def validation_IOU(self, sess, pred, X, Y, keep_prob, is_training):
img_loader2 = data_load.ImgLoader('pascal')
img_loader2.run('val') # train or val
batch_num = 10
input_batch, label_batch = img_loader2.nextbatch(self.input_size, 0)
IOU_sum = 0
for i in range(batch_num):
input_batch, label_batch = img_loader2.nextbatch(
self.input_size, i)
pred_ = sess.run(pred,
feed_dict={
X: input_batch,
Y: label_batch,
keep_prob: 1.0,
is_training: False
})
IOU = util.intersection_over_union(pred_, label_batch)
IOU_sum += IOU
return IOU_sum / batch_num
import data_load
import FCN
import tensorflow as tf
import util
from matplotlib.pyplot import imshow, hist, show, figure, subplot, subplots_adjust, setp
batch_size = 3
image_idx = 2
masker = util.masker()
img_loader = data_load.ImgLoader('pascal')
FCN = FCN.FCN(batch_size, 0.001)
img_loader.run('val') # train or val
input_batch, label_batch = img_loader.nextbatch_for_inference(
batch_size, image_idx)
_, label_batch_cal_loss = img_loader.nextbatch(batch_size, image_idx)
##########################################
##########################################
X = tf.placeholder(
tf.float32,
[None, input_batch.shape[1], input_batch.shape[2], input_batch.shape[3]
]) # h * w * 3
Y = tf.placeholder(tf.float32, [
None, label_batch.shape[1], label_batch.shape[2],
len(masker.class_color_list)
]) # h * w * (class+1) <= 배경포함(+1)
keep_prob = tf.placeholder(tf.float32)
is_training = tf.placeholder(tf.bool)
def run(self, max_iter, already_done):
######already_done = 이미 에폭 돌아간 횟수########
img_loader = data_load.ImgLoader()
img_loader.run('train') # train or val
data_size = len(img_loader.class_path)
batch_num = data_size // self.input_size
# input_batch, label_batch = img_loader.nextbatch(self.input_size, iter)
input_batch, label_batch = img_loader.nextbatch(self.input_size,
0,
stochastic=True)
X = tf.placeholder(tf.float32, [
None, input_batch.shape[1], input_batch.shape[2],
input_batch.shape[3]
]) # h * w * 3
Y = tf.placeholder(tf.float32, [
None, label_batch.shape[1], label_batch.shape[2],
label_batch.shape[3]
]) # h * w * (class+1) <= 배경포함(+1)
keep_prob = tf.placeholder(tf.float32)
is_training = tf.placeholder(tf.bool)
train_op, pred, loss, logit = self.train(X, Y, keep_prob, is_training)
sess = tf.Session()
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state('./model')
if ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path):
saver.restore(sess, ckpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())
for epoch in range(max_iter):
for itr in range(batch_num):
input_batch, label_batch = img_loader.nextbatch(
self.input_size, itr, stochastic=True)
_, loss_, pred_ = sess.run(
[train_op, loss, pred],
feed_dict={
X: input_batch,
Y: label_batch,
keep_prob: 0.5,
is_training: True
})
print('iteration :', itr, ' loss :', loss_)
if (epoch + 1) % 2 == 0:
IOU_this_batch = util.intersection_over_union(
pred_, label_batch)
print('########################\n',
'intersection over union for this batch :',
IOU_this_batch, '\n########################')
IOU_val = self.validation_IOU(sess, pred, X, Y, keep_prob,
is_training)
print('########################\n',
'intersection over union for validation set:', IOU_val,
'\n########################')
model_dir = './model' + str(epoch + 1 +
already_done) + '/model.ckpt'
with open('loss.txt', 'a') as wf:
loss_info = '\nepoch: ' + '%7d' % (
epoch + 1 + already_done
) + ' batch loss: ' + '%7.6f' % loss_ + ' batch IOU: ' + '%7.6f' % IOU_this_batch + ' valiation IOU: ' + '%7.6f' % IOU_val
wf.write(loss_info)
saver.save(sess, model_dir)
else:
model_dir = './model/model.ckpt'
saver.save(sess, model_dir)