def __init__(self, **kwargs):
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.class_names = self._get_class()
self.anchors = self._get_anchors()
self.alpha = 1.4
config = tf.ConfigProto()
tf.keras.backend.set_learning_phase(0)
if self.opt == OPT.XLA:
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
elif self.opt == OPT.MKL:
config.intra_op_parallelism_threads = 4
config.inter_op_parallelism_threads = 4
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
elif self.opt == OPT.DEBUG:
sess = tf_debug.TensorBoardDebugWrapperSession(
tf.get_session(), "fangsixie-Inspiron-7572:6064")
tf.keras.backend.set_session(sess)
else:
sess = tf.get_session()
self.sess = sess
if tf.executing_eagerly():
self.generate()
else:
self.boxes, self.scores, self.classes = self.generate()
def set_value(x, val):
"""
Get parameter value from a shared variable.
"""
if is_theano():
x.set_value(val)
elif is_cgt():
x.op.set_value(val)
elif is_tf():
tf.get_session().run(tf.assign(x, val))
else:
import ipdb; ipdb.set_trace()
def set_value(x, val):
"""
Get parameter value from a shared variable.
"""
if is_theano():
x.set_value(val)
elif is_cgt():
x.op.set_value(val)
elif is_tf():
tf.get_session().run(tf.assign(x, val))
else:
import ipdb
ipdb.set_trace()
def __init__(self, **kwargs):
self.__dict__.update(self._defaults) # set up default values
self.class_names = get_classes(
self.model_config[self.backbone]['classes_path'])
self.anchors = get_anchors(
self.model_config[self.backbone]['anchors_path'])
self.input_shape = self.model_config[self.backbone]['input_size']
config = tf.ConfigProto()
if self.opt == OPT.XLA:
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
elif self.opt == OPT.MKL:
config.intra_op_parallelism_threads = 4
config.inter_op_parallelism_threads = 4
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
elif self.opt == OPT.DEBUG:
tf.logging.set_verbosity(tf.logging.DEBUG)
sess = tf_debug.TensorBoardDebugWrapperSession(
tf.Session(config=tf.ConfigProto(log_device_placement=True)),
"localhost:6064")
tf.keras.backend.set_session(sess)
else:
sess = tf.get_session()
self.sess = sess
self.generate()
print(X_test.shape)
print(ecal_train.shape)
print(ecal_test.shape)
print('*************************************************************************************')
train_history = defaultdict(list)
test_history = defaultdict(list)
# Broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
#callbacks = [
# hvd.callbacks.BroadcastGlobalVariablesCallback(0),
#]
bcast_op = hvd.broadcast_global_variables(0)
tf.get_session().run(bcast_op)
for epoch in range(nb_epochs):
print('Epoch {} of {}'.format(epoch + 1, nb_epochs))
nb_batches = int(X_train.shape[0] / batch_size)
if verbose:
progress_bar = Progbar(target=nb_batches)
epoch_gen_loss = []
epoch_disc_loss = []
for index in range(nb_batches):
if verbose:
progress_bar.update(index)
else:
if index % 100 == 0: