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Made computed_output an optional argument to backward_inplace() so there is 

symmetry between the non-inplace version. This also enables additional
optimizations in the resulting network.
This commit is contained in:
Davis King 2015-12-23 08:03:31 -05:00
parent 122f2fa6b5
commit 28475b8d0d
3 changed files with 39 additions and 4 deletions
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30
dlib/dnn/core.h
View File

@ -113,6 +113,15 @@ namespace dlib
return true;
}
template <typename layer_type, typename SUBNET>
constexpr auto backward_requires_forward_output(
layer_type& layer,
SUBNET& sub
) -> typename alwaysbool<decltype(layer.backward_inplace(rt(),sub.get_gradient_input(),rt()))>::type
{
return false;
}
template <typename layer_type, typename SUBNET>
constexpr auto has_inplace_backward(
layer_type& layer,
@ -140,6 +149,15 @@ namespace dlib
return true;
}
template <typename layer_type, typename SUBNET>
constexpr auto has_inplace_backward(
layer_type& layer,
SUBNET& sub
) -> typename alwaysbool<decltype(layer.backward_inplace(rt(),sub.get_gradient_input(),rt()))>::type
{
return true;
}
template <typename layer_type, typename SUBNET>
constexpr auto is_inplace_layer(
layer_type& layer,
@ -194,6 +212,18 @@ namespace dlib
layer.backward_inplace(computed_output,gradient_input,sub.get_gradient_input(),params_grad);
}
template <typename layer_type, typename SUBNET>
auto call_layer_backward(
layer_type& layer,
const tensor& ,
const tensor& gradient_input,
SUBNET& sub,
tensor& params_grad
) -> decltype(layer.backward_inplace(gradient_input,sub.get_gradient_input(),params_grad))
{
layer.backward_inplace(gradient_input,sub.get_gradient_input(),params_grad);
}
template <typename layer_type, typename SUBNET>
auto call_layer_forward(

1
dlib/dnn/layers.h
View File

@ -559,7 +559,6 @@ namespace dlib
}
void backward_inplace(
const tensor& /*computed_output*/,
const tensor& gradient_input,
tensor& data_grad,
tensor& /*params_grad*/

12
dlib/dnn/layers_abstract.h
View File

@ -99,7 +99,7 @@ namespace dlib
to document the interface that a layer object must implement.
The central work of defining a layer is implementing the forward and backward
methods. When you do this you have three options:
methods. When you do this you have four options:
- Implement the forward() and backward() methods according to the
specification shown below. Do not implement forward_inplace() and
backward_inplace().
@ -113,6 +113,12 @@ namespace dlib
according to the specification shown below. Do not implement
forward() and backward(). These in-place methods allow some types of
layers to be implemented more efficiently.
- Implement the forward_inplace() and backward_inplace() methods
according to the specification shown below, except exclude the
computed_output parameter from backward_inplace(). Doing this will
allow dlib to make some layers execute in-place and therefore run a
little faster and use less memory. Do not implement forward() and
backward().
!*/
public:
@ -239,7 +245,7 @@ namespace dlib
!*/
void backward_inplace(
const tensor& computed_output,
const tensor& computed_output, // this parameter is optional
const tensor& gradient_input,
tensor& data_grad,
tensor& params_grad
@ -503,7 +509,7 @@ namespace dlib
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);
void backward_inplace(const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
const tensor& get_layer_params() const;
tensor& get_layer_params();
/*!