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main.py
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import os
import sys
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from config import cfg
from utils import load_data
from capsNet import CapsNet
def save_to():
if not os.path.exists(cfg.results):
os.mkdir(cfg.results)
if cfg.is_training:
loss = cfg.results + '/loss.csv'
train_acc = cfg.results + '/train_acc.csv'
val_acc = cfg.results + '/val_acc.csv'
if os.path.exists(val_acc):
os.remove(val_acc)
if os.path.exists(loss):
os.remove(loss)
if os.path.exists(train_acc):
os.remove(train_acc)
fd_train_acc = open(train_acc, 'w')
fd_train_acc.write('step,train_acc\n')
fd_loss = open(loss, 'w')
fd_loss.write('step,loss\n')
fd_val_acc = open(val_acc, 'w')
fd_val_acc.write('step,val_acc\n')
return(fd_train_acc, fd_loss, fd_val_acc)
else:
test_acc = cfg.results + '/test_acc.csv'
if os.path.exists(test_acc):
os.remove(test_acc)
fd_test_acc = open(test_acc, 'w')
fd_test_acc.write('test_acc\n')
return(fd_test_acc)
def train(model, supervisor, num_label):
trX, trY, num_tr_batch, valX, valY, num_val_batch = load_data(cfg.dataset, cfg.batch_size, is_training=True)
Y = valY[:num_val_batch * cfg.batch_size].reshape((-1, 1))
fd_train_acc, fd_loss, fd_val_acc = save_to()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with supervisor.managed_session(config=config) as sess:
print("\nNote: all of results will be saved to directory: " + cfg.results)
for epoch in range(cfg.epoch):
sys.stdout.write('Training for epoch ' + str(epoch) + '/' + str(cfg.epoch) + ':')
sys.stdout.flush()
if supervisor.should_stop():
print('supervisor stoped!')
break
for step in tqdm(range(num_tr_batch), total=num_tr_batch, ncols=70, leave=False, unit='b'):
start = step * cfg.batch_size
end = start + cfg.batch_size
global_step = epoch * num_tr_batch + step
if global_step % cfg.train_sum_freq == 0:
_, loss, train_acc, summary_str = sess.run([model.train_op, model.total_loss, model.accuracy, model.train_summary])
assert not np.isnan(loss), 'Something wrong! loss is nan...'
supervisor.summary_writer.add_summary(summary_str, global_step)
fd_loss.write(str(global_step) + ',' + str(loss) + "\n")
fd_loss.flush()
fd_train_acc.write(str(global_step) + ',' + str(train_acc / cfg.batch_size) + "\n")
fd_train_acc.flush()
else:
sess.run(model.train_op)
if cfg.val_sum_freq != 0 and (global_step) % cfg.val_sum_freq == 0:
val_acc = 0
for i in range(num_val_batch):
start = i * cfg.batch_size
end = start + cfg.batch_size
acc = sess.run(model.accuracy, {model.X: valX[start:end], model.labels: valY[start:end]})
val_acc += acc
val_acc = val_acc / (cfg.batch_size * num_val_batch)
fd_val_acc.write(str(global_step) + ',' + str(val_acc) + '\n')
fd_val_acc.flush()
if (epoch + 1) % cfg.save_freq == 0:
supervisor.saver.save(sess, cfg.logdir + '/model_epoch_%04d_step_%02d' % (epoch, global_step))
fd_val_acc.close()
fd_train_acc.close()
fd_loss.close()
def evaluation(model, supervisor, num_label):
teX, teY, num_te_batch = load_data(cfg.dataset, cfg.batch_size, is_training=False)
fd_test_acc = save_to()
with supervisor.managed_session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
supervisor.saver.restore(sess, tf.train.latest_checkpoint(cfg.logdir))
tf.logging.info('Model restored!')
test_acc = 0
for i in tqdm(range(num_te_batch), total=num_te_batch, ncols=70, leave=False, unit='b'):
start = i * cfg.batch_size
end = start + cfg.batch_size
acc = sess.run(model.accuracy, {model.X: teX[start:end], model.labels: teY[start:end]})
test_acc += acc
test_acc = test_acc / (cfg.batch_size * num_te_batch)
fd_test_acc.write(str(test_acc))
fd_test_acc.close()
print('Test accuracy has been saved to ' + cfg.results + '/test_accuracy.txt')
def main(_):
tf.logging.info(' Loading Graph...')
num_label = 10
model = CapsNet()
tf.logging.info(' Graph loaded')
sv = tf.train.Supervisor(graph=model.graph, logdir=cfg.logdir, save_model_secs=0)
if cfg.is_training:
tf.logging.info(' Start trainging...')
train(model, sv, num_label)
tf.logging.info('Training done')
else:
evaluation(model, sv, num_label)
if __name__ == "__main__":
tf.app.run()