public:
BlobShape &resize(uint32_t size)
- {
- _dims.resize(size);
+ {
+ _dims.resize(size);
return (*this);
}
caffe::LayerParameter ¶m(void) { return *_param; }
public:
- caffe::ConvolutionParameter &conv_param(void)
- {
- return *param().mutable_convolution_param();
- }
+ caffe::ConvolutionParameter &conv_param(void) { return *param().mutable_convolution_param(); }
- const caffe::ConvolutionParameter &conv_param(void) const
- {
- return param().convolution_param();
- }
+ const caffe::ConvolutionParameter &conv_param(void) const { return param().convolution_param(); }
public:
const std::string &input_name(void) const;
uint32_t dilation(uint32_t spatial_axe) const;
private:
- const Network * const _net;
- caffe::LayerParameter * const _param;
+ const Network *const _net;
+ caffe::LayerParameter *const _param;
};
#endif // __CONVOLUTION_LAYER_H__
caffe::LayerParameter ¶m(void) { return *_param; }
private:
- caffe::LayerParameter * const _param;
+ caffe::LayerParameter *const _param;
};
#endif // __INPUT_LAYER_H__
#include <iostream>
-int DecodeCommand::run(int, const char * const *) const
+int DecodeCommand::run(int, const char *const *) const
{
caffe::NetParameter param;
struct DecodeCommand final : public cli::Command
{
- int run(int argc, const char * const *argv) const override;
+ int run(int argc, const char *const *argv) const override;
};
#endif // __DECODE_COMMAND_H__
#include <iostream>
-int EncodeCommand::run(int, const char * const *) const
+int EncodeCommand::run(int, const char *const *) const
{
caffe::NetParameter param;
struct EncodeCommand final : public cli::Command
{
- int run(int argc, const char * const *argv) const override;
+ int run(int argc, const char *const *argv) const override;
};
#endif // __ENCODE_COMMAND_H__
#include <random>
#include <iostream>
-int FillCommand::run(int, const char * const *) const
+int FillCommand::run(int, const char *const *) const
{
auto param = nncc::foundation::make_unique<::caffe::NetParameter>();
struct FillCommand final : public cli::Command
{
- int run(int argc, const char * const *argv) const override;
+ int run(int argc, const char *const *argv) const override;
};
#endif // __FILL_COMMAND_H__
#include <iostream>
-int InitCommand::run(int, const char * const *) const
+int InitCommand::run(int, const char *const *) const
{
// Read prototxt from standard input
::caffe::NetParameter in;
struct InitCommand final : public cli::Command
{
- int run(int argc, const char * const *argv) const override;
+ int run(int argc, const char *const *argv) const override;
};
#endif // __INIT_COMMAND_H__
int MergeCommand::run(int argc, const char *const *argv) const
{
- if (argc != 2) {
+ if (argc != 2)
+ {
std::cerr << "ERROR: this command requires exactly 2 arguments" << std::endl;
return 254;
}
*/
struct MergeCommand final : public cli::Command
{
- int run(int argc, const char * const *argv) const override;
+ int run(int argc, const char *const *argv) const override;
};
#endif //__MERGE_COMMAND_H__
#include "internal/BlobContext.h"
-const BlobShape &BlobContext::at(const std::string &name) const
-{
- return _shapes.at(name);
-}
+const BlobShape &BlobContext::at(const std::string &name) const { return _shapes.at(name); }
BlobContext &BlobContext::insert(const std::string &name, const BlobShape &shape)
{
for (uint32_t spatial_axis = 0; spatial_axis < num_spatial_axes(); ++spatial_axis)
{
const uint32_t axis = num_batch_axes() + 1 + spatial_axis;
- const int64_t kernel_ext =
- dilation(spatial_axis) * (kernel_size(spatial_axis) - 1) + 1;
+ const int64_t kernel_ext = dilation(spatial_axis) * (kernel_size(spatial_axis) - 1) + 1;
res.dim(axis) =
- (input_shape().dim(axis) + 2 * pad(spatial_axis) - kernel_ext) / stride(spatial_axis);
+ (input_shape().dim(axis) + 2 * pad(spatial_axis) - kernel_ext) / stride(spatial_axis);
}
return res;
}
-uint32_t ConvolutionLayer::channel_axis(void) const
-{
- return conv_param().axis();
-}
+uint32_t ConvolutionLayer::channel_axis(void) const { return conv_param().axis(); }
-uint32_t ConvolutionLayer::num_effective_output(void) const
-{
- return conv_param().num_output();
-}
+uint32_t ConvolutionLayer::num_effective_output(void) const { return conv_param().num_output(); }
uint32_t ConvolutionLayer::num_spatial_axes(void) const
{
return input_shape().rank() - num_spatial_axes() - 1;
}
-uint32_t ConvolutionLayer::pad(uint32_t /*spatial_axis*/) const
-{
- return conv_param().pad(0);
-}
+uint32_t ConvolutionLayer::pad(uint32_t /*spatial_axis*/) const { return conv_param().pad(0); }
uint32_t ConvolutionLayer::kernel_size(uint32_t /*spatial_axis*/) const
{
return conv_param().stride(0);
}
-uint32_t ConvolutionLayer::dilation(uint32_t /*spatial_axis*/) const
-{
- return 1;
-}
+uint32_t ConvolutionLayer::dilation(uint32_t /*spatial_axis*/) const { return 1; }
uint32_t InputLayer::bottom_size(void) const { return 0; }
-const std::string &InputLayer::bottom_name(uint32_t) const
-{
- throw std::invalid_argument{"n"};
-}
+const std::string &InputLayer::bottom_name(uint32_t) const { throw std::invalid_argument{"n"}; }
-const BlobShape &InputLayer::bottom_shape(uint32_t) const
-{
- throw std::invalid_argument{"n"};
-}
+const BlobShape &InputLayer::bottom_shape(uint32_t) const { throw std::invalid_argument{"n"}; }
uint32_t InputLayer::top_size(void) const { return param().top_size(); }
#include <nncc/foundation/Memory.h>
-template<typename T> std::shared_ptr<LayerFactory> make_factory(void)
+template <typename T> std::shared_ptr<LayerFactory> make_factory(void)
{
struct LayerFactoryImpl final : public LayerFactory
{
caffe::ConvolutionParameter *conv_param = l.param().mutable_convolution_param();
- auto element_count = [] (caffe::BlobShape &shape)
- {
+ auto element_count = [](caffe::BlobShape &shape) {
assert(shape.dim_size() > 0);
int64_t count = 1;