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_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)
# 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)
# Setup Tensorboard logger
configure("log/tensorboard/{}".format(model_name), flush_secs=5)
# Setup logger
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s:%(name)s:%(message)s')
file_handler = logging.FileHandler('log/logger/{}.log'.format(model_name),
mode='w')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
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)
a[i].axis("off")
plt.savefig("best_lest/" +
"{}.png".format(batch_idx * config.batch_size + j),
transparent=0)
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 = {
import os
import numpy as np
import torch
from torch.autograd.variable import Variable
from src.Models.models import Encoder
from src.Models.models import ImitateJoint
from src.Models.models import ParseModelOutput
from src.utils import read_config
from src.utils.generators.mixed_len_generator import MixedGenerateData
from src.utils.train_utils import prepare_input_op, chamfer
config = read_config.Config("config_synthetic.yml")
model_name = config.pretrain_modelpath.split("/")[-1][0:-4]
encoder_net = Encoder()
encoder_net.cuda()
data_labels_paths = {
3: "data/synthetic/one_op/expressions.txt",
5: "data/synthetic/two_ops/expressions.txt",
7: "data/synthetic/three_ops/expressions.txt",
9: "data/synthetic/four_ops/expressions.txt",
11: "data/synthetic/five_ops/expressions.txt",
13: "data/synthetic/six_ops/expressions.txt"
}
# first element of list is num of training examples, and second is number of
# testing examples.
proportion = config.proportion # proportion is in percentage. vary from [1, 100].
dataset_sizes = {
3: [30000, 50 * proportion],
from src.utils import read_config
from src.Models.models import ImitateJoint
from src.Models.models import Encoder
from src.utils.generators.shapenet_generater import Generator
from src.utils.reinforce import Reinforce
from src.utils.train_utils import prepare_input_op
max_len = 13
power = 20
reward = "chamfer"
if len(sys.argv) > 1:
config = read_config.Config(sys.argv[1])
else:
config = read_config.Config("config_synthetic.yml")
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,