More tests for Blob, Layer, copy_from and step, fix some typos

More testes are added into test_net.m and test_solver.m

diff --git a/docs/tutorial/interfaces.md b/docs/tutorial/interfaces.md
index a59a410..1296331 100644 (file)
--- a/docs/tutorial/interfaces.md
+++ b/docs/tutorial/interfaces.md
@@ -82,9 +82,9 @@ In MatCaffe, you can
 * Resume training from solver snapshots
 * Access train net and test nets in a solver
 * Run for a certain number of iterations and give back control to Matlab
-* Intermingle arbitrary Matlab code to with gradient steps
+* Intermingle arbitrary Matlab code with gradient steps
 
-An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m
+An ILSVRC image classification demo is in caffe/matlab/demo/classification_demo.m (you need to download BVLC CaffeNet from [Model Zoo](http://caffe.berkeleyvision.org/model_zoo.html) to run it).
 
 ### Build MatCaffe
 

diff --git a/matlab/+caffe/+test/test_net.m b/matlab/+caffe/+test/test_net.m
index 5d9ba00..3dabe84 100644 (file)
--- a/matlab/+caffe/+test/test_net.m
+++ b/matlab/+caffe/+test/test_net.m
@@ -48,6 +48,24 @@ classdef test_net < matlab.unittest.TestCase
     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();
@@ -60,13 +78,17 @@ classdef test_net < matlab.unittest.TestCase
       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, weights_file, 'train');
+      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

diff --git a/matlab/+caffe/+test/test_solver.m b/matlab/+caffe/+test/test_solver.m
index 682dad4..739258b 100644 (file)
--- a/matlab/+caffe/+test/test_solver.m
+++ b/matlab/+caffe/+test/test_solver.m
@@ -36,6 +36,8 @@ classdef test_solver < matlab.unittest.TestCase
   methods (Test)
     function test_solve(self)
       self.verifyEqual(self.solver.iter(), 0)
+      self.solver.step(30);
+      self.verifyEqual(self.solver.iter(), 30)
       self.solver.solve()
       self.verifyEqual(self.solver.iter(), 100)
     end

diff --git a/matlab/+caffe/Net.m b/matlab/+caffe/Net.m
index a676106..e6295bb 100644 (file)
--- a/matlab/+caffe/Net.m
+++ b/matlab/+caffe/Net.m
@@ -111,7 +111,7 @@ classdef Net < handle
         self.blobs(self.outputs{n}).set_diff(output_diff{n});
       end
       self.backward_prefilled();
-      % retrieve diff from input_blobs
+      % retrieve diff from input blobs
       res = cell(length(self.inputs), 1);
       for n = 1:length(self.inputs)
         res{n} = self.blobs(self.inputs{n}).get_diff();

diff --git a/matlab/+caffe/Solver.m b/matlab/+caffe/Solver.m
index daaa802..f8bdc4e 100644 (file)
--- a/matlab/+caffe/Solver.m
+++ b/matlab/+caffe/Solver.m
@@ -41,7 +41,7 @@ classdef Solver < handle
     end
     function restore(self, snapshot_filename)
       CHECK(ischar(snapshot_filename), 'snapshot_filename must be a string');
-      CHECK_FILE_EXIST(snapshot_filename)
+      CHECK_FILE_EXIST(snapshot_filename);
       caffe_('solver_restore', self.hSolver_self, snapshot_filename);
     end
     function solve(self)

diff --git a/matlab/+caffe/io.m b/matlab/+caffe/io.m
index 7a30bfb..c9e07ae 100644 (file)
--- a/matlab/+caffe/io.m
+++ b/matlab/+caffe/io.m
@@ -17,7 +17,7 @@ classdef io
     function mean_data = read_mean(mean_proto_file)
       % mean_data = read_mean(mean_proto_file)
       %   read image mean data from binaryproto file
-      CHECK(ischar(mean_proto_file), 'im_file must be a string');
+      CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
       CHECK_FILE_EXIST(mean_proto_file);
       mean_data = caffe_('read_mean', mean_proto_file);
     end

diff --git a/matlab/+caffe/run_tests.m b/matlab/+caffe/run_tests.m
index 8773c9f..9389685 100644 (file)
--- a/matlab/+caffe/run_tests.m
+++ b/matlab/+caffe/run_tests.m
@@ -2,6 +2,9 @@ function results = run_tests()
 % results = run_tests()
 %   run all tests in this caffe matlab wrapper package
 
+% use CPU for testing
+caffe.set_mode_cpu();
+
 % reset caffe before testing
 caffe.reset_all();