predictClass.py 文件源码

def _create_joint_tensor(self, tensor, name = 'joint_tensor',debug = False):
        """ TensorFlow Computation of Joint Position
        Args:
            tensor      : Prediction Tensor Shape [nbStack x 64 x 64 x outDim] or [64 x 64 x outDim]
            name        : name of the tensor
        Returns:
            out         : Tensor of joints position

        Comment:
            Genuinely Agreeing this tensor is UGLY. If you don't trust me, look at
            'prediction' node in TensorBoard.
            In my defence, I implement it to compare computation times with numpy.
        """
        with tf.name_scope(name):
            shape = tensor.get_shape().as_list()
            if debug:
                print(shape)
            if len(shape) == 3:
                resh = tf.reshape(tensor[:,:,0], [-1])
            elif len(shape) == 4:
                resh = tf.reshape(tensor[-1,:,:,0], [-1])
            if debug:
                print(resh)
            arg = tf.arg_max(resh,0)
            if debug:
                print(arg, arg.get_shape(), arg.get_shape().as_list())
            joints = tf.expand_dims(tf.stack([arg // tf.to_int64(shape[1]), arg % tf.to_int64(shape[1])], axis = -1), axis = 0)
            for i in range(1, shape[-1]):
                if len(shape) == 3:
                    resh = tf.reshape(tensor[:,:,i], [-1])
                elif len(shape) == 4:
                    resh = tf.reshape(tensor[-1,:,:,i], [-1])
                arg = tf.arg_max(resh,0)
                j = tf.expand_dims(tf.stack([arg // tf.to_int64(shape[1]), arg % tf.to_int64(shape[1])], axis = -1), axis = 0)
                joints = tf.concat([joints, j], axis = 0)
            return tf.identity(joints, name = 'joints')