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paddle.fluid / layers / resize_nearest
resize_nearest¶
-
paddle.fluid.layers.
resize_nearest
( input, out_shape=None, scale=None, name=None, actual_shape=None, align_corners=True, data_format='NCHW' ) [源代码] ¶
该OP对输入图片进行大小调整,在高度方向宽度方向进行最邻近插值(nearest neighbor interpolation)操作。
输出形状按优先级顺序依据 actual_shape
, out_shape
和 scale
而定。
注意: 参数 actual_shape
将被弃用,请使用 out_shape
替代。
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)
Nearest neighbor interpolation:
if align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = \left \lfloor {H_{in} * scale_{}factor}} \right \rfloor
W_out = \left \lfloor {W_{in} * scale_{}factor}} \right \rfloor
else:
align_corners = True
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = round(H_{in} * scale_{factor})
W_out = round(W_{in} * scale_{factor})
最邻近插值的详细介绍请参照: Wiki Nearest-neighbor interpolation
- 参数:
-
input (Variable) - 4-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) - 双线性插值法调整后的输出,维度为[out_h, out_w]的2-D Tensor。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。默认值为None。scale (float|Variable|None) – 输入高宽的乘数因子。
out_shape
和scale
二者至少设置其一。out_shape
具有比scale
更高的优先级。 默认值为None。name (str|None) - 该参数供开发人员打印调试信息时使用,具体用法请参见 Name 。默认值为None。
actual_shape (Variable) - 可选输入,用于动态指定输出形状。如果指定actual_shape,图像将根据给定的形状调整大小,而不是根据指定形状的
out_shape
和scale
进行调整。也就是说,actual_shape
具有最高的优先级。注意:如果希望动态指定输出形状,建议使用out_shape
, 因为actual_shape
未来将被弃用。在使用actual_shape指定输出形状时,仍然需要设置out_shape和scale之一,否则在图形构建阶段会出现错误。默认值为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。 默认值为True。
data_format (str,可选)- 指定输入的数据格式,输出的数据格式将与输入保持一致,可以是"NCHW"和"NHWC"。N是批尺寸,C是通道数,H是特征高度,W是特征宽度。默认值:"NCHW"。
返回:4-D Tensor,形状为 (num_batches, channels, out_h, out_w) 或 (num_batches, out_h, out_w, channels)。
返回类型:Variable
代码示例
import paddle.fluid as fluid
input = fluid.layers.data(name="input", shape=[3,6,9], dtype="float32")
# input.shape = [-1, 3, 6, 9], where -1 indicates batch size, and it will get the exact value in runtime.
out0 = fluid.layers.resize_nearest(input, out_shape=[12, 12])
# out0.shape = [-1, 3, 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_nearest(input, out_shape=[12, dim1])
# out1.shape = [-1, 3, 12, -1]
# out_shape is a 1-D tensor Variable
shape_tensor = fluid.layers.data(name="resize_shape", shape=[2], dtype="int32", append_batch_size=False)
out2 = fluid.layers.resize_nearest(input, out_shape=shape_tensor)
# out2.shape = [-1, 3, -1, -1]
# when use actual_shape
actual_shape_tensor = fluid.layers.data(name="actual_shape_tensor", shape=[2], dtype="int32", append_batch_size=False)
out3 = fluid.layers.resize_nearest(input, out_shape=[4, 4], actual_shape=actual_shape_tensor)
# out3.shape = [-1, 3, 4, 4]
# scale is a Variable
scale_tensor = fluid.layers.data(name="scale", shape=[1], dtype="float32", append_batch_size=False)
out4 = fluid.layers.resize_nearest(input, scale=scale_tensor)
# out4.shape = [-1, 3, -1, -1]
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