def load_network_stageI(self):
from model import STAGE1_G, STAGE1_D
netG = STAGE1_G()
netG.apply(weights_init)
print(netG)
netD = STAGE1_D()
netD.apply(weights_init)
print(netD)
if NET_G != '':
state_dict = \
torch.load(NET_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load from: ', NET_G)
if NET_D != '':
state_dict = \
torch.load(NET_D,
map_location=lambda storage, loc: storage)
netD.load_state_dict(state_dict)
print('Load from: ', NET_D)
if CUDA:
netG.cuda()
netD.cuda()
return netG, netD
def load_network_stageI(self):
from model import STAGE1_G, STAGE1_D
netG = STAGE1_G()
netG.apply(weights_init)
print(netG)
netD = STAGE1_D()
netD.apply(weights_init)
print(netD)
if cfg.NET_G != '':
state_dict = \
torch.load(cfg.NET_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load from: ', cfg.NET_G)
if cfg.NET_D != '':
state_dict = \
torch.load(cfg.NET_D,
map_location=lambda storage, loc: storage)
netD.load_state_dict(state_dict)
print('Load from: ', cfg.NET_D)
if cfg.CUDA:
netG.cuda()
netD.cuda()
start_epoch = 0
if cfg.NET_G != '':
istart = cfg.NET_G.rfind('_') + 1
iend = cfg.NET_G.rfind('.')
start_epoch = cfg.NET_G[istart:iend]
start_epoch = int(start_epoch) + 1
return netG, netD, start_epoch
def load_network_stageI(self):
from model import STAGE1_G, STAGE1_D
netG = STAGE1_G()
netG.apply(weights_init)
# print(netG)
netD = STAGE1_D(self.new_arch)
netD.apply(weights_init)
# print(netD)
if cfg.NET_G != '':
state_dict = \
torch.load(cfg.NET_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load from: ', cfg.NET_G)
if cfg.NET_D != '':
state_dict = \
torch.load(cfg.NET_D,
map_location=lambda storage, loc: storage)
netD.load_state_dict(state_dict)
print('Load from: ', cfg.NET_D)
if cfg.CTModel != '':
state_dict=\
torch.load(cfg.CTModel,
map_location=lambda storage, loc: storage)
self.CTallmodel.load_state_dict(state_dict)
print('Load CT Model From: ', cfg.CTModel)
# print(self.CTallmodel)
if cfg.CUDA:
netG.cuda()
netD.cuda()
return netG, netD
def load_network_stageI(self, text_dim, gf_dim, condition_dim, z_dim, df_dim):
from model import STAGE1_G, STAGE1_D
netG = STAGE1_G(text_dim, gf_dim, condition_dim, z_dim, self.cuda)
netG.apply(weights_init)
print(netG)
netD = STAGE1_D(df_dim, condition_dim)
netD.apply(weights_init)
print(netD)
if self.net_g != '':
state_dict = torch.load(self.net_g)
#torch.load(self.net_g, map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load from: ', self.net_g)
if self.net_d != '':
state_dict = torch.load(self.net_d, map_location=lambda storage, loc: storage)
netD.load_state_dict(state_dict)
print('Load from: ', self.net_d)
if self.cuda:
netG.cuda()
netD.cuda()
return netG, netD
def load_network_stageI(self):
from model import STAGE1_G, STAGE1_D
from model import EncoderRNN, LuongAttnDecoderRNN
from model import STAGE1_ImageEncoder, EncodingDiscriminator
netG = STAGE1_G()
netG.apply(weights_init)
#print(netG)
netD = STAGE1_D()
netD.apply(weights_init)
#print(netD)
emb_dim = 300
encoder = EncoderRNN(emb_dim, self.txt_emb, 1, dropout=0.0)
attn_model = 'general'
decoder = LuongAttnDecoderRNN(attn_model,
emb_dim,
len(self.txt_dico.id2word),
self.txt_emb,
n_layers=1,
dropout=0.0)
image_encoder = STAGE1_ImageEncoder()
image_encoder.apply(weights_init)
e_disc = EncodingDiscriminator(emb_dim)
if cfg.NET_G != '':
state_dict = \
torch.load(cfg.NET_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load from: ', cfg.NET_G)
if cfg.NET_D != '':
state_dict = \
torch.load(cfg.NET_D,
map_location=lambda storage, loc: storage)
netD.load_state_dict(state_dict)
print('Load from: ', cfg.NET_D)
if cfg.ENCODER != '':
state_dict = \
torch.load(cfg.ENCODER,
map_location=lambda storage, loc: storage)
encoder.load_state_dict(state_dict)
print('Load from: ', cfg.ENCODER)
if cfg.DECODER != '':
state_dict = \
torch.load(cfg.DECODER,
map_location=lambda storage, loc: storage)
decoder.load_state_dict(state_dict)
print('Load from: ', cfg.DECODER)
if cfg.IMAGE_ENCODER != '':
state_dict = \
torch.load(cfg.IMAGE_ENCODER,
map_location=lambda storage, loc: storage)
image_encoder.load_state_dict(state_dict)
print('Load from: ', cfg.IMAGE_ENCODER)
# load classification model and freeze weights
#clf_model = models.alexnet(pretrained=True)
clf_model = models.vgg16(pretrained=True)
for param in clf_model.parameters():
param.requires_grad = False
if cfg.CUDA:
netG.cuda()
netD.cuda()
encoder.cuda()
decoder.cuda()
image_encoder.cuda()
e_disc.cuda()
clf_model.cuda()
# ## finetune model for a bit???
# output_size = 512 * 2 * 2
# num_classes = 200
# clf_model.classifier = nn.Sequential(
# nn.Linear(output_size, 1024, bias=True),
# nn.LeakyReLU(0.2),
# nn.Dropout(0.5),
# nn.Linear(1024, num_classes, bias=True)
# )
# clf_optim = optim.SGD(clf_model.parameters(), lr=1e-2, momentum=0.9)
return netG, netD, encoder, decoder, image_encoder, e_disc, clf_model
z = self.reparameterize(mu, logvar)
#z_p = self.decode_lin(z).view(-1, self.nef, 4, 4)
#img = self.decode_img(z_p)
#z = self.encode_img(x).view(-1, 480 * 4 * 4)
img = self.decode(z)
# mu = None
# logvar = None
return img, mu, logvar
model = VAE()
netD = STAGE1_D()
if args.cuda:
model.cuda()
netD.cuda()
model_params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(model_params, lr=1e-3)
optd = optim.Adam(netD.parameters(), lr=1e-4)
# Reconstruction + KL divergence losses summed over all elements and batch
def loss_function(recon_x, x, mu, logvar):
#pdb.set_trace()
#BCE = F.binary_cross_entropy(recon_x, x, size_average=False)
BCE = F.smooth_l1_loss(recon_x, F.sigmoid(x), size_average=False)