# sess.run(loss, feed_dict={inputs:images, b_batch_:b_batch, labels_:labels})
with tf.Session() as sess:
train_writer = tf.summary.FileWriter('/tmp/yolo-tf/train/train',
sess.graph)
merged = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
for i in range(config["EPOCH_SIZE"]):
print("epoch number :{}".format(i))
for j in range(
int(
len(open(dataset_path + "train.txt", "r").readlines()) /
config["BATCH_SIZE"])):
print("doing stuff on {}th batch".format(j))
images, b_batch, labels = loader.next_batch(config["BATCH_SIZE"],
print_img_files=False)
summary = sess.run([merged, train_step],
feed_dict={
inputs: images,
b_batch_: b_batch,
labels_: labels
})
l = sess.run(loss,
feed_dict={
inputs: images,
b_batch_: b_batch,
labels_: labels
})
if np.isnan(l):
exit(0)
train_writer.add_summary(summary[0], j)
if sys.argv[1] == "train":
with tf.Session() as sess:
train_writer = tf.summary.FileWriter('/tmp/yolo-tf/train/train',
sess.graph)
merged = tf.summary.merge_all()
sess.run(tf.global_variables_initializer())
for i in range(config["EPOCH_SIZE"]):
print("epoch number :{}".format(i))
for j in range(
int(
len(open(dataset_path + "train.txt", "r").readlines())
/ config["BATCH_SIZE"])):
print("doing stuff on {}th batch".format(j))
images, labels_ = loader.next_batch(config["BATCH_SIZE"],
print_img_files=False)
summary = sess.run([merged, train_step],
feed_dict={
x: images,
labels: labels_
})
# summary = sess.run([train_step], feed_dict={x:images,labels:labels_})
ls_val = sess.run(ls, feed_dict={x: images, labels: labels_})
print("loss : {}".format(ls_val))
train_writer.add_summary(summary[0], j)
loader.set_batch_ptr(0)
if i % 100 == 0:
save_path = saver.save(
sess,
config["MODEL_SAVE_PATH"] + "model_{}.ckpt".format(i))
print("Model at {} epoch saved at {}".format(i, save_path))
import config.parameters as p
from loss import losses
from data.loader import Loader
dataset_path = sys.argv[1]
config = p.getParams()
loader = Loader(dataset_path, config, "bbox")
arch = Arch(config)
preds = arch.darknet()
x = arch.getX()
saver = tf.train.Saver()
if not os.path.exists(config["MODEL_SAVE_PATH"]):
os.mkdir(config["MODEL_SAVE_PATH"])
with tf.Session() as sess:
for i in range(config["EPOCH_SIZE"]):
for j in range(int(len(open(dataset_path+"train.txt","r").readlines())/config["BATCH_SIZE"])):
print ("doing stuff on {}th batch".format(j))
images,labels = loader.next_batch(config["BATCH_SIZE"])
ls = losses.yolo_loss(preds, config, labels)
train_step = tf.train.AdamOptimizer(1e-4).minimize(ls)
sess.run(tf.global_variables_initializer())
sess.run(train_step, feed_dict={x:images})
ls_val = sess.run(ls, feed_dict={x:images})
print ("loss : {}".format(ls_val))
if i%100 == 0:
save_path = saver.save(sess, config["MODEL_SAVE_PATH"]+"model_{}.ckpt".format(i))
print ("Model at {} epoch saved at {}".format(i, save_path))