Python Encoder.Encoder Examples

Example #1

File: fid_score.py Project: HomerW/CSGNet

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

Example #2

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

Example #3

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)

Example #4

File: train_cad.py Project: ml-lab/CSGNet

# 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)

Example #5

File: test_synthetic.py Project: HomerW/CSGNet

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],