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train_multiple_DRGAN.py
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#!/usr/bin/env python
# encoding: utf-8
import os
import numpy as np
from scipy import misc
import pdb
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import torch
from torch import nn, optim
from torch.autograd import Variable
from util.one_hot import one_hot
from util.Is_D_strong import Is_D_strong
from util.log_learning import log_learning
from util.convert_image import convert_image
from util.create_multiDR_GAN_traindata import create_multiDR_GAN_traindata
def train_multiple_DRGAN(images_whole, id_labels_whole, pose_labels_whole, Nd, Np, Nz, D_model, G_model, args):
if args.cuda:
D_model.cuda()
G_model.cuda()
D_model.train()
G_model.train()
lr_Adam = args.lr
beta1_Adam = args.beta1
beta2_Adam = args.beta2
optimizer_D = optim.Adam(D_model.parameters(), lr = lr_Adam, betas=(beta1_Adam, beta2_Adam))
optimizer_G = optim.Adam(G_model.parameters(), lr = lr_Adam, betas=(beta1_Adam, beta2_Adam))
loss_criterion = nn.CrossEntropyLoss()
loss_criterion_gan = nn.BCEWithLogitsLoss()
loss_log = []
steps = 0
flag_D_strong = False
for epoch in range(1,args.epochs+1):
images, id_labels, pose_labels = create_multiDR_GAN_traindata(images_whole,\
id_labels_whole, pose_labels_whole, args)
image_size = images.shape[0]
epoch_time = np.ceil(image_size / args.batch_size).astype(int)
for i in range(epoch_time):
D_model.zero_grad()
G_model.zero_grad()
start = i*args.batch_size
end = start + args.batch_size
batch_image = torch.FloatTensor(images[start:end])
batch_id_label = torch.LongTensor(id_labels[start:end])
batch_id_label_unique = torch.LongTensor(batch_id_label[::args.images_perID])
batch_pose_label = torch.LongTensor(pose_labels[start:end])
minibatch_size = len(batch_image)
minibatch_size_unique = len(batch_image) // args.images_perID
batch_ones_label = torch.ones(minibatch_size) # 真偽判別用のラベル
batch_zeros_label = torch.zeros(minibatch_size)
# 特徴量をまとめた場合とそれぞれ用いた場合の ノイズと姿勢コードを生成
# それぞれ用いた場合
fixed_noise = torch.FloatTensor(np.random.uniform(-1,1, (minibatch_size, Nz)))
tmp = torch.LongTensor(np.random.randint(Np, size=minibatch_size))
pose_code = one_hot(tmp, Np) # Condition 付に使用
pose_code_label = torch.LongTensor(tmp) # CrossEntropy 誤差に使用
# 同一人物の特徴量をまとめた場合
fixed_noise_unique = torch.FloatTensor(np.random.uniform(-1,1, (minibatch_size_unique, Nz)))
tmp = torch.LongTensor(np.random.randint(Np, size=minibatch_size_unique))
pose_code_unique = one_hot(tmp, Np) # Condition 付に使用
pose_code_label_unique = torch.LongTensor(tmp) # CrossEntropy 誤差に使用
if args.cuda:
batch_image, batch_id_label, batch_pose_label, batch_ones_label, batch_zeros_label = \
batch_image.cuda(), batch_id_label.cuda(), batch_pose_label.cuda(), batch_ones_label.cuda(), batch_zeros_label.cuda()
fixed_noise, pose_code, pose_code_label = \
fixed_noise.cuda(), pose_code.cuda(), pose_code_label.cuda()
batch_id_label_unique, fixed_noise_unique, pose_code_unique, pose_code_label_unique = \
batch_id_label_unique.cuda(), fixed_noise_unique.cuda(), pose_code_unique.cuda(), pose_code_label_unique.cuda()
batch_image, batch_id_label, batch_pose_label, batch_ones_label, batch_zeros_label = \
Variable(batch_image), Variable(batch_id_label), Variable(batch_pose_label), Variable(batch_ones_label), Variable(batch_zeros_label)
fixed_noise, pose_code, pose_code_label = \
Variable(fixed_noise), Variable(pose_code), Variable(pose_code_label)
batch_id_label_unique, fixed_noise_unique, pose_code_unique, pose_code_label_unique = \
Variable(batch_id_label_unique), Variable(fixed_noise_unique), Variable(pose_code_unique), Variable(pose_code_label_unique)
# Generatorでイメージ生成
# 個々の画像特徴量からそれぞれ画像を生成した場合
generated = G_model(batch_image, pose_code, fixed_noise,single=True)
# 同一人物の画像特徴量を一つにまとめた場合
generated_unique = G_model(batch_image, pose_code_unique, fixed_noise_unique)
steps += 1
# バッチ毎に交互に D と G の学習， Dが90%以上の精度の場合は 1:4の比率で学習
if flag_D_strong:
if i%5 == 0:
# Discriminator の学習
flag_D_strong = Learn_D(D_model, loss_criterion, loss_criterion_gan, optimizer_D, batch_image, generated, \
batch_id_label, batch_pose_label, batch_ones_label, batch_zeros_label, epoch, steps, Nd, args)
else:
# Generatorの学習
Learn_G(D_model, loss_criterion, loss_criterion_gan, optimizer_G ,generated, generated_unique, batch_id_label,\
pose_code_label, batch_id_label_unique, pose_code_label_unique, batch_ones_label, minibatch_size_unique, epoch, steps, Nd, args)
else:
if i%2==0:
# Discriminator の学習
flag_D_strong = Learn_D(D_model, loss_criterion, loss_criterion_gan, optimizer_D, batch_image, generated, \
batch_id_label, batch_pose_label, batch_ones_label, batch_zeros_label, epoch, steps, Nd, args)
else:
# Generatorの学習
Learn_G(D_model, loss_criterion, loss_criterion_gan, optimizer_G ,generated, generated_unique, batch_id_label,\
pose_code_label, batch_id_label_unique, pose_code_label_unique, batch_ones_label, minibatch_size_unique, epoch, steps, Nd, args)
if epoch%args.save_freq == 0:
# 各エポックで学習したモデルを保存
if not os.path.isdir(args.save_dir): os.makedirs(args.save_dir)
save_path_D = os.path.join(args.save_dir,'epoch{}_D.pt'.format(epoch))
torch.save(D_model, save_path_D)
save_path_G = os.path.join(args.save_dir,'epoch{}_G.pt'.format(epoch))
torch.save(G_model, save_path_G)
# 最後のエポックの学習前に生成した画像を１枚保存（学習の確認用）
save_generated_image = convert_image(generated[0].cpu().data.numpy())
save_path_image = os.path.join(args.save_dir, 'epoch{}_generatedimage.jpg'.format(epoch))
misc.imsave(save_path_image, save_generated_image.astype(np.uint8))
def Learn_D(D_model, loss_criterion, loss_criterion_gan, optimizer_D, batch_image, generated, \
batch_id_label, batch_pose_label, batch_ones_label, batch_zeros_label, epoch, steps, Nd, args):
real_output = D_model(batch_image)
syn_output = D_model(generated.detach()) # .detach() をすることで Generatorまでの逆伝播計算省略
# id,真偽, pose それぞれのロスを計算
L_id = loss_criterion(real_output[:, :Nd], batch_id_label)
L_gan = loss_criterion_gan(real_output[:, Nd], batch_ones_label) + loss_criterion_gan(syn_output[:, Nd], batch_zeros_label)
L_pose = loss_criterion(real_output[:, Nd+1:], batch_pose_label)
d_loss = L_gan + L_id + L_pose
d_loss.backward()
optimizer_D.step()
log_learning(epoch, steps, 'D', d_loss.data[0], args)
# Discriminator の強さを判別
flag_D_strong = Is_D_strong(real_output, syn_output, batch_id_label, batch_pose_label, Nd)
return flag_D_strong
def Learn_G(D_model, loss_criterion, loss_criterion_gan, optimizer_G ,generated, generated_unique, batch_id_label,\
pose_code_label, batch_id_label_unique, pose_code_label_unique, batch_ones_label, minibatch_size_unique, epoch, steps, Nd, args):
syn_output = D_model(generated)
syn_output_unique = D_model(generated_unique)
# id についての出力と元画像のラベル, 真偽, poseについての出力と生成時に与えたposeコード の ロスを計算
L_id = loss_criterion(syn_output[:, :Nd], batch_id_label)
L_gan = loss_criterion_gan(syn_output[:, Nd], batch_ones_label)
L_pose = loss_criterion(syn_output[:, Nd+1:], pose_code_label)
L_id_unique = loss_criterion(syn_output_unique[:, :Nd], batch_id_label_unique)
L_gan_unique = loss_criterion_gan(syn_output_unique[:, Nd], batch_ones_label[:minibatch_size_unique])
L_pose_unique = loss_criterion(syn_output_unique[:, Nd+1:], pose_code_label_unique)
g_loss = L_gan + L_id + L_pose + L_gan_unique + L_id_unique + L_pose_unique
g_loss.backward()
optimizer_G.step()
log_learning(epoch, steps, 'G', g_loss.data[0], args)