resize_trilinear

paddle.fluid.layers.resize_trilinear(input, out_shape=None, scale=None, name=None, actual_shape=None, align_corners=True, align_mode=1, data_format='NCDHW')[源代码]

注意: 参数 actual_shape 将被弃用,请使用 out_shape 替代。

该层对输入进行放缩,基于给定的由 actual_shape , out_shape , scale 确定的输出shape,进行三线插值。三线插值是包含三个参数的线性插值方程(D方向,H方向, W方向),在一个3D格子上进行三个方向的线性插值。更多细节,请参考维基百科:https://en.wikipedia.org/wiki/Trilinear_interpolation Align_corners和align_mode都是可选参数,可以用来设置插值的计算方法,如下:

Example:

      For scale:

      if align_corners = True && out_size > 1 :

        scale_factor = (in_size-1.0)/(out_size-1.0)

      else:

        scale_factor = float(in_size/out_size)

      Bilinear interpolation:

        if:
            align_corners = False , align_mode = 0

            input : (N,C,D_in,H_in,W_in)
            output: (N,C,D_out,H_out,W_out) where:

            D_out = (D_{in}+0.5) * scale_{factor} - 0.5
            H_out = (H_{in}+0.5) * scale_{factor} - 0.5
            W_out = (W_{in}+0.5) * scale_{factor} - 0.5

        else:

            input : (N,C,D_in,H_in,W_in)
            output: (N,C,D_out,H_out,W_out) where:

            D_out = D_{in} * scale_{factor}
            H_out = H_{in} * scale_{factor}
            W_out = W_{in} * scale_{factor}
参数:
  • input (Variable) – 5-D Tensor,数据类型为float32、float64或uint8,其数据格式由参数 data_format 指定。
  • out_shape (list|tuple|Variable|None) – 调整最近邻层的输出形状,形式为(out_h, out_w)。默认值:None。如果 out_shape 是列表,每一个元素可以是整数或者shape为[1]的变量。如果 out_shape 是变量,则其维度大小为1。
  • scale (float|None) – 输入高、宽的乘法器。 out_shapescale 二者至少设置其一。 out_shape 具有比 scale 更高的优先级。 默认: None
  • name (str|None) – 输出变量的命名
  • actual_shape (Variable) – 可选输入, 动态设置输出张量的形状。 如果提供该值, 图片放缩会依据此形状进行, 而非依据 out_shapescale 。 即为, actual_shape 具有最高的优先级。 如果想动态指明输出形状,推荐使用 out_shape ,因为 actual_shape 未来将被弃用。 当使用 actual_shape 来指明输出形状, out_shapescale 也应该进行设置, 否则在图形生成阶段将会报错。默认: None
  • align_corners (bool)- 一个可选的bool型参数,如果为True,则将输入和输出张量的4个角落像素的中心对齐,并保留角点像素的值。 默认值:True
  • align_mode (bool) - (int,默认为'1'),双线性插值选项,src_idx = scale*(dst_index+0.5)-0.5时取'0',src_idx = scale*dst_index时取'1'。
  • data_format (str,可选)- 支持 NCDHW(num_batches, channels, depth, height, width)或者 NDHWC(num_batches, depth, height, width, channels),默认值:'NCDHW'。

返回:5-D Tensor,形状为 (num_batches, channels, out_d, out_h, out_w) 或 (num_batches, out_d, out_h, out_w, channels)。

代码示例

import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9,11], dtype="float32")
# input.shape = [-1, 3, 6, 9, 11], where -1 indicates batch size, and it will get the exact value in runtime.

out0 = fluid.layers.resize_trilinear(input, out_shape=[12, 12, 12])
# out0.shape = [-1, 3, 12, 12, 12], it means out0.shape[0] = input.shape[0] in runtime.

# out_shape is a list in which each element is a integer or a tensor Variable
dim1 = fluid.layers.data(name="dim1", shape=[1], dtype="int32", append_batch_size=False)
out1 = fluid.layers.resize_trilinear(input, out_shape=[12, dim1, 4])
# out1.shape = [-1, 3, 12, -1, 4]

# out_shape is a 1-D tensor Variable
shape_tensor = fluid.layers.data(name="shape_tensor", shape=[3], dtype="int32", append_batch_size=False)
out2 = fluid.layers.resize_trilinear(input, out_shape=shape_tensor)
# out2.shape = [-1, 3, -1, -1, -1]

# when use actual_shape
actual_shape_tensor = fluid.layers.data(name="actual_shape_tensor", shape=[3], dtype="int32", append_batch_size=False)
out3 = fluid.layers.resize_trilinear(input, out_shape=[4, 4, 8], actual_shape=actual_shape_tensor)
# out3.shape = [-1, 3, 4, 4, 8]

# scale is a Variable
scale_tensor = fluid.layers.data(name="scale", shape=[1], dtype="float32", append_batch_size=False)
out4 = fluid.layers.resize_trilinear(input, scale=scale_tensor)
# out4.shape = [-1, 3, -1, -1, -1]