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Commit b73dacc1 authored by Davis King's avatar Davis King
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Fixing tests

parent 7f77ec65
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Showing with 22 additions and 22 deletions
dlib/test/dnn.cpp
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...@@ -165,13 +165,13 @@ namespace ...@@ -165,13 +165,13 @@ namespace
resizable_tensor running_means; resizable_tensor running_means;
resizable_tensor running_variances; resizable_tensor running_variances;
batch_normalize(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
const double scale = (src.num_samples())/(src.num_samples()-1.0); const double scale = (src.num_samples())/(src.num_samples()-1.0);
// Turn back into biased variance estimate because that's how batch_normalize() works, so if we want to match it this is necessary. // Turn back into biased variance estimate because that's how batch_normalize() works, so if we want to match it this is necessary.
running_variances = mat(running_variances)/scale; running_variances = mat(running_variances)/scale;
batch_normalize_inference(dest2, src, gamma, beta, running_means, running_variances); batch_normalize_inference(DEFAULT_BATCH_NORM_EPS,dest2, src, gamma, beta, running_means, running_variances);
DLIB_TEST_MSG(max(abs(mat(dest2)-mat(dest))) < 1e-5, max(abs(mat(dest2)-mat(dest)))); DLIB_TEST_MSG(max(abs(mat(dest2)-mat(dest))) < 1e-5, max(abs(mat(dest2)-mat(dest))));
cpu::batch_normalize_inference(dest3, src, gamma, beta, running_means, running_variances); cpu::batch_normalize_inference(DEFAULT_BATCH_NORM_EPS,dest3, src, gamma, beta, running_means, running_variances);
DLIB_TEST_MSG(max(abs(mat(dest3)-mat(dest))) < 1e-5, max(abs(mat(dest3)-mat(dest)))); DLIB_TEST_MSG(max(abs(mat(dest3)-mat(dest))) < 1e-5, max(abs(mat(dest3)-mat(dest))));
...@@ -179,7 +179,7 @@ namespace ...@@ -179,7 +179,7 @@ namespace
auto f = [&](float eps) { auto f = [&](float eps) {
const float old = src.host()[idx]; const float old = src.host()[idx];
src.host()[idx] += eps; src.host()[idx] += eps;
batch_normalize(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
float result = dot(gradient_input, dest); float result = dot(gradient_input, dest);
src.host()[idx] = old; src.host()[idx] = old;
return result; return result;
...@@ -191,7 +191,7 @@ namespace ...@@ -191,7 +191,7 @@ namespace
auto f = [&](float eps) { auto f = [&](float eps) {
const float old = gamma.host()[idx]; const float old = gamma.host()[idx];
gamma.host()[idx] += eps; gamma.host()[idx] += eps;
batch_normalize(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
float result = dot(gradient_input, dest); float result = dot(gradient_input, dest);
gamma.host()[idx] = old; gamma.host()[idx] = old;
return result; return result;
...@@ -203,7 +203,7 @@ namespace ...@@ -203,7 +203,7 @@ namespace
auto f = [&](float eps) { auto f = [&](float eps) {
const float old = beta.host()[idx]; const float old = beta.host()[idx];
beta.host()[idx] += eps; beta.host()[idx] += eps;
batch_normalize(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
float result = dot(gradient_input, dest); float result = dot(gradient_input, dest);
beta.host()[idx] = old; beta.host()[idx] = old;
return result; return result;
...@@ -220,7 +220,7 @@ namespace ...@@ -220,7 +220,7 @@ namespace
gamma_grad = 8; gamma_grad = 8;
beta_grad = 8; beta_grad = 8;
batch_normalize_gradient(gradient_input, means, vars, src, gamma, src_grad, gamma_grad, beta_grad); batch_normalize_gradient(DEFAULT_BATCH_NORM_EPS,gradient_input, means, vars, src, gamma, src_grad, gamma_grad, beta_grad);
auto grad_error = compare_gradients(src_grad, grad_src); auto grad_error = compare_gradients(src_grad, grad_src);
dlog << LINFO << "src error: " << grad_error; dlog << LINFO << "src error: " << grad_error;
...@@ -250,14 +250,14 @@ namespace ...@@ -250,14 +250,14 @@ namespace
resizable_tensor running_means; resizable_tensor running_means;
resizable_tensor running_variances; resizable_tensor running_variances;
batch_normalize_conv(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize_conv(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
const double scale = (src.num_samples()*src.nr()*src.nc())/(src.num_samples()*src.nr()*src.nc()-1.0); const double scale = (src.num_samples()*src.nr()*src.nc())/(src.num_samples()*src.nr()*src.nc()-1.0);
// Turn back into biased variance estimate because that's how // Turn back into biased variance estimate because that's how
// batch_normalize_conv() works, so if we want to match it this is necessary. // batch_normalize_conv() works, so if we want to match it this is necessary.
running_variances = mat(running_variances)/scale; running_variances = mat(running_variances)/scale;
batch_normalize_conv_inference(dest2, src, gamma, beta, running_means, running_variances); batch_normalize_conv_inference(DEFAULT_BATCH_NORM_EPS,dest2, src, gamma, beta, running_means, running_variances);
DLIB_TEST(max(abs(mat(dest2)-mat(dest))) < 1e-5); DLIB_TEST(max(abs(mat(dest2)-mat(dest))) < 1e-5);
cpu::batch_normalize_conv_inference(dest3, src, gamma, beta, running_means, running_variances); cpu::batch_normalize_conv_inference(DEFAULT_BATCH_NORM_EPS,dest3, src, gamma, beta, running_means, running_variances);
DLIB_TEST(max(abs(mat(dest3)-mat(dest))) < 1e-5); DLIB_TEST(max(abs(mat(dest3)-mat(dest))) < 1e-5);
...@@ -265,7 +265,7 @@ namespace ...@@ -265,7 +265,7 @@ namespace
auto f = [&](float eps) { auto f = [&](float eps) {
const float old = src.host()[idx]; const float old = src.host()[idx];
src.host()[idx] += eps; src.host()[idx] += eps;
batch_normalize_conv(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize_conv(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
float result = dot(gradient_input, dest); float result = dot(gradient_input, dest);
src.host()[idx] = old; src.host()[idx] = old;
return result; return result;
...@@ -277,7 +277,7 @@ namespace ...@@ -277,7 +277,7 @@ namespace
auto f = [&](float eps) { auto f = [&](float eps) {
const float old = gamma.host()[idx]; const float old = gamma.host()[idx];
gamma.host()[idx] += eps; gamma.host()[idx] += eps;
batch_normalize_conv(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize_conv(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
float result = dot(gradient_input, dest); float result = dot(gradient_input, dest);
gamma.host()[idx] = old; gamma.host()[idx] = old;
return result; return result;
...@@ -289,7 +289,7 @@ namespace ...@@ -289,7 +289,7 @@ namespace
auto f = [&](float eps) { auto f = [&](float eps) {
const float old = beta.host()[idx]; const float old = beta.host()[idx];
beta.host()[idx] += eps; beta.host()[idx] += eps;
batch_normalize_conv(dest, means, vars, 1, running_means, running_variances, src, gamma, beta); batch_normalize_conv(DEFAULT_BATCH_NORM_EPS,dest, means, vars, 1, running_means, running_variances, src, gamma, beta);
float result = dot(gradient_input, dest); float result = dot(gradient_input, dest);
beta.host()[idx] = old; beta.host()[idx] = old;
return result; return result;
...@@ -307,7 +307,7 @@ namespace ...@@ -307,7 +307,7 @@ namespace
gamma_grad = 9; gamma_grad = 9;
beta_grad = 9; beta_grad = 9;
batch_normalize_conv_gradient(gradient_input, means, vars, src, gamma, src_grad, gamma_grad, beta_grad); batch_normalize_conv_gradient(DEFAULT_BATCH_NORM_EPS,gradient_input, means, vars, src, gamma, src_grad, gamma_grad, beta_grad);
auto grad_error = compare_gradients(src_grad, grad_src); auto grad_error = compare_gradients(src_grad, grad_src);
...@@ -888,8 +888,8 @@ namespace ...@@ -888,8 +888,8 @@ namespace
rnd.fill_uniform(src); rnd.fill_uniform(src);
cpu::batch_normalize(dest, means, invstds, 1, running_means, running_variances, src, gamma, beta); cpu::batch_normalize(DEFAULT_BATCH_NORM_EPS,dest, means, invstds, 1, running_means, running_variances, src, gamma, beta);
cuda::batch_normalize(dest2,means2,invstds2, 1, running_means2, running_variances2, src, gamma, beta); cuda::batch_normalize(DEFAULT_BATCH_NORM_EPS,dest2,means2,invstds2, 1, running_means2, running_variances2, src, gamma, beta);
dlog << LINFO << "dest error: "<< max(abs(mat(dest) -mat(dest2))); dlog << LINFO << "dest error: "<< max(abs(mat(dest) -mat(dest2)));
dlog << LINFO << "means error: "<< max(abs(mat(means) -mat(means2))); dlog << LINFO << "means error: "<< max(abs(mat(means) -mat(means2)));
...@@ -915,8 +915,8 @@ namespace ...@@ -915,8 +915,8 @@ namespace
rnd.fill_uniform(gradient_input); rnd.fill_uniform(gradient_input);
cpu::batch_normalize_gradient(gradient_input, means, invstds, src, gamma, src_grad, gamma_grad, beta_grad); cpu::batch_normalize_gradient(DEFAULT_BATCH_NORM_EPS,gradient_input, means, invstds, src, gamma, src_grad, gamma_grad, beta_grad);
cuda::batch_normalize_gradient(gradient_input, means, invstds, src, gamma, src_grad2, gamma_grad2, beta_grad2); cuda::batch_normalize_gradient(DEFAULT_BATCH_NORM_EPS,gradient_input, means, invstds, src, gamma, src_grad2, gamma_grad2, beta_grad2);
dlog << LINFO << "src_grad error: " << max(abs(mat(src_grad)-mat(src_grad2))); dlog << LINFO << "src_grad error: " << max(abs(mat(src_grad)-mat(src_grad2)));
dlog << LINFO << "gamma_grad error: " << max(abs(mat(gamma_grad)-mat(gamma_grad2))); dlog << LINFO << "gamma_grad error: " << max(abs(mat(gamma_grad)-mat(gamma_grad2)));
...@@ -942,8 +942,8 @@ namespace ...@@ -942,8 +942,8 @@ namespace
tt::tensor_rand rnd; tt::tensor_rand rnd;
rnd.fill_uniform(src); rnd.fill_uniform(src);
cpu::batch_normalize_conv(dest,means,invstds,1,running_means,running_variances, src, gamma, beta); cpu::batch_normalize_conv(DEFAULT_BATCH_NORM_EPS,dest,means,invstds,1,running_means,running_variances, src, gamma, beta);
cuda::batch_normalize_conv(dest2,means2,invstds2,1,running_means2,running_variances2, src, gamma, beta); cuda::batch_normalize_conv(DEFAULT_BATCH_NORM_EPS,dest2,means2,invstds2,1,running_means2,running_variances2, src, gamma, beta);
dlog << LINFO << "dest error: "<< max(abs(mat(dest) -mat(dest2))); dlog << LINFO << "dest error: "<< max(abs(mat(dest) -mat(dest2)));
dlog << LINFO << "means error: "<< max(abs(mat(means) -mat(means2))); dlog << LINFO << "means error: "<< max(abs(mat(means) -mat(means2)));
...@@ -967,8 +967,8 @@ namespace ...@@ -967,8 +967,8 @@ namespace
rnd.fill_uniform(gradient_input); rnd.fill_uniform(gradient_input);
cpu::batch_normalize_conv_gradient(gradient_input, means, invstds, src, gamma, src_grad, gamma_grad, beta_grad); cpu::batch_normalize_conv_gradient(DEFAULT_BATCH_NORM_EPS,gradient_input, means, invstds, src, gamma, src_grad, gamma_grad, beta_grad);
cuda::batch_normalize_conv_gradient(gradient_input, means, invstds, src, gamma, src_grad2, gamma_grad2, beta_grad2); cuda::batch_normalize_conv_gradient(DEFAULT_BATCH_NORM_EPS,gradient_input, means, invstds, src, gamma, src_grad2, gamma_grad2, beta_grad2);
dlog << LINFO << "src_grad error: " << max(abs(mat(src_grad)-mat(src_grad2))); dlog << LINFO << "src_grad error: " << max(abs(mat(src_grad)-mat(src_grad2)));
dlog << LINFO << "gamma_grad error: " << max(abs(mat(gamma_grad)-mat(gamma_grad2))); dlog << LINFO << "gamma_grad error: " << max(abs(mat(gamma_grad)-mat(gamma_grad2)));
......
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