framehop_lstm_memory_model.py 文件源码

def resolution(self, model_input_raw, num_frames, resolution, method="SELECT"):
    frame_dim = len(model_input_raw.get_shape()) - 2
    feature_dim = len(model_input_raw.get_shape()) - 1
    max_frames = model_input_raw.get_shape().as_list()[frame_dim]
    num_features = model_input_raw.get_shape().as_list()[feature_dim]
    if resolution > 1:
      new_max_frames = max_frames / resolution
      cut_frames = new_max_frames * resolution
      model_input_raw = model_input_raw[:, :cut_frames, :]
      model_input_raw = tf.reshape(model_input_raw, shape=[-1,new_max_frames,resolution,num_features])
      if method == "MEAN":
        model_input_raw = tf.reduce_mean(model_input_raw, axis=2)
      elif method == "MAX":
        model_input_raw = tf.reduce_max(model_input_raw, axis=2)
      elif method == "SELECT":
        model_input_raw = model_input_raw[:,:,resolution-1,:]
      model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
      num_frames = num_frames / resolution
    else:
      model_input = tf.nn.l2_normalize(model_input_raw, feature_dim)
    return model_input, num_frames