mirror of https://github.com/Jittor/Jittor
fix gan and python3-config path lookup
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# 使用Jittor实现Conditional GAN
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Generative Adversarial Nets（GAN）[1]提出了一种新的方法来训练生成模型。然而，GAN对于要生成的图片缺少控制。Conditional GAN（CGAN）[2]通过添加显式的条件或标签，来控制生成的图像。本教程讲解了CGAN的网络结构、损失函数设计、使用CGAN生成一串数字、从头训练CGAN、以及在mnist手写数字数据集上的训练结果。
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## CGAN网络架构
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通过在生成器generator和判别器discriminator中添加相同的额外信息y，GAN就可以扩展为一个conditional模型。y可以是任何形式的辅助信息，例如类别标签或者其他形式的数据。我们可以通过将y作为额外输入层，添加到生成器和判别器来完成条件控制。
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在生成器generator中，除了y之外，还额外输入随机一维噪声z，为结果生成提供更多灵活性。
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## 损失函数
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### GAN的损失函数
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在解释CGAN的损失函数之前，首先介绍GAN的损失函数。下面是GAN的损失函数设计。
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对于判别器D，我们要训练最大化这个loss。如果D的输入是来自真实样本的数据x,则D的输出D(x)要尽可能地大，log(D(x))也会尽可能大。如果D的输入是来自G生成的假图片G(z)，则D的输出D(G(z))应尽可能地小，从而log(1-D(G(z))会尽可能地大。这样可以达到max D的目的。
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对于生成器G，我们要训练最小化这个loss。对于G生成的假图片G(z)，我们希望尽可能地骗过D，让它觉得我们生成的图片就是真的图片，这样就达到了G“以假乱真”的目的。那么D的输出D(G(z))应尽可能地大，从而log(1-D(G(z))会尽可能地小。这样可以达到min G的目的。
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D和G以这样的方式联合训练，最终达到G的生成能力越来越强，D的判别能力越来越强的目的。
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### CGAN的损失函数
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下面是CGAN的损失函数设计。
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很明显，CGAN的loss跟GAN的loss的区别就是多了条件限定y。D(x/y)代表在条件y下，x为真的概率。D(G(z/y))表示在条件y下，G生成的图片被D判别为真的概率。
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## Jittor代码数字生成
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首先，我们导入需要的包，并且设置好所需的超参数：
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```python
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import jittor as jt
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from jittor import nn
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import numpy as np
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import pylab as pl
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%matplotlib inline
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# 隐空间向量长度
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latent_dim = 100
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# 类别数量
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n_classes = 10
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# 图片大小
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img_size = 32
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# 图片通道数量
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channels = 1
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# 图片张量的形状
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img_shape = (channels, img_size, img_size)
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```
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第一步，定义生成器G。该生成器输入两个一维向量y和noise，生成一张图片。
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```python
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class Generator(nn.Module):
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def __init__(self):
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super(Generator, self).__init__()
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self.label_emb = nn.Embedding(n_classes, n_classes)
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def block(in_feat, out_feat, normalize=True):
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layers = [nn.Linear(in_feat, out_feat)]
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if normalize:
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layers.append(nn.BatchNorm1d(out_feat, 0.8))
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layers.append(nn.LeakyReLU(0.2))
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return layers
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self.model = nn.Sequential(
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*block((latent_dim + n_classes), 128, normalize=False),
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*block(128, 256),
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*block(256, 512),
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*block(512, 1024),
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nn.Linear(1024, int(np.prod(img_shape))),
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nn.Tanh())
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def execute(self, noise, labels):
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gen_input = jt.contrib.concat((self.label_emb(labels), noise), dim=1)
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img = self.model(gen_input)
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img = img.view((img.shape[0], *img_shape))
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return img
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```
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第二步，定义判别器D。D输入一张图片和对应的y，输出是真图片的概率。
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```python
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class Discriminator(nn.Module):
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def __init__(self):
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super(Discriminator, self).__init__()
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self.label_embedding = nn.Embedding(n_classes, n_classes)
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self.model = nn.Sequential(
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nn.Linear((n_classes + int(np.prod(img_shape))), 512),
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nn.LeakyReLU(0.2),
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nn.Linear(512, 512),
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nn.Dropout(0.4),
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nn.LeakyReLU(0.2),
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nn.Linear(512, 512),
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nn.Dropout(0.4),
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nn.LeakyReLU(0.2),
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nn.Linear(512, 1))
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def execute(self, img, labels):
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d_in = jt.contrib.concat((img.view((img.shape[0], (- 1))), self.label_embedding(labels)), dim=1)
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validity = self.model(d_in)
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return validity
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```
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第三步，使用CGAN生成一串数字。
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代码如下。您可以使用您训练好的模型来生成图片，也可以使用我们提供的预训练参数： 模型预训练参数下载：<https://cloud.tsinghua.edu.cn/d/fbe30ae0967942f6991c/>。
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```python
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# 下载提供的预训练参数
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!wget https://cg.cs.tsinghua.edu.cn/jittor/assets/build/generator_last.pkl
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!wget https://cg.cs.tsinghua.edu.cn/jittor/assets/build/discriminator_last.pkl
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```
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生成自定义的数字：
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```python
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# 定义模型
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generator = Generator()
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discriminator = Discriminator()
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generator.eval()
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discriminator.eval()
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# 加载参数
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generator.load('./generator_last.pkl')
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discriminator.load('./discriminator_last.pkl')
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# 定义一串数字
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number = "201962517"
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n_row = len(number)
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z = jt.array(np.random.normal(0, 1, (n_row, latent_dim))).float32().stop_grad()
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labels = jt.array(np.array([int(number[num]) for num in range(n_row)])).float32().stop_grad()
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gen_imgs = generator(z,labels)
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pl.imshow(gen_imgs.data.transpose((1,2,0,3))[0].reshape((gen_imgs.shape[2], -1)))
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```
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## 从头训练Condition GAN
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从头训练 Condition GAN 的完整代码在<https://github.com/Jittor/gan-jittor/blob/master/models/cgan/cgan.py>， 让我们把他下载下来看看！
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```python
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!wget https://raw.githubusercontent.com/Jittor/gan-jittor/master/models/cgan/cgan.py
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!python3.7 ./cgan.py --help
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# 选择合适的batch size，运行试试
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# 运行命令： !python3.7 ./cgan.py --batch_size 64
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```
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## MNIST数据集训练结果
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下面展示了Jittor版CGAN在MNIST数据集的训练结果。下面分别是训练0 epoch和90 epoches的结果。
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## 参考文献
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1. Goodfellow, Ian, et al. “Generative adversarial nets.” Advances in neural information processing systems. 2014.
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2. Mirza, Mehdi, and Simon Osindero. “Conditional generative adversarial nets.” arXiv preprint arXiv:1411.1784 (2014).
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@ -815,12 +815,22 @@ with jit_utils.import_scope(import_flags):
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jit_utils.try_import_jit_utils_core()
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python_path = sys.executable
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py3_config_path = sys.executable+"-config"
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assert os.path.isfile(python_path)
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if not os.path.isfile(py3_config_path) :
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py3_config_path = sys.executable + '3-config'
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py3_config_paths = [
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sys.executable + "-config",
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os.path.dirname(sys.executable) + f"/python3.{sys.version_info.minor}-config",
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f"/usr/bin/python3.{sys.version_info.minor}-config",
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os.path.dirname(sys.executable) + "/python3-config",
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]
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if "python_config_path" in os.environ:
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py3_config_paths.insert(0, os.environ["python_config_path"])
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assert os.path.isfile(py3_config_path)
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for py3_config_path in py3_config_paths:
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if os.path.isfile(py3_config_path):
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break
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else:
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raise RuntimeError(f"python3.{sys.version_info.minor}-config "
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"not found in {py3_config_paths}, please specify "
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"enviroment variable 'python_config_path'")
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nvcc_path = env_or_try_find('nvcc_path', '/usr/local/cuda/bin/nvcc')
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gdb_path = try_find_exe('gdb')
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addr2line_path = try_find_exe('addr2line')
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@ -17,12 +17,19 @@ import jittor as jt
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import jittor.transform as trans
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class MNIST(Dataset):
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def __init__(self, data_root=dataset_root+"/mnist_data/", train=True ,download=True, transform=None):
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def __init__(self, data_root=dataset_root+"/mnist_data/",
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train=True,
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download=True,
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batch_size = 16,
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shuffle = False,
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transform=None):
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# if you want to test resnet etc you should set input_channel = 3, because the net set 3 as the input dimensions
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super().__init__()
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self.data_root = data_root
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self.is_train = train
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self.transform = transform
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self.batch_size = batch_size
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self.shuffle = shuffle
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if download == True:
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self.download_url()
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@ -15,7 +15,7 @@ tests = []
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for mdname in os.listdir(dirname):
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if not mdname.endswith(".src.md"): continue
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# temporary disable model_test
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if "LSGAN" in mdname: continue
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if "GAN" in mdname: continue
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tests.append(mdname[:-3])
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try:
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