def _gen_anchors(self, config, feature_map_shape): model = FasterRCNN(config) results = model._generate_anchors(feature_map_shape) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) results = sess.run(results) return results
def _gen_anchors(self, config, feature_map_shape): model = FasterRCNN(config) results = model._generate_anchors(feature_map_shape) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) results = sess.run(results) return results
def _get_losses(self, config, prediction_dict, image_size): image = tf.placeholder(tf.float32, shape=self.image.shape) gt_boxes = tf.placeholder(tf.float32, shape=self.gt_boxes.shape) model = FasterRCNN(config) model(image, gt_boxes) all_losses = model.loss(prediction_dict, return_all=True) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) all_losses = sess.run(all_losses) return all_losses
def _get_losses(self, config, prediction_dict, image_size): image = tf.placeholder( tf.float32, shape=self.image.shape) gt_boxes = tf.placeholder( tf.float32, shape=self.gt_boxes.shape) model = FasterRCNN(config) model(image, gt_boxes, is_training=True) all_losses = model.loss(prediction_dict, return_all=True) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) all_losses = sess.run(all_losses) return all_losses
def _run_network(self): image = tf.placeholder(tf.float32, shape=self.image.shape) gt_boxes = tf.placeholder(tf.float32, shape=self.gt_boxes.shape) model = FasterRCNN(self.config) results = model(image, gt_boxes) with self.test_session() as sess: sess.run(tf.global_variables_initializer()) results = sess.run(results, feed_dict={ gt_boxes: self.gt_boxes, image: self.image, }) return results