def __init__(self, models_dir, fold_name, writer=None, hyper=None):
self._graph = tf.Graph()
with self._graph.as_default():
reader = Reader(fold_name)
with tf.device('/gpu:1'):
self._input = reader.inputs(Trainer.BATCH_SIZE, is_train=True)
self._network = Network(self._input['images'],
is_train=True,
hyper=hyper)
self._cross_entropy_losses = self._network.cross_entropy_losses(
self._input['labels'])
self._total_loss = self._network.total_loss(
self._input['labels'])
self._lr_placeholder = tf.placeholder(tf.float32)
self._train = self._train_op()
self._all_summaries = tf.merge_all_summaries()
self.models_dir = models_dir
print('Trainer model folder: %s' % self.models_dir)
if not tf.gfile.Exists(self.models_dir):
tf.gfile.MakeDirs(self.models_dir)
self.writer = writer
if (self.writer):
self.writer.write_graph(self._graph)
def inputs(eval_data):
if not FLAGS.data_dir:
raise ValueError('Please supply a data_dir')
data_dir = os.path.join(FLAGS.data_dir, 'Data')
images, labels = reader.inputs(eval_data=eval_data,
data_dir=data_dir,
batch_size=FLAGS.batch_size)
if FLAGS.use_fp16:
images = tf.cast(images, tf.float16)
labels = tf.cast(labels, tf.float16)
return images, labels
def __init__(self, models_dir, fold_name, writer=None, hyper=None):
self._graph = tf.Graph()
with self._graph.as_default():
reader = Reader(fold_name)
self.fold_size = reader.fold_size
with tf.device('/gpu:1'):
self._input = reader.inputs(Tester.BATCH_SIZE, is_train=False)
self._network = Network(self._input['images'], is_train=False, hyper=hyper)
self._probs = self._network.probs()
self._cross_entropy_losses = self._network.cross_entropy_losses(self._input['labels'])
self._all_summaries = tf.merge_all_summaries()
self.models_dir = models_dir
print('Tester model folder: %s' %self.models_dir)
assert os.path.exists(self.models_dir)
self.writer = writer
def __init__(self, models_dir, fold_name, writer=None, hyper=None):
self._graph = tf.Graph()
with self._graph.as_default():
reader = Reader(fold_name)
self.fold_size = reader.fold_size
with tf.device('/gpu:1'):
images, self._labels, scores, self._filenames = reader.inputs(Tester.BATCH_SIZE, is_train=False)
self._network = Network(images, is_train=False, hyper=hyper, features=scores)
self._probs = self._network.probs()
self._loss = self._network.loss(self._labels)
self._all_summaries = tf.merge_all_summaries()
self.models_dir = models_dir
print('Tester model folder: %s' %self.models_dir)
assert os.path.exists(self.models_dir)
self.writer = writer
def __init__(self, models_dir, fold_name, writer=None, hyper=None):
self._graph = tf.Graph()
with self._graph.as_default():
reader = Reader(fold_name)
with tf.device('/gpu:1'):
images, labels, scores, _ = reader.inputs(Trainer.BATCH_SIZE, is_train=True)
self._network = Network(images, is_train=True, hyper=hyper, features=scores)
self._loss = self._network.loss(labels)
self._lr_placeholder = tf.placeholder(tf.float32)
self._train = self._train_op()
self._all_summaries = tf.merge_all_summaries()
self.models_dir = models_dir
print('Trainer model folder: %s' %self.models_dir)
if not tf.gfile.Exists(self.models_dir):
tf.gfile.MakeDirs(self.models_dir)
self.writer = writer
if (self.writer):
self.writer.write_graph(self._graph)