def train():
with tf.Graph().as_default():
global_step = tf.contrib.framework.get_or_create_global_step()
print ("Global step", global_step)
images, labels = model.distorted_inputs()
logits = model.inference(images)
loss = model.loss(logits, labels)
train_op = model.train(loss, global_step)
class _LoggerHook(tf.train.SessionRunHook):
def begin(self):
self._step = -1
def before_run(self, run_context):
self._step += 1
self._start_time = time.time()
return tf.train.SessionRunArgs(loss)
def after_run(self, run_context, run_values):
duration = time.time() - self._start_time
loss_value = run_values.results
if self._step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step /duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir = FLAGS.train_dir,
hooks=[tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()],
config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op)
def tower_loss(scope):
"""Calculate the total loss on a single tower running the cnn model.
Args:
scope: unique prefix string identifying the cnn tower, e.g. 'tower_0'
Returns:
Tensor of shape [] containing the total loss for a batch of data
"""
images, labels = model.distorted_inputs(args.m, train_chunk_name, args.bs,
number_of_labels,
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN,
args.d)
labels['observed'] = 1 - labels['censored']
# images, labels = calc_at_risk(images, labels, args.m)
if args.m == 'image_genome':
labels['genomics'] = tf.cast(labels['genomics'], tf.float32)
logits = model.inference(images, labels, args.kp, args.m, args.bs)
_, risk_diff = model.log_sigmoid_loss(logits, labels, args.bs)
losses = tf.get_collection('losses', scope)
total_loss = tf.add_n(losses, name='total_loss')
for l in losses + [total_loss]:
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
loss_name = re.sub('%s_[0-9]*/' % model.TOWER_NAME, '', l.op.name)
tf.summary.scalar(loss_name, l)
# "labels" is a dictionary in which the keywords are among: 'idx' for
# patients' indexes, 'survival', 'censored', 'observed', 'idh', 'codel',
# 'copynum' for copy numbers, 'at_risk'
return total_loss, logits, labels, images, risk_diff
def train():
"""Train datasets for a number of steps."""
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# Get images and labels for model.
images, labels = model.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = model.inference(images)
# Calculate loss.
loss = model.loss(logits, labels)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = model.train(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))# log_device_placement=True,该参数表示程序会将运行每一个操作的设备输出到屏幕
sess.run(init)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, graph_def=sess.graph_def)
for step in range(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)')
print (format_str % (datetime.now(), step, loss_value, examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images, labels = model.distorted_inputs()
logits = model.inference(images)
loss = model.loss(logits, labels)
train_op = model.train(loss, global_step)
saver = tf.train.Saver(tf.all_variables())
summary_op = tf.merge_all_summaries()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if FLAGS.resume_training and ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
current_step = int(
ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
else:
current_step = 0
init = tf.initialize_all_variables()
sess.run(init)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(SUMMARY_DIR,
graph_def=sess.graph_def)
for step in xrange(current_step, FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f'
'(%.1f examples/sec; %.3f'
'sec/batch)')
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 50 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
if step % 100 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images, labels = model.distorted_inputs()
logits = model.inference(images)
loss = model.loss(logits, labels)
train_op = model.train(loss, global_step)
saver = tf.train.Saver(tf.all_variables())
summary_op = tf.merge_all_summaries()
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if FLAGS.resume_training and ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
current_step = int(ckpt.model_checkpoint_path
.split('/')[-1].split('-')[-1])
else:
current_step = 0
init = tf.initialize_all_variables()
sess.run(init)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(SUMMARY_DIR,
graph_def=sess.graph_def)
for step in xrange(current_step, FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f'
'(%.1f examples/sec; %.3f'
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 50 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
if step % 100 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
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()
# Get images and labels for CIFAR-10.
# Force input pipeline to CPU:0 to avoid operations sometimes ending up on
# GPU and resulting in a slow down.
with tf.device('/cpu:0'):
lefts, disps, confs = model.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
predicted = model.inference(lefts, disps)
# Calculate loss.
loss = model.loss(predicted, confs)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = model.train(loss, global_step)
class _LoggerHook(tf.train.SessionRunHook):
"""Logs loss and runtime."""
def begin(self):
self._step = -1
self._start_time = time.time()
def before_run(self, run_context):
self._step += 1
return tf.train.SessionRunArgs(loss) # Asks for loss value.
def after_run(self, run_context, run_values):
if self._step % FLAGS.log_frequency == 0:
current_time = time.time()
duration = current_time - self._start_time
self._start_time = current_time
loss_value = run_values.results
examples_per_sec = FLAGS.log_frequency * FLAGS.batch_size / duration
sec_per_batch = float(duration / FLAGS.log_frequency)
format_str = (
'%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), self._step, loss_value,
examples_per_sec, sec_per_batch))
with tf.train.MonitoredTrainingSession(
checkpoint_dir=FLAGS.train_dir,
hooks=[
tf.train.StopAtStepHook(last_step=FLAGS.max_steps),
tf.train.NanTensorHook(loss),
_LoggerHook()
],
config=tf.ConfigProto(log_device_placement=FLAGS.
log_device_placement)) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_op)
def train():
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
images, labels = cifar10.distorted_inputs()
logits = cifar10.inference(images, train=True)
loss = cifar10.loss(logits, labels)
train_op = cifar10.train(loss, global_step)
saver = tf.train.Saver(tf.all_variables())
summary_op = tf.merge_all_summaries()
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restoring from checkpoint')
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss=NaN'
if step % 100 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
print('step %d loss = %f (%.1f examples/sec, %.3f sec/batch)' %
(step, loss_value, examples_per_sec, sec_per_batch))
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def tower_loss(scope):
images, labels = model.distorted_inputs()
logits = model.inference(images)
_ = model.loss(logits, labels)
losses = tf.get_collection('losses', scope)
total_loss = tf.add_n(losses, name='total_loss')
for l in losses + [total_loss]:
loss_name = re.sub('%s_[0-9]*/' % model.TOWER_NAME, '', l.op.name)
tf.summary.scalar(loss_name, l)
return total_loss
def train(use_vgg=False):
with tf.Graph().as_default():
global_step = tf.train.get_or_create_global_step()
images, labels = model.distorted_inputs()
if use_vgg:
logits = model_vgg16.inference(images, FLAGS.vgg_model)
loss = model_vgg16.loss(logits, labels)
else:
logits = model.inference(images)
loss = model.loss(logits, labels)
train_op = tf.train.MomentumOptimizer(1e-3, momentum=0.9).minimize(
loss, global_step=global_step)
saver = tf.train.Saver(tf.all_variables())
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Start queue runners
tf.train.start_queue_runners(sess=sess)
for step in xrange(FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
if step % 10 == 0:
examples_per_sec = FLAGS.batch_size / duration
sec_per_batch = float(duration)
format_str = (
'step %d,loss = %.2f (%.1f examples/sec; %.3f sec/batch)')
print(format_str %
(step, loss_value, examples_per_sec, sec_per_batch))
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
if use_vgg:
checkpoint_path = os.path.join(FLAGS.train_dir,
'vgg_model.ckpt')
else:
checkpoint_path = os.path.join(FLAGS.train_dir,
'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def evaluate(use_vgg=False):
with tf.Graph().as_default():
eval_data = FLAGS.eval_data == 'test'
images, labels = model.distorted_inputs()
# Build a Graph that computes the logits predictions from the
# inference model.
logits = model.inference(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(
model.MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
while True:
eval_once(saver, top_k_op, use_vgg)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs)
def train():
print('[Training Configuration]')
print('\tTrain dir: %s' % FLAGS.train_dir)
print('\tTraining max steps: %d' % FLAGS.max_steps)
print('\tSteps per displaying info: %d' % FLAGS.display)
print('\tSteps per testing: %d' % FLAGS.test_interval)
print('\tSteps per saving checkpoints: %d' % FLAGS.checkpoint_interval)
print('\tGPU memory fraction: %f' % FLAGS.gpu_fraction)
"""Train aPascal for a number of steps."""
with tf.Graph().as_default():
init_step = 0
global_step = tf.Variable(0, trainable=False)
# Get images and labels for aPascal.
train_images, train_labels = model.distorted_inputs('train')
test_images, test_labels = model.inputs('eval')
# Build a Graph that computes the predictions from the inference model.
images = tf.placeholder(tf.float32, [FLAGS.batch_size, model.IMAGE_WIDTH, model.IMAGE_WIDTH, 3])
labels = tf.placeholder(tf.int32, [FLAGS.batch_size, model.NUM_ATTRS])
probs = model.inference(images)
# Calculate loss. (cross_entropy loss)
loss, acc = model.loss_acc(probs, labels)
tf.scalar_summary("accuracy", acc)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op, lr = model.train(loss, global_step)
# Build the summary operation based on the TF collection of Summaries.
train_summary_op = tf.merge_all_summaries()
# Loss and accuracy summary used in test phase)
loss_summary = tf.scalar_summary("test/loss", loss)
acc_summary = tf.scalar_summary("test/accuracy", acc)
test_summary_op = tf.merge_summary([loss_summary, acc_summary])
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Start running operations on the Graph.
sess = tf.Session(config=tf.ConfigProto(
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.gpu_fraction),
log_device_placement=FLAGS.log_device_placement))
sess.run(init)
# Create a saver.
saver = tf.train.Saver(tf.all_variables(), max_to_keep=10000)
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
print('\tRestore from %s' % ckpt.model_checkpoint_path)
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
init_step = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1])
else:
print('No checkpoint file found. Start from the scratch.')
# if finetune, load variables of the final predication layers
# from pretrained model
if FLAGS.finetune:
base_variables = tf.trainable_variables()[:-2*model.NUM_ATTRS]
base_saver = tf.train.Saver(base_variables, max_to_keep=10000)
ckpt = tf.train.get_checkpoint_state(FLAGS.pretrained_dir)
print('Initial checkpoint: ' + ckpt.model_checkpoint_path)
base_saver.restore(sess, ckpt.model_checkpoint_path)
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
if not os.path.exists(FLAGS.train_dir):
os.mkdir(FLAGS.train_dir)
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir)
# Training!!
for step in xrange(init_step, FLAGS.max_steps):
start_time = time.time()
try:
train_images_val, train_labels_val = sess.run([train_images, train_labels])
_, lr_value, loss_value, acc_value, train_summary_str = sess.run([train_op, lr, loss, acc, train_summary_op],
feed_dict={images:train_images_val, labels:train_labels_val})
except tf.python.framework.errors.InvalidArgumentError:
embed()
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % FLAGS.display == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: (Training) step %d, loss=%.4f, acc=%.4f, lr=%f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value, acc_value, lr_value,
examples_per_sec, sec_per_batch))
summary_writer.add_summary(train_summary_str, step)
if step % FLAGS.test_interval == 0:
test_images_val, test_labels_val = sess.run([test_images, test_labels])
loss_value, acc_value, test_summary_str = sess.run([loss, acc, test_summary_op],
feed_dict={images:test_images_val, labels:test_labels_val})
format_str = ('%s: (Test) step %d, loss=%.4f, acc=%.4f')
print (format_str % (datetime.now(), step, loss_value, acc_value))
summary_writer.add_summary(test_summary_str, step)
# Save the model checkpoint periodically.
if step % FLAGS.checkpoint_interval == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
def train():
"""Train FSRCNN for a number of steps."""
os.environ["CUDA_VISIBLE_DEVICES"] = FLAGS.gpu
with tf.Graph().as_default(), tf.device('/cpu:0'):
# Create a variable to count the number of train() calls. This equals the
# number of batches processed * FLAGS.num_gpus.
global_step = tf.get_variable('global_step', [],
initializer=tf.constant_initializer(0),
trainable=False)
# Create an optimizer that performs gradient descent.
opt = tf.train.MomentumOptimizer(FLAGS.lr, 0.9)
# Determine number of GPUs to use.
num_gpus = len(FLAGS.gpu.split(','))
# Get images and labels for FSRCNN.
images, labels = model.distorted_inputs()
batch_queue = tf.contrib.slim.prefetch_queue.prefetch_queue(
[images, labels], capacity=2 * num_gpus)
# Calculate the gradients for each model tower.
tower_grads = []
with tf.variable_scope(tf.get_variable_scope()):
for i in xrange(num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('%s_%d' %
(model.TOWER_NAME, i)) as scope:
# Dequeues one batch for the GPU
image_batch, label_batch = batch_queue.dequeue()
# Calculate the loss for one tower of the model. This function
# constructs the entire model but shares the variables across
# all towers.
loss = tower_loss(scope, image_batch, label_batch)
# Reuse variables for the next tower.
tf.get_variable_scope().reuse_variables()
# Retain the summaries from the final tower.
summaries = tf.get_collection(tf.GraphKeys.SUMMARIES,
scope)
# Calculate the gradients for the batch of data on this tower.
if FLAGS.quantize:
tensor_list = tf.get_collection('quantize', scope)
var_list = tf.trainable_variables()
grads = zip(tf.gradients(loss, tensor_list),
var_list)
else:
grads = opt.compute_gradients(loss)
# Keep track of the gradients across all towers.
tower_grads.append(grads)
# We must calculate the mean of each gradient. Note that this is the
# synchronization point across all towers.
grads = average_gradients(tower_grads)
# Add a summary to track the learning rate.
summaries.append(tf.summary.scalar('learning_rate', FLAGS.lr))
# Apply the gradients to adjust the shared variables.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
summaries.append(tf.summary.histogram(var.op.name, var))
# Group all updates to into a single train op.
train_op = tf.group(apply_gradient_op)
# Create a saver.
saver = tf.train.Saver(tf.global_variables())
# Build the summary operation from the last tower summaries.
summary_op = tf.summary.merge(summaries)
# summary_op = tf.summary.merge_all()
# Build an initialization operation to run below.
init = tf.global_variables_initializer()
global_step = 0
# Start running operations on the Graph. allow_soft_placement must be set to
# True to build towers on GPU, as some of the ops do not have GPU
# implementations.
config = tf.ConfigProto(
allow_soft_placement=True,
log_device_placement=FLAGS.log_device_placement)
config.gpu_options.per_process_gpu_memory_fraction = 0.8
# config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(init)
# Restore the model if reload is True.
if FLAGS.reload:
ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
if ckpt and ckpt.model_checkpoint_path:
# Restores from checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
# Assuming model_checkpoint_path looks something like:
# /my-favorite-path/cifar10_train/model.ckpt-0,
# extract global_step from it.
global_step = ckpt.model_checkpoint_path.split('/')[-1].split(
'-')[-1]
# Start the queue runners.
tf.train.start_queue_runners(sess=sess)
summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)
for step in xrange(int(global_step), FLAGS.max_steps):
start_time = time.time()
_, loss_value = sess.run([train_op, loss])
duration = time.time() - start_time
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
if step % 10 == 0:
num_examples_per_step = FLAGS.batch_size * num_gpus
examples_per_sec = num_examples_per_step / duration
sec_per_batch = duration / num_gpus
format_str = (
'%s: step %d, loss = %.4f (%.1f examples/sec; %.3f '
'sec/batch)')
print(format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
if step % 100 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, step)
# Save the model checkpoint periodically.
if step % 5000 == 0 or (step + 1) == FLAGS.max_steps:
checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')
saver.save(sess, checkpoint_path, global_step=step)
