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Python gen_nn_ops.dilation2d方法代码示例

本文整理汇总了Python中tensorflow.python.ops.gen_nn_ops.dilation2d方法的典型用法代码示例。如果您正苦于以下问题:Python gen_nn_ops.dilation2d方法的具体用法?Python gen_nn_ops.dilation2d怎么用?Python gen_nn_ops.dilation2d使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在tensorflow.python.ops.gen_nn_ops的用法示例。


在下文中一共展示了gen_nn_ops.dilation2d方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: erosion2d

# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import dilation2d [as 别名]
def erosion2d(value, kernel, strides, rates, padding, name=None):
  """Computes the grayscale erosion of 4-D `value` and 3-D `kernel` tensors.

  The `value` tensor has shape `[batch, in_height, in_width, depth]` and the
  `kernel` tensor has shape `[kernel_height, kernel_width, depth]`, i.e.,
  each input channel is processed independently of the others with its own
  structuring function. The `output` tensor has shape
  `[batch, out_height, out_width, depth]`. The spatial dimensions of the
  output tensor depend on the `padding` algorithm. We currently only support the
  default "NHWC" `data_format`.

  In detail, the grayscale morphological 2-D erosion is given by:

      output[b, y, x, c] =
         min_{dy, dx} value[b,
                            strides[1] * y - rates[1] * dy,
                            strides[2] * x - rates[2] * dx,
                            c] -
                      kernel[dy, dx, c]

  Duality: The erosion of `value` by the `kernel` is equal to the negation of
  the dilation of `-value` by the reflected `kernel`.

  Args:
    value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`.
    kernel: A `Tensor`. Must have the same type as `value`.
      3-D with shape `[kernel_height, kernel_width, depth]`.
    strides: A list of `ints` that has length `>= 4`.
      1-D of length 4. The stride of the sliding window for each dimension of
      the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
    rates: A list of `ints` that has length `>= 4`.
      1-D of length 4. The input stride for atrous morphological dilation.
      Must be: `[1, rate_height, rate_width, 1]`.
    padding: A `string` from: `"SAME", "VALID"`.
      The type of padding algorithm to use.
    name: A name for the operation (optional). If not specified "erosion2d"
      is used.

  Returns:
    A `Tensor`. Has the same type as `value`.
    4-D with shape `[batch, out_height, out_width, depth]`.

  Raises:
    ValueError: If the `value` depth does not match `kernel`' shape, or if
      padding is other than `'VALID'` or `'SAME'`.
  """
  with ops.name_scope(name, "erosion2d", [value, kernel]) as name:
    # Reduce erosion to dilation by duality.
    return math_ops.negative(
        gen_nn_ops.dilation2d(input=math_ops.negative(value),
                              filter=array_ops.reverse_v2(kernel, [0, 1]),
                              strides=strides,
                              rates=rates,
                              padding=padding,
                              name=name)) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:57,代码来源:nn_ops.py

示例2: erosion2d

# 需要导入模块: from tensorflow.python.ops import gen_nn_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_nn_ops import dilation2d [as 别名]
def erosion2d(value, kernel, strides, rates, padding, name=None):
  """Computes the grayscale erosion of 4-D `value` and 3-D `kernel` tensors.

  The `value` tensor has shape `[batch, in_height, in_width, depth]` and the
  `kernel` tensor has shape `[kernel_height, kernel_width, depth]`, i.e.,
  each input channel is processed independently of the others with its own
  structuring function. The `output` tensor has shape
  `[batch, out_height, out_width, depth]`. The spatial dimensions of the
  output tensor depend on the `padding` algorithm. We currently only support the
  default "NHWC" `data_format`.

  In detail, the grayscale morphological 2-D erosion is given by:

      output[b, y, x, c] =
         min_{dy, dx} value[b,
                            strides[1] * y - rates[1] * dy,
                            strides[2] * x - rates[2] * dx,
                            c] -
                      kernel[dy, dx, c]

  Duality: The erosion of `value` by the `kernel` is equal to the negation of
  the dilation of `-value` by the reflected `kernel`.

  Args:
    value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`.
    kernel: A `Tensor`. Must have the same type as `value`.
      3-D with shape `[kernel_height, kernel_width, depth]`.
    strides: A list of `ints` that has length `>= 4`.
      1-D of length 4. The stride of the sliding window for each dimension of
      the input tensor. Must be: `[1, stride_height, stride_width, 1]`.
    rates: A list of `ints` that has length `>= 4`.
      1-D of length 4. The input stride for atrous morphological dilation.
      Must be: `[1, rate_height, rate_width, 1]`.
    padding: A `string` from: `"SAME", "VALID"`.
      The type of padding algorithm to use.
    name: A name for the operation (optional). If not specified "erosion2d"
      is used.

  Returns:
    A `Tensor`. Has the same type as `value`.
    4-D with shape `[batch, out_height, out_width, depth]`.

  Raises:
    ValueError: If the `value` depth does not match `kernel`' shape, or if
      padding is other than `'VALID'` or `'SAME'`.
  """
  with ops.name_scope(name, "erosion2d", [value, kernel]) as name:
    # Reduce erosion to dilation by duality.
    return math_ops.neg(gen_nn_ops.dilation2d(input=math_ops.neg(value),
                                              filter=array_ops.reverse(
                                                  kernel, [True, True, False]),
                                              strides=strides,
                                              rates=rates,
                                              padding=padding,
                                              name=name)) 
开发者ID:tobegit3hub,项目名称:deep_image_model,代码行数:57,代码来源:nn_ops.py


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