Reference to non-existent field 'normalization'

%%Download and predict using a pretrained ImageNet model

% Setup MatConvNet
run(fullfile('matconvnet-1.0-beta24','matlab','vl_setupnn.m'));

% Download ImageNet model from MatConvNet pretrained networks repository
% urlwrite('http://www.vlfeat.org/matconvnet/models/imagenet-vgg-f.mat', 'imagenet-vgg-f.mat');
cnnModel.net = load('imagenet-vgg-f.mat');

% Load and display an example image
imshow('c1.JPG','InitialMagnification','fit');
img = imread('c1.JPG');

% Predict label using ImageNet trained vgg-f CNN model
label = cnnPredict(cnnModel,img,'display', false);
title(label,'FontSize',20)

Error in cnnPredict/cnnPreprocess (line 84)

im = imresize(im, cnnModel.net.normalization.imageSize(1:2));

Error in cnnPredict (line 26)

resTemp = vl_simplenn(cnnModel.net, cnnPreprocess(predImage(:,:,:,1)), [], []);

function [classLabel, scores, batchTime] = cnnPredict(cnnModel,predImage,varargin)
% Copyright (c) 2015, MathWorks, Inc.

% Parse inputs
p = inputParser;
addParameter(p,'outputLayer',numel(cnnModel.net.layers),@isnumeric);
addParameter(p,'UseGPU',false,@islogical);
addParameter(p,'display',true,@islogical);
parse(p,varargin{:});

% Get batch size and number of images
if ~isfield(cnnModel,'info')
    cnnModel.info.opts.batchSize = 1;
end
batchSize = cnnModel.info.opts.batchSize;
n_obs = size(predImage,4);
isTapLayer = p.Results.outputLayer < numel(cnnModel.net.layers);

if isTapLayer
    cnnModel.net.layers(p.Results.outputLayer+1:end) = [];
else
    cnnModel.net.layers{end} = struct('type', 'softmax');
end

% Preallocate scores
resTemp = vl_simplenn(cnnModel.net, cnnPreprocess(predImage(:,:,:,1)), [], []);
scores = zeros([size(resTemp(end).x), n_obs]);

% Move model to GPU if requested
if p.Results.UseGPU
    cnnModel.net = vl_simplenn_move(cnnModel.net,'gpu');
end

% Make predictions
batchNumber = 0;
numBatches = ceil(n_obs/batchSize);
batchTime = zeros(numBatches,1);
if p.Results.display
    disp(' ')
    fprintf('Using GPU: %s\n',mat2str(p.Results.UseGPU))
    fprintf('Number of images: %d\n',n_obs)
    fprintf('Number of batches: %d\n',numBatches)
    fprintf('Number of layers in the Network: %d\n',numel(cnnModel.net.layers))
    disp('-------------------------------------')
end
for ii = 1:batchSize:n_obs
    tic
    idx = ii:min(ii+batchSize-1,n_obs);
  batchImages = predImage(:,:,:,idx);
    im = cnnPreprocess(batchImages);

  % Move batch to GPU if requested
    if p.Results.UseGPU
        im = gpuArray(im);
    end
    train_res = vl_simplenn(cnnModel.net, im, [], []);
    scores(:,:,:,idx) = squeeze(gather(train_res(end).x));
    batchNumber = batchNumber + 1;
    batchTime(batchNumber) = toc;
    if p.Results.display
        fprintf('Batch: %2d/%d. Execution time: %2.4f\n',batchNumber,numBatches,batchTime(batchNumber))
    end
end

if p.Results.display
  fprintf('Avg. execution time/batch:   %2.4f\n',mean(batchTime))
    disp('-------------------------------------')
    fprintf('Total execution time:        %2.4f\n',sum(batchTime))
    disp('-------------------------------------')
end

if isTapLayer
    classLabel = [];
else
    scores = squeeze(gather(scores))';
    [~, labelId] = max(scores,[],2);
%     classLabel = categorical(cnnModel.net.classes.description(labelId)');
    classLabel = cnnModel.net.classes.description(labelId)';
end

function im = cnnPreprocess(batchImages)
    % Preprocess images
    im = single(batchImages);
    im = imresize(im, cnnModel.net.normalization.imageSize(1:2));
  im = bsxfun(@minus,im,cnnModel.net.normalization.averageImage);
end

end

[Joss Knight]

This is a problem you are having with MatConvNet. That should be stated in your title or at least at the start of your problem description, otherwise people are going to have no chance of reproducing it.

Secondly, consider asking the MatConvNet fora.

Answer 1

[CONG CAO]

use 'net.meta.normalization' instead of 'net.normalization'

[pratyush ghosh]

I have tried doing that it says there is an error in Dilate in the vl_simplenn function

Answer 2

[Sedat METLEK]

Hello, if you examine the matcovnet site, you will see functions with the same name but written differently. So if you test different functions with the same name, your problem will probably be solved.