def train():
opt = Config() # 配置的实例
dataloader = data_loader(opt)
criterion = nn.BCELoss()
device = torch.device("cuda: 0, 1, 2" if torch.cuda.is_available() else "cpu") # 有cuda这个架构就使用0卡，无则cpu;device(可以指定任何设备，cpu,哪块或哪几块显卡)
gnet = GNet(opt).to(device)
dnet = DNet(opt).to(device)
#writer.add_graph(gnet)'''做实验试验下第二个参数'''
#writer.add_graph(dnet)
if device.type == 'cuda':
gnet = nn.DataParallel(gnet, [0, 1, 2])
dnet = nn.DataParallel(dnet, [0, 1, 2])
gnet.apply(weight_init)
dnet.apply(weight_init)
print('Generative NetWork:')
print(gnet)
print('')
print('Discriminative NetWork:')
print(dnet)
g_optimizer = optim.Adam(gnet.parameters(), lr=opt.lr1, betas=(opt.beta1, 0.999))
d_optimizer = optim.Adam(dnet.parameters(), lr=opt.lr2, betas=(opt.beta1, 0.999))
print('g_optimizer:')
print(g_optimizer)
print('d_optimizer:')
print(d_optimizer)
writer = SummaryWriter(log_dir='result_shiyan')
#dummy1_input = torch.rand(opt.batch_size, 3, 96,96)
#dummy2_input = torch.rand(opt.batch_size, opt.nd,1,1)
#writer.add_graph(dnet, dummy1_input.detach())
#writer.add_graph(gnet, dumm2_input.detach())
# Training Loop
# Lists to keep track of progress
img_list = []
G_losses = []
D_losses = []
iters = 0
fixed_noise = torch.randn(opt.batch_size, opt.nd, 1, 1, device=device)
print("Starting Training Loop...")
# For each epoch
for epoch in range(1, opt.max_epoch + 1):
# For each batch in the dataloader
print(len(dataloader))
print(type(dataloader))
for i, (imgs, _) in enumerate(dataloader, 1):
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
############################
## Train with all-real batch
dnet.zero_grad()
'''先训练判别器，再训练生成器'''
# Format batch
real_img = imgs.to(device)
label = torch.full((opt.batch_size, ), opt.real_label, device=device)
# Forward pass real batch through D
output = dnet(real_img) # 在model模块中已经被展成一维的啦
# Calculate loss on all-real batch
d_err_real = criterion(output, label)
# Calculate gradients for D in backward pass
d_err_real.backward()
D_x = output.mean().item() # 真实图片的平均得分，当然是接近1越好
## Train with all-fake batch
# Generate batch of latent vectors latent:隐藏的，潜伏的
noise = torch.randn(opt.batch_size, opt.nd, 1, 1, device=device)
# Generate fake image batch with G
fake = gnet(noise)
label.fill_(opt.fake_label)
# Classify all fake batch with D
output = dnet(fake.detach())
# Calculate D's loss on the all-fake batch
d_err_fake = criterion(output, label)
# Calculate the gradients for this batch
d_err_fake.backward()
D_G_z1 = output.mean().item() # 假图像的分数，自然是越接近0越好
# Add the gradients from the all-real and all-fake batches
d_err = d_err_real + d_err_fake #tensor(1.272)+tensor(0.183)可以直接相加，不需要先取出数值。tensor
# 自成体系，tensor和tensor的加减乘除和标量一模一样；只是tensor和标量之间不能直接算
# Update D
d_optimizer.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
gnet.zero_grad()
label.fill_(opt.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 = dnet(fake) # 更新了一步D网络后，同样一批假图片，自然是希望判别得分output比更新前的假图片得分要小,也就是使下面的g_err扩大
# Calculate G's loss based on this output
g_err = criterion(output, label)
'''生成器就是要把假图片往真标签身上凑；所以假图片+真标签，进行比较后，损失越小越好'''
# Calculate gradients for G
g_err.backward()
D_G_z2 = output.mean().item() # 因为更新过一次判别器，所以这个假图片的output均值应该比上面的假图片的output均值更接近0才健康
# Update G
g_optimizer.step()
# Output training stats
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\treal_img_mean_score: %.4f\tfake_img_mean_score_1/2: %.4f / %.4f'
% (epoch, opt.max_epoch, i, len(dataloader),d_err.item(), g_err.item(), D_x, D_G_z1, D_G_z2))
# Save Losses for plotting later
G_losses.append(g_err.item())
D_losses.append(d_err.item())
writer.add_scalars('dnet_gnet_loss', {'G_losses': G_losses[iters], 'D_losses': D_losses[iters]}, iters)
# Check how the generator is doing by saving G's output on fixed_noise
if (iters % 500 == 0) or ((epoch == opt.max_epoch) and (i == len(dataloader))):
with torch.no_grad():
fake = gnet(fixed_noise)#.detach().cpu()
img_list.append(vutils.make_grid(fake, normalize=True))
'''还不知道合成的图有多少个小图呢'''
writer.add_image('fake%d'%(iters/500), img_list[int(iters/500)], int(iters/500))
iters += 1
#torch.save(dnet, 'dnet1.pth')
#torch.save(gnet, 'gnet1.pth')
torch.save(dnet.state_dict(), 'dd.pth')
torch.save(gnet.state_dict(), 'gg.pth')
#writer = SummaryWriter(log_dir='result_')
print('最后的iters为: %d'%iters)
print('G_losses长度为: %d'%len(G_losses))
print('D_losses长度为: %d'%len(D_losses))
#for i in range(iters):
#writer.add_scalars('dnet_gnet_loss', {'G_losses': G_losses[i], 'D_losses': D_losses[i]}, i)
print('img_list的长度为: %d'%len(img_list))
#for i in range(len(img_list)):
#writer.add_image('fake%d'%i, img_list[i], i)
#writer.add_graph(dnet, input_to_model=(torch.rand(opt.batch_size, 3, 96, 96), ), verbose=False)
#writer.add_graph(gnet, input_to_model=(torch.rand(opt.batch_size, op.nd, 1, 1), ), verbose=False)
writer.close()
L_GAN = nn.MSELoss()
L_cyc = nn.L1Loss()
L_identity = nn.L1Loss()
if opt.cuda:
L_GAN.cuda()
L_cyc.cuda()
L_identity.cuda()
if opt.identity_loss:
lambda_identity = opt.lambda_ / 2
else:
lambda_identity = 0
# Optimizers
optim_G = Adam(itertools.chain(Gnet_AB.parameters(), Gnet_BA.parameters()),
lr=opt.lr,
betas=(0.5, 0.999))
optim_D = Adam(itertools.chain(Dnet_A.parameters(), Dnet_B.parameters()),
lr=opt.lr,
betas=(0.5, 0.999))
# Learning rate schedulers
lr_scheduler_G = LambdaLR(optim_G,
lr_lambda=LR_Decay_Multiplier(
opt.n_epochs, opt.decay_epoch).step)
lr_scheduler_D = LambdaLR(optim_D,
lr_lambda=LR_Decay_Multiplier(
opt.n_epochs, opt.decay_epoch).step)
Tensor = torch.cuda.FloatTensor if opt.cuda else torch.Tensor
# Cuda
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Model
if args.load_model is not None: # Continue training
state_dict = torch.load(args.load_model, map_location=device)
model_gcn = GNet()
model_gcn.load_state_dict(state_dict)
else:
model_gcn = GNet()
# Optimizer
if args.load_optimizer is not None:
state_dict_opt = torch.load(args.load_optimizer, map_location=device)
optimizer = optim.Adam(model_gcn.parameters(), lr=args.lr)
optimizer.load_state_dict(state_dict_opt)
else:
optimizer = optim.Adam(model_gcn.parameters(), lr=args.lr)
model_gcn.train()
# Graph
graph = Graph("./ellipsoid/init_info.pickle")
# Data Loader
folder = CustomDatasetFolder(args.data, extensions = ["dat"])
train_loader = torch.utils.data.DataLoader(folder, batch_size=1, shuffle=True)
# Param
nb_epochs = args.epochs
log_step = args.log_step
def train(opt, device):
dataloader = data_loader(opt)
gnet = GNet(opt).to(device)
dnet = DNet(opt).to(device)
#writer.add_graph(gnet)'''做实验试验下第二个参数'''
#writer.add_graph(dnet)
if device.type == 'cuda': # 就算device里有多个GPU可见，但是若不用分发功能，仍然只有第0块在跑
gnet = nn.DataParallel(gnet, [0, 1, 2]) # list(range(ngpu))不好使，只能用前几个
dnet = nn.DataParallel(dnet, [0, 1, 2])
gnet.apply(
weight_init) # 也就是初始化了下面的d/gnet.parameters()；不进行初始化则会系统给你进行一次随机初始
dnet.apply(weight_init)
print('Generative NetWork:')
print(gnet)
print('')
print('Discriminative NetWork:')
print(dnet)
criterion = nn.BCELoss()
'''
params (iterable): iterable of parameters to optimize or dicts defining parameter groups
除了下面的整体赋值，还可以通过迭代给优化器赋值，把模型中所有需要参数的过程都分别设置值;如学长代码：
optimizer = optim.SGD([
{'params': model.features.parameters(), 'lr': 0.1 * lr},
{'params': model.sample_128.parameters(), 'lr': lr},
{'params': model.sample_256.parameters(), 'lr': lr},
{'params': model.fc_concat.parameters(), 'lr': lr}
], lr=1e-1, momentum=0.9, weight_decay=1e-5)
'''
g_optimizer = optim.Adam(gnet.parameters(),
lr=opt.lr1,
betas=(opt.beta1, 0.999))
d_optimizer = optim.Adam(dnet.parameters(),
lr=opt.lr2,
betas=(opt.beta1, 0.999))
# 优化器只会进行一次初始赋值，其他都是反向调整
print('g_optimizer:')
print(g_optimizer)
print('d_optimizer:')
print(d_optimizer)
writer = SummaryWriter(log_dir='train_result')
# 定义writer时候就会生成events文件，而tensorboard执行时会搜索大文件下的所有路径，找出所有需要的文件
#dummy1_input = torch.rand(opt.batch_size, 3, 96,96)
#dummy2_input = torch.rand(opt.batch_size, opt.nd,1,1)
#writer.add_graph(dnet, dummy1_input)
#writer.add_graph(gnet, dumm2_input
# Training Loop
# Lists to keep track of progress
'''完全可以不用列表，但是为了以后可能有其他用，就保留了'''
img_list = []
G_losses = []
D_losses = []
iters = 0
fixed_noise = torch.randn(opt.batch_size, opt.nd, 1, 1, device=device)
print("Starting Training Loop...")
dnet.train()
gnet.train()
# 不写也默认为train模式；当有BN层和dropout层时，肯定得考虑模式切换，因为训练时这两个层有变化，验证时不能让它变，而eval()模式就不变，train()会变
# For each epoch
for epoch in range(1, opt.max_epoch + 1):
# For each batch in the dataloader
print(len(dataloader))
print(type(dataloader))
for i, (imgs, _) in enumerate(dataloader, 1):
# torch.utils.data.DataLoader()返回的就是二元组组成的一个特殊的对象(不是列表等，也不能切片);
# 在MNIST数据集中img, label = data；这些动漫头像没有标签，打印出来后发现是tensor([0, 0,...0, 0 0])
############################
# (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))
############################
## Train with all-real batch
dnet.zero_grad()
'''先训练判别器，再训练生成器'''
# Format batch
real_img = imgs.to(device) # 每个batch.to(device)
# torch.full((2,3), 1.2),第一个参数必须是元组，可以是任意维数，但想要一维填充时也得为元组，而元组只有一个元素时后面必须有个,
label = torch.full((opt.batch_size, ),
opt.real_label,
device=device)
# Forward pass real batch through D
output = dnet(real_img) # 在model模块中已经被展成一维的啦
# Calculate loss on all-real batch
d_err_real = criterion(output, label) # 平均损失
# Calculate gradients for D in backward pass
d_err_real.backward()
D_x = output.mean().item() # 真实图片的平均得分，当然是接近1越好
## Train with all-fake batch
# Generate batch of latent vectors latent:隐藏的，潜伏的
noise = torch.randn(opt.batch_size, opt.nd, 1, 1, device=device)
# gnet会生成opt.batch_size个图像，因为一个(opt.nd,1,1)可以生成一个图像；在gnet中，每张图有otp.nd个feature maps
# ,每个feature map大小为1 x 1,所以每个opt.nd(也就是一个值)，控制着生成图像中的一个特征
# Generate fake image batch with G
fake = gnet(noise)
label.fill_(opt.fake_label)
# Classify all fake batch with D
output = dnet(fake.detach())
# Calculate D's loss on the all-fake batch
d_err_fake = criterion(output, label)
# Calculate the gradients for this batch
d_err_fake.backward()
D_G_z1 = output.mean().item() # 假图像的分数，自然是越接近0越好
# Add the gradients from the all-real and all-fake batches
d_err = d_err_real + d_err_fake #tensor(1.272)+tensor(0.183)可以直接相加，不需要先取出数值。
# tensor自成体系，tensor和tensor的加减乘除和标量一模一样；只是tensor和标量之间不能直接算
# Update D
d_optimizer.step()
############################
# (2) Update G network: maximize log(D(G(z)))
###########################
gnet.zero_grad()
label.fill_(
opt.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 = dnet(
fake
) # 更新了一步D网络后，同样一批假图片，自然是希望判别得分output比更新前的假图片得分要小,也就是使下面的g_err扩大
# Calculate G's loss based on this output
g_err = criterion(output, label)
'''生成器就是要把假图片往真标签身上凑；所以假图片+真标签，进行比较后，损失越小越好'''
# Calculate gradients for G
g_err.backward()
D_G_z2 = output.mean().item(
) # 因为更新过一次判别器，所以这个假图片的output均值应该比上面的假图片的output均值更接近0才健康
# Update G
g_optimizer.step()
# Output training stats
if i % 50 == 0:
print(
'[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\treal_img_mean_score: %.4f\tfake_img_mean_score_1/2: %.4f / %.4f'
% (epoch, opt.max_epoch, i, len(dataloader), d_err.item(),
g_err.item(), D_x, D_G_z1, D_G_z2))
# Save Losses for plotting later
G_losses.append(g_err.item())
D_losses.append(d_err.item())
writer.add_scalars('dnet_gnet_loss', {
'G_losses': G_losses[iters],
'D_losses': D_losses[iters]
}, iters)
# Check how the generator is doing by saving G's output on fixed_noise
if (iters % 500 == 0) or ((epoch == opt.max_epoch) and
(i == len(dataloader))):
with torch.no_grad(
): # 上下文管理，处于with范围内的tensor待会不反向，所以前向时不用求局部梯度了，节省计算。因为forward时就会把每层对应局部梯度公式求出来
fake = gnet(
fixed_noise
) #.detach().cpu() 截断再放CPU里没什么特殊用啊，有没有效果一样，只是拷贝一份假图片存放cpu里
img_list.append(vutils.make_grid(fake, normalize=True))
'''还不知道合成的图有多少个小图呢'''
writer.add_image('fake%d' % (iters / 500),
img_list[int(iters / 500)], int(iters / 500))
iters += 1
torch.save(dnet.state_dict(), 'dnet.pth')
torch.save(gnet.state_dict(), 'gnet.pth')
writer.close()
'''