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train_classifier.py
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import numpy as np
import pandas as pd
import sys
import os
import pdb
import yaml
import tensorflow as tf
from classifier.DenseNet import pretrained_classifier
from utils import read_data_file, load_images_and_labels
import argparse
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore")
def train():
parser = argparse.ArgumentParser()
parser.add_argument(
'--config', '-c', default='configs/celebA_YSBBB_Classifier.yaml'
)
args = parser.parse_args()
# ============= Load config =============
config_path = args.config
config = yaml.load(open(config_path))
print(config)
# ============= Experiment Folder=============
output_dir = os.path.join(config['log_dir'], config['name'])
try: os.makedirs(output_dir)
except: pass
try: os.makedirs(os.path.join(output_dir, 'logs'))
except: pass
# ============= Experiment Parameters =============
BATCH_SIZE = config['batch_size']
EPOCHS = config['epochs']
channels = config['num_channel']
input_size = config['input_size']
N_CLASSES = config['num_class']
ckpt_dir_continue = config['ckpt_dir_continue']
if ckpt_dir_continue == '':
continue_train = False
else:
continue_train = True
# ============= Data =============
try:
categories, file_names_dict = read_data_file(config['image_label_dict'])
except:
print("Problem in reading input data file : ", config['image_label_dict'])
sys.exit()
data_train = np.load(config['train'])
data_test = np.load(config['test'])
print("The classification categories are: ")
print(categories)
print('The size of the training set: ', data_train.shape[0])
print('The size of the testing set: ', data_test.shape[0])
fp = open(os.path.join(output_dir, 'setting.txt'), 'w')
fp.write('config_file:'+str(config_path)+'\n')
fp.close()
# ============= placeholder =============
with tf.name_scope('input'):
x_ = tf.placeholder(tf.float32, [None, input_size, input_size, channels], name='x-input')
y_ = tf.placeholder(tf.int64, [None, N_CLASSES], name='y-input')
isTrain = tf.placeholder(tf.bool)
# ============= Model =============
if N_CLASSES == 1:
y = tf.reshape(y_, [-1])
y = tf.one_hot(y,2,on_value=1.0,off_value=0.0,axis=-1)
logit,prediction = pretrained_classifier(x_, n_label=2, reuse=False, name='classifier', isTrain =isTrain)
else:
logit,prediction = pretrained_classifier(x_, n_label=N_CLASSES, reuse=False, name='classifier', isTrain =isTrain)
y = y_
classif_loss = tf.losses.sigmoid_cross_entropy(multi_class_labels=y,logits=logit)
loss = tf.losses.get_total_loss()
# ============= Optimization functions =============
train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)
# ============= summary =============
cls_loss = tf.summary.scalar('classif_loss', classif_loss)
total_loss = tf.summary.scalar('total_loss', loss)
sum_train = tf.summary.merge([cls_loss, total_loss])
# ============= Variables =============
# Note that this list of variables only include the weights and biases in the model.
lst_vars = []
for v in tf.global_variables():
lst_vars.append(v)
# ============= Session =============
sess=tf.InteractiveSession()
saver = tf.train.Saver(var_list=lst_vars)
tf.global_variables_initializer().run()
writer = tf.summary.FileWriter(output_dir + '/train', sess.graph)
writer_test = tf.summary.FileWriter(output_dir + '/test', sess.graph)
# ============= Checkpoints =============
if continue_train:
print("Before training, Load checkpoint ")
print("Reading checkpoint...")
ckpt = tf.train.get_checkpoint_state(ckpt_dir_continue)
if ckpt and ckpt.model_checkpoint_path:
ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
saver.restore(sess, os.path.join(ckpt_dir_continue, ckpt_name))
print(ckpt_name)
print("Successful checkpoint upload")
else:
print("Failed checkpoint load")
sys.exit()
# ============= Training =============
train_loss = []
test_loss = []
itr_train = 0
itr_test = 0
for epoch in range(EPOCHS):
total_loss = 0.0
perm = np.arange(data_train.shape[0])
np.random.shuffle(perm)
data_train = data_train[perm]
num_batch = int(data_train.shape[0]/BATCH_SIZE)
for i in range(0, num_batch):
start = i*BATCH_SIZE
ns = data_train[start:start+BATCH_SIZE]
xs, ys = load_images_and_labels(ns, config['image_dir'],N_CLASSES, file_names_dict, input_size, channels, do_center_crop=True)
[_, _loss,summary_str] = sess.run([train_step, loss, sum_train], feed_dict={x_:xs, isTrain:True, y_: ys})
writer.add_summary(summary_str, itr_train)
itr_train+=1
total_loss += _loss
total_loss /= i
print("Epoch: " + str( epoch) + " loss: " + str(total_loss) + '\n')
train_loss.append(total_loss)
total_loss = 0.0
perm = np.arange(data_test.shape[0])
np.random.shuffle(perm)
data_test = data_test[perm]
num_batch = int(data_test.shape[0]/BATCH_SIZE)
for i in range(0, num_batch):
start = i*BATCH_SIZE
ns = data_test[start:start+BATCH_SIZE]
xs, ys = load_images_and_labels(ns, config['image_dir'],N_CLASSES, file_names_dict, input_size, channels, do_center_crop=True)
[_loss, summary_str] = sess.run([loss, sum_train], feed_dict={x_:xs, isTrain:False, y_: ys})
writer_test.add_summary(summary_str, itr_test)
itr_test+=1
total_loss += _loss
total_loss /= i
print("Epoch: "+ str(epoch) + " Test loss: "+ str(total_loss) + '\n')
test_loss.append(total_loss)
if epoch %2 == 0:
checkpoint_name = os.path.join( output_dir, 'cp1_epoch'+str(epoch)+'.ckpt')
save_path = saver.save(sess, checkpoint_name)
np.save( os.path.join( output_dir,'logs','train_loss.npy'), np.asarray(train_loss))
np.save( os.path.join( output_dir,'logs','test_loss.npy'), np.asarray(test_loss))
checkpoint_name = os.path.join( output_dir, 'cp1_epoch'+str(epoch)+'.ckpt')
save_path = saver.save(sess, checkpoint_name)
np.save( os.path.join( output_dir,'logs','train_loss.npy'), np.asarray(train_loss))
np.save( os.path.join( output_dir,'logs','test_loss.npy'), np.asarray(test_loss))
if __name__ == "__main__":
train()