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train.py
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train.py
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#coding:utf-8
import os
import sys
from datetime import datetime
import numpy as np
import tensorflow as tf
from tensorboard.plugins import projector
from train_models.MTCNN_config import config
sys.path.append("../prepare_data")
print(sys.path)
from prepare_data.read_tfrecord_v2 import read_multi_tfrecords,read_single_tfrecord
import random
import cv2
def train_model(base_lr, loss, data_num):
"""
train model
:param base_lr: base learning rate
:param loss: loss
:param data_num:
:return:
train_op, lr_op
"""
lr_factor = 0.1
global_step = tf.Variable(0, trainable=False)
#LR_EPOCH [8,14]
#boundaried [num_batch,num_batch]
boundaries = [int(epoch * data_num / config.BATCH_SIZE) for epoch in config.LR_EPOCH]
#lr_values[0.01,0.001,0.0001,0.00001]
lr_values = [base_lr * (lr_factor ** x) for x in range(0, len(config.LR_EPOCH) + 1)]
#control learning rate
lr_op = tf.train.piecewise_constant(global_step, boundaries, lr_values)
optimizer = tf.train.MomentumOptimizer(lr_op, 0.9)
train_op = optimizer.minimize(loss, global_step)
return train_op, lr_op
'''
certain samples mirror
def random_flip_images(image_batch,label_batch,landmark_batch):
num_images = image_batch.shape[0]
random_number = npr.choice([0,1],num_images,replace=True)
#the index of image needed to flip
indexes = np.where(random_number>0)[0]
fliplandmarkindexes = np.where(label_batch[indexes]==-2)[0]
#random flip
for i in indexes:
cv2.flip(image_batch[i],1,image_batch[i])
#pay attention: flip landmark
for i in fliplandmarkindexes:
landmark_ = landmark_batch[i].reshape((-1,2))
landmark_ = np.asarray([(1-x, y) for (x, y) in landmark_])
landmark_[[0, 1]] = landmark_[[1, 0]]#left eye<->right eye
landmark_[[3, 4]] = landmark_[[4, 3]]#left mouth<->right mouth
landmark_batch[i] = landmark_.ravel()
return image_batch,landmark_batch
'''
# all mini-batch mirror
def random_flip_images(image_batch,label_batch,landmark_batch):
#mirror
if random.choice([0,1]) > 0:
num_images = image_batch.shape[0]
fliplandmarkindexes = np.where(label_batch==-2)[0]
flipposindexes = np.where(label_batch==1)[0]
#only flip
flipindexes = np.concatenate((fliplandmarkindexes,flipposindexes))
#random flip
for i in flipindexes:
cv2.flip(image_batch[i],1,image_batch[i])
#pay attention: flip landmark
for i in fliplandmarkindexes:
landmark_ = landmark_batch[i].reshape((-1,2))
landmark_ = np.asarray([(1-x, y) for (x, y) in landmark_])
landmark_[[0, 1]] = landmark_[[1, 0]]#left eye<->right eye
landmark_[[3, 4]] = landmark_[[4, 3]]#left mouth<->right mouth
landmark_batch[i] = landmark_.ravel()
return image_batch,landmark_batch
def image_color_distort(inputs):
inputs = tf.image.random_contrast(inputs, lower=0.5, upper=1.5)
inputs = tf.image.random_brightness(inputs, max_delta=0.2)
inputs = tf.image.random_hue(inputs,max_delta= 0.2)
inputs = tf.image.random_saturation(inputs,lower = 0.5, upper= 1.5)
return inputs
def train(net_factory, prefix, end_epoch, base_dir,
display=200, base_lr=0.01):
"""
train PNet/RNet/ONet
:param net_factory:
:param prefix: model path
:param end_epoch:
:param dataset:
:param display:
:param base_lr:
:return:
"""
net = prefix.split('/')[-1]
#label file
label_file = os.path.join(base_dir,'train_%s_landmark.txt' % net)
#label_file = os.path.join(base_dir,'landmark_12_few.txt')
print(label_file)
f = open(label_file, 'r')
# get number of training examples
num = len(f.readlines())
print("Total size of the dataset is: ", num)
print(prefix)
#PNet use this method to get data
if net == 'PNet':
#dataset_dir = os.path.join(base_dir,'train_%s_ALL.tfrecord_shuffle' % net)
dataset_dir = os.path.join(base_dir,'train_%s_landmark.tfrecord_shuffle' % net)
print('dataset dir is:',dataset_dir)
image_batch, label_batch, bbox_batch,landmark_batch = read_single_tfrecord(dataset_dir, config.BATCH_SIZE, net)
#RNet use 3 tfrecords to get data
else:
pos_dir = os.path.join(base_dir,'pos_landmark.tfrecord_shuffle')
part_dir = os.path.join(base_dir,'part_landmark.tfrecord_shuffle')
neg_dir = os.path.join(base_dir,'neg_landmark.tfrecord_shuffle')
#landmark_dir = os.path.join(base_dir,'landmark_landmark.tfrecord_shuffle')
landmark_dir = os.path.join('../../DATA/imglists/RNet','landmark_landmark.tfrecord_shuffle')
dataset_dirs = [pos_dir,part_dir,neg_dir,landmark_dir]
pos_radio = 1.0/6;part_radio = 1.0/6;landmark_radio=1.0/6;neg_radio=3.0/6
pos_batch_size = int(np.ceil(config.BATCH_SIZE*pos_radio))
assert pos_batch_size != 0,"Batch Size Error "
part_batch_size = int(np.ceil(config.BATCH_SIZE*part_radio))
assert part_batch_size != 0,"Batch Size Error "
neg_batch_size = int(np.ceil(config.BATCH_SIZE*neg_radio))
assert neg_batch_size != 0,"Batch Size Error "
landmark_batch_size = int(np.ceil(config.BATCH_SIZE*landmark_radio))
assert landmark_batch_size != 0,"Batch Size Error "
batch_sizes = [pos_batch_size,part_batch_size,neg_batch_size,landmark_batch_size]
#print('batch_size is:', batch_sizes)
image_batch, label_batch, bbox_batch,landmark_batch = read_multi_tfrecords(dataset_dirs,batch_sizes, net)
#landmark_dir
if net == 'PNet':
image_size = 12
radio_cls_loss = 1.0;radio_bbox_loss = 0.5;radio_landmark_loss = 0.5;
elif net == 'RNet':
image_size = 24
radio_cls_loss = 1.0;radio_bbox_loss = 0.5;radio_landmark_loss = 0.5;
else:
radio_cls_loss = 1.0;radio_bbox_loss = 0.5;radio_landmark_loss = 1;
image_size = 48
#define placeholder
input_image = tf.placeholder(tf.float32, shape=[config.BATCH_SIZE, image_size, image_size, 3], name='input_image')
label = tf.placeholder(tf.float32, shape=[config.BATCH_SIZE], name='label')
bbox_target = tf.placeholder(tf.float32, shape=[config.BATCH_SIZE, 4], name='bbox_target')
landmark_target = tf.placeholder(tf.float32,shape=[config.BATCH_SIZE,10],name='landmark_target')
#get loss and accuracy
input_image = image_color_distort(input_image)
cls_loss_op,bbox_loss_op,landmark_loss_op,L2_loss_op,accuracy_op = net_factory(input_image, label, bbox_target,landmark_target,training=True)
#train,update learning rate(3 loss)
total_loss_op = radio_cls_loss*cls_loss_op + radio_bbox_loss*bbox_loss_op + radio_landmark_loss*landmark_loss_op + L2_loss_op
train_op, lr_op = train_model(base_lr,
total_loss_op,
num)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
#save model
saver = tf.train.Saver(max_to_keep=0)
sess.run(init)
#visualize some variables
tf.summary.scalar("cls_loss",cls_loss_op)#cls_loss
tf.summary.scalar("bbox_loss",bbox_loss_op)#bbox_loss
tf.summary.scalar("landmark_loss",landmark_loss_op)#landmark_loss
tf.summary.scalar("cls_accuracy",accuracy_op)#cls_acc
tf.summary.scalar("total_loss",total_loss_op)#cls_loss, bbox loss, landmark loss and L2 loss add together
summary_op = tf.summary.merge_all()
logs_dir = "../logs/%s" %(net)
if os.path.exists(logs_dir) == False:
os.mkdir(logs_dir)
writer = tf.summary.FileWriter(logs_dir,sess.graph)
projector_config = projector.ProjectorConfig()
projector.visualize_embeddings(writer,projector_config)
#begin
coord = tf.train.Coordinator()
#begin enqueue thread
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
i = 0
#total steps
MAX_STEP = int(num / config.BATCH_SIZE + 1) * end_epoch
epoch = 0
sess.graph.finalize()
try:
for step in range(MAX_STEP):
i = i + 1
if coord.should_stop():
break
image_batch_array, label_batch_array, bbox_batch_array,landmark_batch_array = sess.run([image_batch, label_batch, bbox_batch,landmark_batch])
#random flip
image_batch_array,landmark_batch_array = random_flip_images(image_batch_array,label_batch_array,landmark_batch_array)
'''
print('im here')
print(image_batch_array.shape)
print(label_batch_array.shape)
print(bbox_batch_array.shape)
print(landmark_batch_array.shape)
print(label_batch_array[0])
print(bbox_batch_array[0])
print(landmark_batch_array[0])
'''
_,_,summary = sess.run([train_op, lr_op ,summary_op], feed_dict={input_image: image_batch_array, label: label_batch_array, bbox_target: bbox_batch_array,landmark_target:landmark_batch_array})
if (step+1) % display == 0:
#acc = accuracy(cls_pred, labels_batch)
cls_loss, bbox_loss,landmark_loss,L2_loss,lr,acc = sess.run([cls_loss_op, bbox_loss_op,landmark_loss_op,L2_loss_op,lr_op,accuracy_op],
feed_dict={input_image: image_batch_array, label: label_batch_array, bbox_target: bbox_batch_array, landmark_target: landmark_batch_array})
total_loss = radio_cls_loss*cls_loss + radio_bbox_loss*bbox_loss + radio_landmark_loss*landmark_loss + L2_loss
# landmark loss: %4f,
print("%s : Step: %d/%d, accuracy: %3f, cls loss: %4f, bbox loss: %4f,Landmark loss :%4f,L2 loss: %4f, Total Loss: %4f ,lr:%f " % (
datetime.now(), step+1,MAX_STEP, acc, cls_loss, bbox_loss,landmark_loss, L2_loss,total_loss, lr))
#save every two epochs
if i * config.BATCH_SIZE > num*2:
epoch = epoch + 1
i = 0
path_prefix = saver.save(sess, prefix, global_step=epoch*2)
print('path prefix is :', path_prefix)
writer.add_summary(summary,global_step=step)
except tf.errors.OutOfRangeError:
print("完成!!!")
finally:
coord.request_stop()
writer.close()
coord.join(threads)
sess.close()