def main(_):
"""
Main script for training.
:return: None.
"""
# Create feature extractor.
mobile_conf = mobileConf.loadMobileConf()
ft_extractor = mobileNet.MobileNet(mobile_conf)
# Load SSD config.
ssd_conf = ssdConf()
ssd_net = ssdNet.SSDNet(ssd_conf, ft_extractor)
# Predict labels and bouding boxes.
input_img = tf.placeholder(
tf.int8,
[None, ssd_conf['input_h'], ssd_conf['input_w'], ssd_conf['input_c']])
labels, logits, bboxes, endpoints = ssd_net.predict(input_img)
# Compute loss and put them to tf.collections.LOSS and other loss.
input_labels = tf.placeholder(tf.int8, [None, ssd_conf['class_num']])
input_bboxes = tf.placeholder(tf.int8, [None, 4])
total_loss = ssd_net.loss(labels=labels,
bboxes=bboxes,
glabels=input_labels,
gbboxes=input_bboxes)
# load traning config.
trainConf = script_conf.loadTrainConf()
# Create optimizer.
optimizer = utils.getOptimizer(trainConf)
# Train model using optimizer.
train_op = optimizer.minimize(total_loss)
# Prepare training data.
dataset = dt.DataSet(trainConf['dataset_path'], trainConf['batch_size'],
trainConf['class_num'])
imgs, sizes, glabels, gbboxes = dataset.getNext()
with tf.Session() as ss:
for i in range(trainConf['epoch_num']):
ss.run(train_op,
feed_dict={
input_img: imgs,
input_labels: glabels,
input_bboxes: gbboxes,
labels: labels,
bboxes: bboxes
})
import network.mobileNet as mobileNet
import common.config as gconf
import tensorflow as tf
import tensorlayer as tl
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
# Create feature extractor.
global_conf = gconf.loadTrainConf()
ft_extractor = mobileNet.MobileNet(global_conf)
path = '/home/autel/data/exp_imgs'
img_name = 'face.jpg'
img = tl.vis.read_image(img_name, path)
img = img[:224, :224]
img = np.reshape(img, [1, 224, 224, 3])
img = tf.constant(np.ones([3, 224, 224, 3]), dtype=tf.float32)
ft, _ = ft_extractor.predict(img)
ss = tf.Session()
# initialize all variables in the session
tl.layers.initialize_global_variables(ss)
# ft = ss.run(ft.outputs, feed_dict={input_img: img})
ft = ss.run(ft.outputs)
ss.close()
print(np.shape(ft))
# print(ft)
def main(_):
"""
Main script for training.
:return: None.
"""
# Create feature extractor.
gconf = conf.loadTrainConf()
# mobilenet = mobileNet.MobileNet(gconf)
# Prepaire data
dataset = dt.DataSet(path=gconf['dataset_path'],
batchsize=gconf['batch_size'],
class_num=gconf['class_num'])
img_name_batch, img_batch, sizes_batch, class_id_batch, box_num_batch, labels_batch, bboxes_batch = dataset.getNext(
)
labels_batch = tf.one_hot(class_id_batch, gconf['class_num'] + 1)
labels_batch = labels_batch[:, 1:]
dataset_itr = dataset._itr
# Predict labels
# img_batch = tf.cast(input_imgs, tf.float32)
input_imgs = tf.placeholder(
tf.float32, [None, gconf['input_size'], gconf['input_size'], 3])
alexnet, _ = nets.resnet_v2.resnet_v2_50(input_imgs,
num_classes=20,
is_training=True)
logits = alexnet
# Compute loss
labels = tf.placeholder(tf.float32, [None, gconf['class_num']])
tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
loss = tf.losses.get_total_loss()
tf.summary.scalar('loss', loss)
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=gconf['learning_rate'])
train_op = optimizer.minimize(loss)
correct_prediction = tf.equal(tf.argmax(logits, axis=1),
tf.argmax(labels, axis=1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('train_acc', accuracy)
summary = tf.summary.merge_all()
init_op = tf.global_variables_initializer()
with open(
'/home/autel/libs/ssd-tensorflow-ljanyst/pascal-voc/trainval/VOCdevkit/VOC2007/classes.json'
) as label_name_file:
class_dict = json.load(label_name_file)
step_cnt = 0
with tf.Session() as sess:
tb_log_writer = tf.summary.FileWriter(gconf['log_dir'], sess.graph)
sess.run(init_op)
for _ in range(gconf['epoch_num']):
sess.run(dataset_itr.initializer)
while True:
step_cnt = step_cnt + 1
try:
# train
imgs_input, labels_input = sess.run(
[img_batch, labels_batch])
# lab_pred, lab_batch = sess.run([labels_pred, class_id_batch], feed_dict={input_imgs:imgs_input,
# labels: labels_input});
# print(lab_pred, lab_batch)
# exit(0)
#
# for img, class_onehot in zip(imgs_input, labels_input):
# utils.visulizeClass(img, class_onehot, class_dict, hold=True)
# plt.waitforbuttonpress()
summary_val, loss_val, train_acc, _ = sess.run(
[summary, loss, accuracy, train_op],
feed_dict={
input_imgs: imgs_input,
labels: labels_input
})
if step_cnt % gconf['log_step'] == 0:
tb_log_writer.add_summary(summary_val, step_cnt)
print('Step %d, loss: %f, train_acc: %f' %
(step_cnt, loss_val, train_acc))
except tf.errors.OutOfRangeError:
# log statistics
# break
break
import network.mobileNet as mobileNet import network.ssdConf as ssdConf import network.ssdNet as ssdNet import common.config as confutil import tensorflow as tf import tensorlayer as tl import numpy as np g_conf = confutil.loadTrainConf() # Create feature extractor. ft_extractor = mobileNet.MobileNet(g_conf) ssd_conf = ssdConf.addSSDConf(g_conf) ssd_net = ssdNet.SSDNet(ssd_conf, ft_extractor) anchors = ssd_net._create_anchors(ssd_net._net_conf['featuremaps'])
import common.dataset as dt
import tensorflow as tf
import common.config as script_conf
import numpy as np
import matplotlib.pyplot as plt
import common.utils as utils
import json
if __name__ == '__main__':
# load traning config.
trainConf = script_conf.loadTrainConf()
dataset = dt.DataSet(path=trainConf['dataset_path'],
batchsize=10,
class_num=trainConf['class_num'])
# img_batch, size_batch, \
img_name_batch, img_batch, sizes_batch, class_id_batch, box_num_batch, labels_batch, bboxes_batch = dataset.getNext(
)
class_id_batch_hot = tf.one_hot(class_id_batch, trainConf['class_num'] + 1)
class_id_batch_hot = class_id_batch_hot[:, 1:]
# labels_mask = tf.greater(labels_batch, 0)
# labels_batch = tf.one_hot(labels_batch, 20)
# labels_batch = tf.boolean_mask(labels_batch, labels_mask, axis=0)
#
# bboxes_batch = tf.boolean_mask(bboxes_batch, labels_mask, axis=0)
# img_fig = plt.imshow(np.zeros([100, 100]))
with open(
'/home/autel/libs/ssd-tensorflow-ljanyst/pascal-voc/trainval/VOCdevkit/VOC2007/classes.json'