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import jittor as jt
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from jittor import init
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import argparse
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import os
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import numpy as np
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import math
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from jittor import nn
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if jt.has_cuda:
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jt.flags.use_cuda = 1
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parser = argparse.ArgumentParser()
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parser.add_argument('--n_epochs', type=int, default=100, help='number of epochs of training')
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parser.add_argument('--batch_size', type=int, default=64, help='size of the batches')
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parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate')
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parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient')
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parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient')
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parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation')
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parser.add_argument('--latent_dim', type=int, default=100, help='dimensionality of the latent space')
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parser.add_argument('--n_classes', type=int, default=10, help='number of classes for dataset')
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parser.add_argument('--img_size', type=int, default=32, help='size of each image dimension')
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parser.add_argument('--channels', type=int, default=1, help='number of image channels')
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parser.add_argument('--sample_interval', type=int, default=1000, help='interval between image sampling')
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opt = parser.parse_args()
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print(opt)
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img_shape = (opt.channels, opt.img_size, opt.img_size)
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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(opt.n_classes, opt.n_classes)
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# nn.Linear(in_dim, out_dim)表示全连接层
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# in_dim：输入向量维度
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# out_dim：输出向量维度
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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(*block((opt.latent_dim + opt.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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# 将img从1024维向量变为32*32矩阵
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img = img.view((img.shape[0], *img_shape))
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return img
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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(opt.n_classes, opt.n_classes)
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self.model = nn.Sequential(nn.Linear((opt.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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# TODO: 添加最后一个线性层，最终输出为一个实数
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nn.Linear(512,1)
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)
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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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# TODO: 将d_in输入到模型中并返回计算结果
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return self.model(d_in)
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# 损失函数：平方误差
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# 调用方法：adversarial_loss(网络输出A, 分类标签B)
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# 计算结果：(A-B)^2
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adversarial_loss = nn.MSELoss()
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generator = Generator()
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discriminator = Discriminator()
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# 导入MNIST数据集
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from jittor.dataset.mnist import MNIST
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import jittor.transform as transform
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transform = transform.Compose([
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transform.Resize(opt.img_size),
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transform.Gray(),
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transform.ImageNormalize(mean=[0.5], std=[0.5]),
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])
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dataloader = MNIST(train=True, transform=transform).set_attrs(batch_size=opt.batch_size, shuffle=True)
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optimizer_G = nn.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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optimizer_D = nn.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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from PIL import Image
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def save_image(img, path, nrow=10, padding=5):
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N,C,W,H = img.shape
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if (N%nrow!=0):
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print("N%nrow!=0")
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return
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ncol=int(N/nrow)
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img_all = []
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for i in range(ncol):
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img_ = []
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for j in range(nrow):
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img_.append(img[i*nrow+j])
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img_.append(np.zeros((C,W,padding)))
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img_all.append(np.concatenate(img_, 2))
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img_all.append(np.zeros((C,padding,img_all[0].shape[2])))
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img = np.concatenate(img_all, 1)
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img = np.concatenate([np.zeros((C,padding,img.shape[2])), img], 1)
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img = np.concatenate([np.zeros((C,img.shape[1],padding)), img], 2)
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min_=img.min()
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max_=img.max()
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img=(img-min_)/(max_-min_)*255
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img=img.transpose((1,2,0))
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if C==3:
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img = img[:,:,::-1]
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elif C==1:
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img = img[:,:,0]
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Image.fromarray(np.uint8(img)).save(path)
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def sample_image(n_row, batches_done):
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# 随机采样输入并保存生成的图片
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z = jt.array(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))).float32().stop_grad()
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labels = jt.array(np.array([num for _ in range(n_row) for num in range(n_row)])).float32().stop_grad()
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gen_imgs = generator(z, labels)
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save_image(gen_imgs.numpy(), "%d.png" % batches_done, nrow=n_row)
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# ----------
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# 模型训练
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# ----------
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for epoch in range(opt.n_epochs):
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for i, (imgs, labels) in enumerate(dataloader):
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batch_size = imgs.shape[0]
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# 数据标签，valid=1表示真实的图片，fake=0表示生成的图片
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valid = jt.ones([batch_size, 1]).float32().stop_grad()
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fake = jt.zeros([batch_size, 1]).float32().stop_grad()
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# 真实图片及其类别
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real_imgs = jt.array(imgs)
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labels = jt.array(labels)
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# -----------------
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# 训练生成器
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# -----------------
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# 采样随机噪声和数字类别作为生成器输入
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z = jt.array(np.random.normal(0, 1, (batch_size, opt.latent_dim))).float32()
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gen_labels = jt.array(np.random.randint(0, opt.n_classes, batch_size)).float32()
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# 生成一组图片
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gen_imgs = generator(z, gen_labels)
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# 损失函数衡量生成器欺骗判别器的能力，即希望判别器将生成图片分类为valid
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validity = discriminator(gen_imgs, gen_labels)
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g_loss = adversarial_loss(validity, valid)
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g_loss.sync()
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optimizer_G.step(g_loss)
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# ---------------------
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# 训练判别器
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# ---------------------
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validity_real = discriminator(real_imgs, labels)
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d_real_loss = adversarial_loss(validity_real, valid) # """TODO: 计算真实类别的损失函数"""
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validity_fake = discriminator(gen_imgs.stop_grad(), gen_labels)
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d_fake_loss = adversarial_loss(validity_fake, fake) # """TODO: 计算虚假类别的损失函数"""
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# 总的判别器损失
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d_loss = (d_real_loss + d_fake_loss) / 2
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d_loss.sync()
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optimizer_D.step(d_loss)
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if i % 50 == 0:
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print(
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"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]"
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% (epoch, opt.n_epochs, i, len(dataloader), d_loss.data, g_loss.data)
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)
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batches_done = epoch * len(dataloader) + i
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if batches_done % opt.sample_interval == 0:
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sample_image(n_row=10, batches_done=batches_done)
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if epoch % 10 == 0:
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generator.save("generator_last.pkl")
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discriminator.save("discriminator_last.pkl")
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generator.eval()
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discriminator.eval()
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generator.load('generator_last.pkl')
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discriminator.load('discriminator_last.pkl')
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number ="20662262061325" #TODO: 写入比赛页面中指定的数字序列（字符串类型）
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n_row = len(number)
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z = jt.array(np.random.normal(0, 1, (n_row, opt.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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img_array = gen_imgs.data.transpose((1,2,0,3))[0].reshape((gen_imgs.shape[2], -1))
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min_=img_array.min()
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max_=img_array.max()
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img_array=(img_array-min_)/(max_-min_)*255
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Image.fromarray(np.uint8(img_array)).save("result.png")
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