import numpy as np
from torch.autograd import Variable
# Hyper Parameters
EPOCH = 100 # 训练的epoch数
Z_DIMENSION = 110 # 生成器的idea数,最后十位为label
G_EPOCH = 1 # 判别器的epoch数
NUM_IMG = 100 # 图像的batch size
LR = 0.0003 # 学习率
OPTIMIZER = torch.optim.Adam # 优化器
CRITERION = nn.BCELoss() # 损失函数
NUM_OF_WORKERS = 10 # 线程数
N_IDEAS = 100 # 随机数，Z_DEMENSION比它多了tag的数量
D = DCGAN.Discriminator()
G = DCGAN.Generator(Z_DIMENSION, 1 * 56 * 56) #
Training_Set, Testing_Set, Training_Loader, Testing_Loader = DCGAN.load_image(
NUM_IMG, NUM_OF_WORKERS)
D = D.cuda()
G = G.cuda()
d_optimizer = OPTIMIZER(D.parameters(), lr=LR)
g_optimizer = OPTIMIZER(G.parameters(), lr=LR)
if __name__ == '__main__':
for count, i in enumerate(range(EPOCH)):
for (img, label) in Training_Loader:
labels_one_hot = np.zeros((NUM_IMG, 10))
labels_one_hot[np.arange(NUM_IMG), label.numpy()] = 1
img = Variable(img).cuda()
real_label = Variable(
torch.from_numpy(labels_one_hot).float()).cuda()
import torch
import DCGAN
import numpy as np
import matplotlib.pyplot as plt
NUM_IMG = 9
Z_DEMENSION = 110
N_IDEAS = 100
D = DCGAN.Discriminator()
G = DCGAN.Generator(Z_DEMENSION, 3136)
D.load_state_dict(torch.load(r'./DC-GAN-Networks/discriminator_cpu_.pkl'))
G.load_state_dict(torch.load(r'./DC-GAN-Networks/generator_cpu_.pkl'))
lis = []
for i in range(10):
z = torch.randn((NUM_IMG, N_IDEAS))
x = np.zeros((NUM_IMG, Z_DEMENSION - N_IDEAS))
x[:, i] = 1
z = np.concatenate((z.numpy(), x), 1)
z = torch.from_numpy(z).float()
fake_img = G(z)
lis.append(fake_img.detach().numpy())
output = D(fake_img)
DCGAN.show(fake_img)
plt.savefig('./GAN_IMAGE/Test %d.png' % i, bbox_inches='tight')
DCGAN.show_all(lis)
plt.savefig('./GAN_IMAGE/Test_All.png', bbox_inches='tight')
plt.show()
from mxnet import nd
from mxnet import random
from matplotlib import pyplot as plt
import numpy as np
#if not updating the seed by system time, you'll get the same results
import time
seed = int(time.time() * 100)
random.seed(seed)
import sys
sys.path.append('./dependencies')
import utils
ctx = utils.try_gpu()
filename = './params/dcgan.netG.save'
netG = dcgan.Generator()
netG.collect_params()
netG.load_params(filename, ctx=ctx)
z = nd.random_normal(0, 1, shape=(4, 100, 1, 1), ctx=ctx)
#print(z)
output = netG(z)
for i in range(4):
plt.subplot(1, 4, i + 1)
plt.imshow(((output[i].asnumpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(
np.uint8))
plt.axis('off')
plt.show()
if not fname.endswith('.jpg'):
continue
img = os.path.join(path, fname)
img_arr = mx.image.imread(img)
img_arr = transform(img_arr)
img_list.append(img_arr)
train_data = mx.io.NDArrayIter(data=nd.concatenate(img_list),
batch_size=batch_size)
# Initial Model
## loss
loss = gluon.loss.SigmoidBinaryCrossEntropyLoss()
## generator and discriminator
filename1 = './params/dcgan.netG.get'
filename2 = './params/dcgan.netD.get'
netG = dc.Generator()
netD = dc.Discriminator()
#netG.load_params(filename1, ctx = ctx)
#netD.load_params(filename2, ctx = ctx)
netG.initialize(mx.init.Normal(0.02), ctx=ctx)
netD.initialize(mx.init.Normal(0.02), ctx=ctx)
## trainer for the generator and the discriminator
trainerG = gluon.Trainer(netG.collect_params(), 'adam', {
'learning_rate': lr,
'beta1': beta1
})
trainerD = gluon.Trainer(netD.collect_params(), 'adam', {
'learning_rate': lr,
'beta1': beta1
})
