solver.py
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import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
import pickle
import os
import scipy.io
import scipy.misc
class Solver(object):
def __init__(self, model, batch_size=100, pretrain_iter=20000, train_iter=2000, sample_iter=100,
svhn_dir='svhn', mnist_dir='mnist', log_dir='logs', sample_save_path='sample',
model_save_path='model', pretrained_model='model/svhn_model-20000', test_model='model/dtn-600'):
self.model = model
self.batch_size = batch_size
self.pretrain_iter = pretrain_iter
self.train_iter = train_iter
self.sample_iter = sample_iter
self.svhn_dir = svhn_dir
self.mnist_dir = mnist_dir
self.log_dir = log_dir
self.sample_save_path = sample_save_path
self.model_save_path = model_save_path
self.pretrained_model = pretrained_model
self.test_model = test_model
self.config = tf.ConfigProto()
self.config.gpu_options.allow_growth=True
def load_svhn(self, image_dir, split='train'):
print ('loading svhn image dataset..')
if self.model.mode == 'pretrain':
image_file = 'extra_32x32.mat' if split=='train' else 'test_32x32.mat'
else:
image_file = 'train_32x32.mat' if split=='train' else 'test_32x32.mat'
image_dir = os.path.join(image_dir, image_file)
svhn = scipy.io.loadmat(image_dir)
images = np.transpose(svhn['X'], [3, 0, 1, 2]) / 127.5 - 1
labels = svhn['y'].reshape(-1)
labels[np.where(labels==10)] = 0
print ('finished loading svhn image dataset..!')
return images, labels
def load_mnist(self, image_dir, split='train'):
print ('loading mnist image dataset..')
image_file = 'train.pkl' if split=='train' else 'test.pkl'
image_dir = os.path.join(image_dir, image_file)
with open(image_dir, 'rb') as f:
mnist = pickle.load(f)
images = mnist['X'] / 127.5 - 1
labels = mnist['y']
print ('finished loading mnist image dataset..!')
return images, labels
def merge_images(self, sources, targets, k=10):
_, h, w, _ = sources.shape
row = int(np.sqrt(self.batch_size))
merged = np.zeros([row*h, row*w*2, 3])
for idx, (s, t) in enumerate(zip(sources, targets)):
i = idx // row
j = idx % row
merged[i*h:(i+1)*h, (j*2)*h:(j*2+1)*h, :] = s
merged[i*h:(i+1)*h, (j*2+1)*h:(j*2+2)*h, :] = t
return merged
def pretrain(self):
# load svhn dataset
train_images, train_labels = self.load_svhn(self.svhn_dir, split='train')
test_images, test_labels = self.load_svhn(self.svhn_dir, split='test')
# build a graph
model = self.model
model.build_model()
with tf.Session(config=self.config) as sess:
tf.global_variables_initializer().run()
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter(logdir=self.log_dir, graph=tf.get_default_graph())
for step in range(self.pretrain_iter+1):
i = step % int(train_images.shape[0] / self.batch_size)
batch_images = train_images[i*self.batch_size:(i+1)*self.batch_size]
batch_labels = train_labels[i*self.batch_size:(i+1)*self.batch_size]
feed_dict = {model.images: batch_images, model.labels: batch_labels}
sess.run(model.train_op, feed_dict)
if (step+1) % 10 == 0:
summary, l, acc = sess.run([model.summary_op, model.loss, model.accuracy], feed_dict)
rand_idxs = np.random.permutation(test_images.shape[0])[:self.batch_size]
test_acc, _ = sess.run(fetches=[model.accuracy, model.loss],
feed_dict={model.images: test_images[rand_idxs],
model.labels: test_labels[rand_idxs]})
summary_writer.add_summary(summary, step)
print ('Step: [%d/%d] loss: [%.6f] train acc: [%.2f] test acc [%.2f]' \
%(step+1, self.pretrain_iter, l, acc, test_acc))
if (step+1) % 1000 == 0:
saver.save(sess, os.path.join(self.model_save_path, 'svhn_model'), global_step=step+1)
print ('svhn_model-%d saved..!' %(step+1))
def train(self):
# load svhn dataset
svhn_images, _ = self.load_svhn(self.svhn_dir, split='train')
mnist_images, _ = self.load_mnist(self.mnist_dir, split='train')
# build a graph
model = self.model
model.build_model()
# make directory if not exists
if tf.gfile.Exists(self.log_dir):
tf.gfile.DeleteRecursively(self.log_dir)
tf.gfile.MakeDirs(self.log_dir)
with tf.Session(config=self.config) as sess:
# initialize G and D
tf.global_variables_initializer().run()
# restore variables of F
print ('loading pretrained model F..')
#variables_to_restore = slim.get_model_variables(scope='content_extractor')
#restorer = tf.train.Saver(variables_to_restore)
#restorer.restore(sess, self.pretrained_model)
restorer = tf.train.Saver()
restorer.restore(sess, 'model/dtn-1600')
summary_writer = tf.summary.FileWriter(logdir=self.log_dir, graph=tf.get_default_graph())
saver = tf.train.Saver()
print ('start training..!')
f_interval = 15
for step in range(self.train_iter+1):
i = step % int(svhn_images.shape[0] / self.batch_size)
# train the model for source domain S
src_images = svhn_images[i*self.batch_size:(i+1)*self.batch_size]
feed_dict = {model.src_images: src_images}
sess.run(model.d_train_op_src, feed_dict)
sess.run([model.g_train_op_src], feed_dict)
sess.run([model.g_train_op_src], feed_dict)
sess.run([model.g_train_op_src], feed_dict)
sess.run([model.g_train_op_src], feed_dict)
sess.run([model.g_train_op_src], feed_dict)
sess.run([model.g_train_op_src], feed_dict)
if step > 1600:
f_interval = 30
if i % f_interval == 0:
sess.run(model.f_train_op_src, feed_dict)
if (step+1) % 10 == 0:
summary, dl, gl, fl = sess.run([model.summary_op_src, \
model.d_loss_src, model.g_loss_src, model.f_loss_src], feed_dict)
summary_writer.add_summary(summary, step)
print ('[Source] step: [%d/%d] d_loss: [%.6f] g_loss: [%.6f] f_loss: [%.6f]' \
%(step+1, self.train_iter, dl, gl, fl))
# train the model for target domain T
j = step % int(mnist_images.shape[0] / self.batch_size)
trg_images = mnist_images[j*self.batch_size:(j+1)*self.batch_size]
feed_dict = {model.src_images: src_images, model.trg_images: trg_images}
sess.run(model.d_train_op_trg, feed_dict)
sess.run(model.d_train_op_trg, feed_dict)
sess.run(model.g_train_op_trg, feed_dict)
sess.run(model.g_train_op_trg, feed_dict)
sess.run(model.g_train_op_trg, feed_dict)
sess.run(model.g_train_op_trg, feed_dict)
if (step+1) % 10 == 0:
summary, dl, gl = sess.run([model.summary_op_trg, \
model.d_loss_trg, model.g_loss_trg], feed_dict)
summary_writer.add_summary(summary, step)
print ('[Target] step: [%d/%d] d_loss: [%.6f] g_loss: [%.6f]' \
%(step+1, self.train_iter, dl, gl))
if (step+1) % 200 == 0:
saver.save(sess, os.path.join(self.model_save_path, 'dtn'), global_step=step+1)
print ('model/dtn-%d saved' %(step+1))
def eval(self):
# build model
model = self.model
model.build_model()
# load svhn dataset
svhn_images, _ = self.load_svhn(self.svhn_dir)
with tf.Session(config=self.config) as sess:
# load trained parameters
print ('loading test model..')
saver = tf.train.Saver()
saver.restore(sess, self.test_model)
print ('start sampling..!')
for i in range(self.sample_iter):
# train model for source domain S
batch_images = svhn_images[i*self.batch_size:(i+1)*self.batch_size]
feed_dict = {model.images: batch_images}
sampled_batch_images = sess.run(model.sampled_images, feed_dict)
# merge and save source images and sampled target images
merged = self.merge_images(batch_images, sampled_batch_images)
path = os.path.join(self.sample_save_path, 'sample-%d-to-%d.png' %(i*self.batch_size, (i+1)*self.batch_size))
scipy.misc.imsave(path, merged)
print ('saved %s' %path)