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Commit f9cb3150 authored by Davis King's avatar Davis King
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upgraded to cudnn v5. Also changed the affine_ layer to not be templated but 

to automatically select the right mode.  The serialization format for bn_
layers has also changed, but the code will still be able to deserialize older
bn_ objects.
parent c81825c3
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Showing with 127 additions and 58 deletions
dlib/CMakeLists.txt
View file @ f9cb3150
...@@ -445,7 +445,7 @@ if (NOT TARGET dlib) ...@@ -445,7 +445,7 @@ if (NOT TARGET dlib)
if (DLIB_USE_CUDA) if (DLIB_USE_CUDA)
find_package(CUDA 7.0) find_package(CUDA 7.5)
if (CUDA_FOUND AND COMPILER_CAN_DO_CPP_11) if (CUDA_FOUND AND COMPILER_CAN_DO_CPP_11)
...@@ -505,7 +505,7 @@ if (NOT TARGET dlib) ...@@ -505,7 +505,7 @@ if (NOT TARGET dlib)
set(DLIB_USE_CUDA OFF CACHE STRING ${DLIB_USE_BLAS_STR} FORCE ) set(DLIB_USE_CUDA OFF CACHE STRING ${DLIB_USE_BLAS_STR} FORCE )
toggle_preprocessor_switch(DLIB_USE_CUDA) toggle_preprocessor_switch(DLIB_USE_CUDA)
if (NOT cudnn OR NOT cudnn_include OR NOT cudnn_test_compile_worked) if (NOT cudnn OR NOT cudnn_include OR NOT cudnn_test_compile_worked)
message(STATUS "*** cuDNN V4.0 OR GREATER NOT FOUND. DLIB WILL NOT USE CUDA. ***") message(STATUS "*** cuDNN V5.0 OR GREATER NOT FOUND. DLIB WILL NOT USE CUDA. ***")
message(STATUS "*** If you have cuDNN then set CMAKE_PREFIX_PATH to include cuDNN's folder.") message(STATUS "*** If you have cuDNN then set CMAKE_PREFIX_PATH to include cuDNN's folder.")
endif() endif()
if (NOT COMPILER_CAN_DO_CPP_11) if (NOT COMPILER_CAN_DO_CPP_11)
......
dlib/dnn/cpu_dlib.cpp
View file @ f9cb3150
...@@ -6,6 +6,7 @@ ...@@ -6,6 +6,7 @@
// This file contains CPU implementations of the GPU based functions in cuda_dlib.h // This file contains CPU implementations of the GPU based functions in cuda_dlib.h
#include "cpu_dlib.h" #include "cpu_dlib.h"
#include "tensor_tools.h"
namespace dlib namespace dlib
{ {
...@@ -510,7 +511,7 @@ namespace dlib ...@@ -510,7 +511,7 @@ namespace dlib
{ {
for (long k = 0; k < num; ++k) for (long k = 0; k < num; ++k)
{ {
*d = g[k]*(*s - m[k])*i[k] + b[k]; *d = g[k]*(*s - m[k])/std::sqrt(i[k]+dlib::tt::BATCH_NORM_EPS) + b[k];
++d; ++d;
++s; ++s;
} }
...@@ -579,10 +580,18 @@ namespace dlib ...@@ -579,10 +580,18 @@ namespace dlib
invstds.host(); means.host(); invstds.host(); means.host();
// compute variances // compute variances
running_invstds.copy_size(invstds);
auto rvar = running_invstds.host();
const double scale = (src.num_samples())/(src.num_samples()-1.0);
for (long i = 0; i < num; ++i) for (long i = 0; i < num; ++i)
{ {
auto actual_var = p_invstds[i] - p_means[i]*p_means[i]; auto actual_var = p_invstds[i] - p_means[i]*p_means[i];
p_invstds[i] = 1.0f/std::sqrt(actual_var+BATCH_NORM_EPS); if (averaging_factor == 1)
rvar[i] = scale*actual_var;
else
rvar[i] = (1-averaging_factor)*rvar[i] + scale*averaging_factor*actual_var;
p_invstds[i] = 1.0f/std::sqrt(actual_var + dlib::tt::BATCH_NORM_EPS);
} }
p_src = src.host(); p_src = src.host();
...@@ -600,19 +609,12 @@ namespace dlib ...@@ -600,19 +609,12 @@ namespace dlib
} }
} }
// now keep track of the running means and invstds // now keep track of the running means
running_means.copy_size(means); running_means.copy_size(means);
running_invstds.copy_size(invstds);
if (averaging_factor != 1) if (averaging_factor != 1)
{
running_means = (1-averaging_factor)*mat(running_means) + averaging_factor*mat(means); running_means = (1-averaging_factor)*mat(running_means) + averaging_factor*mat(means);
running_invstds = (1-averaging_factor)*mat(running_invstds) + averaging_factor*mat(invstds);
}
else else
{
running_means = means; running_means = means;
running_invstds = invstds;
}
} }
void batch_normalize_gradient ( void batch_normalize_gradient (
...@@ -761,9 +763,10 @@ namespace dlib ...@@ -761,9 +763,10 @@ namespace dlib
{ {
for (long k = 0; k < src.k(); ++k) for (long k = 0; k < src.k(); ++k)
{ {
const float invstd = 1.0f/std::sqrt(i[k] + dlib::tt::BATCH_NORM_EPS);
for (long j = 0; j < num; ++j) for (long j = 0; j < num; ++j)
{ {
*d = g[k]*(*s - m[k])*i[k] + b[k]; *d = g[k]*(*s - m[k])*invstd + b[k];
++d; ++d;
++s; ++s;
} }
...@@ -841,10 +844,18 @@ namespace dlib ...@@ -841,10 +844,18 @@ namespace dlib
p_src = src.host(); p_src = src.host();
// compute variances // compute variances
running_invstds.copy_size(invstds);
auto rvar = running_invstds.host();
const double scale = (src.num_samples()*num)/(src.num_samples()*num-1.0);
for (long k = 0; k < src.k(); ++k) for (long k = 0; k < src.k(); ++k)
{ {
float actual_var = p_invstds[k] - p_means[k]*p_means[k]; float actual_var = p_invstds[k] - p_means[k]*p_means[k];
p_invstds[k] = 1.0f/std::sqrt(actual_var + BATCH_NORM_EPS); if (averaging_factor == 1)
rvar[k] = scale*actual_var;
else
rvar[k] = (1-averaging_factor)*rvar[k] + scale*averaging_factor*actual_var;
p_invstds[k] = 1.0f/std::sqrt(actual_var + dlib::tt::BATCH_NORM_EPS);
} }
p_src = src.host(); p_src = src.host();
...@@ -863,19 +874,12 @@ namespace dlib ...@@ -863,19 +874,12 @@ namespace dlib
} }
} }
// now keep track of the running means and invstds // now keep track of the running means
running_means.copy_size(means); running_means.copy_size(means);
running_invstds.copy_size(invstds);
if (averaging_factor != 1) if (averaging_factor != 1)
{
running_means = (1-averaging_factor)*mat(running_means) + averaging_factor*mat(means); running_means = (1-averaging_factor)*mat(running_means) + averaging_factor*mat(means);
running_invstds = (1-averaging_factor)*mat(running_invstds) + averaging_factor*mat(invstds);
}
else else
{
running_means = means; running_means = means;
running_invstds = invstds;
}
} }
void batch_normalize_conv_gradient( void batch_normalize_conv_gradient(
......
dlib/dnn/cpu_dlib.h
View file @ f9cb3150
...@@ -13,10 +13,6 @@ namespace dlib ...@@ -13,10 +13,6 @@ namespace dlib
namespace cpu namespace cpu
{ {
// ----------------------------------------------------------------------------------------
const double BATCH_NORM_EPS = 0.00001;
// ----------------------------------------------------------------------------------- // -----------------------------------------------------------------------------------
void multiply ( void multiply (
......
dlib/dnn/cudnn_dlibapi.cpp
View file @ f9cb3150
...@@ -112,6 +112,57 @@ namespace dlib ...@@ -112,6 +112,57 @@ namespace dlib
return c.get_handle(); return c.get_handle();
} }
// ------------------------------------------------------------------------------------
class cudnn_activation_descriptor
{
public:
// not copyable
cudnn_activation_descriptor(const cudnn_activation_descriptor&) = delete;
cudnn_activation_descriptor& operator=(const cudnn_activation_descriptor&) = delete;
cudnn_activation_descriptor(
cudnnActivationMode_t mode,
cudnnNanPropagation_t reluNanOpt,
double reluCeiling
)
{
CHECK_CUDNN(cudnnCreateActivationDescriptor(&handle));
CHECK_CUDNN(cudnnSetActivationDescriptor(handle, mode, reluNanOpt, reluCeiling));
}
~cudnn_activation_descriptor()
{
cudnnDestroyActivationDescriptor(handle);
}
cudnnActivationDescriptor_t get_handle (
)
{
return handle;
}
private:
cudnnActivationDescriptor_t handle;
};
static cudnnActivationDescriptor_t relu_activation_descriptor()
{
thread_local cudnn_activation_descriptor des(CUDNN_ACTIVATION_RELU, CUDNN_PROPAGATE_NAN,0);
return des.get_handle();
}
static cudnnActivationDescriptor_t sigmoid_activation_descriptor()
{
thread_local cudnn_activation_descriptor des(CUDNN_ACTIVATION_SIGMOID, CUDNN_PROPAGATE_NAN,0);
return des.get_handle();
}
static cudnnActivationDescriptor_t tanh_activation_descriptor()
{
thread_local cudnn_activation_descriptor des(CUDNN_ACTIVATION_TANH, CUDNN_PROPAGATE_NAN,0);
return des.get_handle();
}
// ------------------------------------------------------------------------------------ // ------------------------------------------------------------------------------------
tensor_descriptor:: tensor_descriptor::
...@@ -223,7 +274,7 @@ namespace dlib ...@@ -223,7 +274,7 @@ namespace dlib
return; return;
} }
CHECK_CUDNN(cudnnAddTensor_v3(context(), CHECK_CUDNN(cudnnAddTensor(context(),
&alpha, &alpha,
descriptor(src), descriptor(src),
src.device(), src.device(),
...@@ -342,7 +393,7 @@ namespace dlib ...@@ -342,7 +393,7 @@ namespace dlib
beta.device(), beta.device(),
running_means.device(), running_means.device(),
running_invstds.device(), running_invstds.device(),
dlib::cpu::BATCH_NORM_EPS)); dlib::tt::BATCH_NORM_EPS));
} }
void batch_normalize ( void batch_normalize (
...@@ -404,7 +455,7 @@ namespace dlib ...@@ -404,7 +455,7 @@ namespace dlib
averaging_factor, averaging_factor,
running_means.device(), running_means.device(),
running_invstds.device(), running_invstds.device(),
dlib::cpu::BATCH_NORM_EPS, dlib::tt::BATCH_NORM_EPS,
means.device(), means.device(),
invstds.device())); invstds.device()));
} }
...@@ -452,7 +503,7 @@ namespace dlib ...@@ -452,7 +503,7 @@ namespace dlib
gamma.device(), gamma.device(),
gamma_grad.device(), gamma_grad.device(),
beta_grad.device(), beta_grad.device(),
dlib::cpu::BATCH_NORM_EPS, dlib::tt::BATCH_NORM_EPS,
means.device(), means.device(),
invstds.device())); invstds.device()));
} }
...@@ -515,7 +566,7 @@ namespace dlib ...@@ -515,7 +566,7 @@ namespace dlib
beta.device(), beta.device(),
running_means.device(), running_means.device(),
running_invstds.device(), running_invstds.device(),
dlib::cpu::BATCH_NORM_EPS)); dlib::tt::BATCH_NORM_EPS));
} }
void batch_normalize_conv ( void batch_normalize_conv (
...@@ -578,7 +629,7 @@ namespace dlib ...@@ -578,7 +629,7 @@ namespace dlib
averaging_factor, averaging_factor,
running_means.device(), running_means.device(),
running_invstds.device(), running_invstds.device(),
dlib::cpu::BATCH_NORM_EPS, dlib::tt::BATCH_NORM_EPS,
means.device(), means.device(),
invstds.device())); invstds.device()));
} }
...@@ -625,7 +676,7 @@ namespace dlib ...@@ -625,7 +676,7 @@ namespace dlib
gamma.device(), gamma.device(),
gamma_grad.device(), gamma_grad.device(),
beta_grad.device(), beta_grad.device(),
dlib::cpu::BATCH_NORM_EPS, dlib::tt::BATCH_NORM_EPS,
means.device(), means.device(),
invstds.device())); invstds.device()));
} }
...@@ -739,6 +790,7 @@ namespace dlib ...@@ -739,6 +790,7 @@ namespace dlib
CHECK_CUDNN(cudnnCreateFilterDescriptor((cudnnFilterDescriptor_t*)&filter_handle)); CHECK_CUDNN(cudnnCreateFilterDescriptor((cudnnFilterDescriptor_t*)&filter_handle));
CHECK_CUDNN(cudnnSetFilter4dDescriptor((cudnnFilterDescriptor_t)filter_handle, CHECK_CUDNN(cudnnSetFilter4dDescriptor((cudnnFilterDescriptor_t)filter_handle,
CUDNN_DATA_FLOAT, CUDNN_DATA_FLOAT,
CUDNN_TENSOR_NCHW,
filters.num_samples(), filters.num_samples(),
filters.k(), filters.k(),
filters.nr(), filters.nr(),
...@@ -900,7 +952,7 @@ namespace dlib ...@@ -900,7 +952,7 @@ namespace dlib
const float beta = 1; const float beta = 1;
CHECK_CUDNN(cudnnConvolutionBackwardData_v3(context(), CHECK_CUDNN(cudnnConvolutionBackwardData(context(),
&alpha, &alpha,
(const cudnnFilterDescriptor_t)filter_handle, (const cudnnFilterDescriptor_t)filter_handle,
filters.device(), filters.device(),
...@@ -924,7 +976,7 @@ namespace dlib ...@@ -924,7 +976,7 @@ namespace dlib
{ {
const float alpha = 1; const float alpha = 1;
const float beta = 0; const float beta = 0;
CHECK_CUDNN(cudnnConvolutionBackwardFilter_v3(context(), CHECK_CUDNN(cudnnConvolutionBackwardFilter(context(),
&alpha, &alpha,
descriptor(data), descriptor(data),
data.device(), data.device(),
...@@ -1020,6 +1072,7 @@ namespace dlib ...@@ -1020,6 +1072,7 @@ namespace dlib
CHECK_CUDNN(cudnnSetPooling2dDescriptor(poolingDesc, CHECK_CUDNN(cudnnSetPooling2dDescriptor(poolingDesc,
(cudnnPoolingMode_t)pooling_mode, (cudnnPoolingMode_t)pooling_mode,
CUDNN_PROPAGATE_NAN,
window_height, window_height,
window_width, window_width,
window_height/2, window_height/2,
...@@ -1176,7 +1229,7 @@ namespace dlib ...@@ -1176,7 +1229,7 @@ namespace dlib
const float alpha = 1; const float alpha = 1;
const float beta = 0; const float beta = 0;
CHECK_CUDNN(cudnnActivationForward(context(), CHECK_CUDNN(cudnnActivationForward(context(),
CUDNN_ACTIVATION_SIGMOID, sigmoid_activation_descriptor(),
&alpha, &alpha,
descriptor(src), descriptor(src),
src.device(), src.device(),
...@@ -1200,7 +1253,7 @@ namespace dlib ...@@ -1200,7 +1253,7 @@ namespace dlib
const float alpha = 1; const float alpha = 1;
const float beta = 0; const float beta = 0;
CHECK_CUDNN(cudnnActivationBackward(context(), CHECK_CUDNN(cudnnActivationBackward(context(),
CUDNN_ACTIVATION_SIGMOID, sigmoid_activation_descriptor(),
&alpha, &alpha,
descriptor(dest), descriptor(dest),
dest.device(), dest.device(),
...@@ -1227,7 +1280,7 @@ namespace dlib ...@@ -1227,7 +1280,7 @@ namespace dlib
const float alpha = 1; const float alpha = 1;
const float beta = 0; const float beta = 0;
CHECK_CUDNN(cudnnActivationForward(context(), CHECK_CUDNN(cudnnActivationForward(context(),
CUDNN_ACTIVATION_RELU, relu_activation_descriptor(),
&alpha, &alpha,
descriptor(src), descriptor(src),
src.device(), src.device(),
...@@ -1251,7 +1304,7 @@ namespace dlib ...@@ -1251,7 +1304,7 @@ namespace dlib
const float alpha = 1; const float alpha = 1;
const float beta = 0; const float beta = 0;
CHECK_CUDNN(cudnnActivationBackward(context(), CHECK_CUDNN(cudnnActivationBackward(context(),
CUDNN_ACTIVATION_RELU, relu_activation_descriptor(),
&alpha, &alpha,
descriptor(dest), descriptor(dest),
dest.device(), dest.device(),
...@@ -1278,7 +1331,7 @@ namespace dlib ...@@ -1278,7 +1331,7 @@ namespace dlib
const float alpha = 1; const float alpha = 1;
const float beta = 0; const float beta = 0;
CHECK_CUDNN(cudnnActivationForward(context(), CHECK_CUDNN(cudnnActivationForward(context(),
CUDNN_ACTIVATION_TANH, tanh_activation_descriptor(),
&alpha, &alpha,
descriptor(src), descriptor(src),
src.device(), src.device(),
...@@ -1302,7 +1355,7 @@ namespace dlib ...@@ -1302,7 +1355,7 @@ namespace dlib
const float alpha = 1; const float alpha = 1;
const float beta = 0; const float beta = 0;
CHECK_CUDNN(cudnnActivationBackward(context(), CHECK_CUDNN(cudnnActivationBackward(context(),
CUDNN_ACTIVATION_TANH, tanh_activation_descriptor(),
&alpha, &alpha,
descriptor(dest), descriptor(dest),
dest.device(), dest.device(),
......
dlib/dnn/layers.h
View file @ f9cb3150
This diff is collapsed.
dlib/dnn/layers_abstract.h
View file @ f9cb3150
...@@ -736,9 +736,6 @@ namespace dlib ...@@ -736,9 +736,6 @@ namespace dlib
// ---------------------------------------------------------------------------------------- // ----------------------------------------------------------------------------------------
template <
layer_mode mode
>
class affine_ class affine_
{ {
/*! /*!
...@@ -777,11 +774,22 @@ namespace dlib ...@@ -777,11 +774,22 @@ namespace dlib
affine_( affine_(
); );
/*!
ensures
- #get_mode() == FC_MODE
!*/
affine_(
layer_mode mode
);
/*! /*!
ensures ensures
- #get_mode() == mode - #get_mode() == mode
!*/ !*/
template <
layer_mode mode
>
affine_( affine_(
const bn_<mode>& layer const bn_<mode>& layer
); );
...@@ -812,17 +820,16 @@ namespace dlib ...@@ -812,17 +820,16 @@ namespace dlib
are no learnable parameters in this object. are no learnable parameters in this object.
!*/ !*/
friend void serialize(const affine_& item, std::ostream& out);
friend void deserialize(affine_& item, std::istream& in);
/*!
provides serialization support
!*/
}; };
void serialize(const affine_& item, std::ostream& out);
void deserialize(affine_& item, std::istream& in);
/*!
provides serialization support
!*/
template <typename SUBNET> template <typename SUBNET>
using affine_con = add_layer<affine_<CONV_MODE>, SUBNET>; using affine = add_layer<affine_, SUBNET>;
template <typename SUBNET>
using affine_fc = add_layer<affine_<FC_MODE>, SUBNET>;
// ---------------------------------------------------------------------------------------- // ----------------------------------------------------------------------------------------
......
dlib/dnn/tensor_tools.h
View file @ f9cb3150
...@@ -286,6 +286,8 @@ namespace dlib { namespace tt ...@@ -286,6 +286,8 @@ namespace dlib { namespace tt
// ---------------------------------------------------------------------------------------- // ----------------------------------------------------------------------------------------
const double BATCH_NORM_EPS = 0.00001;
void batch_normalize_inference ( void batch_normalize_inference (
resizable_tensor& dest, resizable_tensor& dest,
const tensor& src, const tensor& src,
......
dlib/dnn/test_for_cuda/CMakeLists.txt
View file @ f9cb3150
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
cmake_minimum_required(VERSION 2.8.4) cmake_minimum_required(VERSION 2.8.4)
project(cuda_test) project(cuda_test)
find_package(CUDA 7.0 REQUIRED) find_package(CUDA 7.5 REQUIRED)
set(CUDA_HOST_COMPILATION_CPP ON) set(CUDA_HOST_COMPILATION_CPP ON)
list(APPEND CUDA_NVCC_FLAGS "-arch=sm_30;-std=c++11;-D__STRICT_ANSI__") list(APPEND CUDA_NVCC_FLAGS "-arch=sm_30;-std=c++11;-D__STRICT_ANSI__")
......
dlib/dnn/test_for_cudnn/CMakeLists.txt
View file @ f9cb3150
...@@ -3,7 +3,7 @@ cmake_minimum_required(VERSION 2.8.4) ...@@ -3,7 +3,7 @@ cmake_minimum_required(VERSION 2.8.4)
project(cudnn_test) project(cudnn_test)
include(../../use_cpp_11.cmake) include(../../use_cpp_11.cmake)
find_package(CUDA 7.0 REQUIRED) find_package(CUDA 7.5 REQUIRED)
set(CUDA_HOST_COMPILATION_CPP ON) set(CUDA_HOST_COMPILATION_CPP ON)
list(APPEND CUDA_NVCC_FLAGS "-arch=sm_30;-std=c++11;-D__STRICT_ANSI__") list(APPEND CUDA_NVCC_FLAGS "-arch=sm_30;-std=c++11;-D__STRICT_ANSI__")
add_definitions(-DDLIB_USE_CUDA) add_definitions(-DDLIB_USE_CUDA)
......
dlib/test/dnn.cpp
View file @ f9cb3150
...@@ -166,8 +166,11 @@ namespace ...@@ -166,8 +166,11 @@ namespace
resizable_tensor running_means; resizable_tensor running_means;
resizable_tensor running_invstds; resizable_tensor running_invstds;
batch_normalize(dest, means, vars, 1, running_means, running_invstds, src, gamma, beta); batch_normalize(dest, means, vars, 1, running_means, running_invstds, src, gamma, beta);
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.
running_invstds = mat(running_invstds)/scale;
batch_normalize_inference(dest2, src, gamma, beta, running_means, running_invstds); batch_normalize_inference(dest2, src, gamma, beta, running_means, running_invstds);
DLIB_TEST(max(abs(mat(dest2)-mat(dest))) < 1e-5); DLIB_TEST_MSG(max(abs(mat(dest2)-mat(dest))) < 1e-5, max(abs(mat(dest2)-mat(dest))));
auto grad_src = [&](long idx) { auto grad_src = [&](long idx) {
...@@ -246,6 +249,10 @@ namespace ...@@ -246,6 +249,10 @@ namespace
resizable_tensor running_means; resizable_tensor running_means;
resizable_tensor running_invstds; resizable_tensor running_invstds;
batch_normalize_conv(dest, means, vars, 1, running_means, running_invstds, src, gamma, beta); batch_normalize_conv(dest, means, vars, 1, running_means, running_invstds, src, gamma, beta);
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
// batch_normalize_conv() works, so if we want to match it this is necessary.
running_invstds = mat(running_invstds)/scale;
batch_normalize_conv_inference(dest2, src, gamma, beta, running_means, running_invstds); batch_normalize_conv_inference(dest2, src, gamma, beta, running_means, running_invstds);
DLIB_TEST(max(abs(mat(dest2)-mat(dest))) < 1e-5); DLIB_TEST(max(abs(mat(dest2)-mat(dest))) < 1e-5);
...@@ -1086,12 +1093,12 @@ namespace ...@@ -1086,12 +1093,12 @@ namespace
} }
{ {
print_spinner(); print_spinner();
affine_<CONV_MODE> l; affine_ l(CONV_MODE);
DLIB_TEST_MSG(test_layer(l), test_layer(l)); DLIB_TEST_MSG(test_layer(l), test_layer(l));
} }
{ {
print_spinner(); print_spinner();
affine_<FC_MODE> l; affine_ l(FC_MODE);
DLIB_TEST_MSG(test_layer(l), test_layer(l)); DLIB_TEST_MSG(test_layer(l), test_layer(l));
} }
{ {
......
examples/dnn_mnist_resnet_ex.cpp
View file @ f9cb3150
...@@ -13,7 +13,7 @@ template <int stride, typename SUBNET> ...@@ -13,7 +13,7 @@ template <int stride, typename SUBNET>
using base_res = relu<add_prev1< bn_con<con<8,3,3,1,1,relu< bn_con<con<8,3,3,stride,stride,tag1<SUBNET>>>>>>>>; using base_res = relu<add_prev1< bn_con<con<8,3,3,1,1,relu< bn_con<con<8,3,3,stride,stride,tag1<SUBNET>>>>>>>>;
template <int stride, typename SUBNET> template <int stride, typename SUBNET>
using base_ares = relu<add_prev1<affine_con<con<8,3,3,1,1,relu<affine_con<con<8,3,3,stride,stride,tag1<SUBNET>>>>>>>>; using base_ares = relu<add_prev1<affine<con<8,3,3,1,1,relu<affine<con<8,3,3,stride,stride,tag1<SUBNET>>>>>>>>;
template <typename SUBNET> using res = base_res<1,SUBNET>; template <typename SUBNET> using res = base_res<1,SUBNET>;
template <typename SUBNET> using res_down = base_res<2,SUBNET>; template <typename SUBNET> using res_down = base_res<2,SUBNET>;
......
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