def get_blank_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)
return imitate_net
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)
logger.info(config.config)
# CNN encoder
encoder_net = Encoder(config.encoder_drop)
encoder_net.cuda()
# 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]
# RNN decoder
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.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)
3: [25000, 50 * proportion],
5: [100000, 500 * proportion],
7: [150000, 500 * proportion],
9: [250000, 500 * proportion],
11: [350000, 1000 * proportion],
13: [350000, 1000 * proportion]
}
#dataset_sizes = {k: [x // 100 for x in v] for k, v in dataset_sizes.items()}
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()
with open("terminals.txt", "r") as file:
unique_draw = file.readlines()
for index, e in enumerate(unique_draw):
unique_draw[index] = e[0:-1]
# 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
plt.close("all")
# with open("best_st_expressions.txt", "w") as file:
# for e in pred_expressions:
# file.write(f"{e}\n")
# break
return CDs / (config.test_size // config.batch_size)
config = read_config.Config("config_synthetic.yml")
device = torch.device("cuda")
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)
try:
pretrained_dict = torch.load("imitate_27.pth", map_location=device)
except Exception as e:
print(e)
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)
3: [30000, 50 * proportion],
5: [110000, 500 * proportion],
7: [170000, 500 * proportion],
9: [270000, 500 * proportion],
11: [370000, 1000 * proportion],
13: [370000, 1000 * proportion]
}
dataset_sizes = {k: [x // 100 for x in v] for k, v in dataset_sizes.items()}
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:
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)
encoder_net = Encoder()
encoder_net.cuda()
# CNN encoder
encoder_net = Encoder(config.encoder_drop)
encoder_net.cuda()
# 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.cuda()
imitate_net.epsilon = 0
test_size = 3000
# This is to find top-1 performance.
paths = [config.pretrain_modelpath]
save_viz = False
for p in paths:
print(p, flush=True)
config.pretrain_modelpath = p
image_path = "data/cad/predicted_images/{}/top_1_prediction/images/".format(
dataset_sizes = {
3: [30000, 50 * proportion],
5: [110000, 500 * proportion],
7: [170000, 500 * proportion],
9: [270000, 500 * proportion],
11: [370000, 1000 * proportion],
13: [370000, 1000 * proportion]
}
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:
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)