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roiMaxPooling2dLayer

Neural network layer used to output fixed-size feature maps for rectangular ROIs

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Description

An ROI max pooling layer outputs fixed size feature maps for every rectangular ROI within the input feature map. Use this layer to create a Fast or Faster R-CNN object detection network.

Given an input feature map of size [HWCN], whereCis the number of channels andNis the number of observations, the output feature map size is [heightwidthCsum(M)], whereheight和widthare the output size.Mis a vector of lengthN和M(i) is the number of ROIs associated with thei-th input feature map.

该层有两个输入：

  • 'in'— The input feature map that will be cropped

  • 'roi'— A list of ROIs to pool

Use the input names when connecting or disconnecting the ROI max pooling layer to other layers using连接器(Deep Learning Toolbox)或者disconnectLayers(Deep Learning Toolbox)(requires Deep Learning Toolbox™).

Creation

Syntax

layer = roimaxpooling2dlayer（outputsize）
layer = roiMaxPooling2dLayer(outputSize,'Name',Name)

Description

layer = roimaxpooling2dlayer（outputsize）creates a max pooling layer for ROIs and sets theOutputSizeproperty.

example

layer = roiMaxPooling2dLayer(outputSize,'Name',Name)creates a max pooling layer for ROIs and sets the optionalNameproperty. To create a network containing an ROI max pooling layer, you must specify a layer name.

Properties

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Pooled output size, specified as a two-element vector of positive integers of the form[heightwidth].

图层名称，指定为字符向量或字符串标量。为了Layerarray input, thetrainNetwork,汇编工作,layerGraph, anddlnetworkfunctions automatically assign names to layers with name''.

Data Types:char|string

Number of inputs of the layer. This layer has two inputs.

Data Types:double

Input names of the layer. This layer has two inputs, named'in'和'roi'.

Data Types:cell

This property is read-only.

Number of outputs of the layer. This layer has a single output only.

Data Types:double

This property is read-only.

层的输出名称。该层仅具有单个输出。

Data Types:cell

Examples

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This example uses:

打开实时脚本

Create an ROI input layer.

roiInput = roiInputLayer('Name','roi_input');

Create an ROI max pooling layer with output size [4 4].

outputSize = [4 4]; roiPool = roiMaxPooling2dLayer(outputSize,'Name','roi_pool');

Add the layers to a LayerGraph.

lgraph = layerGraph; lgraph = addLayers(lgraph,roiInput); lgraph = addLayers(lgraph,roiPool);

Specify that the output of the ROI input layer is the'roi'input of the ROI max pooling layer.

lgraph = connectLayers(lgraph,'roi_input','roi_pool/roi'); plot(lgraph)

Figure contains an axes object. The axes object contains an object of type graphplot.

Version History

Introduced in R2018b

See Also

|||(Deep Learning Toolbox)|(Deep Learning Toolbox)|(Deep Learning Toolbox)|(Deep Learning Toolbox)

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