#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Copyright 2017 ComputerGraphics Tuebingen. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Authors: Fabian Groh, Patrick Wieschollek, Hendrik P.A. Lensch


from misc import FakePointCloud, VerboseTestCase
import tensorflow as tf
import numpy as np

from __init__ import flex_pooling

case = FakePointCloud(B=2, N=32, K=4, Din=2, Dout=6, Dp=3)


class FlexPoolTest(VerboseTestCase):
  def __init__(self, methodName="runTest"):
    super(FlexPoolTest, self).__init__(methodName)

  def _forward(self, use_gpu=False, dtype=np.float32):
    case.init_ops(dtype=dtype)
    with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
      actual_op, winner_op = flex_pooling(
          case.features_op, case.neighborhood_op)
      actual = sess.run(actual_op)
    return actual

  def test_forward_same_float32(self):
    cpu = self._forward(use_gpu=False, dtype=np.float32)
    gpu = self._forward(use_gpu=True, dtype=np.float32)
    self.assertAllClose(cpu, gpu)

  def test_forward_same_float64(self):
    cpu = self._forward(use_gpu=False, dtype=np.float64)
    gpu = self._forward(use_gpu=True, dtype=np.float64)
    self.assertAllClose(cpu, gpu)

  def test_backward_same_float32(self):
    cpu, winner_cpu = self._backward(use_gpu=False, dtype=np.float32)
    gpu, winner_gpu = self._backward(use_gpu=True, dtype=np.float32)
    self.assertAllClose(winner_cpu, winner_gpu)
    self.assertAllClose(cpu, gpu)

  def test_backward_same_float64(self):
    cpu, winner_cpu = self._backward(use_gpu=False, dtype=np.float64)
    gpu, winner_gpu = self._backward(use_gpu=True, dtype=np.float64)
    self.assertAllClose(winner_cpu, winner_gpu)
    self.assertAllClose(cpu, gpu)

  def _backward(self, use_gpu=False, dtype=np.float32):
    case.init_ops(dtype=dtype)
    with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
      actual_op, winner_op = flex_pooling(
          case.features_op,
          case.neighborhood_op)

      graph_features_grad = tf.gradients(actual_op, [case.features_op])[0]

      dx, winner = sess.run([graph_features_grad, winner_op])
      return dx, winner

  def _simple_backward(self, use_gpu=False):
    # BN
    x = np.array([[[1], [2], [5], [3]]]).transpose(0, 2, 1)
    n = np.array([[[0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 0, 1],
                   [3, 0, 1, 2, ]]]).transpose(0, 2, 1)

    x = tf.convert_to_tensor(x.astype(np.float32))
    n = tf.convert_to_tensor(n.astype(np.int32))

    with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
      actual_op, winner_op = flex_pooling(x, n)
      graph_features_grad = tf.gradients(actual_op, [x])[0]
      return sess.run(graph_features_grad)

  def test_backward_simple_cpu(self):
    cpu = self._simple_backward(use_gpu=False)
    cpu[0, 0, 2] -= 4
    self.assertEqual(cpu.sum(), 0)

  def test_backward_simple_gpu(self):
    gpu = self._simple_backward(use_gpu=True)
    gpu[0, 0, 2] -= 4
    self.assertEqual(gpu.sum(), 0)


if __name__ == '__main__':
  tf.test.main()