D0 = model.E_Net_2()
encoder_model = torch.load("./Dmodel")
# 24*24
enet = model.E_Net_2().cuda()
enet.load_state_dict(encoder_model)
add_in_feature = 240+ hidden_d # Add one dimension data for the input_feature data.
gnet = model.G_Net_FM_3(ngpu,add_in_feature,main_gpu=main_gpu).cuda()
# g_model = torch.load("./fm21/G_95000.model")
# gnet.load_state_dict(g_model)
d_in_demension = 2
dnet = model.D_Net_conv(ngpu,d_in_demension,main_gpu=main_gpu).cuda()
# nosie_d = 10
g_net_optimizer = optim.Adam(gnet.parameters(),lr = 1e-4)
d_net_optimizer = optim.Adam(dnet.parameters(), lr = 1e-4)
check_points = 500
num_epoches = 100000
criterion = nn.BCELoss()
for epoch in (range(1,num_epoches)):
# print("give me a clue")
# data, label = mnist.train.next_batch(batch_size)
data, label = mm.batch_next(batch_size, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], shuffle=True)
dnet.zero_grad()
gnet.zero_grad()
mm = data_convert.owndata()
num = '0'
out_dir = 'out_fc_{}_{}/'.format(datetime.now(), num)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
shutil.copyfile(sys.argv[0], out_dir + '/training_script.py')
# else:
# print("you have already creat one.")
# exit(1)
sys.stdout = mutil.Logger(out_dir) #
in_channel = 1
G = model.G_Net_conv(ngpu).cuda()
D = model.D_Net_conv(ngpu, in_channel).cuda()
"""Weight Initialization"""
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
# elif classname.find('BatchNorm') != -1:
# m.weight.data.normal_(1.0, 0.02)
# m.bias.data.fill_(0)
G.apply(weights_init)
# G.load_state_dict(torch.load('./out_conv_part/G_20000.model'))
D.apply(weights_init)
out_dir = './cifar100_result/basic_{}_{}/'.format(datetime.now(), num)
out_dir.replace(" ", "_")
if not os.path.exists(out_dir):
os.makedirs(out_dir)
shutil.copyfile(sys.argv[0], out_dir + '/training_script.py')
sys.stdout = mutil.Logger(out_dir)
in_channel = 4
d_num = 3
G = model.G_Net_conv_32(ngpu,
main_gpu=gpu,
in_channel=Z_dim + label_dim,
out_channel=3).cuda()
D = model.D_Net_conv(ngpu, in_channel, main_gpu=gpu).cuda()
"""Weight Initialization"""
# def weights_init(m):
# classname = m.__class__.__name__
# if classname.find('Conv') != -1:
# m.weight.data.normal_(0.0, 0.02)
""" ===================== TRAINING ======================== """
d_num = 3
# avd_num = 1/d_num
G_solver = optim.Adam(G.parameters(), lr=1e-4)
D_solver = optim.Adam(D.parameters(), lr=2e-5)
ones_label = Variable(torch.ones(mb_size)).cuda()
zeros_label = Variable(torch.zeros(mb_size)).cuda()
c_label[i:(i + 6), i / 6] = 1.
sys.stdout = mutil.Logger(out_dir)
# else:
# print("you have already creat one.")
# exit(1)
#
#
# def xavier_init(size):
# in_dim = size[0]
# xavier_stddev = 1. / np.sqrt(in_dim / 2.)
# return Variable(torch.randn(*size) * xavier_stddev, requires_grad=True)
in_channel = 2
G = model.G_Net_conv(ngpu).cuda()
D = model.D_Net_conv(ngpu, 1).cuda()
E = model.Ev_Net_conv(ngpu, 1).cuda()
"""Weight Initialization"""
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
# elif classname.find('BatchNorm') != -1:
# m.weight.data.normal_(1.0, 0.02)
# m.bias.data.fill_(0)
G.apply(weights_init)
D.apply(weights_init)
cnt = 0
num = '0'
out_dir = './cifar100_result/basic_{}_{}/'.format(datetime.now(), num)
out_dir.replace(" ", "_")
if not os.path.exists(out_dir):
os.makedirs(out_dir)
shutil.copyfile(sys.argv[0], out_dir + '/training_script.py')
sys.stdout = mutil.Logger(out_dir)
in_channel = 2
d_num = 3
G = model.G_Net_conv(ngpu).cuda()
D_list = [model.D_Net_conv(ngpu, in_channel).cuda() for i in range(d_num)]
"""Weight Initialization"""
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
""" ===================== TRAINING ======================== """
d_num = 3
# avd_num = 1/d_num
G_solver = optim.Adam(G.parameters(), lr=1e-4)
