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How to set the input as a two-dimensional feature matrix in the custom Intermediate-layer of deep learning?

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cui
cui on 21 Nov 2020
Edited: cui on 22 Nov 2020
introduction:
As can be seen from the official documents, the custom Intermediate-layer has the following forms:
classdef myLayer < nnet.layer.Layer
properties
% (Optional) Layer properties.
% Layer properties go here.
end
properties (Learnable)
% (Optional) Layer learnable parameters.
% Layer learnable parameters go here.
end
methods
function layer = myLayer()
% (Optional) Create a myLayer.
% This function must have the same name as the class.
% Layer constructor function goes here.
end
function [Z1, , Zm] = predict(layer, X1, , Xn)
% Forward input data through the layer at prediction time and
% output the result.
%
% Inputs:
% layer - Layer to forward propagate through
% X1, ..., Xn - Input data
% Outputs:
% Z1, ..., Zm - Outputs of layer forward function
% Layer forward function for prediction goes here.
end
function [Z1, , Zm, memory] = forward(layer, X1, , Xn)
% (Optional) Forward input data through the layer at training
% time and output the result and a memory value.
%
% Inputs:
% layer - Layer to forward propagate through
% X1, ..., Xn - Input data
% Outputs:
% Z1, ..., Zm - Outputs of layer forward function
% memory - Memory value for custom backward propagation
% Layer forward function for training goes here.
end
function [dLdX1, , dLdXn, dLdW1, , dLdWk] = ...
backward(layer, X1, , Xn, Z1, , Zm, dLdZ1, , dLdZm, memory)
% (Optional) Backward propagate the derivative of the loss
% function through the layer.
%
% Inputs:
% layer - Layer to backward propagate through
% X1, ..., Xn - Input data
% Z1, ..., Zm - Outputs of layer forward function
% dLdZ1, ..., dLdZm - Gradients propagated from the next layers
% memory - Memory value from forward function
% Outputs:
% dLdX1, ..., dLdXn - Derivatives of the loss with respect to the
% inputs
% dLdW1, ..., dLdWk - Derivatives of the loss with respect to each
% learnable parameter
% Layer backward function goes here.
end
end
end
my issue:
when the network is forwarded, how to define the input parameter "X" of the "forward" or "predict" function as a two-dimensional input feature matrix, the label form is 'batchSize*numChannels' instead of ' height*wight*numChannels*batchSize' The default four-dimensional feature matrix?
specific problem:
For example, in the field of face recognition, I customize an "ArcFace Head loss" layer, the complete content is as follows, the syntax seems to be correct, but when I use the "checkLayer" function to verify the custom layer, there is no guarantee that "X" is two-dimensional Feature matrix?
classdef arcfaceLayer < nnet.layer.Layer
% custom ArcFace Head loss
% reference:https://blog.csdn.net/yiran103/article/details/83684613
% https://github.com/cuixing158/jetson_faceTrack_pytorch/blob/main/net/model.py
% offical document: Define Custom Deep Learning Layer with Learnable Parameters
%
properties
cos_m
sin_m
mm
threshold
s
m
end
properties (Learnable)
% Layer learnable parameters
% kernel coefficient ,size: embedding_size*classnum
kernel
end
methods
function layer = arcfaceLayer(name,embedding_size, classnum, s, m)
% init function
arguments
name (1,1) string = "arcface_head"
embedding_size (1,1) {mustBeNumeric}=512
classnum (1,1) {mustBeNumeric}=4
s (1,1) {mustBeNumeric}=64 % scalar value default is 64, see normface https://arxiv.org/abs/1704.06369
m (1,1) {mustBeNumeric}=0.5 % the margin value, default is 0.5
end
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = "ArcFace with " + string(embedding_size) + " embedding size"+" classnum:"+string(classnum)+" scalar:"+string(s)+" m:"+string(m);
% Initialize kernel coefficient
layer.kernel = 2*rand(embedding_size,classnum)-1;
layer.cos_m = cos(m);
layer.sin_m = sin(m);
layer.mm = m.*sin(m);
layer.threshold = cos(pi-m);
layer.s = s;
layer.m = m;
end
function Z = predict(layer, X)
% Z = predict(layer, X) forwards the input data X through the
% layer and outputs the result Z.
%
% 输入:
% X 为 bs*embedding_size二维矩阵
% 输出:
% Z, 定义的大小为bs*classnum.
%
X = X./vecnorm(X,2,2);
kernel_norm = layer.kernel./vecnorm(layer.kernel,2,1); % embedding_size*classnum
% cos(theta+m)
cos_theta = X*kernel_norm; % bs*classnum
cos_theta_2 = cos_theta.^2;
sin_theta_2 = 1 - cos_theta_2;
sin_theta = sqrt(sin_theta_2);
cos_theta_m = (cos_theta * layer.cos_m - sin_theta * layer.sin_m); % bs*classnum,数学公式, cos(alpha+beta) = cos(alpha)*cos(beta)-sin(alpha)*sin(beta)
% this condition controls the theta+m should in range [0, pi]
% 0<=theta+m<=pi
% -m<=theta<=pi-m
cond_v = cos_theta - layer.threshold; % bs*classnum, 按道理此值大于0
cond_mask = cond_v <= 0; % bs*classnum, 找出小于0的索引
keep_val = cos_theta - layer.mm; % when theta not in [0,pi], use cosface instead,# bs*classnum
cos_theta_m(cond_mask) = keep_val(cond_mask); % bs*classnum
output = cos_theta_m;
Z = layer.s.*output; % scale up in order to make softmax work, first introduced in normface
end
end
end
Then ,check my layer:
layer = arcfaceLayer();
validInputSize = [10,512];
checkLayer(layer,validInputSize,'ObservationDimension',2) % or 'ObservationDimension' set to 3,4
failed???
______
Failure Summary:
Name Failed Incomplete Reason(s)
==============================================================================================================================================
nnet.checklayer.TestLayerWithoutBackward/predictDoesNotError(Observations=one) X Failed by verification.
----------------------------------------------------------------------------------------------------------------------------------------------
nnet.checklayer.TestLayerWithoutBackward/predictDoesNotError(Observations=multiple) X Failed by verification.
----------------------------------------------------------------------------------------------------------------------------------------------
nnet.checklayer.TestLayerWithoutBackward/predictIsConsistentInType(Precision=single,Device=cpu) X Filtered by assumption.
----------------------------------------------------------------------------------------------------------------------------------------------
nnet.checklayer.TestLayerWithoutBackward/predictIsConsistentInType(Precision=single,Device=gpu) X Filtered by assumption.
----------------------------------------------------------------------------------------------------------------------------------------------
nnet.checklayer.TestLayerWithoutBackward/predictIsConsistentInType(Precision=double,Device=cpu) X Filtered by assumption.
----------------------------------------------------------------------------------------------------------------------------------------------
nnet.checklayer.TestLayerWithoutBackward/predictIsConsistentInType(Precision=double,Device=gpu) X Filtered by assumption.
----------------------------------------------------------------------------------------------------------------------------------------------
nnet.checklayer.TestLayerWithoutBackward/backwardPropagationDoesNotError(Observations=one) X Filtered by assumption.
----------------------------------------------------------------------------------------------------------------------------------------------
nnet.checklayer.TestLayerWithoutBackward/backwardPropagationDoesNotError(Observations=multiple) X Filtered by assumption.
Test Summary:
9 Passed, 2 Failed, 6 Incomplete, 4 Skipped.
Time elapsed: 14.8035 seconds.
Desired result:
  • The custom layer does not report errors when checking
  • Support two-dimensional feature matrix input

Answers (0)

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