Deep Convolutional Generative Adversarial Networks
作者：互联网
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from torch import nn
import numpy as np
from torch.autograd import Variable
import torch
from torchvision.datasets import ImageFolder
from torchvision.transforms import transforms
import zipfile
cuda = True if torch.cuda.is_available() else False
print(cuda)
data_dir='/home/kesci/input/pokemon8600/'
batch_size=256
transform=transforms.Compose([
transforms.Resize((64,64)),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
pokemon=ImageFolder(data_dir+'pokemon',transform)
data_iter=DataLoader(pokemon,batch_size=batch_size,shuffle=True)
Let’s visualize the first 20 images.
fig=plt.figure(figsize=(4,4))
imgs=data_iter.dataset.imgs
for i in range(20):
img = plt.imread(imgs[i*150][0])
plt.subplot(4,5,i+1)
plt.imshow(img)
plt.axis('off')
plt.show()
class G_block(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=4,strides=2, padding=1):
super(G_block,self).__init__()
self.conv2d_trans=nn.ConvTranspose2d(in_channels, out_channels, kernel_size=kernel_size,
stride=strides, padding=padding, bias=False)
self.batch_norm=nn.BatchNorm2d(out_channels,0.8)
self.activation=nn.ReLU()
def forward(self,x):
return self.activation(self.batch_norm(self.conv2d_trans(x)))
Tensor=torch.cuda.FloatTensor
x=Variable(Tensor(np.zeros((2,3,16,16))))
g_blk=G_block(3,20)
g_blk.cuda()
print(g_blk(x).shape)
x=Variable(Tensor(np.zeros((2,3,1,1))))
g_blk=G_block(3,20,strides=1,padding=0)
g_blk.cuda()
print(g_blk(x).shape)
class net_G(nn.Module):
def __init__(self,in_channels):
super(net_G,self).__init__()
n_G=64
self.model=nn.Sequential(
G_block(in_channels,n_G*8,strides=1,padding=0),
G_block(n_G*8,n_G*4),
G_block(n_G*4,n_G*2),
G_block(n_G*2,n_G),
nn.ConvTranspose2d(
n_G,3,kernel_size=4,stride=2,padding=1,bias=False
),
nn.Tanh()
)
def forward(self,x):
x=self.model(x)
return x
def weights_init_normal(m):
classname = m.__class__.__name__
if classname.find("Conv") != -1:
torch.nn.init.normal_(m.weight.data, mean=0, std=0.02)
elif classname.find("BatchNorm2d") != -1:
torch.nn.init.normal_(m.weight.data, mean=1.0, std=0.02)
torch.nn.init.constant_(m.bias.data, 0.0)
Generate a 100 dimensional latent variable to verify the generator's output shape.
x=Variable(Tensor(np.zeros((1,100,1,1))))
generator=net_G(100)
generator.cuda()
generator.apply(weights_init_normal)
print(generator(x).shape)
alphas = [0, 0.2, 0.4, .6]
x = np.arange(-2, 1, 0.1)
Y = [nn.LeakyReLU(alpha)(Tensor(x)).cpu().numpy()for alpha in alphas]
plt.figure(figsize=(4,4))
for y in Y:
plt.plot(x,y)
plt.show()
The basic block of the discriminator is a convolution layer followed by a batch normalization layer and a leaky ReLU activation. The hyper-parameters of the convolution layer are similar to the transpose convolution layer in the generator block.
class D_block(nn.Module):
def __init__(self,in_channels,out_channels,kernel_size=4,strides=2,
padding=1,alpha=0.2):
super(D_block,self).__init__()
self.conv2d=nn.Conv2d(in_channels,out_channels,kernel_size,strides,padding,bias=False)
self.batch_norm=nn.BatchNorm2d(out_channels,0.8)
self.activation=nn.LeakyReLU(alpha)
def forward(self,X):
return self.activation(self.batch_norm(self.conv2d(X)))
x = Variable(Tensor(np.zeros((2, 3, 16, 16))))
d_blk = D_block(3,20)
d_blk.cuda()
print(d_blk(x).shape)
The discriminator is a mirror of the generator.
class net_D(nn.Module):
def __init__(self,in_channels):
super(net_D,self).__init__()
n_D=64
self.model=nn.Sequential(
D_block(in_channels,n_D),
D_block(n_D,n_D*2),
D_block(n_D*2,n_D*4),
D_block(n_D*4,n_D*8)
)
self.conv=nn.Conv2d(n_D*8,1,kernel_size=4,bias=False)
self.activation=nn.Sigmoid()
# self._initialize_weights()
def forward(self,x):
x=self.model(x)
x=self.conv(x)
x=self.activation(x)
return x
x = Variable(Tensor(np.zeros((1, 3, 64, 64))))
discriminator=net_D(3)
discriminator.cuda()
discriminator.apply(weights_init_normal)
print(discriminator(x).shape)
def update_D(X,Z,net_D,net_G,loss,trainer_D):
batch_size=X.shape[0]
Tensor=torch.cuda.FloatTensor
ones=Variable(Tensor(np.ones(batch_size,)),requires_grad=False).view(batch_size,1)
zeros = Variable(Tensor(np.zeros(batch_size,)),requires_grad=False).view(batch_size,1)
real_Y=net_D(X).view(batch_size,-1)
fake_X=net_G(Z)
fake_Y=net_D(fake_X).view(batch_size,-1)
loss_D=(loss(real_Y,ones)+loss(fake_Y,zeros))/2
loss_D.backward()
trainer_D.step()
return float(loss_D.sum())
def update_G(Z,net_D,net_G,loss,trainer_G):
batch_size=Z.shape[0]
Tensor=torch.cuda.FloatTensor
ones=Variable(Tensor(np.ones((batch_size,))),requires_grad=False).view(batch_size,1)
fake_X=net_G(Z)
fake_Y=net_D(fake_X).view(batch_size,-1)
loss_G=loss(fake_Y,ones)
loss_G.backward()
trainer_G.step()
return float(loss_G.sum())
def train(net_D,net_G,data_iter,num_epochs,lr,latent_dim):
loss=nn.BCELoss()
Tensor=torch.cuda.FloatTensor
trainer_D=torch.optim.Adam(net_D.parameters(),lr=lr,betas=(0.5,0.999))
trainer_G=torch.optim.Adam(net_G.parameters(),lr=lr,betas=(0.5,0.999))
plt.figure(figsize=(7,4))
d_loss_point=[]
g_loss_point=[]
d_loss=0
g_loss=0
for epoch in range(1,num_epochs+1):
d_loss_sum=0
g_loss_sum=0
batch=0
for X in data_iter:
X=X[:][0]
batch+=1
X=Variable(X.type(Tensor))
batch_size=X.shape[0]
Z=Variable(Tensor(np.random.normal(0,1,(batch_size,latent_dim,1,1))))
trainer_D.zero_grad()
d_loss = update_D(X, Z, net_D, net_G, loss, trainer_D)
d_loss_sum+=d_loss
trainer_G.zero_grad()
g_loss = update_G(Z, net_D, net_G, loss, trainer_G)
g_loss_sum+=g_loss
d_loss_point.append(d_loss_sum/batch)
g_loss_point.append(g_loss_sum/batch)
print(
"[Epoch %d/%d] [D loss: %f] [G loss: %f]"
% (epoch, num_epochs, d_loss_sum/batch_size, g_loss_sum/batch_size)
)
plt.ylabel('Loss', fontdict={ 'size': 14})
plt.xlabel('epoch', fontdict={ 'size': 14})
plt.xticks(range(0,num_epochs+1,3))
plt.plot(range(1,num_epochs+1),d_loss_point,color='orange',label='discriminator')
plt.plot(range(1,num_epochs+1),g_loss_point,color='blue',label='generator')
plt.legend()
plt.show()
print(d_loss,g_loss)
Z = Variable(Tensor(np.random.normal(0, 1, size=(21, latent_dim, 1, 1))),requires_grad=False)
fake_x = generator(Z)
fake_x=fake_x.cpu().detach().numpy()
plt.figure(figsize=(14,6))
for i in range(21):
im=np.transpose(fake_x[i])
plt.subplot(3,7,i+1)
plt.imshow(im)
plt.show()
Now let’s train the model.
if __name__ == '__main__':
lr,latent_dim,num_epochs=0.005,100,50
train(discriminator,generator,data_iter,num_epochs,lr,latent_dim)
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标签：Convolutional,loss,plt,self,Adversarial,batch,Generative,net,size 来源： https://blog.csdn.net/qq_40441895/article/details/104455603