mirror of https://github.com/davisking/dlib.git
Added a few more tests
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@ -154,7 +154,7 @@ namespace
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{
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using namespace dlib::tt;
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print_spinner();
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resizable_tensor src(5,5), gamma(1,5), beta(1,5), dest, dest2, means, vars, gradient_input(5,5);
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resizable_tensor src(5,5), gamma(1,5), beta(1,5), dest, dest2, dest3, means, vars, gradient_input(5,5);
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src = matrix_cast<float>(gaussian_randm(5,5, 0));
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gamma = matrix_cast<float>(gaussian_randm(1,5, 1));
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beta = matrix_cast<float>(gaussian_randm(1,5, 2));
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@ -171,6 +171,8 @@ namespace
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running_variances = mat(running_variances)/scale;
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batch_normalize_inference(dest2, src, gamma, beta, running_means, running_variances);
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DLIB_TEST_MSG(max(abs(mat(dest2)-mat(dest))) < 1e-5, max(abs(mat(dest2)-mat(dest))));
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cpu::batch_normalize_inference(dest3, src, gamma, beta, running_means, running_variances);
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DLIB_TEST_MSG(max(abs(mat(dest3)-mat(dest))) < 1e-5, max(abs(mat(dest3)-mat(dest))));
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auto grad_src = [&](long idx) {
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@ -237,7 +239,7 @@ namespace
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{
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using namespace dlib::tt;
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print_spinner();
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resizable_tensor src(5,5,4,4), gamma(1,5), beta(1,5), dest, dest2, means, vars, gradient_input(5,5,4,4);
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resizable_tensor src(5,5,4,4), gamma(1,5), beta(1,5), dest, dest2, dest3, means, vars, gradient_input(5,5,4,4);
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src = matrix_cast<float>(gaussian_randm(5,5*4*4, 0));
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gamma = matrix_cast<float>(gaussian_randm(1,5, 1));
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beta = matrix_cast<float>(gaussian_randm(1,5, 2));
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@ -255,6 +257,8 @@ namespace
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running_variances = mat(running_variances)/scale;
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batch_normalize_conv_inference(dest2, src, gamma, beta, running_means, running_variances);
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DLIB_TEST(max(abs(mat(dest2)-mat(dest))) < 1e-5);
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cpu::batch_normalize_conv_inference(dest3, src, gamma, beta, running_means, running_variances);
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DLIB_TEST(max(abs(mat(dest3)-mat(dest))) < 1e-5);
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auto grad_src = [&](long idx) {
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@ -1233,6 +1237,7 @@ namespace
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test_avg_pool(3,3,2,2);
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test_avg_pool(2,2,2,2);
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test_avg_pool(4,5,3,1);
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test_avg_pool(100,100,100,100);
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test_tanh();
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test_softmax();
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test_sigmoid();
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