xiaoxia0722
2 years ago
1 changed files with 223 additions and 0 deletions
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CGAN.py
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| import jittor as jt | |||
| from jittor import init | |||
| import argparse | |||
| import os | |||
| import numpy as np | |||
| import math | |||
| from jittor import nn | |||
| if jt.has_cuda: | |||
| jt.flags.use_cuda = 1 | |||
| parser = argparse.ArgumentParser() | |||
| parser.add_argument('--n_epochs', type=int, default=100, help='number of epochs of training') | |||
| parser.add_argument('--batch_size', type=int, default=64, help='size of the batches') | |||
| parser.add_argument('--lr', type=float, default=0.0002, help='adam: learning rate') | |||
| parser.add_argument('--b1', type=float, default=0.5, help='adam: decay of first order momentum of gradient') | |||
| parser.add_argument('--b2', type=float, default=0.999, help='adam: decay of first order momentum of gradient') | |||
| parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation') | |||
| parser.add_argument('--latent_dim', type=int, default=100, help='dimensionality of the latent space') | |||
| parser.add_argument('--n_classes', type=int, default=10, help='number of classes for dataset') | |||
| parser.add_argument('--img_size', type=int, default=32, help='size of each image dimension') | |||
| parser.add_argument('--channels', type=int, default=1, help='number of image channels') | |||
| parser.add_argument('--sample_interval', type=int, default=1000, help='interval between image sampling') | |||
| opt = parser.parse_args() | |||
| print(opt) | |||
| img_shape = (opt.channels, opt.img_size, opt.img_size) | |||
| class Generator(nn.Module): | |||
| def __init__(self): | |||
| super(Generator, self).__init__() | |||
| self.label_emb = nn.Embedding(opt.n_classes, opt.n_classes) | |||
| # nn.Linear(in_dim, out_dim)表示全连接层 | |||
| # in_dim：输入向量维度 | |||
| # out_dim：输出向量维度 | |||
| 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)) | |||
| return layers | |||
| self.model = nn.Sequential(*block((opt.latent_dim + opt.n_classes), 128, normalize=False), | |||
| *block(128, 256), | |||
| *block(256, 512), | |||
| *block(512, 1024), | |||
| nn.Linear(1024, int(np.prod(img_shape))), | |||
| nn.Tanh()) | |||
| def execute(self, noise, labels): | |||
| gen_input = jt.contrib.concat((self.label_emb(labels), noise), dim=1) | |||
| img = self.model(gen_input) | |||
| # 将img从1024维向量变为32*32矩阵 | |||
| img = img.view((img.shape[0], *img_shape)) | |||
| return img | |||
| class Discriminator(nn.Module): | |||
| def __init__(self): | |||
| super(Discriminator, self).__init__() | |||
| self.label_embedding = nn.Embedding(opt.n_classes, opt.n_classes) | |||
| self.model = nn.Sequential(nn.Linear((opt.n_classes + int(np.prod(img_shape))), 512), | |||
| nn.LeakyReLU(0.2), | |||
| nn.Linear(512, 512), | |||
| nn.Dropout(0.4), | |||
| nn.LeakyReLU(0.2), | |||
| nn.Linear(512, 512), | |||
| nn.Dropout(0.4), | |||
| nn.LeakyReLU(0.2), | |||
| nn.Linear(512, 1), | |||
| nn.LeakyReLU(0.2) | |||
| # TODO: 添加最后一个线性层，最终输出为一个实数 | |||
| ) | |||
| def execute(self, img, labels): | |||
| d_in = jt.contrib.concat((img.view((img.shape[0], (- 1))), self.label_embedding(labels)), dim=1) | |||
| d_out = self.model(d_in) | |||
| return d_out | |||
| # TODO: 将d_in输入到模型中并返回计算结果 | |||
| # 损失函数：平方误差 | |||
| # 调用方法：adversarial_loss(网络输出A, 分类标签B) | |||
| # 计算结果：(A-B)^2 | |||
| adversarial_loss = nn.MSELoss() | |||
| generator = Generator() | |||
| discriminator = Discriminator() | |||
| # 导入MNIST数据集 | |||
| from jittor.dataset.mnist import MNIST | |||
| import jittor.transform as transform | |||
| transform = transform.Compose([ | |||
| transform.Resize(opt.img_size), | |||
| transform.Gray(), | |||
| transform.ImageNormalize(mean=[0.5], std=[0.5]), | |||
| ]) | |||
| dataloader = MNIST(train=True, transform=transform).set_attrs(batch_size=opt.batch_size, shuffle=True) | |||
| optimizer_G = nn.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) | |||
| optimizer_D = nn.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2)) | |||
| from PIL import Image | |||
| def save_image(img, path, nrow=10, padding=5): | |||
| N, C, W, H = img.shape | |||
| if (N % nrow != 0): | |||
| print("N%nrow!=0") | |||
| return | |||
| ncol = int(N / nrow) | |||
| img_all = [] | |||
| for i in range(ncol): | |||
| img_ = [] | |||
| for j in range(nrow): | |||
| img_.append(img[i * nrow + j]) | |||
| img_.append(np.zeros((C, W, padding))) | |||
| img_all.append(np.concatenate(img_, 2)) | |||
| img_all.append(np.zeros((C, padding, img_all[0].shape[2]))) | |||
| img = np.concatenate(img_all, 1) | |||
| img = np.concatenate([np.zeros((C, padding, img.shape[2])), img], 1) | |||
| img = np.concatenate([np.zeros((C, img.shape[1], padding)), img], 2) | |||
| min_ = img.min() | |||
| max_ = img.max() | |||
| img = (img - min_) / (max_ - min_) * 255 | |||
| img = img.transpose((1, 2, 0)) | |||
| if C == 3: | |||
| img = img[:, :, ::-1] | |||
| elif C == 1: | |||
| img = img[:, :, 0] | |||
| Image.fromarray(np.uint8(img)).save(path) | |||
| def sample_image(n_row, batches_done): | |||
| # 随机采样输入并保存生成的图片 | |||
| z = jt.array(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))).float32().stop_grad() | |||
| labels = jt.array(np.array([num for _ in range(n_row) for num in range(n_row)])).float32().stop_grad() | |||
| gen_imgs = generator(z, labels) | |||
| save_image(gen_imgs.numpy(), "%d.png" % batches_done, nrow=n_row) | |||
| # ---------- | |||
| # 模型训练 | |||
| # ---------- | |||
| for epoch in range(opt.n_epochs): | |||
| for i, (imgs, labels) in enumerate(dataloader): | |||
| batch_size = imgs.shape[0] | |||
| # 数据标签，valid=1表示真实的图片，fake=0表示生成的图片 | |||
| valid = jt.ones([batch_size, 1]).float32().stop_grad() | |||
| fake = jt.zeros([batch_size, 1]).float32().stop_grad() | |||
| # 真实图片及其类别 | |||
| real_imgs = jt.array(imgs) | |||
| labels = jt.array(labels) | |||
| # ----------------- | |||
| # 训练生成器 | |||
| # ----------------- | |||
| # 采样随机噪声和数字类别作为生成器输入 | |||
| z = jt.array(np.random.normal(0, 1, (batch_size, opt.latent_dim))).float32() | |||
| gen_labels = jt.array(np.random.randint(0, opt.n_classes, batch_size)).float32() | |||
| # 生成一组图片 | |||
| gen_imgs = generator(z, gen_labels) | |||
| # 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 | |||
| validity = discriminator(gen_imgs, gen_labels) | |||
| g_loss = adversarial_loss(validity, valid) | |||
| g_loss.sync() | |||
| optimizer_G.step(g_loss) | |||
| # --------------------- | |||
| # 训练判别器 | |||
| # --------------------- | |||
| validity_real = discriminator(real_imgs, labels) | |||
| d_real_loss = adversarial_loss(validity_real, valid) | |||
| validity_fake = discriminator(gen_imgs.stop_grad(), gen_labels) | |||
| d_fake_loss = adversarial_loss(validity_fake, fake) | |||
| # 总的判别器损失 | |||
| d_loss = (d_real_loss + d_fake_loss) / 2 | |||
| d_loss.sync() | |||
| optimizer_D.step(d_loss) | |||
| if i % 50 == 0: | |||
| print( | |||
| "[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" | |||
| % (epoch, opt.n_epochs, i, len(dataloader), d_loss.data, g_loss.data) | |||
| ) | |||
| batches_done = epoch * len(dataloader) + i | |||
| if batches_done % opt.sample_interval == 0: | |||
| sample_image(n_row=10, batches_done=batches_done) | |||
| if epoch % 10 == 0: | |||
| generator.save("generator_last.pkl") | |||
| discriminator.save("discriminator_last.pkl") | |||
| generator.eval() | |||
| discriminator.eval() | |||
| generator.load('generator_last.pkl') | |||
| discriminator.load('discriminator_last.pkl') | |||
| number = '14129461411720' # TODO: 写入比赛页面中指定的数字序列（字符串类型） | |||
| n_row = len(number) | |||
| z = jt.array(np.random.normal(0, 1, (n_row, opt.latent_dim))).float32().stop_grad() | |||
| labels = jt.array(np.array([int(number[num]) for num in range(n_row)])).float32().stop_grad() | |||
| gen_imgs = generator(z, labels) | |||
| img_array = gen_imgs.data.transpose((1, 2, 0, 3))[0].reshape((gen_imgs.shape[2], -1)) | |||
| min_ = img_array.min() | |||
| max_ = img_array.max() | |||
| img_array = (img_array - min_) / (max_ - min_) * 255 | |||
| Image.fromarray(np.uint8(img_array)).save("result.png") | |||