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), # TODO: 添加最后一个线性层，最终输出为一个实数 nn.Linear(512,1) ) def execute(self, img, labels): d_in = jt.contrib.concat((img.view((img.shape[0], (- 1))), self.label_embedding(labels)), dim=1) # TODO: 将d_in输入到模型中并返回计算结果 return self.model(d_in) # 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) # """TODO: 计算真实类别的损失函数""" validity_fake = discriminator(gen_imgs.stop_grad(), gen_labels) d_fake_loss = adversarial_loss(validity_fake, fake) # """TODO: 计算虚假类别的损失函数""" # 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 ="20662262061325" #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")