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()