# resize_bilinear¶

`paddle.fluid.layers.``resize_bilinear`(input, out_shape=None, scale=None, name=None, actual_shape=None, align_corners=True, align_mode=1, data_format='NCHW')[source]

This op resizes the input by performing bilinear interpolation based on given output shape which specified by actual_shape, out_shape and scale in priority order.

Warning: the parameter `actual_shape` will be deprecated in the future and only use `out_shape` instead.

Bilinear interpolation is an extension of linear interpolation for interpolating functions of two variables (e.g. H-direction and W-direction in this op) on a rectilinear 2D grid. The key idea is to perform linear interpolation first in one direction, and then again in the other direction.

For details of bilinear interpolation, please refer to Wikipedia: https://en.wikipedia.org/wiki/Bilinear_interpolation

Align_corners and align_mode are optional parameters,the calculation method of interpolation can be selected by them.

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

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

else:

input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
```
Parameters
• input (Variable) – 4-D Tensor(NCHW), its data type is float32, float64, or uint8, its data format is specified by `data_format`.

• out_shape (list|tuple|Variable|None) – Output shape of resize bilinear layer, the shape is (out_h, out_w).Default: None. If a list, each element can be an integer or a Tensor Variable with shape: [1]. If a Tensor Variable, its dimension size should be 1.

• scale (float|Variable|None) – The multiplier for the input height or width. At least one of `out_shape` or `scale` must be set. And `out_shape` has a higher priority than `scale`. Default: None.

• actual_shape (Variable) – An optional input to specify output shape dynamically. If provided, image resize according to this given shape rather than `out_shape` and `scale` specifying shape. That is to say actual_shape has the highest priority. It is recommended to use `out_shape` if you want to specify output shape dynamically, because `actual_shape` will be deprecated. When using actual_shape to specify output shape, one of `out_shape` and `scale` should also be set, otherwise errors would be occurred in graph constructing stage. Default: None

• align_corners (bool) – an optional bool. Defaults to True. If True, the centers of 4 corner pixels of the input and output tensors are aligned, preserving the values at the corner pixels, If False, are not aligned

• align_mode (bool) – (int, default ‘1’), optional for bilinear interpolation, can be ‘0’ for src_idx = scale*(dst_indx+0.5)-0.5 , can be ‘1’ for src_idx = scale*dst_index

• data_format (str, optional) – Specify the data format of the input, and the data format of the output will be consistent with that of the input. An optional string from: “NCHW”, “NHWC”. The default is “NCHW”. When it is “NCHW”, the data is stored in the order of: [batch_size, input_channels, input_height, input_width].

• name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name

Returns

4-D tensor(NCHW or NHWC).

Return type

Variable

Examples

```#declarative mode
import numpy as np
input = fluid.data(name="input", shape=[None,3,6,10])

#1
output = fluid.layers.resize_bilinear(input=input,out_shape=[12,12])

#2
#x = np.array([2]).astype("int32")
#dim1 = fluid.data(name="dim1", shape=[1], dtype="int32")
#fluid.layers.assign(input=x, output=dim1)
#output = fluid.layers.resize_bilinear(input=input,out_shape=[12,dim1])

#3
#x = np.array([3,12]).astype("int32")
#shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
#fluid.layers.assign(input=x, output=shape_tensor)
#output = fluid.layers.resize_bilinear(input=input,out_shape=shape_tensor)

#4
#x = np.array([0.5]).astype("float32")
#scale_tensor = fluid.data(name="scale", shape=[1], dtype="float32")
#fluid.layers.assign(x,scale_tensor)
#output = fluid.layers.resize_bilinear(input=input,scale=scale_tensor)

place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())

input_data = np.random.rand(2,3,6,10).astype("float32")

output_data = exe.run(fluid.default_main_program(),
feed={"input":input_data},
fetch_list=[output],
return_numpy=True)

print(output_data[0].shape)

#1
# (2, 3, 12, 12)
#2
# (2, 3, 12, 2)
#3
# (2, 3, 3, 12)
#4
# (2, 3, 3, 5)

#imperative mode