add competition
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zhouwy19
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README.md
34
README.md
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@ -26,6 +26,7 @@ In another form of presentation, assuming that Pytorch's training time is 100 ho
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## Table of Contents
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* [Installation](#installation)
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* [第二届计图人工智能挑战赛](#第二届计图人工智能挑战赛)
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* [models](#models)
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+ [Auxiliary Classifier GAN](#auxiliary-classifier-gan)
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+ [Adversarial Autoencoder](#adversarial-autoencoder)
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@ -60,6 +61,39 @@ In another form of presentation, assuming that Pytorch's training time is 100 ho
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$ cd gan-jittor/
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$ sudo python3.7 -m pip install -r requirements.txt
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## 第二届计图人工智能挑战赛
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### 计图挑战热身赛
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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）。
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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。
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选手可以基于示例代码填充注释为 TODO 的部分完成该赛题。
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```
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cd competition/warm_up_comp
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修改 CGAN.py 使其运行
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```
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### 赛题一：风景图片生成赛题
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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。
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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。
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```
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cd competition/landscape_comp
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# 单卡训练，需要修改脚本里的数据路径
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bash scripts/single_gpu.sh
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# 多卡训练，需要修改脚本里的数据路径
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bash scripts/multi_gpu.sh
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```
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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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## models
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### Auxiliary Classifier GAN
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_Auxiliary Classifier Generative Adversarial Network_
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@ -0,0 +1,37 @@
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import glob
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import random
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import os
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import numpy as np
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from jittor.dataset.dataset import Dataset
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import jittor.transform as transform
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from PIL import Image
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class ImageDataset(Dataset):
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def __init__(self, root, mode="train", transforms=None):
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super().__init__()
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self.transforms = transform.Compose(transforms)
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self.mode = mode
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if self.mode == 'train':
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self.files = sorted(glob.glob(os.path.join(root, mode, "imgs") + "/*.*"))
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self.labels = sorted(glob.glob(os.path.join(root, mode, "labels") + "/*.*"))
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self.set_attrs(total_len=len(self.labels))
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print(f"from {mode} split load {self.total_len} images.")
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def __getitem__(self, index):
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label_path = self.labels[index % len(self.labels)]
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photo_id = label_path.split('/')[-1][:-4]
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img_B = Image.open(label_path)
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img_B = Image.fromarray(np.array(img_B).astype("uint8")[:, :, np.newaxis].repeat(3,2))
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if self.mode == "train":
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img_A = Image.open(self.files[index % len(self.files)])
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if np.random.random() < 0.5:
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img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], "RGB")
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img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], "RGB")
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img_A = self.transforms(img_A)
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else:
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img_A = np.empty([1])
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img_B = self.transforms(img_B)
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return img_A, img_B, photo_id
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@ -0,0 +1,177 @@
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import jittor as jt
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from jittor import init
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from jittor import nn
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def start_grad(model):
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for param in model.parameters():
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param.start_grad()
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def stop_grad(model):
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for param in model.parameters():
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param.stop_grad()
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def weights_init_normal(m):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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jt.init.gauss_(m.weight, 0.0, 0.02)
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elif classname.find("BatchNorm") != -1:
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jt.init.gauss_(m.weight, 1.0, 0.02)
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jt.init.constant_(m.bias, 0.0)
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class UNetDown(nn.Module):
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def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
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super(UNetDown, self).__init__()
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layers = [nn.Conv(in_size, out_size, 4, stride=2, padding=1, bias=False)]
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if normalize:
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layers.append(nn.InstanceNorm2d(out_size, affine=None))
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layers.append(nn.LeakyReLU(scale=0.2))
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if dropout:
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layers.append(nn.Dropout(dropout))
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self.model = nn.Sequential(*layers)
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def execute(self, x):
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return self.model(x)
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class UNetUp(nn.Module):
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def __init__(self, in_size, out_size, dropout=0.0):
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super(UNetUp, self).__init__()
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layers = [nn.ConvTranspose(in_size, out_size, 4, stride=2, padding=1, bias=False), nn.InstanceNorm2d(out_size, affine=None), nn.ReLU()]
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if dropout:
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layers.append(nn.Dropout(dropout))
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self.model = nn.Sequential(*layers)
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def execute(self, x, skip_input):
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x = self.model(x)
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x = jt.contrib.concat((x, skip_input), dim=1)
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return x
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class GeneratorUNet(nn.Module):
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def __init__(self, in_channels=3, out_channels=3):
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super(GeneratorUNet, self).__init__()
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self.down1 = UNetDown(in_channels, 64, normalize=False)
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self.down2 = UNetDown(64, 128)
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self.down3 = UNetDown(128, 256)
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self.down4 = UNetDown(256, 512, dropout=0.5)
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self.down5 = UNetDown(512, 512, dropout=0.5)
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self.down6 = UNetDown(512, 512, dropout=0.5)
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self.down7 = UNetDown(512, 512, dropout=0.5)
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self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)
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self.up1 = UNetUp(512, 512, dropout=0.5)
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self.up2 = UNetUp(1024, 512, dropout=0.5)
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self.up3 = UNetUp(1024, 512, dropout=0.5)
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self.up4 = UNetUp(1024, 512, dropout=0.5)
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self.up5 = UNetUp(1024, 256)
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self.up6 = UNetUp(512, 128)
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self.up7 = UNetUp(256, 64)
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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())
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for m in self.modules():
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weights_init_normal(m)
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def execute(self, x):
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d1 = self.down1(x)
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d2 = self.down2(d1)
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d3 = self.down3(d2)
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d4 = self.down4(d3)
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d5 = self.down5(d4)
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d6 = self.down6(d5)
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d7 = self.down7(d6)
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d8 = self.down8(d7)
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u1 = self.up1(d8, d7)
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u2 = self.up2(u1, d6)
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u3 = self.up3(u2, d5)
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u4 = self.up4(u3, d4)
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u5 = self.up5(u4, d3)
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u6 = self.up6(u5, d2)
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u7 = self.up7(u6, d1)
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return self.final(u7)
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class UnetGenerator(nn.Module):
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def __init__(self, input_nc, output_nc, num_downs, ngf=64, norm_layer=nn.BatchNorm2d, use_dropout=False):
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super(UnetGenerator, self).__init__()
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# construct unet structure
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unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True) # add the innermost layer
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for i in range(num_downs - 5): # add intermediate layers with ngf * 8 filters
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unet_block = UnetSkipConnectionBlock(ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout)
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# gradually reduce the number of filters from ngf * 8 to ngf
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unet_block = UnetSkipConnectionBlock(ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
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unet_block = UnetSkipConnectionBlock(ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
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unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
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self.model = UnetSkipConnectionBlock(output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer) # add the outermost layer
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def execute(self, input):
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return self.model(input)
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class UnetSkipConnectionBlock(nn.Module):
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def __init__(self, outer_nc, inner_nc, input_nc=None,
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submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
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super(UnetSkipConnectionBlock, self).__init__()
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self.outermost = outermost
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if input_nc is None:
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input_nc = outer_nc
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downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
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stride=2, padding=1, bias=False)
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downrelu = nn.LeakyReLU(0.2)
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downnorm = norm_layer(inner_nc)
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uprelu = nn.ReLU()
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upnorm = norm_layer(outer_nc)
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if outermost:
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upconv = nn.ConvTranspose(inner_nc * 2, outer_nc,
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kernel_size=4, stride=2,
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padding=1)
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down = [downconv]
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up = [uprelu, upconv, nn.Tanh()]
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model = down + [submodule] + up
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elif innermost:
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upconv = nn.ConvTranspose(inner_nc, outer_nc,
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kernel_size=4, stride=2,
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padding=1, bias=False)
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down = [downrelu, downconv]
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up = [uprelu, upconv, upnorm]
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model = down + up
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else:
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upconv = nn.ConvTranspose(inner_nc * 2, outer_nc,
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kernel_size=4, stride=2,
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padding=1, bias=False)
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down = [downrelu, downconv, downnorm]
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up = [uprelu, upconv, upnorm]
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if use_dropout:
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model = down + [submodule] + up + [nn.Dropout(0.5)]
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else:
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model = down + [submodule] + up
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self.model = nn.Sequential(*model)
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def execute(self, x):
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if self.outermost:
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return self.model(x)
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else: # add skip connections
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return jt.contrib.concat([x, self.model(x)], 1)
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class Discriminator(nn.Module):
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def __init__(self, in_channels=3):
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super(Discriminator, self).__init__()
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def discriminator_block(in_filters, out_filters, stride=2, normalization=True):
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'Returns downsampling layers of each discriminator block'
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layers = [nn.Conv(in_filters, out_filters, 4, stride=stride, padding=1)]
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if normalization:
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layers.append(nn.BatchNorm2d(out_filters, eps=1e-05, momentum=0.1, affine=True))
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layers.append(nn.LeakyReLU(scale=0.2))
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return layers
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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))
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for m in self.modules():
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weights_init_normal(m)
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def execute(self, input):
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return self.model(input)
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@ -0,0 +1,209 @@
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import jittor as jt
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from jittor import init
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from jittor import nn
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import jittor.transform as transform
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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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import itertools
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import time
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import datetime
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import sys
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import cv2
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import time
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from models import *
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from datasets import *
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from tensorboardX import SummaryWriter
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import warnings
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warnings.filterwarnings("ignore")
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jt.flags.use_cuda = 1
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parser = argparse.ArgumentParser()
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parser.add_argument("--epoch", type=int, default=0, help="epoch to start training from")
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parser.add_argument("--n_epochs", type=int, default=200, help="number of epochs of training")
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parser.add_argument("--data_path", type=str, default="./jittor_landscape_200k")
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parser.add_argument("--output_path", type=str, default="./results/flickr")
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parser.add_argument("--batch_size", type=int, default=32, 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("--decay_epoch", type=int, default=100, help="epoch from which to start lr decay")
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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("--img_height", type=int, default=384, help="size of image height")
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parser.add_argument("--img_width", type=int, default=512, help="size of image width")
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parser.add_argument("--channels", type=int, default=3, help="number of image channels")
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parser.add_argument(
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"--sample_interval", type=int, default=500, help="interval between sampling of images from generators"
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)
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parser.add_argument("--checkpoint_interval", type=int, default=1, help="interval between model checkpoints")
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opt = parser.parse_args()
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print(opt)
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def save_image(img, path, nrow=10):
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N,C,W,H = img.shape
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if (N%nrow!=0):
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print("save_image error: N%nrow!=0")
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return
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img=img.transpose((1,0,2,3))
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ncol=int(N/nrow)
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img2=img.reshape([img.shape[0],-1,H])
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img=img2[:,:W*ncol,:]
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for i in range(1,int(img2.shape[1]/W/ncol)):
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img=np.concatenate([img,img2[:,W*ncol*i:W*ncol*(i+1),:]],axis=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 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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cv2.imwrite(path,img)
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return img
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os.makedirs(f"{opt.output_path}/images/", exist_ok=True)
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os.makedirs(f"{opt.output_path}/saved_models/", exist_ok=True)
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writer = SummaryWriter(opt.output_path)
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# Loss functions
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criterion_GAN = nn.BCEWithLogitsLoss()
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criterion_pixelwise = nn.L1Loss()
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# Loss weight of L1 pixel-wise loss between translated image and real image
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lambda_pixel = 100
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# Calculate output of image discriminator (PatchGAN)
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patch = (1, opt.img_height // 2 ** 4, opt.img_width // 2 ** 4)
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# Initialize generator and discriminator
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generator = UnetGenerator(3, 3, 7, 64, norm_layer=nn.BatchNorm2d, use_dropout=True)
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discriminator = Discriminator()
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if opt.epoch != 0:
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# Load pretrained models
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generator.load(f"{opt.output_path}/saved_models/generator_{opt.epoch}.pkl")
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discriminator.load(f"{opt.output_path}/saved_models/discriminator_{opt.epoch}.pkl")
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# Optimizers
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optimizer_G = jt.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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optimizer_D = jt.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
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# Configure dataloaders
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transforms = [
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transform.Resize(size=(opt.img_height, opt.img_width), mode=Image.BICUBIC),
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transform.ToTensor(),
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transform.ImageNormalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
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]
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dataloader = ImageDataset(opt.data_path, mode="train", transforms=transforms).set_attrs(
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batch_size=opt.batch_size,
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shuffle=True,
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num_workers=opt.n_cpu,
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)
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# val_dataloader = ImageDataset(opt.data_path, mode="val", transforms=transforms).set_attrs(
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# batch_size=10,
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# shuffle=False,
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# num_workers=1,
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# )
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# @jt.single_process_scope()
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# def eval(epoch, writer):
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# 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"))
|
||||
|
|
@ -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。
|
||||
|
|
@ -0,0 +1 @@
|
|||
mpirun -np 4 python pix2pix.py --output_path ./results/multi_gpu --batch_size 128 --data_path path_to_your_data
|
||||
|
|
@ -0,0 +1 @@
|
|||
python pix2pix.py --output_path ./results/single_gpu --batch_size 32 --data_path path_to_your_data
|
||||
|
|
@ -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")
|
||||
Loading…
Reference in New Issue