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cnn_experiment_train_alex.py
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#!/usr/bin/env python3.5
from datetime import datetime
import time
import os
import cnn_db_loader
import tf_utils
import cnn_tf_graphs
import tensorflow as tf
from tensorflow.contrib import learn
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string('experiment_folder', '21',
"""Directory where to write event logs """
"""and checkpoint.""")
tf.app.flags.DEFINE_integer('max_steps', 1000000,
"""Number of batches to run.""")
tf.app.flags.DEFINE_boolean('log_device_placement', False,
"""Whether to log device placement.""")
tf.app.flags.DEFINE_integer('batch_size', 100,
"""Size of a batch.""")
cnn_db_loader.NUMBER_ALPHAS = 0
cnn_db_loader.NUMBER_IMAGES = 1
cnn_db_loader.NUMBER_XYZ = 0
os.environ['CUDA_VISIBLE_DEVICES']='0'
MOMENTUM = 0.9
LEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor.
INITIAL_LEARNING_RATE = 0.001 # Initial learning rate.
PaSC_still_BASE = '/user/HS204/m09113/my_project_folder/PaSC/still/multi_fit_CCR_iter75_reg30_256/'
#PaSC_still_BASE = '/user/HS204/m09113/my_project_folder/PaSC/still/face_boxes/'
PaSC_video_BASE = '/user/HS204/m09113/my_project_folder/PaSC/video/multi_fit_CCR_iter75_reg30_256/'
#PaSC_video_BASE = '/user/HS204/m09113/my_project_folder/PaSC/video/face_boxes/'
CASIA_BASE = '/user/HS204/m09113/my_project_folder/CASIA_webface/multi_fit_CCR_iter75_reg30_256/'
#CASIA_BASE = '/user/HS204/m09113/my_project_folder/CASIA_webface/face_boxes/'
Experint_BASE = '/user/HS204/m09113/my_project_folder/cnn_experiments/'
experiment_dir = Experint_BASE+FLAGS.experiment_folder
db_dir = experiment_dir+'/db_input/'
train_dir = experiment_dir+'/train'
tf.logging.set_verbosity(tf.logging.DEBUG)
def train():
"""Train CIFAR-10 for a number of steps."""
with tf.Graph().as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
#cnn_db_loader.IMAGE_FILE_ENDING = '/*'
cnn_db_loader.IMAGE_FILE_ENDING = '/*isomap.png'
pasc_still = cnn_db_loader.PaSC_still_loader(outputfolder=db_dir, db_base=PaSC_still_BASE)
#pasc_still.analyse_isomaps()
#pasc_still.remove_bad_isomaps()
pasc_video = cnn_db_loader.PaSC_video_loader(outputfolder=db_dir, db_base=PaSC_video_BASE)
#pasc_video.analyse_isomaps()
#pasc_video.remove_bad_isomaps()
#cnn_db_loader.IMAGE_FILE_ENDING = '/*.jpg'
casia = cnn_db_loader.CASIA_webface_loader(outputfolder=db_dir, db_base=CASIA_BASE)
#casia.analyse_isomaps()
#casia.remove_bad_isomaps()
cnn_db_loader.IMAGE_FILE_ENDING = '/*'
pasc_still_merges = cnn_db_loader.PaSC_still_loader(outputfolder=experiment_dir+'/db_input_merges/', db_base='/user/HS204/m09113/my_project_folder/PaSC/still/random_merges_256_conf13/')
pasc_video_merges = cnn_db_loader.PaSC_video_loader(outputfolder=experiment_dir+'/db_input_merges/', db_base='/user/HS204/m09113/my_project_folder/PaSC/video/random_merges_256_conf13/')
casia_merges = cnn_db_loader.CASIA_webface_loader(outputfolder=experiment_dir+'/db_input_merges/', db_base='/user/HS204/m09113/my_project_folder/CASIA_webface/random_merges_256_conf13/')
pasc_still_merges.set_all_as_train()
pasc_video_merges.split_train_eval(train_proportion=0.8)
casia_merges.set_all_as_train()
pasc_still.set_all_as_train()
casia.set_all_as_train()
pasc_video.split_train_eval(train_proportion=0.8)
#db_loader = cnn_db_loader.Aggregator(pasc_video, pasc_still, casia)
db_loader = cnn_db_loader.Aggregator(pasc_still, pasc_still_merges, pasc_video, pasc_video_merges, casia, casia_merges)
#db_loader = cnn_db_loader.Aggregator(casia)
#db_loader.make_sure_nothings_empty()
num_batches_per_epoch = len(db_loader.examples_train) / FLAGS.batch_size
if cnn_db_loader.NUMBER_ALPHAS > 0 and cnn_db_loader.NUMBER_XYZ == 0:
image_list, alphas_list, labels_list = db_loader.get_training_image_alphas_and_label_lists()
images, alphas, labels = tf_utils.inputs_with_alphas(image_list, alphas_list, labels_list, FLAGS.batch_size, db_loader.get_mean_image_path())
# Build a Graph that computes the logits predictions from the inference model.
logits, _ = cnn_tf_graphs.inference(network="alex_with_alpha", mode=learn.ModeKeys.TRAIN, batch_size=FLAGS.batch_size, num_classes=db_loader.number_ids, input_image_tensor=images, input_alpha_tensor=alphas)
elif cnn_db_loader.NUMBER_ALPHAS == 0 and cnn_db_loader.NUMBER_IMAGES == 1 and cnn_db_loader.NUMBER_XYZ == 0:
image_list, labels_list = db_loader.get_training_image_and_label_lists()
# image_name_tensor = tf.placeholder(tf.string)
# image_contents = tf.read_file(image_name_tensor)
# image = tf.image.decode_image(image_contents, channels=3)
# init_op = tf.initialize_all_tables()
# with tf.Session() as sess:
# sess.run(init_op)
# for image_name in image_list[210000:]:
# print (image_name)
# tmp = sess.run(image, feed_dict={image_name_tensor: image_name})
images, labels = tf_utils.inputs(image_list, labels_list, FLAGS.batch_size, db_loader.get_mean_image_path(), image_size=256)
# Build a Graph that computes the logits predictions from the inference model.
logits, _ = cnn_tf_graphs.inference(network="alex", mode=learn.ModeKeys.TRAIN, batch_size=FLAGS.batch_size, num_classes=db_loader.number_ids, input_image_tensor=images, image_size=256)
elif cnn_db_loader.NUMBER_ALPHAS == 0 and cnn_db_loader.NUMBER_IMAGES == 0 and cnn_db_loader.NUMBER_XYZ == 1:
image_list, labels_list = db_loader.get_training_xyz_and_label_lists()
images, labels = tf_utils.inputs(image_list, labels_list, FLAGS.batch_size, db_loader.get_mean_xyz_path())
# Build a Graph that computes the logits predictions from the inference model.
logits, _ = cnn_tf_graphs.inference(network="alex", mode=learn.ModeKeys.TRAIN, batch_size=FLAGS.batch_size, num_classes=db_loader.number_ids, input_image_tensor=images)
elif cnn_db_loader.NUMBER_ALPHAS == 0 and cnn_db_loader.NUMBER_IMAGES == 1 and cnn_db_loader.NUMBER_XYZ == 1:
image_list, xyz_list, labels_list = db_loader.get_training_image_xyz_and_label_lists()
isomap_stacks, labels = tf_utils.inputs_stack_image_and_xyz(image_list, xyz_list, labels_list, FLAGS.batch_size, db_loader.get_mean_image_path(), db_loader.get_mean_xyz_path())
# Build a Graph that computes the logits predictions from the inference model.
logits, _ = cnn_tf_graphs.inference(network="alex", mode=learn.ModeKeys.TRAIN, batch_size=FLAGS.batch_size, num_classes=db_loader.number_ids, input_image_tensor=isomap_stacks)
#exit(0)
# Calculate loss.
#loss = cnn_tf_graphs.l2_loss(logits, labels)
loss = cnn_tf_graphs.softmax_loss(logits, labels, db_loader.number_ids)
top_k_op = tf.nn.in_top_k(logits, labels, 1)
sum_correct = tf.reduce_sum(tf.cast(top_k_op, tf.float32))
accuracy = tf.divide(tf.multiply(sum_correct,tf.constant(100.0)),tf.constant(float(FLAGS.batch_size)))
#accuracy, accuracy_update = tf.contrib.metrics.streaming_accuracy(tf.argmax(logits,1), tf.argmax(labels, 1))
lr = tf.constant(INITIAL_LEARNING_RATE, tf.float32)
tf.summary.scalar('learning_rate', lr)
tf.summary.scalar('momentum', MOMENTUM)
tf.summary.scalar('batch_size', FLAGS.batch_size)
tf.summary.scalar('accuracy', accuracy)
optimizer=tf.train.MomentumOptimizer(learning_rate=lr, momentum=MOMENTUM)
#optimizer=tf.train.AdadeltaOptimizer(learning_rate=lr)
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=lr,
optimizer=optimizer)
logging_hook = tf.train.LoggingTensorHook(
tensors={'step': tf.contrib.framework.get_global_step(),
'loss': loss,
'lr': lr,
'acc': accuracy},
every_n_iter=100)
#saver = tf.train.Saver(var_list=None, keep_checkpoint_every_n_hours=1)
saver = tf.train.Saver(var_list=None, max_to_keep=None)
class _LearningRateSetterHook(tf.train.SessionRunHook):
"""Sets learning_rate based on global step."""
def begin(self):
self._lrn_rate = INITIAL_LEARNING_RATE * LEARNING_RATE_DECAY_FACTOR**6
#print(self.num_batches_per_epoch)
def before_run(self, run_context):
return tf.train.SessionRunArgs(
tf.contrib.framework.get_global_step(), # Asks for global step value.
feed_dict={lr: self._lrn_rate}) # Sets learning rate
def after_run(self, run_context, run_values):
train_step = run_values.results
self._lrn_rate = INITIAL_LEARNING_RATE
#training_epoch = int(train_step/num_batches_per_epoch)
#self._lrn_rate = INITIAL_LEARNING_RATE * LEARNING_RATE_DECAY_FACTOR**int(train_step/num_batches_per_epoch/2.7)
if train_step < 2*num_batches_per_epoch:
self._lrn_rate = INITIAL_LEARNING_RATE
elif train_step < 4*num_batches_per_epoch:
self._lrn_rate = INITIAL_LEARNING_RATE * LEARNING_RATE_DECAY_FACTOR**1
elif train_step < 6*num_batches_per_epoch:
self._lrn_rate = INITIAL_LEARNING_RATE * LEARNING_RATE_DECAY_FACTOR**2
elif train_step < 9*num_batches_per_epoch:
self._lrn_rate = INITIAL_LEARNING_RATE * LEARNING_RATE_DECAY_FACTOR**3
elif train_step < 12*num_batches_per_epoch:
self._lrn_rate = INITIAL_LEARNING_RATE * LEARNING_RATE_DECAY_FACTOR**4
else:
self._lrn_rate = INITIAL_LEARNING_RATE * LEARNING_RATE_DECAY_FACTOR**5
config = tf.ConfigProto( allow_soft_placement=False, log_device_placement=FLAGS.log_device_placement)
config.gpu_options.allow_growth = True
with tf.train.MonitoredTrainingSession(
checkpoint_dir=train_dir,
hooks=[ tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
tf.train.CheckpointSaverHook(checkpoint_dir=train_dir, save_steps=num_batches_per_epoch, saver=saver),
logging_hook,
_LearningRateSetterHook()],
config=config,
save_checkpoint_secs=3600) as mon_sess:
#saver.restore(mon_sess,'/vol/vssp/facer2vm/people/Philipp/cnn_experiments/03/train/model.ckpt-21575')
while True: # not mon_sess.should_stop():
mon_sess.run(train_op)
#mon_sess.run(train_op)
#my_summary_op = tf.summary.merge_all()
#sv = tf.train.Supervisor(logdir="/my/training/directory", summary_op=None) # Do not run the summary service
def main(argv=None): # pylint: disable=unused-argument
if not os.path.exists(experiment_dir):
os.mkdir(experiment_dir)
if not os.path.exists(train_dir):
os.mkdir(train_dir)
if not os.path.exists(db_dir):
os.mkdir(db_dir)
with tf.device('/gpu:0'):
train()
if __name__ == '__main__':
tf.app.run()