Re: [FFmpeg-devel] [PATCH 2/7] libavfilter: Code style fixes for pointers in DNN module and sr filter.
2018-08-06 18:11 GMT-03:00 Sergey Lavrushkin :
> Updated patch.
>
> 2018-08-06 17:55 GMT+03:00 Pedro Arthur :
>
>> 2018-08-02 15:52 GMT-03:00 Sergey Lavrushkin :
>> > ---
>> > libavfilter/dnn_backend_native.c | 84 +++---
>> > libavfilter/dnn_backend_native.h | 8 +--
>> > libavfilter/dnn_backend_tf.c | 108 +++---
>> -
>> > libavfilter/dnn_backend_tf.h | 8 +--
>> > libavfilter/dnn_espcn.h | 6 +--
>> > libavfilter/dnn_interface.c | 4 +-
>> > libavfilter/dnn_interface.h | 16 +++---
>> > libavfilter/dnn_srcnn.h | 6 +--
>> > libavfilter/vf_sr.c | 60 +++---
>> > 9 files changed, 150 insertions(+), 150 deletions(-)
>> >
>> > diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_
>> native.c
>> > index 3e6b86280d..baefea7fcb 100644
>> > --- a/libavfilter/dnn_backend_native.c
>> > +++ b/libavfilter/dnn_backend_native.c
>> > @@ -34,15 +34,15 @@ typedef enum {RELU, TANH, SIGMOID} ActivationFunc;
>> >
>> > typedef struct Layer{
>> > LayerType type;
>> > -float* output;
>> > -void* params;
>> > +float *output;
>> > +void *params;
>> > } Layer;
>> >
>> > typedef struct ConvolutionalParams{
>> > int32_t input_num, output_num, kernel_size;
>> > ActivationFunc activation;
>> > -float* kernel;
>> > -float* biases;
>> > +float *kernel;
>> > +float *biases;
>> > } ConvolutionalParams;
>> >
>> > typedef struct InputParams{
>> > @@ -55,16 +55,16 @@ typedef struct DepthToSpaceParams{
>> >
>> > // Represents simple feed-forward convolutional network.
>> > typedef struct ConvolutionalNetwork{
>> > -Layer* layers;
>> > +Layer *layers;
>> > int32_t layers_num;
>> > } ConvolutionalNetwork;
>> >
>> > -static DNNReturnType set_input_output_native(void* model, DNNData*
>> input, DNNData* output)
>> > +static DNNReturnType set_input_output_native(void *model, DNNData
>> *input, DNNData *output)
>> > {
>> > -ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
>> > -InputParams* input_params;
>> > -ConvolutionalParams* conv_params;
>> > -DepthToSpaceParams* depth_to_space_params;
>> > +ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
>> > +InputParams *input_params;
>> > +ConvolutionalParams *conv_params;
>> > +DepthToSpaceParams *depth_to_space_params;
>> > int cur_width, cur_height, cur_channels;
>> > int32_t layer;
>> >
>> > @@ -72,7 +72,7 @@ static DNNReturnType set_input_output_native(void*
>> model, DNNData* input, DNNDat
>> > return DNN_ERROR;
>> > }
>> > else{
>> > -input_params = (InputParams*)network->layers[0].params;
>> > +input_params = (InputParams *)network->layers[0].params;
>> > input_params->width = cur_width = input->width;
>> > input_params->height = cur_height = input->height;
>> > input_params->channels = cur_channels = input->channels;
>> > @@ -88,14 +88,14 @@ static DNNReturnType set_input_output_native(void*
>> model, DNNData* input, DNNDat
>> > for (layer = 1; layer < network->layers_num; ++layer){
>> > switch (network->layers[layer].type){
>> > case CONV:
>> > -conv_params = (ConvolutionalParams*)network-
>> >layers[layer].params;
>> > +conv_params = (ConvolutionalParams *)network->layers[layer].
>> params;
>> > if (conv_params->input_num != cur_channels){
>> > return DNN_ERROR;
>> > }
>> > cur_channels = conv_params->output_num;
>> > break;
>> > case DEPTH_TO_SPACE:
>> > -depth_to_space_params = (DepthToSpaceParams*)network->
>> layers[layer].params;
>> > +depth_to_space_params = (DepthToSpaceParams
>> *)network->layers[layer].params;
>> > if (cur_channels % (depth_to_space_params->block_size *
>> depth_to_space_params->block_size) != 0){
>> > return DNN_ERROR;
>> > }
>> > @@ -127,16 +127,16 @@ static DNNReturnType set_input_output_native(void*
>> model, DNNData* input, DNNDat
>> > // layers_num,layer_type,layer_parameterss,layer_type,layer_
>> parameters...
>> > // For CONV layer: activation_function, input_num, output_num,
>> kernel_size, kernel, biases
>> > // For DEPTH_TO_SPACE layer: block_size
>> > -DNNModel* ff_dnn_load_model_native(const char* model_filename)
>> > +DNNModel *ff_dnn_load_model_native(const char *model_filename)
>> > {
>> > -DNNModel* model = NULL;
>> > -ConvolutionalNetwork* network = NULL;
>> > -AVIOContext* model_file_context;
>> > +DNNModel *model = NULL;
>> > +ConvolutionalNetwork *network = NULL;
>> > +AVIOContext *model_file_context;
>> > int file_size, dnn_size, kernel_size, i;
>> > int32_t layer;
>> > LayerType layer_type;
>> > -ConvolutionalParams* conv_params;
>> > -DepthToSpaceParams*
Re: [FFmpeg-devel] [PATCH 2/7] libavfilter: Code style fixes for pointers in DNN module and sr filter.
Updated patch.
2018-08-06 17:55 GMT+03:00 Pedro Arthur :
> 2018-08-02 15:52 GMT-03:00 Sergey Lavrushkin :
> > ---
> > libavfilter/dnn_backend_native.c | 84 +++---
> > libavfilter/dnn_backend_native.h | 8 +--
> > libavfilter/dnn_backend_tf.c | 108 +++---
> -
> > libavfilter/dnn_backend_tf.h | 8 +--
> > libavfilter/dnn_espcn.h | 6 +--
> > libavfilter/dnn_interface.c | 4 +-
> > libavfilter/dnn_interface.h | 16 +++---
> > libavfilter/dnn_srcnn.h | 6 +--
> > libavfilter/vf_sr.c | 60 +++---
> > 9 files changed, 150 insertions(+), 150 deletions(-)
> >
> > diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_
> native.c
> > index 3e6b86280d..baefea7fcb 100644
> > --- a/libavfilter/dnn_backend_native.c
> > +++ b/libavfilter/dnn_backend_native.c
> > @@ -34,15 +34,15 @@ typedef enum {RELU, TANH, SIGMOID} ActivationFunc;
> >
> > typedef struct Layer{
> > LayerType type;
> > -float* output;
> > -void* params;
> > +float *output;
> > +void *params;
> > } Layer;
> >
> > typedef struct ConvolutionalParams{
> > int32_t input_num, output_num, kernel_size;
> > ActivationFunc activation;
> > -float* kernel;
> > -float* biases;
> > +float *kernel;
> > +float *biases;
> > } ConvolutionalParams;
> >
> > typedef struct InputParams{
> > @@ -55,16 +55,16 @@ typedef struct DepthToSpaceParams{
> >
> > // Represents simple feed-forward convolutional network.
> > typedef struct ConvolutionalNetwork{
> > -Layer* layers;
> > +Layer *layers;
> > int32_t layers_num;
> > } ConvolutionalNetwork;
> >
> > -static DNNReturnType set_input_output_native(void* model, DNNData*
> input, DNNData* output)
> > +static DNNReturnType set_input_output_native(void *model, DNNData
> *input, DNNData *output)
> > {
> > -ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
> > -InputParams* input_params;
> > -ConvolutionalParams* conv_params;
> > -DepthToSpaceParams* depth_to_space_params;
> > +ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
> > +InputParams *input_params;
> > +ConvolutionalParams *conv_params;
> > +DepthToSpaceParams *depth_to_space_params;
> > int cur_width, cur_height, cur_channels;
> > int32_t layer;
> >
> > @@ -72,7 +72,7 @@ static DNNReturnType set_input_output_native(void*
> model, DNNData* input, DNNDat
> > return DNN_ERROR;
> > }
> > else{
> > -input_params = (InputParams*)network->layers[0].params;
> > +input_params = (InputParams *)network->layers[0].params;
> > input_params->width = cur_width = input->width;
> > input_params->height = cur_height = input->height;
> > input_params->channels = cur_channels = input->channels;
> > @@ -88,14 +88,14 @@ static DNNReturnType set_input_output_native(void*
> model, DNNData* input, DNNDat
> > for (layer = 1; layer < network->layers_num; ++layer){
> > switch (network->layers[layer].type){
> > case CONV:
> > -conv_params = (ConvolutionalParams*)network-
> >layers[layer].params;
> > +conv_params = (ConvolutionalParams *)network->layers[layer].
> params;
> > if (conv_params->input_num != cur_channels){
> > return DNN_ERROR;
> > }
> > cur_channels = conv_params->output_num;
> > break;
> > case DEPTH_TO_SPACE:
> > -depth_to_space_params = (DepthToSpaceParams*)network->
> layers[layer].params;
> > +depth_to_space_params = (DepthToSpaceParams
> *)network->layers[layer].params;
> > if (cur_channels % (depth_to_space_params->block_size *
> depth_to_space_params->block_size) != 0){
> > return DNN_ERROR;
> > }
> > @@ -127,16 +127,16 @@ static DNNReturnType set_input_output_native(void*
> model, DNNData* input, DNNDat
> > // layers_num,layer_type,layer_parameterss,layer_type,layer_
> parameters...
> > // For CONV layer: activation_function, input_num, output_num,
> kernel_size, kernel, biases
> > // For DEPTH_TO_SPACE layer: block_size
> > -DNNModel* ff_dnn_load_model_native(const char* model_filename)
> > +DNNModel *ff_dnn_load_model_native(const char *model_filename)
> > {
> > -DNNModel* model = NULL;
> > -ConvolutionalNetwork* network = NULL;
> > -AVIOContext* model_file_context;
> > +DNNModel *model = NULL;
> > +ConvolutionalNetwork *network = NULL;
> > +AVIOContext *model_file_context;
> > int file_size, dnn_size, kernel_size, i;
> > int32_t layer;
> > LayerType layer_type;
> > -ConvolutionalParams* conv_params;
> > -DepthToSpaceParams* depth_to_space_params;
> > +ConvolutionalParams *conv_params;
> > +DepthToSpaceParams *depth_to_space_params;
> >
> > model = av_malloc(sizeof(DNNModel));
> >
Re: [FFmpeg-devel] [PATCH 2/7] libavfilter: Code style fixes for pointers in DNN module and sr filter.
2018-08-02 15:52 GMT-03:00 Sergey Lavrushkin :
> ---
> libavfilter/dnn_backend_native.c | 84 +++---
> libavfilter/dnn_backend_native.h | 8 +--
> libavfilter/dnn_backend_tf.c | 108
> +++
> libavfilter/dnn_backend_tf.h | 8 +--
> libavfilter/dnn_espcn.h | 6 +--
> libavfilter/dnn_interface.c | 4 +-
> libavfilter/dnn_interface.h | 16 +++---
> libavfilter/dnn_srcnn.h | 6 +--
> libavfilter/vf_sr.c | 60 +++---
> 9 files changed, 150 insertions(+), 150 deletions(-)
>
> diff --git a/libavfilter/dnn_backend_native.c
> b/libavfilter/dnn_backend_native.c
> index 3e6b86280d..baefea7fcb 100644
> --- a/libavfilter/dnn_backend_native.c
> +++ b/libavfilter/dnn_backend_native.c
> @@ -34,15 +34,15 @@ typedef enum {RELU, TANH, SIGMOID} ActivationFunc;
>
> typedef struct Layer{
> LayerType type;
> -float* output;
> -void* params;
> +float *output;
> +void *params;
> } Layer;
>
> typedef struct ConvolutionalParams{
> int32_t input_num, output_num, kernel_size;
> ActivationFunc activation;
> -float* kernel;
> -float* biases;
> +float *kernel;
> +float *biases;
> } ConvolutionalParams;
>
> typedef struct InputParams{
> @@ -55,16 +55,16 @@ typedef struct DepthToSpaceParams{
>
> // Represents simple feed-forward convolutional network.
> typedef struct ConvolutionalNetwork{
> -Layer* layers;
> +Layer *layers;
> int32_t layers_num;
> } ConvolutionalNetwork;
>
> -static DNNReturnType set_input_output_native(void* model, DNNData* input,
> DNNData* output)
> +static DNNReturnType set_input_output_native(void *model, DNNData *input,
> DNNData *output)
> {
> -ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
> -InputParams* input_params;
> -ConvolutionalParams* conv_params;
> -DepthToSpaceParams* depth_to_space_params;
> +ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
> +InputParams *input_params;
> +ConvolutionalParams *conv_params;
> +DepthToSpaceParams *depth_to_space_params;
> int cur_width, cur_height, cur_channels;
> int32_t layer;
>
> @@ -72,7 +72,7 @@ static DNNReturnType set_input_output_native(void* model,
> DNNData* input, DNNDat
> return DNN_ERROR;
> }
> else{
> -input_params = (InputParams*)network->layers[0].params;
> +input_params = (InputParams *)network->layers[0].params;
> input_params->width = cur_width = input->width;
> input_params->height = cur_height = input->height;
> input_params->channels = cur_channels = input->channels;
> @@ -88,14 +88,14 @@ static DNNReturnType set_input_output_native(void* model,
> DNNData* input, DNNDat
> for (layer = 1; layer < network->layers_num; ++layer){
> switch (network->layers[layer].type){
> case CONV:
> -conv_params =
> (ConvolutionalParams*)network->layers[layer].params;
> +conv_params = (ConvolutionalParams
> *)network->layers[layer].params;
> if (conv_params->input_num != cur_channels){
> return DNN_ERROR;
> }
> cur_channels = conv_params->output_num;
> break;
> case DEPTH_TO_SPACE:
> -depth_to_space_params =
> (DepthToSpaceParams*)network->layers[layer].params;
> +depth_to_space_params = (DepthToSpaceParams
> *)network->layers[layer].params;
> if (cur_channels % (depth_to_space_params->block_size *
> depth_to_space_params->block_size) != 0){
> return DNN_ERROR;
> }
> @@ -127,16 +127,16 @@ static DNNReturnType set_input_output_native(void*
> model, DNNData* input, DNNDat
> // layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
> // For CONV layer: activation_function, input_num, output_num, kernel_size,
> kernel, biases
> // For DEPTH_TO_SPACE layer: block_size
> -DNNModel* ff_dnn_load_model_native(const char* model_filename)
> +DNNModel *ff_dnn_load_model_native(const char *model_filename)
> {
> -DNNModel* model = NULL;
> -ConvolutionalNetwork* network = NULL;
> -AVIOContext* model_file_context;
> +DNNModel *model = NULL;
> +ConvolutionalNetwork *network = NULL;
> +AVIOContext *model_file_context;
> int file_size, dnn_size, kernel_size, i;
> int32_t layer;
> LayerType layer_type;
> -ConvolutionalParams* conv_params;
> -DepthToSpaceParams* depth_to_space_params;
> +ConvolutionalParams *conv_params;
> +DepthToSpaceParams *depth_to_space_params;
>
> model = av_malloc(sizeof(DNNModel));
> if (!model){
> @@ -155,7 +155,7 @@ DNNModel* ff_dnn_load_model_native(const char*
> model_filename)
> av_freep();
> return NULL;
> }
> -model->model = (void*)network;
> +model->model = (void *)network;
>
> network->layers_num = 1 +
[FFmpeg-devel] [PATCH 2/7] libavfilter: Code style fixes for pointers in DNN module and sr filter.
---
libavfilter/dnn_backend_native.c | 84 +++---
libavfilter/dnn_backend_native.h | 8 +--
libavfilter/dnn_backend_tf.c | 108 +++
libavfilter/dnn_backend_tf.h | 8 +--
libavfilter/dnn_espcn.h | 6 +--
libavfilter/dnn_interface.c | 4 +-
libavfilter/dnn_interface.h | 16 +++---
libavfilter/dnn_srcnn.h | 6 +--
libavfilter/vf_sr.c | 60 +++---
9 files changed, 150 insertions(+), 150 deletions(-)
diff --git a/libavfilter/dnn_backend_native.c b/libavfilter/dnn_backend_native.c
index 3e6b86280d..baefea7fcb 100644
--- a/libavfilter/dnn_backend_native.c
+++ b/libavfilter/dnn_backend_native.c
@@ -34,15 +34,15 @@ typedef enum {RELU, TANH, SIGMOID} ActivationFunc;
typedef struct Layer{
LayerType type;
-float* output;
-void* params;
+float *output;
+void *params;
} Layer;
typedef struct ConvolutionalParams{
int32_t input_num, output_num, kernel_size;
ActivationFunc activation;
-float* kernel;
-float* biases;
+float *kernel;
+float *biases;
} ConvolutionalParams;
typedef struct InputParams{
@@ -55,16 +55,16 @@ typedef struct DepthToSpaceParams{
// Represents simple feed-forward convolutional network.
typedef struct ConvolutionalNetwork{
-Layer* layers;
+Layer *layers;
int32_t layers_num;
} ConvolutionalNetwork;
-static DNNReturnType set_input_output_native(void* model, DNNData* input,
DNNData* output)
+static DNNReturnType set_input_output_native(void *model, DNNData *input,
DNNData *output)
{
-ConvolutionalNetwork* network = (ConvolutionalNetwork*)model;
-InputParams* input_params;
-ConvolutionalParams* conv_params;
-DepthToSpaceParams* depth_to_space_params;
+ConvolutionalNetwork *network = (ConvolutionalNetwork *)model;
+InputParams *input_params;
+ConvolutionalParams *conv_params;
+DepthToSpaceParams *depth_to_space_params;
int cur_width, cur_height, cur_channels;
int32_t layer;
@@ -72,7 +72,7 @@ static DNNReturnType set_input_output_native(void* model,
DNNData* input, DNNDat
return DNN_ERROR;
}
else{
-input_params = (InputParams*)network->layers[0].params;
+input_params = (InputParams *)network->layers[0].params;
input_params->width = cur_width = input->width;
input_params->height = cur_height = input->height;
input_params->channels = cur_channels = input->channels;
@@ -88,14 +88,14 @@ static DNNReturnType set_input_output_native(void* model,
DNNData* input, DNNDat
for (layer = 1; layer < network->layers_num; ++layer){
switch (network->layers[layer].type){
case CONV:
-conv_params = (ConvolutionalParams*)network->layers[layer].params;
+conv_params = (ConvolutionalParams *)network->layers[layer].params;
if (conv_params->input_num != cur_channels){
return DNN_ERROR;
}
cur_channels = conv_params->output_num;
break;
case DEPTH_TO_SPACE:
-depth_to_space_params =
(DepthToSpaceParams*)network->layers[layer].params;
+depth_to_space_params = (DepthToSpaceParams
*)network->layers[layer].params;
if (cur_channels % (depth_to_space_params->block_size *
depth_to_space_params->block_size) != 0){
return DNN_ERROR;
}
@@ -127,16 +127,16 @@ static DNNReturnType set_input_output_native(void* model,
DNNData* input, DNNDat
// layers_num,layer_type,layer_parameterss,layer_type,layer_parameters...
// For CONV layer: activation_function, input_num, output_num, kernel_size,
kernel, biases
// For DEPTH_TO_SPACE layer: block_size
-DNNModel* ff_dnn_load_model_native(const char* model_filename)
+DNNModel *ff_dnn_load_model_native(const char *model_filename)
{
-DNNModel* model = NULL;
-ConvolutionalNetwork* network = NULL;
-AVIOContext* model_file_context;
+DNNModel *model = NULL;
+ConvolutionalNetwork *network = NULL;
+AVIOContext *model_file_context;
int file_size, dnn_size, kernel_size, i;
int32_t layer;
LayerType layer_type;
-ConvolutionalParams* conv_params;
-DepthToSpaceParams* depth_to_space_params;
+ConvolutionalParams *conv_params;
+DepthToSpaceParams *depth_to_space_params;
model = av_malloc(sizeof(DNNModel));
if (!model){
@@ -155,7 +155,7 @@ DNNModel* ff_dnn_load_model_native(const char*
model_filename)
av_freep();
return NULL;
}
-model->model = (void*)network;
+model->model = (void *)network;
network->layers_num = 1 + (int32_t)avio_rl32(model_file_context);
dnn_size = 4;
@@ -251,10 +251,10 @@ DNNModel* ff_dnn_load_model_native(const char*
model_filename)
return model;
}
-static int set_up_conv_layer(Layer* layer, const float* kernel, const float*
biases, ActivationFunc activation,
+static
