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draw.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: draw.py
# Author: Qian Ge <[email protected]>
import os
import argparse
import tensorflow as tf
import numpy as np
import scipy.io
import sys
sys.path.append('../')
import loader
# from src.dataflow.synthetic import Circle
# from src.dataflow.generator import Generator
from src.nets.draw_attention import DRAW
# from src.models.interpolate import linear_interpolate
# from src.helper.visualizer import Visualizer
import config
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--train', action='store_true',
help='Train the model')
parser.add_argument('--viz', action='store_true',
help='')
parser.add_argument('--dataset', type=str, default='mnist')
parser.add_argument('--step', type=int, default=10)
parser.add_argument('--epoch', type=int, default=100)
parser.add_argument('--embed', type=int, default=100)
parser.add_argument('--load', type=int, default=49)
parser.add_argument('--lr', type=float, default=1e-3)
return parser.parse_args()
def read_mnist():
def binary_im(im):
thr = 0.7
im = im / 255.
im[np.where(im < thr)] = 0
im[np.where(im > 0)] = 1
return im
train_generator = loader.loadMNIST(pf=binary_im)
return train_generator
def train():
FLAGS = get_args()
n_code = FLAGS.embed
n_encoder_hidden=256
n_decoder_hidden=256
n_step = FLAGS.step
max_epoch = FLAGS.epoch
save_draw_path = config.save_draw_path
data_name = 'mnist'
im_size = 28
im_channel = 1
max_step = None
read_N = 2
write_N = 5
train_generator = read_mnist()
save_path = os.path.join(save_draw_path, data_name)
train_net = DRAW(
im_channel=im_channel,
n_encoder_hidden=n_encoder_hidden,
n_decoder_hidden=n_decoder_hidden,
n_code=n_code,
im_size=im_size,
n_step=n_step,
read_N=read_N,
write_N=write_N)
train_net.create_train_model(train_generator.batch_data)
generate_net = DRAW(
im_channel=im_channel,
n_encoder_hidden=n_encoder_hidden,
n_decoder_hidden=n_decoder_hidden,
n_code=n_code,
im_size=im_size,
n_step=n_step,
read_N=read_N,
write_N=write_N)
generate_net.create_generate_model(b_size=10)
writer = tf.summary.FileWriter(save_path)
saver = tf.train.Saver()
sessconfig = tf.ConfigProto()
sessconfig.gpu_options.allow_growth = True
with tf.Session(config=sessconfig) as sess:
sess.run(tf.global_variables_initializer())
writer.add_graph(sess.graph)
for i in range(max_epoch):
train_generator.init_iterator(sess)
# train_net.test(sess)
train_net.train_epoch(sess, max_step=max_step, lr=FLAGS.lr, summary_writer=writer)
generate_net.generate_batch(sess, summary_writer=writer)
generate_net.viz_generate_step(sess, save_path, file_id=i)
if i % 10 == 0:
saver.save(sess, '{}/draw_step_{}'.format(save_path, i))
saver.save(sess, '{}/draw_step_{}'.format(save_path, i))
def viz():
FLAGS = get_args()
n_code = FLAGS.embed
n_encoder_hidden=256
n_decoder_hidden=256
n_step = FLAGS.step
max_epoch = FLAGS.epoch
save_draw_path = config.save_draw_path
data_name = 'mnist'
im_size = 28
im_channel = 1
max_step = None
read_N = 2
write_N = 5
train_generator = read_mnist()
save_path = os.path.join(save_draw_path, data_name)
generate_net = DRAW(
im_channel=im_channel,
n_encoder_hidden=n_encoder_hidden,
n_decoder_hidden=n_decoder_hidden,
n_code=n_code,
im_size=im_size,
n_step=n_step,
read_N=read_N,
write_N=write_N)
generate_net.create_generate_model(b_size=400)
saver = tf.train.Saver()
sessconfig = tf.ConfigProto()
sessconfig.gpu_options.allow_growth = True
with tf.Session(config=sessconfig) as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, '{}/draw_step_{}'.format(save_path, FLAGS.load))
generate_net.viz_generate_step(sess, save_path, is_animation=True)
if __name__ == '__main__':
FLAGS = get_args()
if FLAGS.train:
train()
elif FLAGS.viz:
viz()