affine_channel¶

paddle.fluid.layers.nn. affine_channel ( x, scale=None, bias=None, data_layout='NCHW', name=None, act=None ) [source]

Applies a separate affine transformation to each channel of the input. Useful for replacing spatial batch norm with its equivalent fixed transformation. The input also can be 2D tensor and applies a affine transformation in second dimension.

Parameters

• x (Variable) – Feature map input can be a 4D tensor with order NCHW or NHWC. It also can be a 2D tensor and the affine transformation is applied in the second dimension.The data type is float32 or float64.

• scale (Variable) – 1D input of shape (C), the c-th element is the scale factor of the affine transformation for the c-th channel of the input.The data type is float32 or float64.

• bias (Variable) – 1D input of shape (C), the c-th element is the bias of the affine transformation for the c-th channel of the input. The data type is float32 or float64.

• data_layout (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]. If input is 2D Tensor, you can ignore data_layout.

• name (str, default None) – The name of this layer. For more information, please refer to Name .

• act (str, default None) – Activation to be applied to the output of this layer.

Returns

A tensor which has the same shape, data layout and data type with x.

Return type

Variable

Examples

import numpy as np
import paddle.fluid as fluid
import paddle.fluid as fluid
import paddle

paddle.enable_static()
use_gpu = False
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)

data = fluid.data(name='data', shape=[None, 1, 2, 2], dtype='float32')
input_scale = fluid.layers.create_parameter(shape=[1], dtype="float32",
                        default_initializer=fluid.initializer.Constant(2.0))
input_bias = fluid.layers.create_parameter(shape=[1],dtype="float32",
                        default_initializer=fluid.initializer.Constant(0.5))
out = fluid.layers.affine_channel(data,scale=input_scale,
                        bias=input_bias)

exe.run(fluid.default_startup_program())
test_program = fluid.default_main_program().clone(for_test=True)

[out_array] = exe.run(test_program,
                      fetch_list=out,
                      feed={'data': np.ones([1,1,2,2]).astype('float32')})
# out_array is [[[[2.5, 2.5],
#                [2.5, 2.5]]]] with shape: [1, 1, 2, 2]