 判别器计算损失过程
def discriminator_forward(real_data, test_noises):
    fake_data = net_g(test_noises)
    fake_out = net_d(fake_data)
    real_out = net_d(real_data)
    real_loss = adversarial_loss(real_out, ops.ones_like(real_out))
    fake_loss = adversarial_loss(fake_out, ops.zeros_like(fake_out))
    loss_d = real_loss + fake_loss
    return loss_d

# 梯度方法
grad_g = ms.value_and_grad(generator_forward, None, net_g.trainable_params())
grad_d = ms.value_and_grad(discriminator_forward, None, net_d.trainable_params())

def train_step(real_data, latent_code):
    # 计算判别器损失和梯度
    loss_d, grads_d = grad_d(real_data, latent_code)
    optimizer_d(grads_d)
    loss_g, grads_g = grad_g(latent_code)
    optimizer_g(grads_g)

    return loss_d, loss_g

# 保存生成的test图像
def save_imgs(gen_imgs1, idx):
    for i3 in range(gen_imgs1.shape[0]):
        plt.subplot(5, 5, i3 + 1)
        plt.imshow(gen_imgs1[i3, 0, :, :] / 2 + 0.5, cmap="gray")
        plt.axis("off")
    plt.savefig(image_path + "/test_{}.png".format(idx))

# 设置参数保存路径
os.makedirs(checkpoints_path, exist_ok=True)
# 设置中间过程生成图片保存路径
os.makedirs(image_path, exist_ok=True)

net_g.set_train()
net_d.set_train()

# 储存生成器和判别器loss
losses_g, losses_d = [], []

for epoch in range(total_epoch):
    start = time.time()
    for (iter, data) in enumerate(mnist_ds):
        start1 = time.time()
        image, latent_code = data
        image = (image - 127.5) / 127.5  # [0, 255] -> [-1, 1]
        image = image.reshape(image.shape[0], 1, image.shape[1], image.shape[2])
        d_loss, g_loss = train_step(image, latent_code)
        end1 = time.time()
        if iter % 10 == 0:
            print(f"Epoch:[{int(epoch):>3d}/{int(total_epoch):>3d}], "
                  f"step:[{int(iter):>4d}/{int(iter_size):>4d}], "
                  f"loss_d:{d_loss.asnumpy():>4f} , "
                  f"loss_g:{g_loss.asnumpy():>4f} , "
                  f"time:{(end1 - start1):>3f}s, "
                  f"lr:{lr:>6f}")

    end = time.time()
    print("time of epoch {} is {:.2f}s".format(epoch + 1, end - start))

    losses_d.append(d_loss.asnumpy())
    losses_g.append(g_loss.asnumpy())

    # 每个epoch结束后，使用生成器生成一组图片
    gen_imgs = net_g(test_noise)
    save_imgs(gen_imgs.asnumpy(), epoch)

    # 根据epoch保存模型权重文件
    if epoch % 1 == 0:
        save_checkpoint(net_g, checkpoints_path + "/Generator%d.ckpt" % (epoch))
        save_checkpoint(net_d, checkpoints_path + "/Discriminator%d.ckpt" % (epoch))

训练过程

Epoch:[  0/200], step:[   0/ 468], loss_d:1.383930 , loss_g:0.693423 , time:0.864688s, lr:0.000200
Epoch:[  0/200], step:[  10/ 468], loss_d:1.356453 , loss_g:0.548430 , time:0.122673s, lr:0.000200
Epoch:[  0/200], step:[  20/ 468], loss_d:1.386923 , loss_g:0.628228 , time:0.120677s, lr:0.000200
Epoch:[  0/200], step:[  30/ 468], loss_d:1.385639 , loss_g:0.649491 , time:0.124667s, lr:0.000200
Epoch:[  0/200], step:[  40/ 468], loss_d:1.365866 , loss_g:0.683650 , time:0.122672s, lr:0.000200
...
Epoch:[ 99/200], step:[ 440/ 468], loss_d:1.170306 , loss_g:0.954169 , time:0.113697s, lr:0.000200
Epoch:[ 99/200], step:[ 450/ 468], loss_d:1.187954 , loss_g:0.970897 , time:0.113697s, lr:0.000200
Epoch:[ 99/200], step:[ 460/ 468], loss_d:1.277891 , loss_g:0.930688 , time:0.116688s, lr:0.000200
time of epoch 100 is 61.76s
Epoch:[100/200], step:[   0/ 468], loss_d:1.197745 , loss_g:0.951075 , time:0.134640s, lr:0.000200
Epoch:[100/200], step:[  10/ 468], loss_d:1.241353 , loss_g:0.939583 , time:0.131648s, lr:0.000200
Epoch:[100/200], step:[  20/ 468], loss_d:1.222481 , loss_g:0.900680 , time:0.129653s, lr:0.000200
...
Epoch:[199/200], step:[ 420/ 468], loss_d:1.215858 , loss_g:1.071604 , time:0.151593s, lr:0.000200
Epoch:[199/200], step:[ 430/ 468], loss_d:1.238803 , loss_g:0.920928 , time:0.135638s, lr:0.000200
Epoch:[199/200], step:[ 440/ 468], loss_d:1.212080 , loss_g:0.954983 , time:0.134640s, lr:0.000200
Epoch:[199/200], step:[ 450/ 468], loss_d:1.236587 , loss_g:0.897825 , time:0.133643s, lr:0.000200
Epoch:[199/200], step:[ 460/ 468], loss_d:1.214701 , loss_g:0.939405 , time:0.135638s, lr:0.000200
time of epoch 200 is 71.98s

效果展示

plt.figure(figsize=(6, 4))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(losses_g, label="G", color='blue')
plt.plot(losses_d, label="D", color='orange')
plt.xlim(-20, 220)
plt.ylim(0, 3.5)
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()

可视化训练过程中的图像

import cv2
import matplotlib.animation as animation

# 将训练过程中生成的测试图转为动态图
image_list = []
for i in range(total_epoch):
    image_list.append(cv2.imread(image_path + "/test_{}.png".format(i), cv2.IMREAD_GRAYSCALE))
show_list = []
fig = plt.figure(dpi=70)
for epoch in range(0, len(image_list), 5):
    plt.axis("off")
    show_list.append([plt.imshow(image_list[epoch], cmap='gray')])

ani = animation.ArtistAnimation(fig, show_list, interval=1000, repeat_delay=1000, blit=True)
ani.save('train_test.gif', writer='pillow', fps=1)

模型推理

import mindspore as ms

test_ckpt = './result/checkpoints/Generator199.ckpt'

parameter = ms.load_checkpoint(test_ckpt)
ms.load_param_into_net(net_g, parameter)
# 模型生成结果
test_data = Tensor(np.random.normal(0, 1, (25, 100)).astype(np.float32))
images = net_g(test_data).transpose(0, 2, 3, 1).asnumpy()
# 结果展示
fig = plt.figure(figsize=(3, 3), dpi=120)
for i in range(25):
    fig.add_subplot(5, 5, i + 1)
    plt.axis("off")
    plt.imshow(images[i].squeeze(), cmap="gray")
plt.show()