def get_csgnet():
config = read_config.Config("config_synthetic.yml")
# Encoder
encoder_net = Encoder(config.encoder_drop)
encoder_net = encoder_net.to(device)
imitate_net = ImitateJoint(hd_sz=config.hidden_size,
input_size=config.input_size,
encoder=encoder_net,
mode=config.mode,
num_draws=400,
canvas_shape=config.canvas_shape)
imitate_net = imitate_net.to(device)
print("pre loading model")
pretrained_dict = torch.load(config.pretrain_modelpath,
map_location=device)
imitate_net_dict = imitate_net.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in imitate_net_dict
}
imitate_net_dict.update(pretrained_dict)
imitate_net.load_state_dict(imitate_net_dict)
return imitate_net.encoder
def get_csgnet():
config = read_config.Config("config_synthetic.yml")
# Encoder
encoder_net = Encoder(config.encoder_drop)
encoder_net = encoder_net.to(device)
# Load the terminals symbols of the grammar
with open("terminals.txt", "r") as file:
unique_draw = file.readlines()
for index, e in enumerate(unique_draw):
unique_draw[index] = e[0:-1]
imitate_net = ImitateJoint(hd_sz=config.hidden_size,
input_size=config.input_size,
encoder=encoder_net,
mode=config.mode,
num_draws=len(unique_draw),
canvas_shape=config.canvas_shape)
imitate_net = imitate_net.to(device)
print("pre loading model")
pretrained_dict = torch.load(config.pretrain_modelpath,
map_location=device)
imitate_net_dict = imitate_net.state_dict()
imitate_pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in imitate_net_dict
}
imitate_net_dict.update(imitate_pretrained_dict)
imitate_net.load_state_dict(imitate_net_dict)
for param in imitate_net.parameters():
param.requires_grad = True
for param in encoder_net.parameters():
param.requires_grad = True
return (encoder_net, imitate_net)
mode=config.mode,
num_draws=len(unique_draw),
canvas_shape=config.canvas_shape)
imitate_net.cuda()
imitate_net.epsilon = config.eps
if config.preload_model:
print("pre loading model")
pretrained_dict = torch.load(config.pretrain_modelpath)
imitate_net_dict = imitate_net.state_dict()
pretrained_dict = {
k: v
for k, v in pretrained_dict.items() if k in imitate_net_dict
}
imitate_net_dict.update(pretrained_dict)
imitate_net.load_state_dict(imitate_net_dict)
for param in imitate_net.parameters():
param.requires_grad = True
for param in encoder_net.parameters():
param.requires_grad = True
generator = Generator()
reinforce = Reinforce(unique_draws=unique_draw)
if config.optim == "sgd":
optimizer = optim.SGD(
[para for para in imitate_net.parameters() if para.requires_grad],
weight_decay=config.weight_decay,
momentum=0.9,
lr=config.lr,
generator = MixedGenerateData(
data_labels_paths=data_labels_paths,
batch_size=config.batch_size,
canvas_shape=config.canvas_shape)
imitate_net = ImitateJoint(
hd_sz=config.hidden_size,
input_size=config.input_size,
encoder=encoder_net,
mode=config.mode,
num_draws=len(generator.unique_draw),
canvas_shape=config.canvas_shape)
imitate_net.cuda()
if config.preload_model:
imitate_net.load_state_dict(torch.load(config.pretrain_modelpath))
for param in imitate_net.parameters():
param.requires_grad = True
for param in encoder_net.parameters():
param.requires_grad = True
max_len = max(data_labels_paths.keys())
optimizer = optim.Adam(
[para for para in imitate_net.parameters() if para.requires_grad],
weight_decay=config.weight_decay,
lr=config.lr)
reduce_plat = LearningRate(
