def main(args):
"""Get dataset hyperparameters."""
assert len(args) == 3 and isinstance(args[1], str) and isinstance(
args[2], str)
dataset_name = args[1]
model_name = args[2]
"""Set reproduciable random seed"""
tf.set_random_seed(1234)
coord_add = get_coord_add(dataset_name)
dataset_size_train = get_dataset_size_train(dataset_name)
dataset_size_test = get_dataset_size_test(dataset_name)
num_classes = get_num_classes(dataset_name)
create_inputs = get_create_inputs(dataset_name,
is_train=False,
epochs=cfg.epoch)
with tf.Graph().as_default():
num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size)
# num_batches_test = 2 # int(dataset_size_test / cfg.batch_size * 0.1)
num_batches_test = int(dataset_size_test / cfg.batch_size * 0.5)
batch_x, batch_labels = create_inputs()
batch_squash = tf.divide(batch_x, 255.)
batch_x_norm = slim.batch_norm(batch_x,
center=False,
is_training=False,
trainable=False)
output, pose_out = net.build_arch(batch_x_norm,
coord_add,
is_train=False,
num_classes=num_classes)
tf.logging.debug(pose_out.get_shape())
batch_acc = net.test_accuracy(output, batch_labels)
m_op = tf.constant(0.9)
loss, spread_loss, mse, recon_img_squash = net.spread_loss(
output, pose_out, batch_squash, batch_labels, m_op)
tf.summary.scalar('spread_loss', spread_loss)
tf.summary.scalar('reconstruction_loss', mse)
tf.summary.scalar('all_loss', loss)
data_size = int(batch_x.get_shape()[1])
recon_img = tf.multiply(
tf.reshape(recon_img_squash,
shape=[cfg.batch_size, data_size, data_size, 1]), 255.)
orig_img = tf.reshape(batch_x,
shape=[cfg.batch_size, data_size, data_size, 1])
tf.summary.image('orig_image', orig_img)
tf.summary.image('recon_image', recon_img)
saver = tf.train.Saver()
step = 0
tf.summary.scalar('accuracy', batch_acc)
summary_op = tf.summary.merge_all()
session_config = tf.ConfigProto(
device_count={'GPU': 0},
gpu_options={
'allow_growth': 1,
# 'per_process_gpu_memory_fraction': 0.1,
'visible_device_list': '0'
},
allow_soft_placement=True)
with tf.Session(config=session_config) as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if not os.path.exists(cfg.test_logdir +
'/{}/{}/'.format(model_name, dataset_name)):
os.makedirs(cfg.test_logdir +
'/{}/{}/'.format(model_name, dataset_name))
summary_writer = tf.summary.FileWriter(
cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name),
graph=sess.graph) # graph=sess.graph, huge!
files = os.listdir(cfg.logdir +
'/{}/{}/'.format(model_name, dataset_name))
for epoch in range(start_epoch, end_epoch):
# requires a regex to adapt the loss value in the file name here
ckpt_re = ".ckpt-%d" % (num_batches_per_epoch_train * epoch)
for __file in files:
if __file.endswith(ckpt_re + ".index"):
ckpt = os.path.join(
cfg.logdir +
'/{}/{}/'.format(model_name, dataset_name),
__file[:-6])
# ckpt = os.path.join(cfg.logdir, "model.ckpt-%d" % (num_batches_per_epoch_train * epoch))
saver.restore(sess, ckpt)
accuracy_sum = 0
for i in range(num_batches_test):
batch_acc_v, summary_str, orig_image, recon_image = sess.run(
[batch_acc, summary_op, orig_img, recon_img])
print('%d batches are tested.' % step)
summary_writer.add_summary(summary_str, step)
accuracy_sum += batch_acc_v
step += 1
# display original/reconstructed images in matplotlib
plot_imgs(orig_image, i, 'ori')
plot_imgs(recon_image, i, 'rec')
ave_acc = accuracy_sum / num_batches_test
print('the average accuracy is %f' % ave_acc)
def main(args):
"""Get dataset hyperparameters."""
assert len(args) == 3 and isinstance(args[1], str) and isinstance(args[2], str)
dataset_name = args[1]
model_name = args[2]
"""Set reproduciable random seed"""
tf.set_random_seed(1234)
coord_add = get_coord_add(dataset_name)
dataset_size_train = get_dataset_size_train(dataset_name)
dataset_size_test = get_dataset_size_test(dataset_name)
num_classes = get_num_classes(dataset_name)
create_inputs = get_create_inputs(
dataset_name, is_train=False, epochs=cfg.epoch)
with tf.Graph().as_default():
num_batches_per_epoch_train = int(dataset_size_train / cfg.batch_size)
num_batches_test = 2 # int(dataset_size_test / cfg.batch_size * 0.1)
batch_x, batch_labels = create_inputs()
batch_squash = tf.divide(batch_x, 255.)
batch_x_norm = slim.batch_norm(batch_x, center=False, is_training=False, trainable=False)
output, pose_out = net.build_arch(batch_x_norm, coord_add,
is_train=False, num_classes=num_classes)
tf.logging.debug(pose_out.get_shape())
batch_acc = net.test_accuracy(output, batch_labels)
m_op = tf.constant(0.9)
loss, spread_loss, mse, recon_img_squash = net.spread_loss(
output, pose_out, batch_squash, batch_labels, m_op)
tf.summary.scalar('spread_loss', spread_loss)
tf.summary.scalar('reconstruction_loss', mse)
tf.summary.scalar('all_loss', loss)
data_size = int(batch_x.get_shape()[1])
recon_img = tf.multiply(tf.reshape(recon_img_squash, shape=[
cfg.batch_size, data_size, data_size, 1]), 255.)
orig_img = tf.reshape(batch_x, shape=[
cfg.batch_size, data_size, data_size, 1])
tf.summary.image('orig_image', orig_img)
tf.summary.image('recon_image', recon_img)
saver = tf.train.Saver()
step = 0
tf.summary.scalar('accuracy', batch_acc)
summary_op = tf.summary.merge_all()
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True, log_device_placement=False)) as sess:
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
if not os.path.exists(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name)):
os.makedirs(cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name))
summary_writer = tf.summary.FileWriter(
cfg.test_logdir + '/{}/{}/'.format(model_name, dataset_name), graph=sess.graph) # graph=sess.graph, huge!
files = os.listdir(cfg.logdir + '/{}/{}/'.format(model_name, dataset_name))
for epoch in range(14, 15):
# requires a regex to adapt the loss value in the file name here
ckpt_re = ".ckpt-%d" % (num_batches_per_epoch_train * epoch)
for __file in files:
if __file.endswith(ckpt_re + ".index"):
ckpt = os.path.join(
cfg.logdir + '/{}/{}/'.format(model_name, dataset_name), __file[:-6])
# ckpt = os.path.join(cfg.logdir, "model.ckpt-%d" % (num_batches_per_epoch_train * epoch))
saver.restore(sess, ckpt)
accuracy_sum = 0
for i in range(num_batches_test):
batch_acc_v, summary_str, orig_image, recon_image = sess.run(
[batch_acc, summary_op, orig_img, recon_img])
print('%d batches are tested.' % step)
summary_writer.add_summary(summary_str, step)
accuracy_sum += batch_acc_v
step += 1
# display original/reconstructed images in matplotlib
plot_imgs(orig_image, i, 'ori')
plot_imgs(recon_image, i, 'rec')
ave_acc = accuracy_sum / num_batches_test
print('the average accuracy is %f' % ave_acc)