#!/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_convolution_transpose

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


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

  def _forward(self, use_gpu=False, force_gpu=False):
    case.init_ops()
    with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
      actual_op = flex_convolution_transpose(case.features_op,
                                             case.position_op,
                                             case.neighborhood_op,
                                             case.theta_op, case.bias_op)
      actual = sess.run(actual_op)
    return actual

  def test_forward(self):
    cpu = self._forward(use_gpu=False)
    gpu = self._forward(use_gpu=True)
    self.assertAllClose(cpu, gpu, 1e-4)

  def _backward_features(self, use_gpu=False, dtype=np.float32, numdiff=True):
    case.init_ops(dtype=dtype)
    with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
      actual_op = flex_convolution_transpose(case.features_op,
                                             case.position_op,
                                             case.neighborhood_op,
                                             case.theta_op, case.bias_op)
      if numdiff:
        return tf.test.compute_gradient(
            [case.features_op], [case.features.shape], actual_op,
            case.expected_output_shape())[0]
      else:
        return sess.run(tf.gradients(actual_op, [case.features_op]))[0]

  def _backward_bias(self, use_gpu=False, dtype=np.float32, numdiff=True):
    case.init_ops(dtype=dtype)
    with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
      actual_op = flex_convolution_transpose(case.features_op,
                                             case.position_op,
                                             case.neighborhood_op,
                                             case.theta_op, case.bias_op)

      if numdiff:
        return tf.test.compute_gradient(
            [case.bias_op], [case.bias.shape], actual_op,
            case.expected_output_shape())[0]
      else:
        return sess.run(tf.gradients(actual_op, [case.bias_op]))[0]

  def _backward_theta(self, use_gpu=False, dtype=np.float32, numdiff=True):
    case.init_ops(dtype=dtype)
    with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
      actual_op = flex_convolution_transpose(case.features_op,
                                             case.position_op,
                                             case.neighborhood_op,
                                             case.theta_op, case.bias_op)

      if numdiff:
        return tf.test.compute_gradient(
            [case.theta_op], [case.theta.shape], actual_op,
            case.expected_output_shape())[0]
      else:
        return sess.run(tf.gradients(actual_op, [case.theta_op]))[0]

  def test_backward_features_cpu_float64(self):
    actual, expected = self._backward_features(use_gpu=False, dtype=np.float64)
    self.assertAllClose(actual, expected)

  def test_backward_bias_cpu_float64(self):
    actual, expected = self._backward_bias(use_gpu=False, dtype=np.float64)
    self.assertAllClose(actual, expected)

  def test_backward_theta_cpu_float64(self):
    actual, expected = self._backward_theta(use_gpu=False, dtype=np.float64)
    self.assertAllClose(actual, expected)

  def test_backward_features_gpu_float64(self):
    actual, expected = self._backward_features(use_gpu=True, dtype=np.float64)
    self.assertAllClose(actual, expected)

  def test_backward_bias_gpu_float64(self):
    actual, expected = self._backward_bias(use_gpu=True, dtype=np.float64)
    self.assertAllClose(actual, expected)

  def test_backward_theta_gpu_float64(self):
    actual, expected = self._backward_theta(use_gpu=True, dtype=np.float64)
    self.assertAllClose(actual, expected)

  def test_backward_features_gpu_float32(self, dtype=np.float32):
    cpu = self._backward_features(use_gpu=False, dtype=dtype, numdiff=False)
    gpu = self._backward_features(use_gpu=True, dtype=dtype, numdiff=False)
    self.assertAllClose(cpu, gpu)

  def test_backward_bias_gpu_float32(self, dtype=np.float32):
    cpu = self._backward_bias(use_gpu=False, dtype=dtype, numdiff=False)
    gpu = self._backward_bias(use_gpu=True, dtype=dtype, numdiff=False)
    self.assertAllClose(cpu, gpu, 1e-5)

  def test_backward_theta_gpu_float32(self, dtype=np.float32):
    cpu = self._backward_theta(use_gpu=False, dtype=dtype, numdiff=False)
    gpu = self._backward_theta(use_gpu=True, dtype=dtype, numdiff=False)
    self.assertAllClose(cpu, gpu)


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