pytorch DCGAN
REF
https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html#data
此处data 位于celeba\img中，celeba下载地址
http://pan.baidu.com/s/1eSNpdRG
此处只需要img_align_celeba.zip一个
但是dataroot 要写到上一级，因为dset.ImageFolder(root=dataroot,…）函数找的是子文件夹
dataroot = ‘H:\code\python_code\GAN\DCGAN\dcgan\data\celeba\’
# -*- coding: utf-8 -*-
"""
DCGAN Tutorial
==============
**Author**: `Nathan Inkawhich <https://github.com/inkawhich>`__
"""
from __future__ import print_function
#%matplotlib inline
import argparse
import os
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torchvision.utils as vutils
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
# Set random seem for reproducibility
manualSeed = 999
#manualSeed = random.randint(1, 10000) # use if you want new results
print("Random Seed: ", manualSeed)
random.seed(manualSeed)
torch.manual_seed(manualSeed)
######################################################################
### https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html
# Number of workers for dataloader
workers = 2
# Batch size during training
batch_size = 2
# Spatial size of training images. All images will be resized to this
# size using a transformer.
image_size = 64
# Number of channels in the training images. For color images this is 3
nc = 3
# Size of z latent vector (i.e. size of generator input)
nz = 100
# Size of feature maps in generator
ngf = 64
# Size of feature maps in discriminator
ndf = 64
# Number of training epochs
num_epochs = 5
# Learning rate for optimizers
lr = 0.0002
# Beta1 hyperparam for Adam optimizers
beta1 = 0.5
# Number of GPUs available. Use 0 for CPU mode.
ngpu = 1
######################################################################
# Data
#
# We can use an image folder dataset the way we have it setup.
# Create the dataset
# Root directory for dataset
dataroot = 'H:\\code\\python_code\\GAN\\DCGAN\\dcgan\\data\\celeba\\'
dataset = dset.ImageFolder(root=dataroot,
transform=transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]))
# Create the dataloader
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,
shuffle=True, num_workers=workers)
# Decide which device we want to run on
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")
# Plot some training images
real_batch = next(iter(dataloader))
plt.figure(figsize=(8,8))
plt.axis("off")
plt.title("Training Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))
######################################################################
# **Visualization of G’s progression**
#
# Remember how we saved the generator’s output on the fixed_noise batch
# after every epoch of training. Now, we can visualize the training
# progression of G with an animation. Press the play button to start the
# animation.
#
#%%capture
fig = plt.figure(figsize=(8,8))
plt.axis("off")
ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list]
ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)
HTML(ani.to_jshtml())
######################################################################
# **Real Images vs. Fake Images**
#
# Finally, lets take a look at some real images and fake images side by
# side.
#
# Grab a batch of real images from the dataloader
real_batch = next(iter(dataloader))
# Plot the real images
plt.figure(figsize=(15,15))
plt.subplot(1,2,1)
plt.axis("off")
plt.title("Real Images")
plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=5, normalize=True).cpu(),(1,2,0)))
# Plot the fake images from the last epoch
plt.subplot(1,2,2)
plt.axis("off")
plt.title("Fake Images")
plt.imshow(np.transpose(img_list[-1],(1,2,0)))
plt.show()
######################################################################
# Where to Go Next
# ----------------
#
# We have reached the end of our journey, but there are several places you
# could go from here. You could:
#
# - Train for longer to see how good the results get
# - Modify this model to take a different dataset and possibly change the
# size of the images and the model architecture
# - Check out some other cool GAN projects
# `here <https://github.com/nashory/gans-awesome-applications>`__
# - Create GANs that generate
# `music <https://deepmind.com/blog/wavenet-generative-model-raw-audio/>`__
#
######################################################################
# Implementation
# --------------
#
# With our input parameters set and the dataset prepared, we can now get
# into the implementation. We will start with the weigth initialization
# strategy, then talk about the generator, discriminator, loss functions,
# and training loop in detail.
#
# Weight Initialization
# ~~~~~~~~~~~~~~~~~~~~~
#
# custom weights initialization called on netG and netD
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
nn.init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('BatchNorm') != -1:
nn.init.normal_(m.weight.data, 1.0, 0.02)
nn.init.constant_(m.bias.data, 0)
######################################################################
# Generator
#
# Generator Code
class Generator(nn.Module):
def __init__(self, ngpu):
super(Generator, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is Z, going into a convolution
nn.ConvTranspose2d( nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.ReLU(True),
# state size. (ngf*8) x 4 x 4
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.ReLU(True),
# state size. (ngf*4) x 8 x 8
nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.ReLU(True),
# state size. (ngf*2) x 16 x 16
nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.ReLU(True),
# state size. (ngf) x 32 x 32
nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
# state size. (nc) x 64 x 64
)
def forward(self, input):
return self.main(input)
######################################################################
# Now, we can instantiate the generator and apply the ``weights_init``
# function. Check out the printed model to see how the generator object is
# structured.
#
# Create the generator
netG = Generator(ngpu).to(device)
# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
netG = nn.DataParallel(netG, list(range(ngpu)))
# 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)
######################################################################
# Discriminator
# ~~~~~~~~~~~~~
#
#########################################################################
# Discriminator Code
class Discriminator(nn.Module):
def __init__(self, ngpu):
super(Discriminator, self).__init__()
self.ngpu = ngpu
self.main = nn.Sequential(
# input is (nc) x 64 x 64
nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf) x 32 x 32
nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*2) x 16 x 16
nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*4) x 8 x 8
nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm2d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
# state size. (ndf*8) x 4 x 4
nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input)
######################################################################
# Now, as with the generator, we can create the discriminator, apply the
# ``weights_init`` function, and print the model’s structure.
#
# Create the Discriminator
netD = Discriminator(ngpu).to(device)
# Handle multi-gpu if desired
if (device.type == 'cuda') and (ngpu > 1):
netD = nn.DataParallel(netD, list(range(ngpu)))
# Apply the weights_init function to randomly initialize all weights
# to mean=0, stdev=0.2.
netD.apply(weights_init)
# Print the model
print(netD)
######################################################################
# Loss Functions and Optimizers
# images form out of the noise.
# Initialize BCELoss function
criterion = nn.BCELoss()
# Create batch of latent vectors that we will use to visualize
# the progression of the generator
fixed_noise = torch.randn(64, nz, 1, 1, device=device)
# 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(netD.parameters(), lr=lr, betas=(beta1, 0.999))
optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))
######################################################################
# Training
# ~~~~~~~~
#
# Finally, now that we have all of the parts of the GAN framework defined,
# we can train it. Be mindful that training GANs is somewhat of an art
# form, as incorrect hyperparameter settings lead to mode collapse with
# little explanation of what went wrong. Here, we will closely follow
# Algorithm 1 from Goodfellow’s paper, while abiding by some of the best
# practices shown in `ganhacks <https://github.com/soumith/ganhacks>`__.
# Namely, we will “construct different mini-batches for real and fake”
# images, and also adjust G’s objective function to maximize
# :math:`logD(G(z))`. Training is split up into two main parts. Part 1
# updates the Discriminator and Part 2 updates the Generator.
#
# **Part 1 - Train the Discriminator**
#
# Recall, the goal of training the discriminator is to maximize the
# probability of correctly classifying a given input as real or fake. In
# terms of Goodfellow, we wish to “update the discriminator by ascending
# its stochastic gradient”. Practically, we want to maximize
# :math:`log(D(x)) + log(1-D(G(z)))`. Due to the separate mini-batch
# suggestion from ganhacks, we will calculate this in two steps. First, we
# will construct a batch of real samples from the training set, forward
# pass through :math:`D`, calculate the loss (:math:`log(D(x))`), then
# calculate the gradients in a backward pass. Secondly, we will construct
# a batch of fake samples with the current generator, forward pass this
# batch through :math:`D`, calculate the loss (:math:`log(1-D(G(z)))`),
# and *accumulate* the gradients with a backward pass. Now, with the
# gradients accumulated from both the all-real and all-fake batches, we
# call a step of the Discriminator’s optimizer.
#
# **Part 2 - Train the Generator**
#
# As stated in the original paper, we want to train the Generator by
# minimizing :math:`log(1-D(G(z)))` in an effort to generate better fakes.
# As mentioned, this was shown by Goodfellow to not provide sufficient
# gradients, especially early in the learning process. As a fix, we
# instead wish to maximize :math:`log(D(G(z)))`. In the code we accomplish
# this by: classifying the Generator output from Part 1 with the
# Discriminator, computing G’s loss *using real labels as GT*, computing
# G’s gradients in a backward pass, and finally updating G’s parameters
# with an optimizer step. It may seem counter-intuitive to use the real
# labels as GT labels for the loss function, but this allows us to use the
# :math:`log(x)` part of the BCELoss (rather than the :math:`log(1-x)`
# part) which is exactly what we want.
#
# Finally, we will do some statistic reporting and at the end of each
# epoch we will push our fixed_noise batch through the generator to
# visually track the progress of G’s training. The training statistics
# reported are:
#
# - **Loss_D** - discriminator loss calculated as the sum of losses for
# the all real and all fake batches (:math:`log(D(x)) + log(D(G(z)))`).
# - **Loss_G** - generator loss calculated as :math:`log(D(G(z)))`
# - **D(x)** - the average output (across the batch) of the discriminator
# for the all real batch. This should start close to 1 then
# theoretically converge to 0.5 when G gets better. Think about why
# this is.
# - **D(G(z))** - average discriminator outputs for the all fake batch.
# The first number is before D is updated and the second number is
# after D is updated. These numbers should start near 0 and converge to
# 0.5 as G gets better. Think about why this is.
#
# **Note:** This step might take a while, depending on how many epochs you
# run and if you removed some data from the dataset.
#
# Training Loop
# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0
print("Starting Training Loop...")
# For each epoch
for epoch in range(num_epochs):
# For each batch in the dataloader
for i, data in enumerate(dataloader, 0):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
###########################
## Train with all-real batch
netD.zero_grad()
# Format batch
real_cpu = data[0].to(device)
b_size = real_cpu.size(0)
label = torch.full((b_size,), real_label, device=device)
# Forward pass real batch through D
output = netD(real_cpu).view(-1)
# Calculate loss on all-real batch
errD_real = criterion(output, label)
# Calculate gradients for D in backward pass
errD_real.backward()
D_x = output.mean().item()
## Train with all-fake batch
# Generate batch of latent vectors
noise = torch.randn(b_size, nz, 1, 1, device=device)
# Generate fake image batch with G
fake = netG(noise)
label.fill_(fake_label)
# Classify all fake batch with D
output = netD(fake.detach()).view(-1)
# Calculate D's loss on the all-fake batch
errD_fake = criterion(output, label)
# Calculate the gradients for this batch
errD_fake.backward()
D_G_z1 = output.mean().item()
# Add the gradients from the all-real and all-fake batches
errD = errD_real + errD_fake
# Update D
optimizerD.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
netG.zero_grad()
label.fill_(real_label) # fake labels are real for generator cost
# Since we just updated D, perform another forward pass of all-fake batch through D
output = netD(fake).view(-1)
# Calculate G's loss based on this output
errG = criterion(output, label)
# Calculate gradients for G
errG.backward()
D_G_z2 = output.mean().item()
# Update G
optimizerG.step()
# Output training stats
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(dataloader),
errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))
# Save Losses for plotting later
G_losses.append(errG.item())
D_losses.append(errD.item())
# Check how the generator is doing by saving G's output on fixed_noise
if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):
with torch.no_grad():
fake = netG(fixed_noise).detach().cpu()
img_list.append(vutils.make_grid(fake, padding=2, normalize=True))
iters += 1
######################################################################
# Results
# -------
#
# Finally, lets check out how we did. Here, we will look at three
# different results. First, we will see how D and G’s losses changed
# during training. Second, we will visualize G’s output on the fixed_noise
# batch for every epoch. And third, we will look at a batch of real data
# next to a batch of fake data from G.
#
# **Loss versus training iteration**
#
# Below is a plot of D & G’s losses versus training iterations.
#
plt.figure(figsize=(10,5))
plt.title("Generator and Discriminator Loss During Training")
plt.plot(G_losses,label="G")
plt.plot(D_losses,label="D")
plt.xlabel("iterations")
plt.ylabel("Loss")
plt.legend()
plt.show()