Python gen_nn_ops._avg_pool方法代码示例

# 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 _avg_pool [as 别名]
def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
  """Performs the average pooling on the input.

  Each entry in `output` is the mean of the corresponding size `ksize`
  window in `value`.

  Args:
    value: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
      `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
    ksize: A list of ints that has length >= 4.
      The size of the window for each dimension of the input tensor.
    strides: A list of ints that has length >= 4.
      The stride of the sliding window for each dimension of the
      input tensor.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
      See the @{tf.nn.convolution$comment here}
    data_format: A string. 'NHWC' and 'NCHW' are supported.
    name: Optional name for the operation.

  Returns:
    A `Tensor` with the same type as `value`.  The average pooled output tensor.
  """
  with ops.name_scope(name, "AvgPool", [value]) as name:
    value = ops.convert_to_tensor(value, name="input")
    return gen_nn_ops._avg_pool(value,
                                ksize=ksize,
                                strides=strides,
                                padding=padding,
                                data_format=data_format,
                                name=name) 

# 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 _avg_pool [as 别名]
def _AvgPoolGradGrad(op, grad):
  return (array_ops.stop_gradient(op.inputs[0]), gen_nn_ops._avg_pool(
      grad,
      op.get_attr("ksize"),
      op.get_attr("strides"),
      op.get_attr("padding"),
      data_format=op.get_attr("data_format"))) 

# 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 _avg_pool [as 别名]
def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
  """Performs the average pooling on the input.

  Each entry in `output` is the mean of the corresponding size `ksize`
  window in `value`.

  Args:
    value: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
      `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
    ksize: A list of ints that has length >= 4.
      The size of the window for each dimension of the input tensor.
    strides: A list of ints that has length >= 4.
      The stride of the sliding window for each dimension of the
      input tensor.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
      See the [comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution)
    data_format: A string. 'NHWC' and 'NCHW' are supported.
    name: Optional name for the operation.

  Returns:
    A `Tensor` with the same type as `value`.  The average pooled output tensor.
  """
  with ops.name_scope(name, "AvgPool", [value]) as name:
    value = ops.convert_to_tensor(value, name="input")
    return gen_nn_ops._avg_pool(value,
                                ksize=ksize,
                                strides=strides,
                                padding=padding,
                                data_format=data_format,
                                name=name) 

# 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 _avg_pool [as 别名]
def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
  """Performs the average pooling on the input.

  Each entry in `output` is the mean of the corresponding size `ksize`
  window in `value`.

  Args:
    value: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type
      `float32`, `float64`, `qint8`, `quint8`, or `qint32`.
    ksize: A 1-D int Tensor of 4 elements.
      The size of the window for each dimension of the input tensor.
    strides: A 1-D int Tensor of 4 elements
      The stride of the sliding window for each dimension of the
      input tensor.
    padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
      See the @{tf.nn.convolution$comment here}
    data_format: A string. 'NHWC' and 'NCHW' are supported.
    name: Optional name for the operation.

  Returns:
    A `Tensor` with the same type as `value`.  The average pooled output tensor.
  """
  with ops.name_scope(name, "AvgPool", [value]) as name:
    value = ops.convert_to_tensor(value, name="input")
    return gen_nn_ops._avg_pool(value,
                                ksize=ksize,
                                strides=strides,
                                padding=padding,
                                data_format=data_format,
                                name=name) 

# 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 _avg_pool [as 别名]
def _rmac(feat_map, rmac_step, reduce_method, deploy):
    """Extract regional vector from the raw feature map.
    The overlap ratio of neighboring regions is 0.5
    Args:
        feat_map: input raw feature map.
        step: the length of the output feature map.
        e.g., step = 3 gives 3 x 3 = 9 regional vectors.
    Returns:
        all_rvec: regional vectors.
    """
    all_rvec = None
    fully_defined = False
    if feat_map.get_shape().is_fully_defined() or deploy:
        batch_size, feat_h, feat_w, feat_dim = feat_map.get_shape().as_list()
        fully_defined = True
    else:
        batch_size, feat_h, feat_w, feat_dim = tf.unstack(tf.shape(feat_map))

    for step in rmac_step:
        if step > 1:
            k_h = (feat_h / (step + 1)) * 2
            s_h = (feat_h - k_h) / (step - 1)
            k_w = (feat_w / (step + 1)) * 2
            s_w = (feat_w - k_w) / (step - 1)
        else:
            # reduce all.
            k_h = feat_h
            s_h = 1
            k_w = feat_w
            s_w = 1

        if fully_defined and (k_h < 1 or k_w < 1):
            # skip the step if the kernal size is smaller that 1.
            continue

        if reduce_method == 'AVG':
            rvec = gen_nn_ops._avg_pool(feat_map, [1, k_h, k_w, 1], [1, s_h, s_w, 1], 'VALID')
        elif reduce_method == 'L2':
            rvec = tf.sqrt(gen_nn_ops._avg_pool(tf.square(feat_map), [
                - 1, k_h, k_w, 1], [1, s_h, s_w, 1], 'VALID'))
        elif reduce_method == 'MAX':
            #rvec = gen_nn_ops._max_pool_v2(feat_map, [1, k_h, k_w, 1], [1, s_h, s_w, 1], 'VALID')
            rvec = tf.nn.max_pool(feat_map, [1, k_h, k_w, 1], [1, s_h, s_w, 1], 'VALID')
        else:
            print(Notify.FAIL, 'Known reduce method:', reduce_method, Notify.ENDC)

        rvec = tf.reshape(rvec, [tf.shape(feat_map)[0], -1, feat_dim])
        rvec = tf.transpose(rvec, [0, 2, 1])
        if all_rvec is None:
            all_rvec = rvec
        else:
            all_rvec = tf.concat([all_rvec, rvec], axis=2)
    return all_rvec