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classdef test_net < matlab.unittest.TestCase
properties
num_output
model_file
net
end
methods (Static)
function model_file = simple_net_file(num_output)
model_file = tempname();
fid = fopen(model_file, 'w');
fprintf(fid, [ ...
'name: "testnet" force_backward: true\n' ...
'layer { type: "DummyData" name: "data" top: "data" top: "label"\n' ...
'dummy_data_param { num: 5 channels: 2 height: 3 width: 4\n' ...
' num: 5 channels: 1 height: 1 width: 1\n' ...
' data_filler { type: "gaussian" std: 1 }\n' ...
' data_filler { type: "constant" } } }\n' ...
'layer { type: "Convolution" name: "conv" bottom: "data" top: "conv"\n' ...
' convolution_param { num_output: 11 kernel_size: 2 pad: 3\n' ...
' weight_filler { type: "gaussian" std: 1 }\n' ...
' bias_filler { type: "constant" value: 2 } }\n' ...
' param { decay_mult: 1 } param { decay_mult: 0 }\n' ...
' }\n' ...
'layer { type: "InnerProduct" name: "ip" bottom: "conv" top: "ip"\n' ...
' inner_product_param { num_output: ' num2str(num_output) ...
' weight_filler { type: "gaussian" std: 2.5 }\n' ...
' bias_filler { type: "constant" value: -3 } } }\n' ...
'layer { type: "SoftmaxWithLoss" name: "loss" bottom: "ip" bottom: "label"\n' ...
' top: "loss" }' ]);
fclose(fid);
end
end
methods
function self = test_net()
self.num_output = 13;
self.model_file = caffe.test.test_net.simple_net_file(self.num_output);
self.net = caffe.Net(self.model_file, 'train');
% also make sure get_solver runs
caffe.get_net(self.model_file, 'train');
% fill in valid labels
self.net.blobs('label').set_data(randi( ...
self.num_output - 1, self.net.blobs('label').shape));
delete(self.model_file);
end
end
methods (Test)
function self = test_blob(self)
self.net.blobs('data').set_data(10 * ones(self.net.blobs('data').shape));
self.verifyEqual(self.net.blobs('data').get_data(), ...
10 * ones(self.net.blobs('data').shape, 'single'));
self.net.blobs('data').set_diff(-2 * ones(self.net.blobs('data').shape));
self.verifyEqual(self.net.blobs('data').get_diff(), ...
-2 * ones(self.net.blobs('data').shape, 'single'));
original_shape = self.net.blobs('data').shape;
self.net.blobs('data').reshape([6 5 4 3 2 1]);
self.verifyEqual(self.net.blobs('data').shape, [6 5 4 3 2 1]);
self.net.blobs('data').reshape(original_shape);
self.net.reshape();
end
function self = test_layer(self)
self.verifyEqual(self.net.params('conv', 1).shape, [2 2 2 11]);
self.verifyEqual(self.net.layers('conv').params(2).shape, 11);
self.verifyEqual(self.net.layers('conv').type(), 'Convolution');
end
function test_forward_backward(self)
self.net.forward_prefilled();
self.net.backward_prefilled();
end
function test_inputs_outputs(self)
self.verifyEqual(self.net.inputs, cell(0, 1))
self.verifyEqual(self.net.outputs, {'loss'});
end
function test_save_and_read(self)
weights_file = tempname();
self.net.save(weights_file);
model_file2 = caffe.test.test_net.simple_net_file(self.num_output);
net2 = caffe.Net(model_file2, 'train');
net2.copy_from(weights_file);
net3 = caffe.Net(model_file2, weights_file, 'train');
delete(model_file2);
delete(weights_file);
for l = 1:length(self.net.layer_vec)
for i = 1:length(self.net.layer_vec(l).params)
self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
net2.layer_vec(l).params(i).get_data());
self.verifyEqual(self.net.layer_vec(l).params(i).get_data(), ...
net3.layer_vec(l).params(i).get_data());
end
end
end
end
end
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