diff --git a/README.md b/README.md
index 9d5647c..b621484 100644
--- a/README.md
+++ b/README.md
@@ -26,6 +26,7 @@ In another form of presentation, assuming that Pytorch's training time is 100 ho
 
 ## Table of Contents
   * [Installation](#installation)
+  * [第二届计图人工智能挑战赛](#第二届计图人工智能挑战赛)
   * [models](#models)
     + [Auxiliary Classifier GAN](#auxiliary-classifier-gan)
     + [Adversarial Autoencoder](#adversarial-autoencoder)
@@ -60,6 +61,39 @@ In another form of presentation, assuming that Pytorch's training time is 100 ho
     $ cd gan-jittor/
     $ sudo python3.7 -m pip install -r requirements.txt
 
+## 第二届计图人工智能挑战赛
+
+### 计图挑战热身赛
+
+本赛题将会提供数字图片数据集 MNIST，参赛选手需要训练一个将随机噪声和类别标签映射为数字图片的Conditional GAN模型，并生成注册时绑定的手机号（如果没有绑定手机号请先绑定再进行提交）。
+
+本赛题提供示例代码框架，提供数据下载、模型定义、训练步骤等功能。
+
+选手可以基于示例代码填充注释为 TODO 的部分完成该赛题。
+
+```
+cd competition/warm_up_comp
+修改 CGAN.py 使其运行
+```
+
+### 赛题一：风景图片生成赛题
+
+图像生成任务一直以来都是十分具有应用场景的计算机视觉任务，从语义分割图生成有意义、高质量的图片仍然存在诸多挑战，如保证生成图片的真实性、清晰程度、多样性、美观性等。
+
+清华大学计算机系图形学实验室从Flickr官网收集了1万张高清（宽1024、高768）的风景图片，并制作了它们的语义分割图。其中，1万对图片被用来训练。训练数据集可以从[这里](https://cloud.tsinghua.edu.cn/f/1d734cbb68b545d6bdf2/?dl=1)下载。
+
+```
+cd competition/landscape_comp
+
+# 单卡训练，需要修改脚本里的数据路径
+bash scripts/single_gpu.sh
+
+# 多卡训练，需要修改脚本里的数据路径
+bash scripts/multi_gpu.sh
+```
+
+注：代码中注释掉了eval的部分，等到测试数据发布之后，您可以取消注释进行评测。也可在训练阶段自动分配一部分数据集为测试集进行训练。
+
 ## models   
 ### Auxiliary Classifier GAN
 _Auxiliary Classifier Generative Adversarial Network_
diff --git a/competition/landscape_comp/datasets.py b/competition/landscape_comp/datasets.py
new file mode 100644
index 0000000..4fc7ad9
--- /dev/null
+++ b/competition/landscape_comp/datasets.py
@@ -0,0 +1,37 @@
+import glob
+import random
+import os
+import numpy as np
+
+from jittor.dataset.dataset import Dataset
+import jittor.transform as transform
+from PIL import Image
+
+class ImageDataset(Dataset):
+    def __init__(self, root, mode="train", transforms=None):
+        super().__init__()
+        self.transforms = transform.Compose(transforms)
+        self.mode = mode
+        if self.mode == 'train':
+            self.files = sorted(glob.glob(os.path.join(root, mode, "imgs") + "/*.*"))
+        self.labels = sorted(glob.glob(os.path.join(root, mode, "labels") + "/*.*"))
+        self.set_attrs(total_len=len(self.labels))
+        print(f"from {mode} split load {self.total_len} images.")
+
+    def __getitem__(self, index):
+        label_path = self.labels[index % len(self.labels)]
+        photo_id = label_path.split('/')[-1][:-4]
+        img_B = Image.open(label_path)
+        img_B = Image.fromarray(np.array(img_B).astype("uint8")[:, :, np.newaxis].repeat(3,2))
+
+        if self.mode == "train":
+            img_A = Image.open(self.files[index % len(self.files)])
+            if np.random.random() < 0.5:
+                img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], "RGB")
+                img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], "RGB")
+            img_A = self.transforms(img_A)
+        else:
+            img_A = np.empty([1])
+        img_B = self.transforms(img_B)
+
+        return img_A, img_B, photo_id
diff --git a/competition/landscape_comp/models.py b/competition/landscape_comp/models.py
new file mode 100644
index 0000000..c20f995
--- /dev/null
+++ b/competition/landscape_comp/models.py
@@ -0,0 +1,177 @@
+
+import jittor as jt
+from jittor import init
+from jittor import nn
+
+
+def start_grad(model):
+    for param in model.parameters():
+        param.start_grad()
+
+def stop_grad(model):
+    for param in model.parameters():
+        param.stop_grad()
+
+def weights_init_normal(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        jt.init.gauss_(m.weight, 0.0, 0.02)
+    elif classname.find("BatchNorm") != -1:
+        jt.init.gauss_(m.weight, 1.0, 0.02)
+        jt.init.constant_(m.bias, 0.0)
+
+class UNetDown(nn.Module):
+
+    def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
+        super(UNetDown, self).__init__()
+        layers = [nn.Conv(in_size, out_size, 4, stride=2, padding=1, bias=False)]
+        if normalize:
+            layers.append(nn.InstanceNorm2d(out_size, affine=None))
+        layers.append(nn.LeakyReLU(scale=0.2))
+        if dropout:
+            layers.append(nn.Dropout(dropout))
+        self.model = nn.Sequential(*layers)
+
+    def execute(self, x):
+        return self.model(x)
+
+class UNetUp(nn.Module):
+
+    def __init__(self, in_size, out_size, dropout=0.0):
+        super(UNetUp, self).__init__()
+        layers = [nn.ConvTranspose(in_size, out_size, 4, stride=2, padding=1, bias=False), nn.InstanceNorm2d(out_size, affine=None), nn.ReLU()]
+        if dropout:
+            layers.append(nn.Dropout(dropout))
+        self.model = nn.Sequential(*layers)
+
+    def execute(self, x, skip_input):
+        x = self.model(x)
+        x = jt.contrib.concat((x, skip_input), dim=1)
+        return x
+
+class GeneratorUNet(nn.Module):
+
+    def __init__(self, in_channels=3, out_channels=3):
+        super(GeneratorUNet, self).__init__()
+        self.down1 = UNetDown(in_channels, 64, normalize=False)
+        self.down2 = UNetDown(64, 128)
+        self.down3 = UNetDown(128, 256)
+        self.down4 = UNetDown(256, 512, dropout=0.5)
+        self.down5 = UNetDown(512, 512, dropout=0.5)
+        self.down6 = UNetDown(512, 512, dropout=0.5)
+        self.down7 = UNetDown(512, 512, dropout=0.5)
+        self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)
+        self.up1 = UNetUp(512, 512, dropout=0.5)
+        self.up2 = UNetUp(1024, 512, dropout=0.5)
+        self.up3 = UNetUp(1024, 512, dropout=0.5)
+        self.up4 = UNetUp(1024, 512, dropout=0.5)
+        self.up5 = UNetUp(1024, 256)
+        self.up6 = UNetUp(512, 128)
+        self.up7 = UNetUp(256, 64)
+        self.final = nn.Sequential(nn.Upsample(scale_factor=2), nn.ZeroPad2d((1, 0, 1, 0)), nn.Conv(128, out_channels, 4, padding=1), nn.Tanh())
+
+        for m in self.modules():
+            weights_init_normal(m)
+
+    def execute(self, x):
+        d1 = self.down1(x)
+        d2 = self.down2(d1)
+        d3 = self.down3(d2)
+        d4 = self.down4(d3)
+        d5 = self.down5(d4)
+        d6 = self.down6(d5)
+        d7 = self.down7(d6)
+        d8 = self.down8(d7)
+        u1 = self.up1(d8, d7)
+        u2 = self.up2(u1, d6)
+        u3 = self.up3(u2, d5)
+        u4 = self.up4(u3, d4)
+        u5 = self.up5(u4, d3)
+        u6 = self.up6(u5, d2)
+        u7 = self.up7(u6, d1)
+        return self.final(u7)
+
+
+class UnetGenerator(nn.Module):
+    def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
+        super(UnetGenerator, self).__init__()
+        # construct unet structure
+        unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True)  # add the innermost layer
+        for i in range(num_downs - 5):          # add intermediate layers with ngf * 8 filters
+            unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
+        # gradually reduce the number of filters from ngf * 8 to ngf
+        unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
+        self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer)  # add the outermost layer
+
+    def execute(self, input):
+        return self.model(input)
+
+class UnetSkipConnectionBlock(nn.Module):
+    def __init__(self, outer_nc, inner_nc, input_nc=None,
+                 submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
+        super(UnetSkipConnectionBlock, self).__init__()
+        self.outermost = outermost
+        if input_nc is None:
+            input_nc = outer_nc
+        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
+                             stride=2, padding=1, bias=False)
+        downrelu = nn.LeakyReLU(0.2)
+        downnorm = norm_layer(inner_nc)
+        uprelu = nn.ReLU()
+        upnorm = norm_layer(outer_nc)
+
+        if outermost:
+            upconv = nn.ConvTranspose(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1)
+            down = [downconv]
+            up = [uprelu, upconv, nn.Tanh()]
+            model = down + [submodule] + up
+        elif innermost:
+            upconv = nn.ConvTranspose(inner_nc, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=False)
+            down = [downrelu, downconv]
+            up = [uprelu, upconv, upnorm]
+            model = down + up
+        else:
+            upconv = nn.ConvTranspose(inner_nc * 2, outer_nc,
+                                        kernel_size=4, stride=2,
+                                        padding=1, bias=False)
+            down = [downrelu, downconv, downnorm]
+            up = [uprelu, upconv, upnorm]
+
+            if use_dropout:
+                model = down + [submodule] + up + [nn.Dropout(0.5)]
+            else:
+                model = down + [submodule] + up
+
+        self.model = nn.Sequential(*model)
+
+    def execute(self, x):
+        if self.outermost:
+            return self.model(x)
+        else:   # add skip connections
+            return jt.contrib.concat([x, self.model(x)], 1)
+
+class Discriminator(nn.Module):
+
+    def __init__(self, in_channels=3):
+        super(Discriminator, self).__init__()
+
+        def discriminator_block(in_filters, out_filters, stride=2, normalization=True):
+            'Returns downsampling layers of each discriminator block'
+            layers = [nn.Conv(in_filters, out_filters, 4, stride=stride, padding=1)]
+            if normalization:
+                layers.append(nn.BatchNorm2d(out_filters, eps=1e-05, momentum=0.1, affine=True))
+            layers.append(nn.LeakyReLU(scale=0.2))
+            return layers
+        self.model = nn.Sequential(*discriminator_block((in_channels * 2), 64, normalization=False), *discriminator_block(64, 128), *discriminator_block(128, 256), *discriminator_block(256, 512, stride=1), nn.Conv(512, 1, 4, padding=1, bias=False))
+        
+        for m in self.modules():
+            weights_init_normal(m)
+
+    def execute(self, input):
+        return self.model(input)
diff --git a/competition/landscape_comp/pix2pix.py b/competition/landscape_comp/pix2pix.py
new file mode 100644
index 0000000..cc2ab3a
--- /dev/null
+++ b/competition/landscape_comp/pix2pix.py
@@ -0,0 +1,209 @@
+import jittor as jt
+from jittor import init
+from jittor import nn
+import jittor.transform as transform
+import argparse
+import os
+import numpy as np
+import math
+import itertools
+import time
+import datetime
+import sys
+import cv2
+import time
+
+from models import *
+from datasets import *
+
+from tensorboardX import SummaryWriter
+
+import warnings
+warnings.filterwarnings("ignore")
+
+jt.flags.use_cuda = 1
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
+parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
+parser.add_argument("--data_path", type=str, default="./jittor_landscape_200k")
+parser.add_argument("--output_path", type=str, default="./results/flickr")
+parser.add_argument("--batch_size", type=int, default=32, 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("--decay_epoch", type=int, default=100, help="epoch from which to start lr decay")
+parser.add_argument("--n_cpu", type=int, default=8, help="number of cpu threads to use during batch generation")
+parser.add_argument("--img_height", type=int, default=384, help="size of image height")
+parser.add_argument("--img_width", type=int, default=512, help="size of image width")
+parser.add_argument("--channels", type=int, default=3, help="number of image channels")
+parser.add_argument(
+    "--sample_interval", type=int, default=500, help="interval between sampling of images from generators"
+)
+parser.add_argument("--checkpoint_interval", type=int, default=1, help="interval between model checkpoints")
+opt = parser.parse_args()
+print(opt)
+
+def save_image(img, path, nrow=10):
+    N,C,W,H = img.shape
+    if (N%nrow!=0):
+        print("save_image error: N%nrow!=0")
+        return
+    img=img.transpose((1,0,2,3))
+    ncol=int(N/nrow)
+    img2=img.reshape([img.shape[0],-1,H])
+    img=img2[:,:W*ncol,:]
+    for i in range(1,int(img2.shape[1]/W/ncol)):
+        img=np.concatenate([img,img2[:,W*ncol*i:W*ncol*(i+1),:]],axis=2)
+    min_=img.min()
+    max_=img.max()
+    img=(img-min_)/(max_-min_)*255
+    img=img.transpose((1,2,0))
+    if C==3:
+        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    cv2.imwrite(path,img)
+    return img
+
+os.makedirs(f"{opt.output_path}/images/", exist_ok=True)
+os.makedirs(f"{opt.output_path}/saved_models/", exist_ok=True)
+
+writer = SummaryWriter(opt.output_path)
+
+# Loss functions
+criterion_GAN = nn.BCEWithLogitsLoss()
+criterion_pixelwise = nn.L1Loss()
+
+# Loss weight of L1 pixel-wise loss between translated image and real image
+lambda_pixel = 100
+
+# Calculate output of image discriminator (PatchGAN)
+patch = (1, opt.img_height // 2 ** 4, opt.img_width // 2 ** 4)
+
+# Initialize generator and discriminator
+generator = UnetGenerator(3, 3, 7, 64, norm_layer=nn.BatchNorm2d, use_dropout=True)
+discriminator = Discriminator()
+
+if opt.epoch != 0:
+    # Load pretrained models
+    generator.load(f"{opt.output_path}/saved_models/generator_{opt.epoch}.pkl")
+    discriminator.load(f"{opt.output_path}/saved_models/discriminator_{opt.epoch}.pkl")
+
+# Optimizers
+optimizer_G = jt.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
+optimizer_D = jt.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
+
+# Configure dataloaders
+transforms = [
+    transform.Resize(size=(opt.img_height, opt.img_width), mode=Image.BICUBIC),
+    transform.ToTensor(),
+    transform.ImageNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+]
+
+dataloader = ImageDataset(opt.data_path, mode="train", transforms=transforms).set_attrs(
+    batch_size=opt.batch_size,
+    shuffle=True,
+    num_workers=opt.n_cpu,
+)
+# val_dataloader = ImageDataset(opt.data_path, mode="val", transforms=transforms).set_attrs(
+#     batch_size=10,
+#     shuffle=False,
+#     num_workers=1,
+# )
+
+# @jt.single_process_scope()
+# def eval(epoch, writer):
+#     cnt = 1
+#     os.makedirs(f"{opt.output_path}/images/test_fake_imgs/epoch_{epoch}", exist_ok=True)
+#     for i, (_, real_A, photo_id) in enumerate(val_dataloader):
+#         fake_B = generator(real_A)
+        
+#         if i == 0:
+#             # visual image result
+#             img_sample = np.concatenate([real_A.data, fake_B.data], -2)
+#             img = save_image(img_sample, f"{opt.output_path}/images/epoch_{epoch}_sample.png", nrow=5)
+#             writer.add_image('val/image', img.transpose(2,0,1), epoch)
+
+#         fake_B = ((fake_B + 1) / 2 * 255).numpy().astype('uint8')
+#         for idx in range(fake_B.shape[0]):
+#             cv2.imwrite(f"{opt.output_path}/images/test_fake_imgs/epoch_{epoch}/{photo_id[idx]}.jpg", fake_B[idx].transpose(1,2,0)[:,:,::-1])
+#             cnt += 1
+
+warmup_times = -1
+run_times = 3000
+total_time = 0.
+cnt = 0
+
+# ----------
+#  Training
+# ----------
+
+prev_time = time.time()
+for epoch in range(opt.epoch, opt.n_epochs):
+    for i, (real_B, real_A, _) in enumerate(dataloader):
+        # Adversarial ground truths
+        valid = jt.ones([real_A.shape[0], 1]).stop_grad()
+        fake = jt.zeros([real_A.shape[0], 1]).stop_grad()
+        fake_B = generator(real_A)
+
+        # ---------------------
+        #  Train Discriminator
+        # ---------------------
+        start_grad(discriminator)
+        fake_AB = jt.contrib.concat((real_A, fake_B), 1) 
+        pred_fake = discriminator(fake_AB.detach())
+        loss_D_fake = criterion_GAN(pred_fake, False)
+        real_AB = jt.contrib.concat((real_A, real_B), 1)
+        pred_real = discriminator(real_AB)
+        loss_D_real = criterion_GAN(pred_real, True)
+        loss_D = (loss_D_fake + loss_D_real) * 0.5
+        optimizer_D.step(loss_D)
+        writer.add_scalar('train/loss_D', loss_D.item(), epoch * len(dataloader) + i)
+
+        # ------------------
+        #  Train Generators
+        # ------------------
+        stop_grad(discriminator)        
+        fake_AB = jt.contrib.concat((real_A, fake_B), 1) 
+        pred_fake = discriminator(fake_AB)
+        loss_G_GAN = criterion_GAN(pred_fake, True)
+        loss_G_L1 = criterion_pixelwise(fake_B, real_B)
+        loss_G = loss_G_GAN + lambda_pixel * loss_G_L1
+        optimizer_G.step(loss_G)
+        writer.add_scalar('train/loss_G', loss_G.item(), epoch * len(dataloader) + i)
+
+        jt.sync_all(True)
+
+        if jt.rank == 0:
+            # --------------
+            #  Log Progress
+            # --------------
+
+            # Determine approximate time left
+            batches_done = epoch * len(dataloader) + i
+            batches_left = opt.n_epochs * len(dataloader) - batches_done
+            time_left = datetime.timedelta(seconds=batches_left * (time.time() - prev_time))
+            prev_time = time.time()
+
+            # Print log
+            jt.sync_all()
+            if batches_done % 5 == 0:
+                sys.stdout.write(
+                    "\r[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f, pixel: %f, adv: %f] ETA: %s"
+                    % (
+                        epoch,
+                        opt.n_epochs,
+                        i,
+                        len(dataloader),
+                        loss_D.numpy()[0],
+                        loss_G.numpy()[0],
+                        loss_G_L1.numpy()[0],
+                        loss_G_GAN.numpy()[0],
+                        time_left,
+                    )   
+                )
+
+    if jt.rank == 0 and opt.checkpoint_interval != -1 and epoch % opt.checkpoint_interval == 0:
+        # eval(epoch, writer)
+        # Save model checkpoints
+        generator.save(os.path.join(f"{opt.output_path}/saved_models/generator_{epoch}.pkl"))
+        discriminator.save(os.path.join(f"{opt.output_path}/saved_models/discriminator_{epoch}.pkl"))
\ No newline at end of file
diff --git a/competition/landscape_comp/readme.md b/competition/landscape_comp/readme.md
new file mode 100644
index 0000000..e1b4985
--- /dev/null
+++ b/competition/landscape_comp/readme.md
@@ -0,0 +1,24 @@
+# Jittor 第二届草图生成风景比赛 baseline
+
+
+## Requirements
+
+```
+jittor
+pillow
+opencv-python
+```
+
+## Train
+
+单卡训练，需要修改脚本里的数据路径
+```
+bash scripts/single_gpu.sh
+```
+
+多卡训练，需要修改脚本里的数据路径
+```
+bash scripts/multi_gpu.sh
+```
+
+注：代码中注释掉了eval的部分，等到测试数据发布之后，您可以取消注释进行评测。也可在训练阶段自动分配一部分数据集为测试集进行训练。
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diff --git a/competition/landscape_comp/scripts/multi_gpu.sh b/competition/landscape_comp/scripts/multi_gpu.sh
new file mode 100644
index 0000000..74d6510
--- /dev/null
+++ b/competition/landscape_comp/scripts/multi_gpu.sh
@@ -0,0 +1 @@
+mpirun -np 4 python pix2pix.py --output_path ./results/multi_gpu --batch_size 128 --data_path path_to_your_data
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diff --git a/competition/landscape_comp/scripts/single_gpu.sh b/competition/landscape_comp/scripts/single_gpu.sh
new file mode 100644
index 0000000..e45cd23
--- /dev/null
+++ b/competition/landscape_comp/scripts/single_gpu.sh
@@ -0,0 +1 @@
+python pix2pix.py --output_path ./results/single_gpu --batch_size 32 --data_path path_to_your_data
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diff --git a/competition/warm_up_comp/CGAN.py b/competition/warm_up_comp/CGAN.py
new file mode 100644
index 0000000..5fd6e8b
--- /dev/null
+++ b/competition/warm_up_comp/CGAN.py
@@ -0,0 +1,208 @@
+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
+                                   )
+
+    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
+
+# 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("""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("""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 = #TODO: 写入你注册时绑定的手机号（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")