def __init__(self, mc, gpu_id):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, mc)
self.BN = mc.BN
self._add_forward_graph()
self._add_yolo_interpret_graph()
def __init__(self, gpu_id, params):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, params)
self._add_datagen_graph()
self._add_forward_graph()
self._add_loss_graph()
self._add_train_graph()
def __init__(self, mc, gpu_id=0):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph()
self._add_interpretation_graph()
self._add_loss_graph()
self._add_train_graph()
self._add_viz_graph()
def __init__(self, mc):
with tf.device('/gpu:0'):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph()
self._add_interpretation_graph()
self._add_loss_graph()
self._add_train_graph()
self._add_viz_graph()
def __init__(self, mc, gpu_id=0):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph()
self._add_output_graph()
self._add_loss_graph()
self._add_train_graph()
self._add_viz_graph()
self._add_summary_ops()
def __init__(self, mc, gpu_id=0):
with tf.device('/gpu:{}'.format(gpu_id)):
print ('SSDNet __init__')
ModelSkeleton.__init__(self, mc)
self.init()
self.add_forward_graph()
self.add_interpretation_graph()
self.add_loss_graph()
self.add_train_graph()
def __init__(self, mc, gpu_id=0):
with tf.device('/gpu:{}'.format(gpu_id)):
#with tf.device('/cpu:{}'.format(0)):
print('SSDNet __init__')
ModelSkeleton.__init__(self, mc)
self.mutibox_loss_layer = MutiBoxLossLayer(mc)
self.add_forward_graph()
self.add_interpretation_graph()
self.add_loss_graph()
self.add_train_graph()
def __init__(self, mc, gpu_id=0):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph() # ed: SqueezeNet Model
self._add_output_graph() # pred_prob, pred_cls
self._add_loss_graph() # cls_loss, total_loss
self._add_train_graph() #
self._add_viz_graph() # label_to_show, depth_image_to_show, pred_image_to_show
self._add_summary_ops() #
def __init__(self, mc, gpu_id=0):
with tf.device('/gpu:{}'.format(gpu_id)):
print("Squeezeseg: Init")
print("Batch: ", mc.BATCH_SIZE)
ModelSkeleton.__init__(self, mc)
print("Adding forward graph")
self._add_forward_graph()
self._add_output_graph()
self._add_loss_graph()
self._add_train_graph()
self._add_viz_graph()
self._add_summary_ops()
print("Complete")
def __init__(self, mc, gpu_id_1=0, gpu_id_2=1, gpu_id_3=2):
# Distribute the tensors to gpus vertically
with tf.device('/gpu:{}'.format(gpu_id_1)):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph_1()
with tf.device('/gpu:{}'.format(gpu_id_2)):
self._add_forward_graph_2()
with tf.device('/gpu:{}'.format(gpu_id_3)):
self._add_forward_graph_3()
self._add_output_graph()
self._add_loss_graph()
self._add_train_graph()
self._add_viz_graph()
self._add_summary_ops()
def __init__(self, mc, gpu_id):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph()
self._add_interpretation_graph()
assert mc.LOSS_TYPE in ['SQT', 'YOLO'], \
'Loss type {0} not defined'.format(mc.LOSS_TYPE)
if mc.LOSS_TYPE == 'SQT':
self._add_sqt_loss_graph()
elif mc.LOSS_TYPE == 'YOLO':
self._add_yolo_loss_graph()
self._add_train_graph()
self._add_viz_graph()
def __init__(self, mc, gpu_id=0):
if gpu_id < 0:
device_set = '/cpu:{}'.format(0)
else:
device_set = '/gpu:{}'.format(gpu_id)
print('device set ' + device_set)
#with tf.device('/gpu:{}'.format(gpu_id)):
with tf.device(device_set):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph()
self._add_interpretation_graph()
self._add_loss_graph()
self._add_train_graph()
self._add_viz_graph()
def __init__(self, mc, gpu_id):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph()
self._add_filter_loss_graph()
self._add_train_graph()
self._add_viz_graph()
self.mask_pred = tf.placeholder(tf.float32,
[mc.BATCH_SIZE, 12, 39, 1],
name='mask_pred')
self.mask_pred_viz_op = tf.summary.image(
'pred_masks',
self.mask_pred,
collections='image_summary',
max_outputs=mc.BATCH_SIZE)
tf.summary.histogram('preds_hist', self.preds)
def __init__(self, gpu_id):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, 50, 28, 28)
self._add_forward_graph()
self._add_loss_graph()
self._add_train_graph()
def __init__(self, mc, gpu_id=0):
with tf.device('/gpu:{}'.format(gpu_id)):
ModelSkeleton.__init__(self, mc)
self._add_forward_graph()