def generate(**kwargs): ''' random create caton images and chose the highest scords top 60 ''' for k_,v_, in kwargs.items(): setattr(opt,k_,v_) netg,netd = NetG(opt).eval(),NetD(opt).eval() noises = Variable(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.use_gpu is True: noises.cuda() netd.cuda() netg.cuda() ipdb.set_trace() fake_img = netg(noises) scores = netd(fake_img).data 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),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_)
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 = Variable(noises, volatile=True)
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:
netd.cuda()
netg.cuda()
noises = noises.cuda()
# 生成图片，并计算图片在判别器的分数
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 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_)
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 = Variable(noises, volatile=True)
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:
netd.cuda()
netg.cuda()
noises = noises.cuda()
# 生成图片，并计算图片在判别器的分数
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 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_)
# 将网络模型置为预测模式 不保存中间结果，加速
netg, netd = NetG(opt).eval(), NetD(opt).eval()
# 初始化gen_search_num张噪声，期望生成gen_search_num张预测图像
noises = t.randn(opt.gen_search_num, opt.nz, 1,
1).normal_(opt.gen_mean, opt.gen_std)
noises = Variable(noises, volatile=True)
# 将模型参数加载到cpu中
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))
# 模型和输入噪声转到GPU中
if opt.gpu:
netd.cuda()
netg.cuda()
noises = noises.cuda()
# 生成图片，并计算图片在判别器的分数
fake_img = netg(noises)
scores = netd(fake_img).data
# 挑选最好的某几张 从512章图片中按分数排序，取前64张 的下标
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))
betas=(0.0, 0.9))
optimizerD_enc = torch.optim.Adam(netD.feature_encoder.parameters(),
lr=0.0004,
betas=(0.0, 0.9))
optimizerD_proj = torch.optim.Adam(netD.COND_DNET.parameters(),
lr=0.004,
betas=(0.0, 0.9))
if state_epoch != 0:
netG.load_state_dict(
torch.load('%s/models/netG_%03d.pth' % (output_dir, state_epoch),
map_location='cpu'))
netD.load_state_dict(
torch.load('%s/models/netD_%03d.pth' % (output_dir, state_epoch),
map_location='cpu'))
netG = netG.cuda()
netD = netD.cuda()
optimizerG.load_state_dict(
torch.load('%s/models/optimizerG.pth' % (output_dir)))
optimizerD.load_state_dict(
torch.load('%s/models/optimizerD.pth' % (output_dir)))
netG.cuda()
netD.cuda()
if cfg.B_VALIDATION:
sampling(text_encoder, netG, dataloader,
device) # generate images for the whole valid dataset
logger.info('state_epoch: %d' % (state_epoch))
else:
train(dataloader, netG, netD, text_encoder, optimizerG, optimizerD_enc,
def train(**kwargs):
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
if opt.vis:
from visualize import Visualizer
vis = Visualizer(opt.env)
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)
# 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
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() # 二分类交叉熵
# 真图片label为1，假图片label为0
# noises为生成网络的输入
true_labels = Variable(t.ones(opt.batch_size))
fake_labels = Variable(t.zeros(opt.batch_size))
fix_noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1))
noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1))
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
if opt.gpu:
netd.cuda()
netg.cuda()
criterion.cuda()
true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda()
fix_noises, noises = fix_noises.cuda(), noises.cuda()
epochs = range(opt.max_epoch)
for epoch in iter(epochs):
for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)):
real_img = Variable(img)
if opt.gpu:
real_img = real_img.cuda()
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.data[0])
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.data[0])
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.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])
fix_fake_imgs = netg(fix_noises)
if epoch % opt.decay_every == 0:
# 保存图片
tv.utils.save_image(fix_fake_imgs.data[:64],
'%s/%s.png' % (opt.save_path, epoch),
normalize=True,
range=(-1, 1))
# 保存模型
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()
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))
def main(args):
# manualSeed to control the noise
manualSeed = 100
random.seed(manualSeed)
np.random.seed(manualSeed)
torch.manual_seed(manualSeed)
with open(args.json_file, 'r') as f:
dataset_json = json.load(f)
# load rnn encoder
text_encoder = RNN_ENCODER(dataset_json['n_words'], nhidden=dataset_json['text_embed_dim'])
text_encoder_dir = args.rnn_encoder
state_dict = torch.load(text_encoder_dir, map_location=lambda storage, loc: storage)
text_encoder.load_state_dict(state_dict)
# load netG
state_dict = torch.load(args.model_path, map_location=torch.device('cpu'))
# netG = NetG(int(dataset_json['n_channels']), int(dataset_json['cond_dim']))
netG = NetG(64, int(dataset_json['cond_dim']))
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`nvidia
new_state_dict[name] = v
model_dict = netG.state_dict()
pretrained_dict = {k: v for k, v in new_state_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
netG.load_state_dict(model_dict)
# use gpu or not, change model to evaluation mode
if args.use_gpu:
text_encoder.cuda()
netG.cuda()
caption_idx.cuda()
caption_len.cuda()
noise.cuda()
text_encoder.eval()
netG.eval()
# generate noise
num_noise = 100
noise = torch.FloatTensor(num_noise, 100)
# cub bird captions
# caption = 'this small bird has a light yellow breast and brown wings'
# caption = 'this small bird has a short beak a light gray breast a darker gray crown and black wing tips'
# caption = 'this small bird has wings that are gray and has a white belly'
# caption = 'this bird has a yellow throat belly abdomen and sides with lots of brown streaks on them'
# caption = 'this little bird has a yellow belly and breast with a gray wing with white wingbars'
# caption = 'this bird has a white belly and breast wit ha blue crown and nape'
# caption = 'a bird with brown and black wings red crown and throat and the bill is short and pointed'
# caption = 'this small bird has a yellow crown and a white belly'
# caption = 'this bird has a blue crown with white throat and brown secondaries'
# caption = 'this bird has wings that are black and has a white belly'
# caption = 'a yellow bird has wings with dark stripes and small eyes'
# caption = 'a black bird has wings with dark stripes and small eyes'
# caption = 'a red bird has wings with dark stripes and small eyes'
# caption = 'a white bird has wings with dark stripes and small eyes'
# caption = 'a blue bird has wings with dark stripes and small eyes'
# caption = 'a pink bird has wings with dark stripes and small eyes'
# caption = 'this is a white and grey bird with black wings and a black stripe by its eyes'
# caption = 'a small bird with an orange bill and grey crown and breast'
# caption = 'a small bird with black gray and white wingbars'
# caption = 'this bird is white and light orange in color with a black beak'
# caption = 'a small sized bird that has tones of brown and a short pointed bill' # beak?
# MS coco captions
# caption = 'two men skiing down a snow covered mountain in the evening'
# caption = 'a man walking down a grass covered mountain'
# caption = 'a close up of a boat on a field under a sunset'
# caption = 'a close up of a boat on a field with a clear sky'
# caption = 'a herd of black and white cattle standing on a field'
# caption = 'a herd of black and white sheep standing on a field'
# caption = 'a herd of black and white dogs standing on a field'
# caption = 'a herd of brown cattle standing on a field'
# caption = 'a herd of black and white cattle standing in a river'
# caption = 'some horses in a field of green grass with a sky in the background'
# caption = 'some horses in a field of yellow grass with a sky in the background'
caption = 'some horses in a field of green grass with a sunset in the background'
# convert caption to index
caption_idx, caption_len = get_caption_idx(dataset_json, caption)
caption_idx = torch.LongTensor(caption_idx)
caption_len = torch.LongTensor([caption_len])
caption_idx = caption_idx.view(1, -1)
caption_len = caption_len.view(-1)
# use rnn encoder to get caption embedding
hidden = text_encoder.init_hidden(1)
words_embs, sent_emb = text_encoder(caption_idx, caption_len, hidden)
# generate fake image
noise.data.normal_(0, 1)
sent_emb = sent_emb.repeat(num_noise, 1)
words_embs = words_embs.repeat(num_noise, 1, 1)
with torch.no_grad():
fake_imgs, fusion_mask = netG(noise, sent_emb)
# create path to save image, caption and mask
cap_number = 10000
main_path = 'result/mani/cap_%s_0_coco_ch64' % (str(cap_number))
img_save_path = '%s/image' % main_path
mask_save_path = '%s/mask_' % main_path
mkdir_p(img_save_path)
for i in range(7):
mkdir_p(mask_save_path + str(i))
# save caption as image
ixtoword = {v: k for k, v in dataset_json['word2idx'].items()}
cap_img = cap2img(ixtoword, caption_idx, caption_len)
im = cap_img[0].data.cpu().numpy()
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
full_path = '%s/caption.png' % main_path
im.save(full_path)
# save generated images and masks
for i in tqdm(range(num_noise)):
full_path = '%s/image_%d.png' % (img_save_path, i)
im = fake_imgs[i].data.cpu().numpy()
im = (im + 1.0) * 127.5
im = im.astype(np.uint8)
im = np.transpose(im, (1, 2, 0))
im = Image.fromarray(im)
im.save(full_path)
for j in range(7):
full_path = '%s%1d/mask_%d.png' % (mask_save_path, j, i)
im = fusion_mask[j][i][0].data.cpu().numpy()
im = im * 255
im = im.astype(np.uint8)
im = Image.fromarray(im)
im.save(full_path)
def train(**kwargs):
for k_,v_ in kwargs.items():
setattr(opt,k_,v_)
if opt.vis:
from visualize import Visualizer
vis = Visualizer(opt.env)
transforms = tv.transforms.Compose([
tv.transforms.Scale(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
)
# 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
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
# noises为生成网络的输入
true_labels = Variable(t.ones(opt.batch_size))
fake_labels = Variable(t.zeros(opt.batch_size))
fix_noises = Variable(t.randn(opt.batch_size,opt.nz,1,1))
noises = Variable(t.randn(opt.batch_size,opt.nz,1,1))
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
if opt.gpu:
netd.cuda()
netg.cuda()
criterion.cuda()
true_labels,fake_labels = true_labels.cuda(), fake_labels.cuda()
fix_noises,noises = fix_noises.cuda(),noises.cuda()
epochs = range(opt.max_epoch)
for epoch in iter(epochs):
for ii,(img,_) in tqdm.tqdm(enumerate(dataloader)):
real_img = Variable(img)
if opt.gpu:
real_img=real_img.cuda()
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.data[0])
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.data[0])
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.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==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()
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))
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()
betas=(opt.beta1, 0.999))
optimizer_d = torch.optim.Adam(netd.parameters(),
opt.lr_netd,
betas=(opt.beta1, 0.999))
criterion = torch.nn.BCELoss()
# 真图片 label 为 1，假图片为 0
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))
if opt.gpu:
netg.cuda()
netd.cuda()
criterion.cuda()
true_labels = true_labels.cuda()
fake_labels = fake_labels.cuda()
#fix_noises = fix_noises.cuda()
noises = noises.cuda()
while True:
action = input('Train(t) or Generate(g) or Quit(q)> ').lower()
if action == 't':
epochs = int(input('Epoch times > '))
train(max_epoch=epochs)
elif action == 'g':
def train(**kwargs):
# 读取参数赋值
for k_, v_ in kwargs.items():
setattr(opt, k_, v_)
# 可视化
if opt.vis:
from visualize import Visualizer
vis = Visualizer(opt.env)
# 对图片进行操作
transforms = tv.transforms.Compose([
tv.transforms.Scale(opt.image_size),
tv.transforms.CenterCrop(opt.image_size),
tv.transforms.ToTensor(),
# 均值方差
tv.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# ImageFolder 使用pytorch原生的方法读取图片，并进行操作 封装数据集
dataset = tv.datasets.ImageFolder(opt.data_path, transform=transforms)
#数据加载器
dataloader = t.utils.data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
drop_last=True)
# 定义网络
netg, netd = NetG(opt), NetD(opt)
# 把map内容加载到CPU中
map_location = lambda storage, loc: storage
# 将预训练的模型都先加载到cpu上
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
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))
# BinaryCrossEntropy二分类交叉熵，常用于二分类问题，当然也可以用于多分类问题
criterion = t.nn.BCELoss()
# 真图片label为1，假图片label为0
# noises为生成网络的输入
true_labels = Variable(t.ones(opt.batch_size))
fake_labels = Variable(t.zeros(opt.batch_size))
# fix_noises是固定值，用来查看每个epoch的变化效果
fix_noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1))
noises = Variable(t.randn(opt.batch_size, opt.nz, 1, 1))
# AverageValueMeter统计任意添加的变量的方差和均值 可视化的仪表盘
errord_meter = AverageValueMeter()
errorg_meter = AverageValueMeter()
if opt.gpu:
# 网络转移到GPU
netd.cuda()
netg.cuda()
# 损失函数转移到GPU
criterion.cuda()
# 标签转移到GPU
true_labels, fake_labels = true_labels.cuda(), fake_labels.cuda()
# 输入噪声转移到GPU
fix_noises, noises = fix_noises.cuda(), noises.cuda()
epochs = range(opt.max_epoch)
for epoch in iter(epochs):
for ii, (img, _) in tqdm.tqdm(enumerate(dataloader)):
real_img = Variable(img)
if opt.gpu:
real_img = real_img.cuda()
# 每d_every个batch训练判别器
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
#一个batchd的真照片判定为1 并反向传播
output = netd(real_img)
error_d_real = criterion(output, true_labels)
#反向传播
error_d_real.backward()
## 尽可能把假图片判别为错误
# 一个batchd的假照片判定为0 并反向传播
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()
#更新可学习参数
optimizer_d.step()
# 总误差=识别真实图片误差+假图片误差
error_d = error_d_fake + error_d_real
# 将总误差加入仪表板用于可视化显示
errord_meter.add(error_d.data[0])
# 每g_every个batch训练生成器
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))
# 生成器：噪声生成假图片
fake_img = netg(noises)
# 判别器：假图片判别份数
output = netd(fake_img)
# 尽量让假图片的份数与真标签接近,让判别器分不出来
error_g = criterion(output, true_labels)
error_g.backward()
# 更新参数
optimizer_g.step()
# 将误差加入仪表板用于可视化显示
errorg_meter.add(error_g.data[0])
if opt.vis and ii % opt.plot_every == opt.plot_every - 1:
## 可视化
# 进入debug模式
if os.path.exists(opt.debug_file):
ipdb.set_trace()
# 固定噪声生成假图片
fix_fake_imgs = netg(fix_noises)
# 可视化 固定噪声产生的假图片
vis.images(fix_fake_imgs.data.cpu().numpy()[:64] * 0.5 + 0.5,
win='fixfake')
# 可视化一张真图片。作为对比
vis.images(real_img.data.cpu().numpy()[:64] * 0.5 + 0.5,
win='real')
# 可视化仪表盘 判别器误差 生成器误差
vis.plot('errord', errord_meter.value()[0])
vis.plot('errorg', errorg_meter.value()[0])
# 每decay_every个epoch之后保存一次模型
if epoch % opt.decay_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))
# 保存判别器 生成器
t.save(netd.state_dict(), 'checkpoints/netd_%s.pth' % epoch)
t.save(netg.state_dict(), 'checkpoints/netg_%s.pth' % epoch)
# 清空误差仪表盘
errord_meter.reset()
errorg_meter.reset()
# 重置优化器参数为刚开始的参数
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))
class TACGAN():
def __init__(self, args):
self.lr = args.lr
self.cuda = args.use_cuda
self.batch_size = args.batch_size
self.image_size = args.image_size
self.epochs = args.epochs
self.data_root = args.data_root
self.dataset = args.dataset
self.num_classes = args.num_cls
self.save_dir = args.save_dir
self.save_prefix = args.save_prefix
self.continue_training = args.continue_training
self.netG_path = args.netg_path
self.netD_path = args.netd_path
self.save_after = args.save_after
self.trainset_loader = None
self.evalset_loader = None
self.num_workers = args.num_workers
self.n_z = args.n_z # length of the noise vector
self.nl_d = args.nl_d
self.nl_g = args.nl_g
self.nf_g = args.nf_g
self.nf_d = args.nf_d
self.bce_loss = nn.BCELoss()
self.nll_loss = nn.NLLLoss()
self.netD = NetD(n_cls=self.num_classes, n_t=self.nl_d, n_f=self.nf_d)
self.netG = NetG(n_z=self.n_z, n_l=self.nl_g, n_c=self.nf_g)
# convert to cuda tensors
if self.cuda and torch.cuda.is_available():
print('CUDA is enabled')
self.netD = self.netD.cuda()
self.netG = self.netG.cuda()
self.bce_loss = self.bce_loss.cuda()
self.nll_loss = self.nll_loss.cuda()
# optimizers for netD and netG
self.optimizerD = optim.Adam(params=self.netD.parameters(),
lr=self.lr,
betas=(0.5, 0.999))
self.optimizerG = optim.Adam(params=self.netG.parameters(),
lr=self.lr,
betas=(0.5, 0.999))
# create dir for saving checkpoints and other results if do not exist
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
if not os.path.exists(os.path.join(self.save_dir, 'netd_checkpoints')):
os.makedirs(os.path.join(self.save_dir, 'netd_checkpoints'))
if not os.path.exists(os.path.join(self.save_dir, 'netg_checkpoints')):
os.makedirs(os.path.join(self.save_dir, 'netg_checkpoints'))
if not os.path.exists(os.path.join(self.save_dir, 'generated_images')):
os.makedirs(os.path.join(self.save_dir, 'generated_images'))
# start training process
def train(self):
# write to the log file and print it
log_msg = '********************************************\n'
log_msg += ' Training Parameters\n'
log_msg += 'Dataset:%s\nImage size:%dx%d\n' % (
self.dataset, self.image_size, self.image_size)
log_msg += 'Batch size:%d\n' % (self.batch_size)
log_msg += 'Number of epochs:%d\nlr:%f\n' % (self.epochs, self.lr)
log_msg += 'nz:%d\nnl-d:%d\nnl-g:%d\n' % (self.n_z, self.nl_d,
self.nl_g)
log_msg += 'nf-g:%d\nnf-d:%d\n' % (self.nf_g, self.nf_d)
log_msg += '********************************************\n\n'
print(log_msg)
with open(os.path.join(self.save_dir, 'training_log.txt'),
'a') as log_file:
log_file.write(log_msg)
# load trainset and evalset
imtext_ds = ImTextDataset(data_dir=self.data_root,
dataset=self.dataset,
train=True,
image_size=self.image_size)
self.trainset_loader = DataLoader(dataset=imtext_ds,
batch_size=self.batch_size,
shuffle=True,
num_workers=2)
print("Dataset loaded successfuly")
# load checkpoints for continuing training
if args.continue_training:
self.loadCheckpoints()
# repeat for the number of epochs
netd_losses = []
netg_losses = []
for epoch in range(self.epochs):
netd_loss, netg_loss = self.trainEpoch(epoch)
netd_losses.append(netd_loss)
netg_losses.append(netg_loss)
self.saveGraph(netd_losses, netg_losses)
#self.evalEpoch(epoch)
self.saveCheckpoints(epoch)
# train epoch
def trainEpoch(self, epoch):
self.netD.train() # set to train mode
self.netG.train() #! set to train mode???
netd_loss_sum = 0
netg_loss_sum = 0
start_time = time()
for i, (images, labels, captions,
_) in enumerate(self.trainset_loader):
batch_size = images.size(
0
) # !batch size my be different (from self.batch_size) for the last batch
images, labels, captions = Variable(images), Variable(
labels), Variable(captions) # !labels should be LongTensor
labels = labels.type(
torch.FloatTensor
) # convert to FloatTensor (from DoubleTensor)
lbl_real = Variable(torch.ones(batch_size, 1))
lbl_fake = Variable(torch.zeros(batch_size, 1))
noise = Variable(torch.randn(batch_size,
self.n_z)) # create random noise
noise.data.normal_(0, 1) # normalize the noise
rnd_perm1 = torch.randperm(
batch_size
) # random permutations for different sets of training tuples
rnd_perm2 = torch.randperm(batch_size)
rnd_perm3 = torch.randperm(batch_size)
rnd_perm4 = torch.randperm(batch_size)
if self.cuda:
images, labels, captions = images.cuda(), labels.cuda(
), captions.cuda()
lbl_real, lbl_fake = lbl_real.cuda(), lbl_fake.cuda()
noise = noise.cuda()
rnd_perm1, rnd_perm2, rnd_perm3, rnd_perm4 = rnd_perm1.cuda(
), rnd_perm2.cuda(), rnd_perm3.cuda(), rnd_perm4.cuda()
############### Update NetD ###############
self.netD.zero_grad()
# train with wrong image, wrong label, real caption
outD_wrong, outC_wrong = self.netD(images[rnd_perm1],
captions[rnd_perm2])
lossD_wrong = self.bce_loss(outD_wrong, lbl_fake)
lossC_wrong = self.bce_loss(outC_wrong, labels[rnd_perm1])
# train with real image, real label, real caption
outD_real, outC_real = self.netD(images, captions)
lossD_real = self.bce_loss(outD_real, lbl_real)
lossC_real = self.bce_loss(outC_real, labels)
# train with fake image, real label, real caption
fake = self.netG(noise, captions)
outD_fake, outC_fake = self.netD(fake.detach(),
captions[rnd_perm3])
lossD_fake = self.bce_loss(outD_fake, lbl_fake)
lossC_fake = self.bce_loss(outC_fake, labels[rnd_perm3])
# backward and forwad for NetD
netD_loss = lossC_wrong + lossC_real + lossC_fake + lossD_wrong + lossD_real + lossD_fake
netD_loss.backward()
self.optimizerD.step()
########## Update NetG ##########
# train with fake data
self.netG.zero_grad()
noise.data.normal_(0, 1) # normalize the noise vector
fake = self.netG(noise, captions[rnd_perm4])
d_fake, c_fake = self.netD(fake, captions[rnd_perm4])
lossD_fake_G = self.bce_loss(d_fake, lbl_real)
lossC_fake_G = self.bce_loss(c_fake, labels[rnd_perm4])
netG_loss = lossD_fake_G + lossC_fake_G
netG_loss.backward()
self.optimizerG.step()
netd_loss_sum += netD_loss.data[0]
netg_loss_sum += netG_loss.data[0]
### print progress info ###
print(
'Epoch %d/%d, %.2f%% completed. Loss_NetD: %.4f, Loss_NetG: %.4f'
% (epoch, self.epochs,
(float(i) / len(self.trainset_loader)) * 100,
netD_loss.data[0], netG_loss.data[0]))
end_time = time()
netd_avg_loss = netd_loss_sum / len(self.trainset_loader)
netg_avg_loss = netg_loss_sum / len(self.trainset_loader)
epoch_time = (end_time - start_time) / 60
log_msg = '-------------------------------------------\n'
log_msg += 'Epoch %d took %.2f minutes\n' % (epoch, epoch_time)
log_msg += 'NetD average loss: %.4f, NetG average loss: %.4f\n\n' % (
netd_avg_loss, netg_avg_loss)
print(log_msg)
with open(os.path.join(self.save_dir, 'training_log.txt'),
'a') as log_file:
log_file.write(log_msg)
return netd_avg_loss, netg_avg_loss
# eval epoch
def evalEpoch(self, epoch):
#self.netD.eval()
#self.netG.eval()
return 0
# draws and saves the loss graph upto the current epoch
def saveGraph(self, netd_losses, netg_losses):
plt.plot(netd_losses, color='red', label='NetD Loss')
plt.plot(netg_losses, color='blue', label='NetG Loss')
plt.xlabel('epoch')
plt.ylabel('error')
plt.legend(loc='best')
plt.savefig(os.path.join(self.save_dir, 'loss_graph.png'))
plt.close()
# save after each epoch
def saveCheckpoints(self, epoch):
if epoch % self.save_after == 0:
name_netD = "netd_checkpoints/netD_" + self.save_prefix + "_epoch_" + str(
epoch) + ".pth"
name_netG = "netg_checkpoints/netG_" + self.save_prefix + "_epoch_" + str(
epoch) + ".pth"
torch.save(self.netD.state_dict(),
os.path.join(self.save_dir, name_netD))
torch.save(self.netG.state_dict(),
os.path.join(self.save_dir, name_netG))
print("Checkpoints for epoch %d saved successfuly" % (epoch))
# load checkpoints to continue training
def loadCheckpoints(self):
self.netG.load_state_dict(torch.load(self.netG_path))
self.netD.load_state_dict(torch.load(self.netD_path))
print("Checkpoints loaded successfuly")
