(inputs, num_outputs, kernel_size, stride, rate=1, scope=None)
Strided 2-D convolution with 'SAME' padding.

  When stride > 1, then we do explicit zero-padding, followed by conv2d with
  'VALID' padding.

  Note that

     net = conv2d_same(inputs, num_outputs, 3, stride=stride)

  is equivalent to

     net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME')
     net = subsample(net, factor=stride)

  whereas

     net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME')

  is different when the input's height or width is even, which is why we add the
  current function. For more details, see ResnetUtilsTest.testConv2DSameEven().

  Args:
    inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
    num_outputs: An integer, the number of output filters.
    kernel_size: An int with the kernel_size of the filters.
    stride: An integer, the output stride.
    rate: An integer, rate for atrous convolution.
    scope: Scope.

  Returns:
    output: A 4-D tensor of size [batch, height_out, width_out, channels] with
      the convolution output.
Strided 2-D convolution with 'SAME' padding.
Strided 2 - D convolution with SAME padding .
def conv2d_same(inputs, num_outputs, kernel_size, stride, rate=1, scope=None):
  """Strided 2-D convolution with 'SAME' padding.

  When stride > 1, then we do explicit zero-padding, followed by conv2d with
  'VALID' padding.

  Note that

     net = conv2d_same(inputs, num_outputs, 3, stride=stride)

  is equivalent to

     net = slim.conv2d(inputs, num_outputs, 3, stride=1, padding='SAME')
     net = subsample(net, factor=stride)

  whereas

     net = slim.conv2d(inputs, num_outputs, 3, stride=stride, padding='SAME')

  is different when the input's height or width is even, which is why we add the
  current function. For more details, see ResnetUtilsTest.testConv2DSameEven().

  Args:
    inputs: A 4-D tensor of size [batch, height_in, width_in, channels].
    num_outputs: An integer, the number of output filters.
    kernel_size: An int with the kernel_size of the filters.
    stride: An integer, the output stride.
    rate: An integer, rate for atrous convolution.
    scope: Scope.

  Returns:
    output: A 4-D tensor of size [batch, height_out, width_out, channels] with
      the convolution output.
  """
  if stride == 1:
    return slim.conv2d(inputs, num_outputs, kernel_size, stride=1, rate=rate,
                       padding='SAME', scope=scope)
  else:
    kernel_size_effective = kernel_size + (kernel_size - 1) * (rate - 1)
    pad_total = kernel_size_effective - 1
    pad_beg = pad_total // 2
    pad_end = pad_total - pad_beg
    inputs = tf.pad(inputs,
                    [[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
    return slim.conv2d(inputs, num_outputs, kernel_size, stride=stride,
                       rate=rate, padding='VALID', scope=scope)
def conv2d_same ( inputs , num_outputs , kernel_size , stride , rate = 1 , scope = None ) : if stride == 1 : return slim . conv2d ( inputs , num_outputs , kernel_size , stride = 1 , rate = rate , padding = 'SAME' , scope = scope ) else : kernel_size_effective = kernel_size + ( kernel_size - 1 ) * ( rate - 1 ) pad_total = kernel_size_effective - 1 pad_beg = pad_total // 2 pad_end = pad_total - pad_beg inputs = tf . pad ( inputs , [ [ 0 , 0 ] , [ pad_beg , pad_end ] , [ pad_beg , pad_end ] , [ 0 , 0 ] ] ) return slim . conv2d ( inputs , num_outputs , kernel_size , stride = stride , rate = rate , padding = 'VALID' , scope = scope )