scripts/tf_cnn_benchmarks/models/tf1_only/mobilenet.py [36:69]:
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def _fixed_padding(inputs, kernel_size, rate=1):
  """Pads the input along the spatial dimensions independently of input size.

  Pads the input such that if it was used in a convolution with 'VALID' padding,
  the output would have the same dimensions as if the unpadded input was used
  in a convolution with 'SAME' padding.

  Args:
    inputs: A tensor of size [batch, height_in, width_in, channels].
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
    rate: An integer, rate for atrous convolution.

  Returns:
    output: A tensor of size [batch, height_out, width_out, channels] with the
      input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),
                           kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]
  pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]
  pad_beg = [pad_total[0] // 2, pad_total[1] // 2]
  pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]
  padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
                                  [pad_beg[1], pad_end[1]], [0, 0]])
  return padded_inputs


def _make_divisible(v, divisor, min_value=None):
  if min_value is None:
    min_value = divisor
  new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
  # Make sure that round down does not go down by more than 10%.
  if new_v < 0.9 * v:
    new_v += divisor
  return new_v
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scripts/tf_cnn_benchmarks/models/tf1_only/mobilenet_conv_blocks.py [28:61]:
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def _fixed_padding(inputs, kernel_size, rate=1):
  """Pads the input along the spatial dimensions independently of input size.

  Pads the input such that if it was used in a convolution with 'VALID' padding,
  the output would have the same dimensions as if the unpadded input was used
  in a convolution with 'SAME' padding.

  Args:
    inputs: A tensor of size [batch, height_in, width_in, channels].
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
    rate: An integer, rate for atrous convolution.

  Returns:
    output: A tensor of size [batch, height_out, width_out, channels] with the
      input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),
                           kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]
  pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]
  pad_beg = [pad_total[0] // 2, pad_total[1] // 2]
  pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]
  padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
                                  [pad_beg[1], pad_end[1]], [0, 0]])
  return padded_inputs


def _make_divisible(v, divisor, min_value=None):
  if min_value is None:
    min_value = divisor
  new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
  # Make sure that round down does not go down by more than 10%.
  if new_v < 0.9 * v:
    new_v += divisor
  return new_v
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