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Updated tests now that the resizable_tensor has an assignment mode that 

automatically sizes the tensor.
This commit is contained in:
Davis King 2016-06-11 08:04:51 -04:00
parent 6008ad2195
commit 87dff094da
1 changed files with 24 additions and 20 deletions
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44
dlib/test/dnn.cpp
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@ -43,7 +43,7 @@ namespace
{
using namespace dlib::tt;
print_spinner();
resizable_tensor src(5,5), dest(5,5), gradient_input(5,5);
resizable_tensor src, dest, gradient_input;
src = matrix_cast<float>(gaussian_randm(5,5, 0));
dest = matrix_cast<float>(gaussian_randm(5,5, 1));
gradient_input = matrix_cast<float>(gaussian_randm(5,5, 2));
@ -79,7 +79,7 @@ namespace
{
using namespace dlib::tt;
print_spinner();
resizable_tensor src(5,5), dest(5,5), gradient_input(5,5);
resizable_tensor src, dest, gradient_input;
src = matrix_cast<float>(gaussian_randm(5,5, 0));
dest = matrix_cast<float>(gaussian_randm(5,5, 1));
gradient_input = matrix_cast<float>(gaussian_randm(5,5, 2));
@ -118,8 +118,10 @@ namespace
const long nr = 3;
const long nc = 3;
resizable_tensor src(5,5,nr,nr), dest(5,5,nr,nc), gradient_input(5,5,nr,nc);
src = matrix_cast<float>(gaussian_randm(5,5*nr*nc, 0));
dest = matrix_cast<float>(gaussian_randm(5,5*nr*nc, 1));
tt::tensor_rand rnd;
rnd.fill_uniform(src);
rnd.fill_uniform(dest);
// fill like this as a test of the assignment operator.
gradient_input = matrix_cast<float>(gaussian_randm(5,5*nr*nc, 2));
@ -153,7 +155,7 @@ namespace
{
using namespace dlib::tt;
print_spinner();
resizable_tensor src(5,5), gamma(1,5), beta(1,5), dest, dest2, dest3, means, vars, gradient_input(5,5);
resizable_tensor src, gamma, beta, dest, dest2, dest3, means, vars, gradient_input;
src = matrix_cast<float>(gaussian_randm(5,5, 0));
gamma = matrix_cast<float>(gaussian_randm(1,5, 1));
beta = matrix_cast<float>(gaussian_randm(1,5, 2));
@ -238,11 +240,12 @@ namespace
{
using namespace dlib::tt;
print_spinner();
resizable_tensor src(5,5,4,4), gamma(1,5), beta(1,5), dest, dest2, dest3, means, vars, gradient_input(5,5,4,4);
src = matrix_cast<float>(gaussian_randm(5,5*4*4, 0));
resizable_tensor src(5,5,4,4), gamma, beta, dest, dest2, dest3, means, vars, gradient_input(5,5,4,4);
tt::tensor_rand rnd;
rnd.fill_gaussian(src);
rnd.fill_gaussian(gradient_input);
gamma = matrix_cast<float>(gaussian_randm(1,5, 1));
beta = matrix_cast<float>(gaussian_randm(1,5, 2));
gradient_input = matrix_cast<float>(gaussian_randm(5,5*4*4, 3));
gamma = 1;
beta = 0;
@ -351,8 +354,6 @@ namespace
dlog << LINFO << mat(dest);
DLIB_TEST(max(abs(truth2-mat(dest))) < 1e-5);
A.set_size(3,4);
B.set_size(3,4);
A = matrix_cast<float>(gaussian_randm(3,4, 1));
B = matrix_cast<float>(gaussian_randm(3,4, 2));
affine_transform(dest, src, A, B);
@ -1418,10 +1419,11 @@ namespace
resizable_tensor src1(10, 3, 7, 15);
resizable_tensor src2(10, 3, 7, 15);
resizable_tensor src3(10, 9, 7, 15);
dest = matrix_cast<float>(gaussian_randm(dest.num_samples(), dest.k() * dest.nr() * dest.nc(), 1));
src1 = matrix_cast<float>(gaussian_randm(src1.num_samples(), src1.k() * src1.nr() * src1.nc(), 0));
src2 = matrix_cast<float>(gaussian_randm(src1.num_samples(), src2.k() * src2.nr() * src2.nc(), 0));
src3 = matrix_cast<float>(gaussian_randm(src1.num_samples(), src3.k() * src3.nr() * src3.nc(), 0));
tt::tensor_rand rnd;
rnd.fill_gaussian(dest);
rnd.fill_gaussian(src1);
rnd.fill_gaussian(src2);
rnd.fill_gaussian(src3);
cpu::copy_tensor(dest, 0, src1, 0, src1.k()); //full copy src1->dest
cpu::copy_tensor(dest, src1.k(), src2, 0, src2.k()); //full copy src2->dest with offset of src1
@ -1469,10 +1471,11 @@ namespace
resizable_tensor src1(10, 3, 7, 15);
resizable_tensor src2(10, 3, 7, 15);
resizable_tensor src3(10, 9, 7, 15);
dest = matrix_cast<float>(gaussian_randm(dest.num_samples(), dest.k() * dest.nr() * dest.nc(), 1));
src1 = matrix_cast<float>(gaussian_randm(src1.num_samples(), src1.k() * src1.nr() * src1.nc(), 0));
src2 = matrix_cast<float>(gaussian_randm(src1.num_samples(), src2.k() * src2.nr() * src2.nc(), 0));
src3 = matrix_cast<float>(gaussian_randm(src1.num_samples(), src3.k() * src3.nr() * src3.nc(), 0));
tt::tensor_rand rnd;
rnd.fill_gaussian(dest);
rnd.fill_gaussian(src1);
rnd.fill_gaussian(src2);
rnd.fill_gaussian(src3);
cuda::copy_tensor(dest, 0, src1, 0, src1.k()); //full copy src1->dest
cuda::copy_tensor(dest, src1.k(), src2, 0, src2.k()); //full copy src2->dest with offset of src1
cuda::copy_tensor(dest, src1.k() + src2.k(), src3, 3, 3); //partial copy src3 into the rest place of dest
@ -1525,7 +1528,8 @@ namespace
using net_type = concat_incept<input<matrix<float>>>;
resizable_tensor data(10, 1, 111, 222);
data = matrix_cast<float>(gaussian_randm(data.num_samples(), data.k() * data.nr() * data.nc(), 1));
tt::tensor_rand rnd;
rnd.fill_gaussian(data);
net_type net;
@ -1546,7 +1550,7 @@ namespace
DLIB_TEST(error == 0);
resizable_tensor gr(10, 14, 111, 222);
gr = matrix_cast<float>(gaussian_randm(gr.num_samples(), gr.k() * gr.nr() * gr.nc(), 1));
rnd.fill_gaussian(gr);
resizable_tensor params;
net.layer_details().backward(gr, net, params);