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* dep
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(&model); > return NULL; > } > -model->model = (void*)network; > +model->model = (void *)network; > > network->layers_num
[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(&model); 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, +stat