# Copyright 2015 The TensorFlow Authors. 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.
# ==============================================================================
"""Functional tests for depthwise convolutional operations."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import nn_impl
from tensorflow.python.ops import nn_ops
import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging


def _DepthwiseConv2dNumpyBasic(x1, x2, strides):
  """Compute depthwise_conv2d using Numpy.

  This allows use to test TensorFlow's depthwise_conv2d by comparing to the
  Numpy version.

  Args:
    x1: The input Numpy array, in NHWC format.
    x2: The filter Numpy array.
    strides: A Python list of 4 elements representing the strides.

  Returns:
    The depthwise conv2d output as a Numpy array.
  """
  n, h, w, c = x1.shape
  fh, fw, c2, o = x2.shape
  assert c == c2
  _, sh, sw, _ = strides
  out_rows = (h - fh + sh) // sh
  out_cols = (w - fw + sw) // sw
  out = np.zeros([n, out_rows, out_cols, c * o])
  for i in range(out_rows):
    for j in range(out_cols):
      for k in range(c):
        start_height = i * sh
        end_height = start_height + fh
        start_width = j * sw
        end_width = start_width + fw
        # multiplied_slice.shape: (b, fh, fw, o)
        multiplied_slice = (
            x1[:, start_height:end_height, start_width:end_width, k, np.newaxis]
            * x2[:, :, k, :])
        # Set a slice of b * o elements of 'out'.
        out[:, i, j, k * o:(k + 1) * o] = np.sum(multiplied_slice, axis=(1, 2))
  return out


def _DepthwiseConv2dNumpy(x1, x2, strides, padding, data_format, dilations):
  """Compute depthwise_conv2d using Numpy.

  This allows use to test TensorFlow's depthwise_conv2d by comparing to the
  Numpy version.

  Unlike `_DepthwiseConv2dNumpyBasic`, this supports more advanced features
  like padding.

  Args:
    x1: The input Numpy array.
    x2: The filter Numpy array.
    strides: A Python list of 4 elements representing the strides.
    padding: The padding. "SAME", "VALID", or a list of explicit paddings.
    data_format: "NHWC" or "NCHW".
    dilations: A list of 2 elements, representing the dilations.

  Returns:
    The depthwise conv2d as a Numpy array.
  """
  if data_format == "NCHW":
    # Transpose arguments to NHWC format.
    x1 = np.transpose(x1, (0, 3, 1, 2))
    strides = [strides[0], strides[3], strides[1], strides[2]]
    if dilations:
      dilations = [dilations[0], dilations[3], dilations[1], dilations[2]]

  if dilations:
    # Dilate the filter so _DepthwiseConv2dNumpyBasic doesn't have to deal with
    # dilations.
    fh, fw, c, o = x2.shape
    new_fh = (fh - 1) * dilations[0] + 1
    new_fw = (fw - 1) * dilations[1] + 1
    new_x2 = np.zeros((new_fh, new_fw, c, o))
    for i in range(fh):
      for j in range(fw):
        new_x2[i * dilations[0], j * dilations[1], : :] = x2[i, j, :, :]
    x2 = new_x2

  # Pad input so _DepthwiseConv2dNumpyBasic doesn't have to deal with padding.
  if padding == "SAME":
    def PaddingsForDim(input_dim, filter_dim, stride):
      """Computes paddings for a single dimension."""
      if input_dim % stride == 0:
        total_padding = max(filter_dim - stride, 0)
      else:
        total_padding = max(filter_dim - (input_dim % stride), 0)
      pad_before = total_padding // 2
      pad_after = total_padding - pad_before
      return pad_before, pad_after
    padding = [(0, 0),
               PaddingsForDim(x1.shape[1], x2.shape[0], strides[1]),
               PaddingsForDim(x1.shape[2], x2.shape[1], strides[2]),
               (0, 0)]
  elif padding == "VALID":
    padding = [(0, 0)] * 4
  x1 = np.pad(x1, padding, "constant")

  y = _DepthwiseConv2dNumpyBasic(x1, x2, strides)

  if data_format == "NCHW":
    # Transpose back to NCHW format.
    y = np.transpose(y, (0, 2, 3, 1))

  return y


def ConfigsToTest():
  """Iterator for different convolution shapes, strides and paddings.

  Returns:
    List of tuples (input_size, filter_size, out_size, stride, padding,
    dilations), the depthwise convolution parameters.
  """
  def Config(input_size, filter_size, out_size, stride=1, padding="SAME",
             dilations=None):
    return input_size, filter_size, out_size, stride, padding, dilations
  return [
      Config([4, 5, 5, 48], [1, 1, 48, 2], [4, 5, 5, 96]),
      Config([4, 8, 8, 84], [1, 3, 84, 1], [4, 8, 8, 84]),
      Config([4, 17, 17, 48], [3, 1, 48, 4], [4, 17, 17, 192]),
      Config([4, 9, 27, 8], [3, 3, 8, 1], [4, 9, 27, 8]),
      Config([4, 31, 31, 7], [3, 3, 7, 1], [4, 31, 31, 7]),
      Config([4, 35, 35, 2], [5, 5, 2, 1], [4, 35, 35, 2]),
      Config([4, 147, 147, 2], [3, 3, 2, 8], [4, 49, 49, 16], 3,
             padding="VALID"),
      Config([3, 299, 299, 3], [3, 2, 3, 8], [3, 150, 150, 24], 2),
      Config([5, 183, 183, 1], [5, 5, 1, 2], [5, 92, 92, 2], 2),
      Config([5, 183, 183, 1], [5, 5, 1, 2], [5, 183, 183, 2],
             dilations=[2, 2]),
      Config([5, 41, 35, 2], [4, 7, 2, 2], [5, 32, 23, 4], padding="VALID",
             dilations=[3, 2]),
  ]


def ConfigsToTestExplicit():
  """Iterator for different convolution shapes, strides and explicit paddings.

  Returns:
    List of tuples (input_size, filter_size, out_size, stride, padding,
    dilations), the depthwise convolution parameters.
  """
  def Config(input_size, filter_size, out_size, stride=1, padding=None,
             dilations=None):
    return input_size, filter_size, out_size, stride, padding, dilations
  return [
      Config([4, 5, 5, 48], [1, 1, 48, 2], [4, 8, 12, 96],
             padding=[[1, 2], [3, 4]]),
      Config([4, 1, 1, 3], [3, 3, 3, 2], [4, 29, 39, 6],
             padding=[[10, 20], [15, 25]]),
      Config([4, 9, 27, 8], [3, 3, 8, 1], [4, 14, 31, 8],
             padding=[[3, 4], [4, 2]]),
      Config([4, 31, 31, 7], [3, 3, 7, 1], [4, 29, 29, 7],
             padding=[[0, 0], [0, 0]]),
      Config([3, 299, 299, 3], [3, 2, 3, 8], [3, 150, 153, 24], 2,
             padding=[[1, 2], [3, 5]]),
      Config([5, 183, 183, 1], [5, 5, 1, 2], [5, 62, 60, 2], 3,
             padding=[[3, 2], [1, 0]]),
      Config([5, 29, 31, 1], [5, 4, 1, 2], [5, 26, 23, 2],
             padding=[[3, 2], [1, 0]], dilations=[2, 3]),
      # These cases test the kernels in depthwise_conv_op_gpu.h which are used
      # if the input size is small.
      Config([4, 5, 5, 48], [3, 3, 48, 1], [4, 5, 5, 48],
             padding=[[0, 2], [0, 2]]),
      Config([1, 8, 7, 2], [8, 7, 2, 1], [1, 8, 7, 2],
             padding=[[0, 7], [3, 3]]),
      Config([2, 4, 3, 2], [3, 2, 2, 1], [2, 4, 3, 2],
             padding=[[2, 0], [1, 0]]),

  ]


def CheckGradConfigsToTest():
  """Iterator for different convolution shapes, strides and paddings.

  compute_gradient_error() is very expensive. So the configs should be
  relatively small.

  Returns:
    List of tuples (input_size, filter_size, out_size, stride, padding,
    dilations), the depthwise convolution parameters.
  """
  def Config(input_size, filter_size, out_size, stride=1, padding="SAME",
             dilations=None):
    return input_size, filter_size, out_size, stride, padding, dilations
  return [
      Config([2, 5, 8, 1], [4, 4, 1, 2], [2, 5, 8, 2]),
      Config([4, 5, 5, 1], [2, 2, 1, 2], [4, 2, 2, 2], 2, padding="VALID"),
      Config([2, 4, 4, 2], [3, 1, 2, 2], [2, 4, 4, 4]),
      Config([1, 15, 15, 2], [1, 3, 2, 1], [1, 15, 15, 2]),
      Config([2, 15, 16, 1], [3, 3, 1, 2], [2, 5, 5, 2], 3, padding="VALID"),
      Config([2, 5, 8, 1], [4, 3, 1, 2], [2, 5, 8, 2], dilations=[1, 2]),
      # These cases test the kernels in depthwise_conv_op_gpu.h which are used
      # if the input size is small.
      Config([1, 3, 1, 2], [2, 1, 2, 1], [1, 3, 1, 2]),
      Config([2, 2, 3, 2], [2, 1, 2, 1], [2, 2, 3, 2]),
      Config([2, 2, 3, 1], [2, 2, 1, 1], [2, 2, 3, 1]),
  ]


def CheckGradConfigsToTestExplicit():
  """Iterator for different convolution shapes, strides and explicit paddings.

  compute_gradient_error() is very expensive. So the configs should be
  relatively small.

  Returns:
    List of tuples (input_size, filter_size, out_size, stride, padding,
    dilations), the depthwise convolution parameters.
  """
  def Config(input_size, filter_size, out_size, stride=1, padding=None,
             dilations=None):
    return input_size, filter_size, out_size, stride, padding, dilations
  return [
      Config([2, 5, 8, 1], [4, 4, 1, 2], [2, 3, 10, 2],
             padding=[[0, 1], [2, 3]]),
      Config([4, 5, 5, 1], [2, 2, 1, 2], [4, 4, 5, 2], 2,
             padding=[[3, 1], [5, 0]]),
      Config([2, 4, 4, 2], [3, 1, 2, 2], [2, 7, 11, 4],
             padding=[[4, 1], [3, 4]]),
      Config([1, 15, 15, 2], [1, 3, 2, 1], [1, 18, 23, 2],
             padding=[[3, 0], [2, 8]]),
      Config([2, 15, 16, 1], [3, 3, 1, 2], [2, 5, 8, 2], 3,
             padding=[[0, 0], [10, 0]]),
      Config([2, 5, 8, 1], [3, 4, 1, 2], [2, 5, 10, 2],
             padding=[[3, 1], [2, 3]], dilations=[2, 1]),
      # These cases test the kernels in depthwise_conv_op_gpu.h which are used
      # if the input size is small.
      Config([2, 4, 3, 2], [3, 2, 2, 1], [2, 4, 3, 2],
             padding=[[2, 0], [1, 0]]),
  ]


class DepthwiseConv2DTest(test.TestCase):

  # This tests depthwise_conv2d and depthwise_conv2d_native
  def _VerifyValues(self,
                    tensor_in_sizes,
                    filter_in_sizes,
                    stride,
                    padding,
                    data_type,
                    use_gpu,
                    grouped_conv=False,
                    data_format="NHWC",
                    dilations=None):
    """Verifies the output values of the convolution function.

    Args:
      tensor_in_sizes: Input tensor dimensions in
        [batch, input_rows, input_cols, input_depth].
      filter_in_sizes: Filter tensor dimensions in
        [filter_rows, filter_cols, input_depth, depth_multiplier].
      stride: Stride.
      padding: Padding type.
      data_type: The data type to use.
      use_gpu: Whether to use GPU.
      grouped_conv: Whether to use cuDNN 7's grouped convolution.
      data_format: The data_format of the input. "NHWC" or "NCHW".
      dilations: A list of 2 elements, representing the dilations.
    """
    input_size = 1
    filter_size = 1
    for s in tensor_in_sizes:
      input_size *= s
    for s in filter_in_sizes:
      filter_size *= s
    # Initializes the input and filter tensor with numbers incrementing from 1.
    x1 = [f * 1.0 / input_size for f in range(1, input_size + 1)]
    x1 = np.array(x1).reshape(tensor_in_sizes)
    x2 = [f * 1.0 / filter_size for f in range(1, filter_size + 1)]
    x2 = np.array(x2).reshape(filter_in_sizes)
    # Compute reference result
    strides = [1, stride, stride, 1]
    if isinstance(padding, list):
      padding = [(0, 0)] + padding + [(0, 0)]
    np_result = _DepthwiseConv2dNumpy(x1, x2, strides, padding, "NHWC",
                                      dilations)

    ops.reset_default_graph()
    graph = ops.get_default_graph()
    with self.session(graph=graph, use_gpu=use_gpu) as sess:
      tolerance = {
          dtypes.float16: 4e-2,
          dtypes.float32: 1e-5,
          dtypes.float64: 1e-12,
      }[data_type]

      t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=data_type)
      t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=data_type)

      if data_format == "NCHW":
        # Transpose from NHWC input to NCHW
        # Ex. [4, 5, 5, 48] to [4, 48, 5, 5]
        t1 = array_ops.transpose(t1, [0, 3, 1, 2])
        strides = [1, 1, stride, stride]
        if isinstance(padding, list):
          padding = [padding[0], padding[3], padding[1], padding[2]]

      # depthwise_conv2d_native does not support dilations except on TPUs.
      if dilations is None:
        with sess.graph._kernel_label_map({
            "DepthwiseConv2dNative": "cudnn_grouped_convolution"
        } if grouped_conv else {}):
          conv_native = nn_ops.depthwise_conv2d_native(
              t1,
              t2,
              strides=strides,
              data_format=data_format,
              padding=padding)

        if data_format == "NCHW":
          # Transpose back from NCHW to NHWC
          conv_native = array_ops.transpose(conv_native, [0, 2, 3, 1])

        try:
          # The Numpy array from calling depthwise_conv2d_native
          native_result = self.evaluate(conv_native)
        except errors.InvalidArgumentError as e:
          # Grouped convolution kernel is only registered for cuDNN 7. Silently
          # return when we are running on an earlier version or without GPU.
          if e.message.startswith(
              "No OpKernel was registered to support Op "
              "'DepthwiseConv2dNative'"):
            tf_logging.warn("Skipping grouped convolution test")
            return
          raise e

      conv_interface = nn_impl.depthwise_conv2d(
          t1, t2, strides=strides, padding=padding,
          data_format=data_format, dilations=dilations)
      if data_format == "NCHW":
        # Transpose back from NCHW to NHWC
        conv_interface = array_ops.transpose(conv_interface, [0, 2, 3, 1])

      # The Numpy array from calling depthwise_conv2d
      interface_result = self.evaluate(conv_interface)

    if dilations is None:
      self.assertAllClose(native_result, np_result, atol=tolerance, rtol=0.)
    self.assertAllClose(interface_result, np_result, atol=tolerance, rtol=0.)

  @test_util.run_v1_only("b/120545219")
  @test_util.run_cuda_only
  def testDepthwiseConv2DCudnn(self):
    for index, (input_size, filter_size, _, stride,
                padding, dilations) in enumerate(ConfigsToTest()):
      # The CuDNN depthwise conv is turned on only when input/output is NCHW and
      # float16(half). See cudnn release note 7.6.3.
      tf_logging.info(
          "Testing DepthwiseConv2DCudnn, %dth config: %r * %r, stride: %d, "
          "padding: %s", index, input_size, filter_size, stride, padding)
      data_type = dtypes.float16
      self._VerifyValues(
          input_size,
          filter_size,
          stride,
          padding,
          data_type,
          use_gpu=True,
          data_format="NCHW",
          dilations=dilations)

  @test_util.run_v1_only("b/120545219")
  def testDepthwiseConv2D(self):
    for index, (input_size, filter_size, _, stride,
                padding, dilations) in enumerate(ConfigsToTest()):
      tf_logging.info(
          "Testing DepthwiseConv2D, %dth config: %r * %r, stride: %d, padding: "
          "%s", index, input_size, filter_size, stride, padding)
      # double datatype is currently not supported for convolution ops
      # on the ROCm platform
      optional_float64 = [] if test.is_built_with_rocm() else [dtypes.float64]
      for data_type in ([dtypes.float32] + optional_float64):
        tf_logging.info("Testing without grouped_conv")
        self._VerifyValues(
            input_size, filter_size, stride, padding, data_type, use_gpu=True,