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Added prelu layer

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This commit is contained in:
Davis King 2016-03-31 21:07:04 -04:00
parent ebf7a89a7c
commit dcb5b46b5f
8 changed files with 331 additions and 1 deletions
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48
dlib/dnn/cpu_dlib.cpp
View File

@ -1160,6 +1160,54 @@ namespace dlib
}
}
// ----------------------------------------------------------------------------------------
void prelu (
tensor& dest,
const tensor& src,
const tensor& param
)
{
const float p = param.host()[0];
const float* s = src.host();
float* d = dest.host();
for (size_t i = 0; i < dest.size(); ++i)
{
if (s[i] > 0)
d[i] = s[i];
else
d[i] = p*s[i];
}
}
void prelu_gradient (
tensor& grad,
const tensor& src,
const tensor& gradient_input,
const tensor& param,
tensor& params_grad
)
{
const float p = param.host()[0];
const float* gi = gradient_input.host();
const float* s = src.host();
float* out = grad.host();
float pgrad = 0;
for (size_t i = 0; i < src.size(); ++i)
{
if (s[i] > 0)
{
out[i] += gi[i];
}
else
{
out[i] += p*gi[i];
pgrad += gi[i]*s[i];
}
}
params_grad.host()[0] = pgrad;
}
// ------------------------------------------------------------------------------------
void tanh (

16
dlib/dnn/cpu_dlib.h
View File

@ -235,6 +235,22 @@ namespace dlib
const tensor& gradient_input
);
// ----------------------------------------------------------------------------------------
void prelu (
tensor& dest,
const tensor& src,
const tensor& param
);
void prelu_gradient (
tensor& grad,
const tensor& src,
const tensor& gradient_input,
const tensor& param,
tensor& params_grad
);
// ------------------------------------------------------------------------------------
void tanh (

64
dlib/dnn/cuda_dlib.cu
View File

@ -538,7 +538,69 @@ namespace dlib
launch_kernel(_cuda_dot, max_jobs(a.size()), a.device(), b.device(), a.size(), result.device()+idx);
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
__global__ void _cuda_prelu(const float* s, float* d, size_t n, const float* pp)
{
const float p = *pp;
for (auto i : grid_stride_range(0, n))
{
if (s[i] > 0)
d[i] = s[i];
else
d[i] = p*s[i];
}
}
void prelu (
tensor& dest,
const tensor& src,
const tensor& param
)
{
launch_kernel(_cuda_prelu, max_jobs(dest.size()),
src.device(), dest.device(), src.size(), param.device());
}
// ----------------------------------------------------------------------------------------
__global__ void _cuda_prelu_gradient(float* out, const float* s, const float* gi, size_t n, const float* pp, float* ppgrad)
{
const float p = *pp;
float pgrad = 0;
for(auto i : grid_stride_range(0, n))
{
if (s[i] > 0)
{
out[i] += gi[i];
}
else
{
out[i] += p*gi[i];
pgrad += gi[i]*s[i];
}
}
// Then do the warp reduce add thing to merge into one output value.
warp_reduce_atomic_add(*ppgrad, pgrad);
}
void prelu_gradient (
tensor& grad,
const tensor& src,
const tensor& gradient_input,
const tensor& param,
tensor& params_grad
)
{
params_grad = 0;
launch_kernel(_cuda_prelu_gradient, max_jobs(grad.size()),
grad.device(), src.device(), gradient_input.device(), grad.size(),
param.device(), params_grad.device());
}
// ----------------------------------------------------------------------------------------
}
}

16
dlib/dnn/cuda_dlib.h
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@ -135,6 +135,22 @@ namespace dlib
size_t idx
);
// ----------------------------------------------------------------------------------------
void prelu (
tensor& dest,
const tensor& src,
const tensor& param
);
void prelu_gradient (
tensor& grad,
const tensor& src,
const tensor& gradient_input,
const tensor& param,
tensor& params_grad
);
// ------------------------------------------------------------------------------------
// ------------------------------------------------------------------------------------
// ------------------------------------------------------------------------------------

67
dlib/dnn/layers.h
View File

@ -1119,6 +1119,73 @@ namespace dlib
template <typename SUBNET>
using relu = add_layer<relu_, SUBNET>;
// ----------------------------------------------------------------------------------------
class prelu_
{
public:
explicit prelu_(
float initial_param_value_ = 0.25
) : initial_param_value(initial_param_value_)
{
}
template <typename SUBNET>
void setup (const SUBNET& /*sub*/)
{
params.set_size(1);
params = initial_param_value;
}
template <typename SUBNET>
void forward(
const SUBNET& sub,
resizable_tensor& data_output
)
{
data_output.copy_size(sub.get_output());
tt::prelu(data_output, sub.get_output(), params);
}
template <typename SUBNET>
void backward(
const tensor& gradient_input,
SUBNET& sub,
tensor& params_grad
)
{
tt::prelu_gradient(sub.get_gradient_input(), sub.get_output(),
gradient_input, params, params_grad);
}
const tensor& get_layer_params() const { return params; }
tensor& get_layer_params() { return params; }
friend void serialize(const prelu_& item, std::ostream& out)
{
serialize("prelu_", out);
serialize(item.params, out);
serialize(item.initial_param_value, out);
}
friend void deserialize(prelu_& item, std::istream& in)
{
std::string version;
deserialize(version, in);
if (version != "prelu_")
throw serialization_error("Unexpected version found while deserializing dlib::prelu_.");
deserialize(item.params, in);
deserialize(item.initial_param_value, in);
}
private:
resizable_tensor params;
float initial_param_value;
};
template <typename SUBNET>
using prelu = add_layer<prelu_, SUBNET>;
// ----------------------------------------------------------------------------------------
class sig_

49
dlib/dnn/layers_abstract.h
View File

@ -1066,6 +1066,55 @@ namespace dlib
template <typename SUBNET>
using relu = add_layer<relu_, SUBNET>;
// ----------------------------------------------------------------------------------------
class prelu_
{
/*!
WHAT THIS OBJECT REPRESENTS
This is an implementation of the EXAMPLE_LAYER_ interface defined above.
In particular, it defines a parametric rectified linear layer. Therefore,
it passes its inputs through the function
f(x) = x>0 ? x : p*x
where f() is applied pointwise across the input tensor and p is a scalar
parameter learned by this layer.
This is the layer type introduced in the paper:
He, Kaiming, et al. "Delving deep into rectifiers: Surpassing
human-level performance on imagenet classification." Proceedings of the
IEEE International Conference on Computer Vision. 2015.
!*/
public:
explicit prelu_(
float initial_param_value = 0.25
);
/*!
ensures
- The p parameter will be initialized with initial_param_value.
!*/
template <typename SUBNET> void setup (const SUBNET& sub);
void forward_inplace(const tensor& input, tensor& output);
void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
const tensor& get_layer_params() const;
tensor& get_layer_params();
/*!
These functions are implemented as described in the EXAMPLE_LAYER_ interface.
!*/
};
void serialize(const prelu_& item, std::ostream& out);
void deserialize(prelu_& item, std::istream& in);
/*!
provides serialization support
!*/
template <typename SUBNET>
using prelu = add_layer<prelu_, SUBNET>;
// ----------------------------------------------------------------------------------------
class sig_

30
dlib/dnn/tensor_tools.cpp
View File

@ -515,6 +515,36 @@ namespace dlib { namespace tt
#endif
}
// ----------------------------------------------------------------------------------------
void prelu (
tensor& dest,
const tensor& src,
const tensor& param
)
{
#ifdef DLIB_USE_CUDA
cuda::prelu(dest, src, param);
#else
cpu::prelu(dest, src, param);
#endif
}
void prelu_gradient (
tensor& grad,
const tensor& src,
const tensor& gradient_input,
const tensor& param,
tensor& params_grad
)
{
#ifdef DLIB_USE_CUDA
cuda::prelu_gradient(grad, src, gradient_input, param, params_grad);
#else
cpu::prelu_gradient(grad, src, gradient_input, param, params_grad);
#endif
}
// ----------------------------------------------------------------------------------------
void tanh (

42
dlib/dnn/tensor_tools.h
View File

@ -896,6 +896,48 @@ namespace dlib { namespace tt
is_same_object(grad, gradient_input)==true
!*/
// ----------------------------------------------------------------------------------------
void prelu (
tensor& dest,
const tensor& src,
const tensor& param
);
/*!
requires
- have_same_dimensions(dest, src) == true
- param.size() == 1
ensures
- for all valid i:
- if (src.host()[i] > 0) then
- #dest.host()[i] == src.host()[i]
- else
- #dest.host()[i] == src.host()[i] * param.host()[0]
- This function supports in-place operation, i.e. having
is_same_object(dest, src)==true
!*/
void prelu_gradient (
tensor& grad,
const tensor& src,
const tensor& gradient_input,
const tensor& param,
tensor& params_grad
);
/*!
requires
- have_same_dimensions(grad,src) == true
- have_same_dimensions(grad,gradient_input) == true
- param.size() == 1
- params_grad.size() == 1
ensures
- Recalling that dest is the output of prelu(dest,src,param) let
f(src,param) == dot(gradient_input,dest)
- Then this function computes the gradient of f() with respect to src and
param. It assigns the gradient with respect to param to #params_grad and
adds the gradient with respect to src to #grad.
!*/
// ----------------------------------------------------------------------------------------
void tanh (