From 83f7962d0caeee4fad3830361115ebd9857d3a61 Mon Sep 17 00:00:00 2001 From: hy22 Date: Wed, 15 May 2024 19:50:35 +0800 Subject: [PATCH] ADD file via upload --- CGAN.py | 210 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 210 insertions(+) create mode 100644 CGAN.py diff --git a/CGAN.py b/CGAN.py new file mode 100644 index 0000000..4a405a2 --- /dev/null +++ b/CGAN.py @@ -0,0 +1,210 @@ +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")