def generate(**kwargs):
'''
随机生成动漫头像，并根据netd的分数选择较好的结果
:param kwargs:
:return:
'''
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
netg,netd = NetG(opt).eval(),NetD(opt).eval()
noises = t.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std)
map_location = lambda storage, loc: storage
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
if opt.gpu:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
netd.to(device)
netg.to(device)
noises.to(device)
#生成图片
fake_img = netg(noises)
scores = netd(fake_img).data
#挑选最好的某几张
indexs = scores.topk(opt.gen_num)[1]
result = []
for i in indexs:
result.append(fake_img.data[ii])
#保存图片
tv.utils.save_image(t.stack(result),opt.gen_img,normalize=True,range=(-1,1))
def generate(**kwargs):
"""
随机生成动漫头像，并根据netd的分数选择较好的
"""
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device = t.device('cuda') if opt.gpu else t.device('cpu')
netg, netd = NetG(opt).eval(), NetD(opt).eval()
noises = t.randn(opt.gen_search_num, opt.nz, 1,
1).normal_(opt.gen_mean, opt.gen_std)
noises = noises.to(device)
map_location = lambda storage, loc: storage
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
# 生成图片，并计算图片在判别器的分数
fake_img = netg(noises)
scores = netd(fake_img).detach()
# 挑选最好的某几张
indexs = scores.topk(opt.gen_num)[1]
result = []
for ii in indexs:
result.append(fake_img.data[ii])
# 保存图片
tv.utils.save_image(t.stack(result),
opt.gen_img,
normalize=True,
range=(-1, 1))
def generate(**kwargs):
"""
随机生成动漫头像，并根据netd的分数选择较好的
"""
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device=t.device('cuda') if opt.gpu else t.device('cpu')
netg, netd = NetG(opt).eval(), NetD(opt).eval()
noises = t.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std)
noises = noises.to(device)
map_location = lambda storage, loc: storage
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
# 生成图片，并计算图片在判别器的分数
fake_img = netg(noises)
scores = netd(fake_img).detach()
# 挑选最好的某几张
indexs = scores.topk(opt.gen_num)[1]
result = []
for ii in indexs:
result.append(fake_img.data[ii])
# 保存图片
tv.utils.save_image(t.stack(result), opt.gen_img, normalize=True, range=(-1, 1))
def generate(**kwargs):
"""
随机生成动漫头像，并根据netd的分数选择较好的
"""
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device=t.device('cuda') if opt.gpu else t.device('cpu')
netg, netd = NetG(opt).eval(), NetD(opt).eval()
noises = t.randn(opt.gen_search_num, opt.nz, 1, 1).normal_(opt.gen_mean, opt.gen_std)
noises = noises.to(device)
map_location = lambda storage, loc: storage
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
# 生成图片，并计算图片在判别器的分数
fake_img = netg(noises)
scores = netd(fake_img).detach()
# 挑选最好的某几张
indexs = scores.topk(opt.gen_num)[1]
result = []
for ii in indexs:
result.append(fake_img.data[ii])
# 保存图片
epoch_num = opt.netd_path[opt.netd_path.find("_") + 1:opt.netd_path.rfind(".")]
randid = random.randint(0, 1000)
pic_stack_name = "".join(["result_", str(epoch_num), "_", str(randid), ".png"])
tv.utils.save_image(t.stack(result), pic_stack_name, normalize=True, range=(-1, 1))
base_dir = "./imgs/tiny_epoch_{0}_{1}".format(epoch_num, randid)
if not os.path.exists(base_dir):
os.mkdir(base_dir)
for picx in range(len(result)):
picx_name = "".join([base_dir, "/picid_", str(picx), ".png"])
tv.utils.save_image(result[picx], picx_name, normalize=True, range=(-1, 1))
import cv2
import torch as th
from model import NetG, NetD, NetA
from my_dataSet import CASIABDatasetGenerate
netg = NetG(nc=1)
netd = NetD(nc=1)
neta = NetA(nc=1)
device = th.device("cuda:0")
netg = netg.to(device)
netd = netd.to(device)
neta = neta.to(device)
fineSize = 64
checkpoint = '/home/mg/code/my_GAN_dataSet/snapshots/snapshot_449.t7'
checkpoint = th.load(checkpoint)
neta.load_state_dict(checkpoint['netA'])
netg.load_state_dict(checkpoint['netG'])
netd.load_state_dict(checkpoint['netD'])
neta.eval()
netg.eval()
netd.eval()
angles = [
'000', '018', '036', '054', '072', '090', '108', '126', '144', '162', '180'
]
for cond in ['nm-01', 'nm-02', 'nm-03', 'nm-04', 'cl-01', 'cl-02']:
dataset = CASIABDatasetGenerate(
data_dir='/home/mg/code/data/GEI_CASIA_B/gei/', cond=cond)
def train(**kwargs):
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device = t.device('cuda') if opt.gpu else t.device('cpu')
# if opt.vis:
# from visualize import Visualizer
# vis = Visualizer(opt.env)
# 数据
transforms = tv.transforms.Compose([
tv.transforms.Resize(opt.image_size),
tv.transforms.CenterCrop(opt.image_size),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = tv.datasets.ImageFolder(root=opt.data_path, transform=transforms)
dataloader = t.utils.data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
drop_last=True)
# 网络
netg, netd = NetG(opt), NetD(opt)
map_location = lambda storage, loc: storage
if opt.netd_path:
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
if opt.netg_path:
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
# 定义优化器和损失
optimizer_g = t.optim.Adam(netg.parameters(),
opt.lr1,
betas=(opt.beta1, 0.999))
optimizer_d = t.optim.Adam(netd.parameters(),
opt.lr2,
betas=(opt.beta1, 0.999))
criterion = t.nn.BCELoss().to(device)
# 真图片label为1，假图片label为0
# noises为生成网络的输入
true_labels = t.ones(opt.batch_size).to(device)
fake_labels = t.zeros(opt.batch_size).to(device)
fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
epochs = range(opt.max_epoch)
for epoch in iter(epochs):
for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)):
real_img = img.to(device)
if ii % opt.d_every == 0:
# 训练判别器
optimizer_d.zero_grad()
## 尽可能的把真图片判别为正确
output = netd(real_img)
error_d_real = criterion(output, true_labels)
error_d_real.backward()
## 尽可能把假图片判别为错误
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises).detach() # 根据噪声生成假图
output = netd(fake_img)
error_d_fake = criterion(output, fake_labels)
error_d_fake.backward()
optimizer_d.step()
error_d = error_d_fake + error_d_real
errord_meter.add(error_d.item())
if ii % opt.g_every == 0:
# 训练生成器
optimizer_g.zero_grad()
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises)
output = netd(fake_img)
error_g = criterion(output, true_labels)
error_g.backward()
optimizer_g.step()
errorg_meter.add(error_g.item())
if opt.vis and ii % opt.plot_every == opt.plot_every - 1:
## 可视化
if os.path.exists(opt.debug_file):
ipdb.set_trace()
fix_fake_imgs = netg(fix_noises)
vis.images(fix_fake_imgs.detach().cpu().numpy()[:64] * 0.5 +
0.5,
win='fixfake')
vis.images(real_img.data.cpu().numpy()[:64] * 0.5 + 0.5,
win='real')
vis.plot('errord', errord_meter.value()[0])
vis.plot('errorg', errorg_meter.value()[0])
if (epoch + 1) % opt.save_every == 0:
# 保存模型、图片
tv.utils.save_image(fix_fake_imgs.data[:64],
'%s/%s.png' % (opt.save_path, epoch),
normalize=True,
range=(-1, 1))
t.save(netd.state_dict(), 'checkpoints/netd_%s.pth' % epoch)
t.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % epoch)
errord_meter.reset()
errorg_meter.reset()
def generate(**kwargs):
"""
随机生成动漫头像，并根据netd的分数选择较好的
"""
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device = torch.device('cuda') if opt.gpu else torch.device('cpu')
hash_id = hash(opt.gen_id) % 51603
torch.manual_seed(hash_id)
netg, netd = NetG(opt).eval(), NetD(opt).eval()
noises = torch.randn(opt.gen_search_num, opt.nz, 1,
1).normal_(opt.gen_mean, opt.gen_std)
noises = noises.to(device)
map_location = lambda storage, loc: storage
netd.load_state_dict(torch.load(opt.netd_path, map_location=map_location))
netg.load_state_dict(torch.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
# 生成图片，并计算图片在判别器的分数
fake_img = netg(noises)
scores = netd(fake_img).detach()
# 挑选最好的某几张
indexs = scores.topk(opt.gen_num)[1]
result = []
for ii in indexs:
result.append(fake_img.data[ii])
# 保存图片
# epoch_num = opt.netd_path[opt.netd_path.find("_") + 1:opt.netd_path.rfind(".")]
# pic_stack_name = "".join(["result_", str(epoch_num), "_", str(randid), ".png"])
print(result[0])
print(result[0].size())
# np_array = result[0].cpu().numpy()
#
# trans_array = np.zeros((96,96,3))
#
# trans_array[:,:,0] = np_array[0,:,:]
# trans_array[:,:,1] = np_array[1,:,:]
# trans_array[:,:,2] = np_array[2,:,:]
#
# print(trans_array.shape)
#
# img_ = Image.fromarray(np.uint8(trans_array*255))
# img_ = img_.resize((224, 224), Image.BILINEAR) #resize to
# t_img = torch.from_numpy(np.array(img_))
# tv.utils.save_image(t_img, "PIL_test_img.png", normalize=True, range=(-0.8, 0.8))
#tv.utils.save_image(torch.stack(result), "save_test.png", normalize=True, range=(-1, 1))
base_dir = opt.gen_dst
base_name = opt.base_name
if not os.path.exists(base_dir):
os.mkdir(base_dir)
for picx in range(len(result)):
picx_name = "".join([base_dir, base_name, str(picx), ".png"])
tv.utils.save_image(result[picx],
picx_name,
normalize=True,
range=(-0.8, 0.8))
img = Image.open(picx_name)
img = img.resize((188, 188), Image.ANTIALIAS)
img.save(base_dir + "/" + str(picx) + ".png", "png")
os.remove(picx_name)
def train(**kwargs):
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device = t.device("cuda") if opt.gpu else t.device("cpu")
# 数据处理，输出规范为-1~1
transforms = tv.transforms.Compose([
tv.transforms.Resize(opt.image_size),
tv.transforms.CenterCrop(opt.image_size),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = tv.datasets.ImageFolder(opt.data_path, transform=transforms)
dataloader = t.utils.data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
drop_last=True)
# 网络
netg, netd = NetG(opt), NetD(opt)
map_location = lambda storage, loc: storage
if opt.netd_path:
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
if opt.netg_path:
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
# 定义优化器和损失
optimizer_g = t.optim.Adam(netg.parameters(),
opt.lr1,
betas=(opt.beta1, 0.999))
optimizer_d = t.optim.Adam(netd.parameters(),
opt.lr2,
betas=(opt.beta1, 0.999))
criterion = t.nn.BCELoss()
# 真图片label为1，假图片label为0， noise为生成网络的输入
true_labels = t.ones(opt.batch_size).to(device)
fake_labels = t.zeros(opt.batch_size).to(device)
fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
# 用来结果的均值和标准差
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
epochs = range(opt.max_epoch)
for epoch in iter(epochs):
for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)):
real_img = img.to(device)
if ii % opt.d_every == 0:
# 训练判别器
optimizer_d.zero_grad()
## 尽可能把真图片判别为正
output = netd(real_img)
error_d_real = criterion(output, true_labels)
error_d_real.backward()
## 尽可能把假图片判断为错误
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
# 使用detach来关闭G求梯度，加速训练
fake_img = netg(noises).detach()
output = netd(fake_img)
error_d_fake = criterion(output, fake_labels)
error_d_fake.backward()
optimizer_d.step()
error_d = error_d_fake + error_d_real
errord_meter.add(error_d.item())
if ii % opt.g_every == 0:
# 训练生成器
optimizer_g.zero_grad()
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises)
output = netd(fake_img)
# 尽可能把假的图片也判别为1
error_g = criterion(output, true_labels)
error_g.backward()
optimizer_g.step()
errorg_meter.add(error_g.item())
# 可视化
# 保存模型、图片
if (epoch + 1) % opt.save_every == 0:
fix_fake_imgs = netg(fix_noises)
tv.utils.save_image(fix_fake_imgs.data[:64],
"%s%s.png" % (opt.save_path, epoch),
normalize=True,
range=(-1, 1))
t.save(netd.state_dict(), r"./checkpoints/netd_%s.pth" % epoch)
t.save(netg.state_dict(), r"./checkpoints/netg_%s.pth" % epoch)
errord_meter.reset()
errorg_meter.reset()
class GANAgent(object):
def __init__(self,
input_size,
output_size,
num_env,
num_step,
gamma,
lam=0.95,
learning_rate=1e-4,
ent_coef=0.01,
clip_grad_norm=0.5,
epoch=3,
batch_size=128,
ppo_eps=0.1,
update_proportion=0.25,
use_gae=True,
use_cuda=False,
use_noisy_net=False,
hidden_dim=512):
self.model = CnnActorCriticNetwork(input_size, output_size,
use_noisy_net)
self.num_env = num_env
self.output_size = output_size
self.input_size = input_size
self.num_step = num_step
self.gamma = gamma
self.lam = lam
self.epoch = epoch
self.batch_size = batch_size
self.use_gae = use_gae
self.ent_coef = ent_coef
self.ppo_eps = ppo_eps
self.clip_grad_norm = clip_grad_norm
self.update_proportion = update_proportion
self.device = torch.device('cuda' if use_cuda else 'cpu')
self.netG = NetG(z_dim=hidden_dim) #(input_size, z_dim=hidden_dim)
self.netD = NetD(z_dim=1)
self.netG.apply(weights_init)
self.netD.apply(weights_init)
self.optimizer_policy = optim.Adam(list(self.model.parameters()),
lr=learning_rate)
self.optimizer_G = optim.Adam(list(self.netG.parameters()),
lr=learning_rate,
betas=(0.5, 0.999))
self.optimizer_D = optim.Adam(list(self.netD.parameters()),
lr=learning_rate,
betas=(0.5, 0.999))
self.netG = self.netG.to(self.device)
self.netD = self.netD.to(self.device)
self.model = self.model.to(self.device)
def reconstruct(self, state):
state = torch.Tensor(state).to(self.device)
state = state.float()
reconstructed = self.vae(state.unsqueeze(0))[0].squeeze(0)
return reconstructed.detach().cpu().numpy()
def get_action(self, state):
state = torch.Tensor(state).to(self.device)
state = state.float()
policy, value_ext, value_int = self.model(state)
action_prob = F.softmax(policy, dim=-1).data.cpu().numpy()
action = self.random_choice_prob_index(action_prob)
return action, value_ext.data.cpu().numpy().squeeze(
), value_int.data.cpu().numpy().squeeze(), policy.detach()
@staticmethod
def random_choice_prob_index(p, axis=1):
r = np.expand_dims(np.random.rand(p.shape[1 - axis]), axis=axis)
return (p.cumsum(axis=axis) > r).argmax(axis=axis)
def compute_intrinsic_reward(self, obs):
obs = torch.FloatTensor(obs).to(self.device)
#embedding = self.vae.representation(obs)
#reconstructed_embedding = self.vae.representation(self.vae(obs)[0]) # why use index[0]
reconstructed_img, embedding, reconstructed_embedding = self.netG(obs)
intrinsic_reward = (embedding - reconstructed_embedding
).pow(2).sum(1) / 2 # Not use reconstructed loss
return intrinsic_reward.detach().cpu().numpy()
def train_model(self, s_batch, target_ext_batch, target_int_batch, y_batch,
adv_batch, next_obs_batch, old_policy):
s_batch = torch.FloatTensor(s_batch).to(self.device)
target_ext_batch = torch.FloatTensor(target_ext_batch).to(self.device)
target_int_batch = torch.FloatTensor(target_int_batch).to(self.device)
y_batch = torch.LongTensor(y_batch).to(self.device)
adv_batch = torch.FloatTensor(adv_batch).to(self.device)
next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device)
sample_range = np.arange(len(s_batch))
#reconstruction_loss = nn.MSELoss(reduction='none')]
l_adv = nn.MSELoss(reduction='none')
l_con = nn.L1Loss(reduction='none')
l_enc = nn.MSELoss(reduction='none')
l_bce = nn.BCELoss(reduction='none')
with torch.no_grad():
policy_old_list = torch.stack(old_policy).permute(
1, 0, 2).contiguous().view(-1,
self.output_size).to(self.device)
m_old = Categorical(F.softmax(policy_old_list, dim=-1))
log_prob_old = m_old.log_prob(y_batch)
# ------------------------------------------------------------
#recon_losses = np.array([])
#kld_losses = np.array([])
mean_err_g_adv_per_batch = np.array([])
mean_err_g_con_per_batch = np.array([])
mean_err_g_enc_per_batch = np.array([])
mean_err_d_per_batch = np.array([])
for i in range(self.epoch):
np.random.shuffle(sample_range)
for j in range(int(len(s_batch) / self.batch_size)):
sample_idx = sample_range[self.batch_size * j:self.batch_size *
(j + 1)]
# --------------------------------------------------------------------------------
# for generative curiosity (GAN loss)
#gen_next_state, mu, logvar = self.vae(next_obs_batch[sample_idx])
############### netG forward ##############################################
gen_next_state, latent_i, latent_o = self.netG(
next_obs_batch[sample_idx])
############### netD forward ##############################################
pred_real, feature_real = self.netD(next_obs_batch[sample_idx])
pred_fake, feature_fake = self.netD(gen_next_state)
#d = len(gen_next_state.shape)
#recon_loss = reconstruction_loss(gen_next_state, next_obs_batch[sample_idx]).mean(axis=list(range(1, d)))
############### netG backward #############################################
self.optimizer_G.zero_grad()
err_g_adv_per_img = l_adv(
self.netD(next_obs_batch[sample_idx])[1],
self.netD(gen_next_state)[1]).mean(
axis=list(range(1, len(feature_real.shape))))
err_g_con_per_img = l_con(
next_obs_batch[sample_idx], gen_next_state).mean(
axis=list(range(1, len(gen_next_state.shape))))
err_g_enc_per_img = l_enc(latent_i, latent_o).mean(-1)
#kld_loss = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(axis=1)
# TODO: keep this proportion of experience used for VAE update?
# Proportion of experience used for VAE update
img_num = len(err_g_con_per_img)
mask = torch.rand(img_num).to(self.device)
mask = (mask < self.update_proportion).type(
torch.FloatTensor).to(self.device)
mean_err_g_adv = (err_g_adv_per_img * mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_g_con = (err_g_con_per_img * mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_g_enc = (err_g_enc_per_img * mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
# hyperparameter weights:
w_adv = 1
w_con = 50
w_enc = 1
mean_err_g = mean_err_g_adv * w_adv +\
mean_err_g_con * w_con +\
mean_err_g_enc * w_enc
mean_err_g.backward(retain_graph=True)
self.optimizer_G.step()
mean_err_g_adv_per_batch = np.append(
mean_err_g_adv_per_batch,
mean_err_g_adv.detach().cpu().numpy())
mean_err_g_con_per_batch = np.append(
mean_err_g_con_per_batch,
mean_err_g_con.detach().cpu().numpy())
mean_err_g_enc_per_batch = np.append(
mean_err_g_enc_per_batch,
mean_err_g_enc.detach().cpu().numpy())
############## netD backward ##############################################
self.optimizer_D.zero_grad()
real_label = torch.ones_like(pred_real).to(self.device)
fake_label = torch.zeros_like(pred_fake).to(self.device)
err_d_real_per_img = l_bce(pred_real, real_label)
err_d_fake_per_img = l_bce(pred_fake, fake_label)
mean_err_d_real = (err_d_real_per_img *
mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_d_fake = (err_d_fake_per_img *
mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_d = (mean_err_d_real + mean_err_d_fake) / 2
mean_err_d.backward()
self.optimizer_D.step()
mean_err_d_per_batch = np.append(
mean_err_d_per_batch,
mean_err_d.detach().cpu().numpy())
if mean_err_d.item() < 1e-5:
self.netD.apply(weights_init)
print('Reloading net d')
############# policy update ###############################################
policy, value_ext, value_int = self.model(s_batch[sample_idx])
m = Categorical(F.softmax(policy, dim=-1))
log_prob = m.log_prob(y_batch[sample_idx])
ratio = torch.exp(log_prob - log_prob_old[sample_idx])
surr1 = ratio * adv_batch[sample_idx]
surr2 = torch.clamp(ratio, 1.0 - self.ppo_eps,
1.0 + self.ppo_eps) * adv_batch[sample_idx]
actor_loss = -torch.min(surr1, surr2).mean()
critic_ext_loss = F.mse_loss(value_ext.sum(1),
target_ext_batch[sample_idx])
critic_int_loss = F.mse_loss(value_int.sum(1),
target_int_batch[sample_idx])
critic_loss = critic_ext_loss + critic_int_loss
entropy = m.entropy().mean()
self.optimizer_policy.zero_grad()
loss = actor_loss + 0.5 * critic_loss - self.ent_coef * entropy
loss.backward()
#global_grad_norm_(list(self.model.parameters())+list(self.vae.parameters())) do we need this step
#global_grad_norm_(list(self.model.parameter())) or just norm policy
self.optimizer_poilicy.step()
return mean_err_g_adv_per_batch, mean_err_g_con_per_batch, mean_err_g_enc_per_batch, mean_err_d_per_batch
def train_just_vae(self, s_batch, next_obs_batch):
s_batch = torch.FloatTensor(s_batch).to(self.device)
next_obs_batch = torch.FloatTensor(next_obs_batch).to(self.device)
sample_range = np.arange(len(s_batch))
l_adv = nn.MSELoss(reduction='none')
l_con = nn.L1Loss(reduction='none')
l_enc = nn.MSELoss(reduction='none')
l_bce = nn.BCELoss(reduction='none')
mean_err_g_adv_per_batch = np.array([])
mean_err_g_con_per_batch = np.array([])
mean_err_g_enc_per_batch = np.array([])
mean_err_d_per_batch = np.array([])
for i in range(self.epoch):
np.random.shuffle(sample_range)
for j in range(int(len(s_batch) / self.batch_size)):
sample_idx = sample_range[self.batch_size * j:self.batch_size *
(j + 1)]
############### netG forward ##############################################
gen_next_state, latent_i, latent_o = self.netG(
next_obs_batch[sample_idx])
############### netD forward ##############################################
pred_real, feature_real = self.netD(next_obs_batch[sample_idx])
pred_fake, feature_fake = self.netD(gen_next_state)
#d = len(gen_next_state.shape)
#recon_loss = reconstruction_loss(gen_next_state, next_obs_batch[sample_idx]).mean(axis=list(range(1, d)))
############### netG backward #############################################
self.optimizer_G.zero_grad()
err_g_adv_per_img = l_adv(
self.netD(next_obs_batch[sample_idx])[1],
self.netD(gen_next_state)[1]).mean(
axis=list(range(1, len(feature_real.shape))))
err_g_con_per_img = l_con(
next_obs_batch[sample_idx], gen_next_state).mean(
axis=list(range(1, len(gen_next_state.shape))))
err_g_enc_per_img = l_enc(latent_i, latent_o).mean(-1)
#kld_loss = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).sum(axis=1)
# TODO: keep this proportion of experience used for VAE update?
# Proportion of experience used for VAE update
img_num = len(err_g_con_per_img)
mask = torch.rand(img_num).to(self.device)
mask = (mask < self.update_proportion).type(
torch.FloatTensor).to(self.device)
mean_err_g_adv = (err_g_adv_per_img * mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_g_con = (err_g_con_per_img * mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_g_enc = (err_g_enc_per_img * mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
# hyperparameter weights:
w_adv = 1
w_con = 50
w_enc = 1
mean_err_g = mean_err_g_adv * w_adv +\
mean_err_g_con * w_con +\
mean_err_g_enc * w_enc
mean_err_g.backward(retain_graph=True)
self.optimizer_G.step()
mean_err_g_adv_per_batch = np.append(
mean_err_g_adv_per_batch,
mean_err_g_adv.detach().cpu().numpy())
mean_err_g_con_per_batch = np.append(
mean_err_g_con_per_batch,
mean_err_g_con.detach().cpu().numpy())
mean_err_g_enc_per_batch = np.append(
mean_err_g_enc_per_batch,
mean_err_g_enc.detach().cpu().numpy())
############## netD backward ##############################################
self.optimizer_D.zero_grad()
real_label = torch.ones_like(pred_real).to(self.device)
fake_label = torch.zeros_like(pred_fake).to(self.device)
err_d_real_per_img = l_bce(pred_real, real_label)
err_d_fake_per_img = l_bce(pred_fake, fake_label)
mean_err_d_real = (err_d_real_per_img *
mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_d_fake = (err_d_fake_per_img *
mask).sum() / torch.max(
mask.sum(),
torch.Tensor([1]).to(self.device))
mean_err_d = (mean_err_d_real + mean_err_d_fake) / 2
mean_err_d.backward()
self.optimizer_D.step()
mean_err_d_per_batch = np.append(
mean_err_d_per_batch,
mean_err_d.detach().cpu().numpy())
return mean_err_g_adv_per_batch, mean_err_g_con_per_batch, mean_err_g_enc_per_batch, mean_err_d_per_batch
def train(**kwargs):
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device=t.device('cuda') if opt.gpu else t.device('cpu')
if opt.vis:
from visualize import Visualizer
vis = Visualizer(opt.env)
# 数据
transforms = tv.transforms.Compose([
tv.transforms.Resize(opt.image_size),
tv.transforms.CenterCrop(opt.image_size),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = tv.datasets.ImageFolder(opt.data_path, transform=transforms)
dataloader = t.utils.data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
drop_last=True
)
# 网络
netg, netd = NetG(opt), NetD(opt)
map_location = lambda storage, loc: storage
if opt.netd_path:
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
if opt.netg_path:
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
# 定义优化器和损失
optimizer_g = t.optim.Adam(netg.parameters(), opt.lr1, betas=(opt.beta1, 0.999))
optimizer_d = t.optim.Adam(netd.parameters(), opt.lr2, betas=(opt.beta1, 0.999))
criterion = t.nn.BCELoss().to(device)
# 真图片label为1，假图片label为0
# noises为生成网络的输入
true_labels = t.ones(opt.batch_size).to(device)
fake_labels = t.zeros(opt.batch_size).to(device)
fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
epochs = range(opt.max_epoch)
for epoch in iter(epochs):
for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)):
real_img = img.to(device)
if ii % opt.d_every == 0:
# 训练判别器
optimizer_d.zero_grad()
## 尽可能的把真图片判别为正确
output = netd(real_img)
error_d_real = criterion(output, true_labels)
error_d_real.backward()
## 尽可能把假图片判别为错误
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises).detach() # 根据噪声生成假图
output = netd(fake_img)
error_d_fake = criterion(output, fake_labels)
error_d_fake.backward()
optimizer_d.step()
error_d = error_d_fake + error_d_real
errord_meter.add(error_d.item())
if ii % opt.g_every == 0:
# 训练生成器
optimizer_g.zero_grad()
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises)
output = netd(fake_img)
error_g = criterion(output, true_labels)
error_g.backward()
optimizer_g.step()
errorg_meter.add(error_g.item())
if opt.vis and ii % opt.plot_every == opt.plot_every - 1:
## 可视化
if os.path.exists(opt.debug_file):
ipdb.set_trace()
fix_fake_imgs = netg(fix_noises)
vis.images(fix_fake_imgs.detach().cpu().numpy()[:64] * 0.5 + 0.5, win='fixfake')
vis.images(real_img.data.cpu().numpy()[:64] * 0.5 + 0.5, win='real')
vis.plot('errord', errord_meter.value()[0])
vis.plot('errorg', errorg_meter.value()[0])
if (epoch+1) % opt.save_every == 0:
# 保存模型、图片
tv.utils.save_image(fix_fake_imgs.data[:64], '%s/%s.png' % (opt.save_path, epoch), normalize=True,
range=(-1, 1))
t.save(netd.state_dict(), 'checkpoints/netd_%s.pth' % epoch)
t.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % epoch)
errord_meter.reset()
errorg_meter.reset()
def train(**kwargs):
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
device = t.device('cuda') if opt.gpu else t.device('cpu')
# 可视化
if opt.vis:
from visualize import Visualizer
vis = Visualizer(opt.env)
# 数据
transforms = tv.transforms.Compose([
tv.transforms.Resize(opt.image_size),
tv.transforms.CenterCrop(opt.image_size),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = tv.datasets.ImageFolder(opt.data_path, transforms)
dataloader = t.utils.data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
drop_last=True)
netg, netd = NetG(opt), NetD(opt)
if opt.netd_path:
netd.load_state_dict(
t.load(opt.netd_path, map_location=t.device('cpu')))
if opt.netg_path:
netg.load_state_dict(
t.load(opt.netg_path, map_location=t.device('cpu')))
netd.to(device)
netg.to(device)
# 定义优化器和损失
optimizer_g = t.optim.Adam(netg.parameters(),
opt.lr1,
betas=(opt.beta1, 0.999))
optimizer_d = t.optim.Adam(netd.parameters(),
opt.lr1,
betas=(opt.beta1, 0.999))
criterion = t.nn.BCELoss().to(device)
# 真图片label为1，假图片label为0
# noise为生成网络的输入
true_labels = t.ones(opt.batch_size).to(device)
fake_labels = t.zeros(opt.batch_size).to(device)
fix_noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
noises = t.randn(opt.batch_size, opt.nz, 1, 1).to(device)
errord = 0
errorg = 0
epochs = range(opt.max_epoch)
for epoch in epochs:
for ii, (img, _) in tqdm(enumerate(dataloader)):
real_img = img.to(device)
if (ii + 1) % opt.d_every == 0:
# 训练判别器
optimizer_d.zero_grad()
# 把真图片判断为正确
output = netd(real_img)
error_d_real = criterion(output, true_labels)
error_d_real.backward()
# 把假图片(netg通过噪声生成的图片)判断为错误
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises).detach()
output = netd(fake_img)
error_d_fake = criterion(output, fake_labels)
error_d_fake.backward()
optimizer_d.step()
errord += (error_d_fake + error_d_real).item()
if (ii + 1) % opt.g_every == 0:
# 训练生成器
optimizer_g.zero_grad()
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises)
output = netd(fake_img)
error_g = criterion(output, true_labels)
error_g.backward()
optimizer_g.step()
errorg += error_g.item()
if opt.vis and (ii + 1) % opt.plot_every == 0:
fix_fake_imgs = netg(fix_noises)
vis.images(fix_fake_imgs.detach().cpu().numpy()[:64] * 0.5 +
0.5,
win='fixfake')
vis.images(real_img.data.cpu().numpy()[:64] * 0.5 + 0.5,
win='real')
vis.plot('errord', errord / (opt.plot_every))
vis.plot('errorg', errorg / (opt.plot_every))
errord = 0
errorg = 0
if (epoch + 1) % opt.save_every == 0:
tv.utils.save_image(fix_fake_imgs.data[:64],
'%s%s.png' % (opt.save_path, epoch),
normalize=True,
range=(-1, 1))
t.save(netd.state_dict(),
'checkpoints/netd_%s.pth' % (epoch + 1))
t.save(netg.state_dict(),
'checkpoints/netg_%s.pth' % (epoch + 1))
def train():
# change opt
# for k_, v_ in kwargs.items():
# setattr(opt, k_, v_)
device = torch.device('cuda') if torch.cuda.is_available else torch.device(
'cpu')
if opt.vis:
from visualizer import Visualizer
vis = Visualizer(opt.env)
# rescale to -1~1
transform = transforms.Compose([
transforms.Resize(opt.image_size),
transforms.CenterCrop(opt.image_size),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = datasets.ImageFolder(opt.data_path, transform=transform)
dataloader = DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
drop_last=True)
netd = NetD(opt)
netg = NetG(opt)
map_location = lambda storage, loc: storage
if opt.netd_path:
netd.load_state_dict(torch.load(opt.netd_path),
map_location=map_location)
if opt.netg_path:
netg.load_state_dict(torch.load(opt.netg_path),
map_location=map_location)
if torch.cuda.is_available():
netd.to(device)
netg.to(device)
# 定义优化器和损失
optimizer_g = torch.optim.Adam(netg.parameters(),
opt.lr1,
betas=(opt.beta1, 0.999))
optimizer_d = torch.optim.Adam(netd.parameters(),
opt.lr2,
betas=(opt.beta1, 0.999))
criterion = torch.nn.BCELoss().to(device)
# 真label为1， noises是输入噪声
true_labels = Variable(torch.ones(opt.batch_size))
fake_labels = Variable(torch.zeros(opt.batch_size))
fix_noises = Variable(torch.randn(opt.batch_size, opt.nz, 1, 1))
noises = Variable(torch.randn(opt.batch_size, opt.nz, 1, 1))
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
if torch.cuda.is_available():
netd.cuda()
netg.cuda()
criterion.cuda()
true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda()
fix_noises, noises = fix_noises.cuda(), noises.cuda()
for epoch in range(opt.max_epoch):
print("epoch:", epoch, end='\r')
# sys.stdout.flush()
for ii, (img, _) in enumerate(dataloader):
real_img = Variable(img)
if torch.cuda.is_available():
real_img = real_img.cuda()
# 训练判别器, real -> 1, fake -> 0
if (ii + 1) % opt.d_every == 0:
# real
optimizer_d.zero_grad()
output = netd(real_img)
# print(output.shape, true_labels.shape)
error_d_real = criterion(output, true_labels)
error_d_real.backward()
# fake
noises.data.copy_(torch.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises).detach() # 随机噪声生成假图
fake_output = netd(fake_img)
error_d_fake = criterion(fake_output, fake_labels)
error_d_fake.backward()
# update optimizer
optimizer_d.step()
error_d = error_d_fake + error_d_real
errord_meter.add(error_d.item())
# 训练生成器, 让生成器得到的图片能够被判别器判别为真
if (ii + 1) % opt.g_every == 0:
optimizer_g.zero_grad()
noises.data.copy_(torch.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises)
fake_output = netd(fake_img)
error_g = criterion(fake_output, true_labels)
error_g.backward()
optimizer_g.step()
errorg_meter.add(error_g.item())
if opt.vis and ii % opt.plot_every == opt.plot_every - 1:
# 进行可视化
# if os.path.exists(opt.debug_file):
# import ipdb
# ipdb.set_trace()
fix_fake_img = netg(fix_noises)
vis.images(
fix_fake_img.detach().cpu().numpy()[:opt.batch_size] * 0.5
+ 0.5,
win='fixfake')
vis.images(real_img.data.cpu().numpy()[:opt.batch_size] * 0.5 +
0.5,
win='real')
vis.plot('errord', errord_meter.value()[0])
vis.plot('errorg', errorg_meter.value()[0])
if (epoch + 1) % opt.save_every == 0:
# 保存模型、图片
tv.utils.save_image(fix_fake_img.data[:opt.batch_size],
'%s/%s.png' % (opt.save_path, epoch),
normalize=True,
range=(-1, 1))
torch.save(netd.state_dict(), 'checkpoints/netd_%s.pth' % epoch)
torch.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % epoch)
errord_meter.reset()
errorg_meter.reset()
ndf = 64
nz = 100
opt = Config()
for gen in range(gen_num):
Result = random.randint(1000000, 100000000)
device = t.device('cuda') if opt.gpu else t.device('cpu')
netg, netd = NetG(opt).eval(), NetD(opt).eval()
noises = t.randn(opt.gen_search_num, opt.nz, 1,
1).normal_(opt.gen_mean, opt.gen_std)
noises = noises.to(device)
map_location = lambda storage, loc: storage
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
netd.to(device)
netg.to(device)
fake_img = netg(noises)
scores = netd(fake_img).detach()
indexs = scores.topk(opt.gen_num)[1]
result = []
for ii in indexs:
result.append(fake_img.data[ii])
tv.utils.save_image(t.stack(result),
str(Result) + opt.gen_img,
normalize=True,
range=(-1, 1))
project="mnist", # have to change when you want to change project
job_type="anomaly detection",
)
state_dict_G = torch.load("weights/G.prm",
map_location=lambda storage, loc: storage)
state_dict_D = torch.load("weights/D.prm",
map_location=lambda storage, loc: storage)
G_update = NetG(CONFIG)
D_update = NetD(CONFIG)
G_update.load_state_dict(state_dict_G)
D_update.load_state_dict(state_dict_D)
G_update.to(device)
D_update.to(device)
mean = (0.5, )
std = (0.5, )
test_dataset = Dataset(csv_file=CONFIG.test_csv_file,
transform=ImageTransform(mean, std))
test_dataloader = DataLoader(test_dataset,
batch_size=CONFIG.test_batch_size,
shuffle=True)
for sample in test_dataloader:
test_img = sample["img"]
def train(**kwargs):
'''
训练函数
:param kwargs: fire传进来的训练参数
:return:
'''
opt.parse(kwargs)
for k_,v_ in kwargs.items():
setattr(opt,k_,v_)
if opt.vis:
vis = Visualizer(opt.env)
#step1：数据预处理
transforms = tv.transforms.Compose([
tv.transforms.Resize(opt.image_size),
tv.transforms.CenterCrop(opt.image_size),
tv.transforms.ToTensor(),
tv.transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))
])
dataset = tv.datasets.ImageFolder(opt.data_path,transform=transforms)
dataloader = t.utils.data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
drop_last=True)
#step2: 定义网络
netg,netd = NetG(opt),NetD(opt)
map_location = lambda storage,loc:storage
if opt.netd_path:
netd.load_state_dict(t.load(opt.netd_path, map_location=map_location))
if opt.netg_path:
netg.load_state_dict(t.load(opt.netg_path, map_location=map_location))
#定义优化器和损失函数
optimizer_g = t.optim.Adam(netg.parameters(), opt.lrG, betas=(0.5, 0.999))
optimizer_d = t.optim.Adam(netd.parameters(), opt.lrD, betas=(0.5, 0.999))
criterion = t.nn.BCELoss()
#真图片label为1，加图片label为0
#noise为网络的输入
true_labels = t.ones(opt.batch_size)
fake_labels = t.zeros(opt.batch_size)
fix_noises = t.randn(opt.batch_size,opt.nz,1,1)
noises = t.randn(opt.batch_size,opt.nz,1,1)
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
if opt.gpu:
device = t.device("cuda:0" if t.cuda.is_available() else "cpu")
netd.to(device)
netg.to(device)
criterion.to(device)
true_labels,fake_labels = true_labels.to(device),fake_labels.to(device)
fix_noises,noises = fix_noises.to(device),noises.to(device)
epochs = range(140)
for epoch in iter(epochs):
for ii,(img,_) in tqdm.tqdm(enumerate(dataloader),total=len(dataloader)):
if opt.gpu:
real_img = img.to(device)
if ii%opt.d_every == 0: #每个batch训练一次鉴别器
optimizer_d.zero_grad()
output = netd(real_img) #判断真图片(使其尽可能大)
error_d_real = criterion(output,true_labels)
error_d_real.backward()
##尽可能把假图片判断为错误
noises.data.copy_(t.randn(opt.batch_size,opt.nz,1,1))
fake_img = netg(noises).detach() #根据噪声生成假图
output = netd(fake_img)
error_d_fake = criterion(output,fake_labels)
error_d_fake.backward()
optimizer_d.step()
error_d = error_d_fake + error_d_real
errord_meter.add(error_d.item())
if ii%opt.g_every == 0: #每5个batch更新一次生成器
#训练生成器
optimizer_g.zero_grad()
noises.data.copy_(t.randn(opt.batch_size, opt.nz, 1, 1))
fake_img = netg(noises)
output = netd(fake_img)
error_g = criterion(output,true_labels)
error_g.backward()
optimizer_g.step()
errord_meter.add(error_g.item())
if opt.vis and ii%opt.plot_time == opt.plot_time - 1:
##可视化
fix_fake_img = netg(fix_noises) #使用噪声生成图片
vis.images(fix_fake_img.data.cpu().numpy()[:64]*0.5+0.5,win = 'fixfake')
# vis.images(real_img.data.cpu().numpy()[:64]*0.5+0.5,win = 'real')
vis.plot('errord', errord_meter.value()[0])
vis.plot('errorg', errorg_meter.value()[0])
if epoch%opt.decay_every == opt.decay_every-1:
#保存模型，图片
tv.utils.save_image(fix_fake_img.data[:64],'%s/new%s.png'%(opt.save_path,epoch),
normalize=True,range=(-1,1))
t.save(netd.state_dict(), 'checkpoints/new_netd_%s.pth' % epoch)
t.save(netg.state_dict(), 'checkpoints/new_netg_%s.pth' % epoch)
errord_meter.reset()
errorg_meter.reset()
optimizer_g = t.optim.Adam(netg.parameters(), opt.lrG, betas=(0.5, 0.999))
optimizer_d = t.optim.Adam(netd.parameters(), opt.lrD, betas=(0.5, 0.999))
