#导入一些必要的包 import os import random import paddle import paddle.nn as nn import paddle.optimizer as optim import paddle.vision.datasets as dset import paddle.vision.transforms as transforms import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation dataset = paddle.vision.datasets.MNIST(mode='train', transform=transforms.Compose([ # resize ->(32,32) transforms.Resize((32,32)), # 归一化到-1~1 transforms.Normalize([127.5], [127.5]) ])) dataloader = paddle.io.DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4) #参数初始化的模块 @paddle.no_grad() def normal_(x, mean=0., std=1.): temp_value = paddle.normal(mean, std, shape=x.shape) x.set_value(temp_value) return x @paddle.no_grad() def uniform_(x, a=-1., b=1.): temp_value = paddle.uniform(min=a, max=b, shape=x.shape) x.set_value(temp_value) return x @paddle.no_grad() def constant_(x, value): temp_value = paddle.full(x.shape, value, x.dtype) x.set_value(temp_value) return x def weights_init(m): classname = m.__class__.__name__ if hasattr(m, 'weight') and classname.find('Conv') != -1: normal_(m.weight, 0.0, 0.02) elif classname.find('BatchNorm') != -1: normal_(m.weight, 1.0, 0.02) constant_(m.bias, 0) # Generator Code class Generator(nn.Layer): def __init__(self, ): super(Generator, self).__init__() self.gen = nn.Sequential( # input is Z, [B, 100, 1, 1] -> [B, 64 * 4, 4, 4] nn.Conv2DTranspose(100, 64 * 4, 4, 1, 0, bias_attr=False), nn.BatchNorm2D(64 * 4), nn.ReLU(True), # state size. [B, 64 * 4, 4, 4] -> [B, 64 * 2, 8, 8] nn.Conv2DTranspose(64 * 4, 64 * 2, 4, 2, 1, bias_attr=False), nn.BatchNorm2D(64 * 2), nn.ReLU(True), # state size. [B, 64 * 2, 8, 8] -> [B, 64, 16, 16] nn.Conv2DTranspose( 64 * 2, 64, 4, 2, 1, bias_attr=False), nn.BatchNorm2D(64), nn.ReLU(True), # state size. [B, 64, 16, 16] -> [B, 1, 32, 32] nn.Conv2DTranspose( 64, 1, 4, 2, 1, bias_attr=False), nn.Tanh() ) def forward(self, x): return self.gen(x) netG = Generator() # Apply the weights_init function to randomly initialize all weights # to mean=0, stdev=0.2. netG.apply(weights_init) # Print the model print(netG) class Discriminator(nn.Layer): def __init__(self,): super(Discriminator, self).__init__() self.dis = nn.Sequential( # input [B, 1, 32, 32] -> [B, 64, 16, 16] nn.Conv2D(1, 64, 4, 2, 1, bias_attr=False), nn.LeakyReLU(0.2), # state size. [B, 64, 16, 16] -> [B, 128, 8, 8] nn.Conv2D(64, 64 * 2, 4, 2, 1, bias_attr=False), nn.BatchNorm2D(64 * 2), nn.LeakyReLU(0.2), # state size. [B, 128, 8, 8] -> [B, 256, 4, 4] nn.Conv2D(64 * 2, 64 * 4, 4, 2, 1, bias_attr=False), nn.BatchNorm2D(64 * 4), nn.LeakyReLU(0.2), # state size. [B, 256, 4, 4] -> [B, 1, 1, 1] nn.Conv2D(64 * 4, 1, 4, 1, 0, bias_attr=False), # 这里为需要改变的地方 # nn.Sigmoid() nn.LeakyReLU() ) def forward(self, x): return self.dis(x) netD = Discriminator() netD.apply(weights_init) print(netD) # Initialize BCELoss function # 这里为需要改变的地方 # loss = nn.BCELoss() loss = nn.MSELoss() # Create batch of latent vectors that we will use to visualize # the progression of the generator fixed_noise = paddle.randn([32, 100, 1, 1], dtype='float32') # Establish convention for real and fake labels during training real_label = 1. fake_label = 0. # Setup Adam optimizers for both G and D optimizerD = optim.Adam(parameters=netD.parameters(), learning_rate=0.0002, beta1=0.5, beta2=0.999) optimizerG = optim.Adam(parameters=netG.parameters(), learning_rate=0.0002, beta1=0.5, beta2=0.999) losses = [[], []] #plt.ion() now = 0 for pass_id in range(100): for batch_id, (data, target) in enumerate(dataloader): ############################ # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z))) ########################### optimizerD.clear_grad() real_img = data bs_size = real_img.shape[0] label = paddle.full((bs_size, 1, 1, 1), real_label, dtype='float32') real_out = netD(real_img) errD_real = loss(real_out, label) errD_real.backward() noise = paddle.randn([bs_size, 100, 1, 1], 'float32') fake_img = netG(noise) label = paddle.full((bs_size, 1, 1, 1), fake_label, dtype='float32') fake_out = netD(fake_img.detach()) errD_fake = loss(fake_out,label) errD_fake.backward() optimizerD.step() optimizerD.clear_grad() errD = errD_real + errD_fake losses[0].append(errD.numpy()[0]) ############################ # (2) Update G network: maximize log(D(G(z))) ########################### optimizerG.clear_grad() noise = paddle.randn([bs_size, 100, 1, 1],'float32') fake = netG(noise) label = paddle.full((bs_size, 1, 1, 1), real_label, dtype=np.float32,) output = netD(fake) errG = loss(output,label) errG.backward() optimizerG.step() optimizerG.clear_grad() losses[1].append(errG.numpy()[0]) ############################ # visualize ########################### if batch_id % 100 == 0: generated_image = netG(noise).numpy() imgs = [] plt.figure(figsize=(15,15)) try: for i in range(10): image = generated_image[i].transpose() image = np.where(image > 0, image, 0) image = image.transpose((1,0,2)) plt.subplot(10, 10, i + 1) plt.imshow(image[...,0], vmin=-1, vmax=1) plt.axis('off') plt.xticks([]) plt.yticks([]) plt.subplots_adjust(wspace=0.1, hspace=0.1) msg = 'Epoch ID={0} Batch ID={1} \n\n D-Loss={2} G-Loss={3}'.format(pass_id, batch_id, errD.numpy()[0], errG.numpy()[0]) print(msg) plt.suptitle(msg,fontsize=20) plt.draw() plt.savefig('{}/{:04d}_{:04d}.png'.format('work', pass_id, batch_id), bbox_inches='tight') plt.pause(0.01) except IOError: print(IOError) paddle.save(netG.state_dict(), "work/generator.params")