roiMaxPooling2dLayer

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

Since R2018b

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 [H W C N], where C is the number of channels and N is the number of observations, the output feature map size is [height width C sum(M)], where height and width are the output size. M is a vector of length N and M(i) is the number of ROIs associated with the i-th input feature map.

There are two inputs to this layer:

• '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 connectLayers (Deep Learning Toolbox) or 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 the OutputSize property.

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

Properties

OutputSize — Pooled output size

two-element vector of positive integers

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

Name — Layer name

'' (default) | character vector | string scalar

Layer name, specified as a character vector or a string scalar. For Layer array input, the trainNetwork (Deep Learning Toolbox), assembleNetwork (Deep Learning Toolbox), layerGraph (Deep Learning Toolbox), and dlnetwork (Deep Learning Toolbox) functions automatically assign names to layers with the name ''.

Data Types: char | string

NumInputs — Number of inputs

2 (default)

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

Data Types: double

InputNames — Input names

{'in' 'roi'} (default)

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

Data Types: cell

NumOutputs — Number of outputs

1 (default)

This property is read-only.

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

Data Types: double

OutputNames — Output names

{'out'} (default)

This property is read-only.

Output names of the layer. This layer has a single output only.

Data Types: cell

Examples

Connect ROI Input Layer to ROI Max Pooling Layer

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)

Version History

Introduced in R2018b