2018-08-06 18:11 GMT-03:00 Sergey Lavrushkin <dual...@gmail.com>:
> Updated patch.
>
> 2018-08-06 17:55 GMT+03:00 Pedro Arthur <bygran...@gmail.com>:
>
>> 2018-08-02 15:52 GMT-03:00 Sergey Lavrushkin <dual...@gmail.com>:
>> > ---
>> > 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,
>> > +static int set_up_conv_layer(Layer *layer, const float *kernel, const
>> float *biases, ActivationFunc activation,
>> > int32_t input_num, int32_t output_num,
>> int32_t size)
>> > {
>> > - ConvolutionalParams* conv_params;
>> > + ConvolutionalParams *conv_params;
>> > int kernel_size;
>> >
>> > conv_params = av_malloc(sizeof(ConvolutionalParams));
>> > @@ -282,11 +282,11 @@ static int set_up_conv_layer(Layer* layer, const
>> float* kernel, const float* bia
>> > return DNN_SUCCESS;
>> > }
>> >
>> > -DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type)
>> > +DNNModel *ff_dnn_load_default_model_native(DNNDefaultModel model_type)
>> > {
>> > - DNNModel* model = NULL;
>> > - ConvolutionalNetwork* network = NULL;
>> > - DepthToSpaceParams* depth_to_space_params;
>> > + DNNModel *model = NULL;
>> > + ConvolutionalNetwork *network = NULL;
>> > + DepthToSpaceParams *depth_to_space_params;
>> > int32_t layer;
>> >
>> > model = av_malloc(sizeof(DNNModel));
>> > @@ -299,7 +299,7 @@ DNNModel*
>> > ff_dnn_load_default_model_native(DNNDefaultModel
>> model_type)
>> > av_freep(&model);
>> > return NULL;
>> > }
>> > - model->model = (void*)network;
>> > + model->model = (void *)network;
>> >
>> > switch (model_type){
>> > case DNN_SRCNN:
>> > @@ -365,7 +365,7 @@ DNNModel*
>> > ff_dnn_load_default_model_native(DNNDefaultModel
>> model_type)
>> >
>> > #define CLAMP_TO_EDGE(x, w) ((x) < 0 ? 0 : ((x) >= (w) ? (w - 1) : (x)))
>> >
>> > -static void convolve(const float* input, float* output, const
>> ConvolutionalParams* conv_params, int width, int height)
>> > +static void convolve(const float *input, float *output, const
>> ConvolutionalParams *conv_params, int width, int height)
>> > {
>> > int y, x, n_filter, ch, kernel_y, kernel_x;
>> > int radius = conv_params->kernel_size >> 1;
>> > @@ -403,7 +403,7 @@ static void convolve(const float* input, float*
>> output, const ConvolutionalParam
>> > }
>> > }
>> >
>> > -static void depth_to_space(const float* input, float* output, int
>> block_size, int width, int height, int channels)
>> > +static void depth_to_space(const float *input, float *output, int
>> block_size, int width, int height, int channels)
>> > {
>> > int y, x, by, bx, ch;
>> > int new_channels = channels / (block_size * block_size);
>> > @@ -426,20 +426,20 @@ static void depth_to_space(const float* input,
>> float* output, int block_size, in
>> > }
>> > }
>> >
>> > -DNNReturnType ff_dnn_execute_model_native(const DNNModel* model)
>> > +DNNReturnType ff_dnn_execute_model_native(const DNNModel *model)
>> > {
>> > - ConvolutionalNetwork* network = (ConvolutionalNetwork*)model->
>> model;
>> > + ConvolutionalNetwork *network = (ConvolutionalNetwork
>> *)model->model;
>> > int cur_width, cur_height, cur_channels;
>> > int32_t layer;
>> > - InputParams* input_params;
>> > - ConvolutionalParams* conv_params;
>> > - DepthToSpaceParams* depth_to_space_params;
>> > + InputParams *input_params;
>> > + ConvolutionalParams *conv_params;
>> > + DepthToSpaceParams *depth_to_space_params;
>> >
>> > if (network->layers_num <= 0 || network->layers[0].type != INPUT ||
>> !network->layers[0].output){
>> > return DNN_ERROR;
>> > }
>> > else{
>> > - input_params = (InputParams*)network->layers[0].params;
>> > + input_params = (InputParams *)network->layers[0].params;
>> > cur_width = input_params->width;
>> > cur_height = input_params->height;
>> > cur_channels = input_params->channels;
>> > @@ -451,12 +451,12 @@ DNNReturnType ff_dnn_execute_model_native(const
>> DNNModel* model)
>> > }
>> > switch (network->layers[layer].type){
>> > case CONV:
>> > - conv_params = (ConvolutionalParams*)network-
>> >layers[layer].params;
>> > + conv_params = (ConvolutionalParams *)network->layers[layer].
>> params;
>> > convolve(network->layers[layer - 1].output,
>> network->layers[layer].output, conv_params, cur_width, cur_height);
>> > 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;
>> > depth_to_space(network->layers[layer - 1].output,
>> network->layers[layer].output,
>> > depth_to_space_params->block_size,
>> cur_width, cur_height, cur_channels);
>> > cur_height *= depth_to_space_params->block_size;
>> > @@ -471,19 +471,19 @@ DNNReturnType ff_dnn_execute_model_native(const
>> DNNModel* model)
>> > return DNN_SUCCESS;
>> > }
>> >
>> > -void ff_dnn_free_model_native(DNNModel** model)
>> > +void ff_dnn_free_model_native(DNNModel **model)
>> > {
>> > - ConvolutionalNetwork* network;
>> > - ConvolutionalParams* conv_params;
>> > + ConvolutionalNetwork *network;
>> > + ConvolutionalParams *conv_params;
>> > int32_t layer;
>> >
>> > if (*model)
>> > {
>> > - network = (ConvolutionalNetwork*)(*model)->model;
>> > + network = (ConvolutionalNetwork *)(*model)->model;
>> > for (layer = 0; layer < network->layers_num; ++layer){
>> > av_freep(&network->layers[layer].output);
>> > if (network->layers[layer].type == CONV){
>> > - conv_params = (ConvolutionalParams*)network-
>> >layers[layer].params;
>> > + conv_params = (ConvolutionalParams
>> *)network->layers[layer].params;
>> > av_freep(&conv_params->kernel);
>> > av_freep(&conv_params->biases);
>> > }
>> > diff --git a/libavfilter/dnn_backend_native.h b/libavfilter/dnn_backend_
>> native.h
>> > index 599c1302e2..adbb7088b4 100644
>> > --- a/libavfilter/dnn_backend_native.h
>> > +++ b/libavfilter/dnn_backend_native.h
>> > @@ -29,12 +29,12 @@
>> >
>> > #include "dnn_interface.h"
>> >
>> > -DNNModel* ff_dnn_load_model_native(const char* model_filename);
>> > +DNNModel *ff_dnn_load_model_native(const char *model_filename);
>> >
>> > -DNNModel* ff_dnn_load_default_model_native(DNNDefaultModel model_type);
>> > +DNNModel *ff_dnn_load_default_model_native(DNNDefaultModel model_type);
>> >
>> > -DNNReturnType ff_dnn_execute_model_native(const DNNModel* model);
>> > +DNNReturnType ff_dnn_execute_model_native(const DNNModel *model);
>> >
>> > -void ff_dnn_free_model_native(DNNModel** model);
>> > +void ff_dnn_free_model_native(DNNModel **model);
>> >
>> > #endif
>> > diff --git a/libavfilter/dnn_backend_tf.c b/libavfilter/dnn_backend_tf.c
>> > index 21516471c3..6307c794a5 100644
>> > --- a/libavfilter/dnn_backend_tf.c
>> > +++ b/libavfilter/dnn_backend_tf.c
>> > @@ -31,24 +31,24 @@
>> > #include <tensorflow/c/c_api.h>
>> >
>> > typedef struct TFModel{
>> > - TF_Graph* graph;
>> > - TF_Session* session;
>> > - TF_Status* status;
>> > + TF_Graph *graph;
>> > + TF_Session *session;
>> > + TF_Status *status;
>> > TF_Output input, output;
>> > - TF_Tensor* input_tensor;
>> > - DNNData* output_data;
>> > + TF_Tensor *input_tensor;
>> > + DNNData *output_data;
>> > } TFModel;
>> >
>> > -static void free_buffer(void* data, size_t length)
>> > +static void free_buffer(void *data, size_t length)
>> > {
>> > av_freep(&data);
>> > }
>> >
>> > -static TF_Buffer* read_graph(const char* model_filename)
>> > +static TF_Buffer *read_graph(const char *model_filename)
>> > {
>> > - TF_Buffer* graph_buf;
>> > - unsigned char* graph_data = NULL;
>> > - AVIOContext* model_file_context;
>> > + TF_Buffer *graph_buf;
>> > + unsigned char *graph_data = NULL;
>> > + AVIOContext *model_file_context;
>> > long size, bytes_read;
>> >
>> > if (avio_open(&model_file_context, model_filename, AVIO_FLAG_READ)
>> < 0){
>> > @@ -70,20 +70,20 @@ static TF_Buffer* read_graph(const char*
>> model_filename)
>> > }
>> >
>> > graph_buf = TF_NewBuffer();
>> > - graph_buf->data = (void*)graph_data;
>> > + graph_buf->data = (void *)graph_data;
>> > graph_buf->length = size;
>> > graph_buf->data_deallocator = free_buffer;
>> >
>> > return graph_buf;
>> > }
>> >
>> > -static DNNReturnType set_input_output_tf(void* model, DNNData* input,
>> DNNData* output)
>> > +static DNNReturnType set_input_output_tf(void *model, DNNData *input,
>> DNNData *output)
>> > {
>> > - TFModel* tf_model = (TFModel*)model;
>> > + TFModel *tf_model = (TFModel *)model;
>> > int64_t input_dims[] = {1, input->height, input->width,
>> input->channels};
>> > - TF_SessionOptions* sess_opts;
>> > - const TF_Operation* init_op = TF_GraphOperationByName(tf_model->graph,
>> "init");
>> > - TF_Tensor* output_tensor;
>> > + TF_SessionOptions *sess_opts;
>> > + const TF_Operation *init_op = TF_GraphOperationByName(tf_model->graph,
>> "init");
>> > + TF_Tensor *output_tensor;
>> >
>> > // Input operation should be named 'x'
>> > tf_model->input.oper = TF_GraphOperationByName(tf_model->graph,
>> "x");
>> > @@ -99,7 +99,7 @@ static DNNReturnType set_input_output_tf(void* model,
>> DNNData* input, DNNData* o
>> > if (!tf_model->input_tensor){
>> > return DNN_ERROR;
>> > }
>> > - input->data = (float*)TF_TensorData(tf_model->input_tensor);
>> > + input->data = (float *)TF_TensorData(tf_model->input_tensor);
>> >
>> > // Output operation should be named 'y'
>> > tf_model->output.oper = TF_GraphOperationByName(tf_model->graph,
>> "y");
>> > @@ -156,12 +156,12 @@ static DNNReturnType set_input_output_tf(void*
>> model, DNNData* input, DNNData* o
>> > return DNN_SUCCESS;
>> > }
>> >
>> > -DNNModel* ff_dnn_load_model_tf(const char* model_filename)
>> > +DNNModel *ff_dnn_load_model_tf(const char *model_filename)
>> > {
>> > - DNNModel* model = NULL;
>> > - TFModel* tf_model = NULL;
>> > - TF_Buffer* graph_def;
>> > - TF_ImportGraphDefOptions* graph_opts;
>> > + DNNModel *model = NULL;
>> > + TFModel *tf_model = NULL;
>> > + TF_Buffer *graph_def;
>> > + TF_ImportGraphDefOptions *graph_opts;
>> >
>> > model = av_malloc(sizeof(DNNModel));
>> > if (!model){
>> > @@ -197,25 +197,25 @@ DNNModel* ff_dnn_load_model_tf(const char*
>> model_filename)
>> > return NULL;
>> > }
>> >
>> > - model->model = (void*)tf_model;
>> > + model->model = (void *)tf_model;
>> > model->set_input_output = &set_input_output_tf;
>> >
>> > return model;
>> > }
>> >
>> > -static TF_Operation* add_pad_op(TFModel* tf_model, TF_Operation*
>> input_op, int32_t pad)
>> > +static TF_Operation *add_pad_op(TFModel *tf_model, TF_Operation
>> *input_op, int32_t pad)
>> > {
>> > - TF_OperationDescription* op_desc;
>> > - TF_Operation* op;
>> > - TF_Tensor* tensor;
>> > + TF_OperationDescription *op_desc;
>> > + TF_Operation *op;
>> > + TF_Tensor *tensor;
>> > TF_Output input;
>> > - int32_t* pads;
>> > + int32_t *pads;
>> > int64_t pads_shape[] = {4, 2};
>> >
>> > op_desc = TF_NewOperation(tf_model->graph, "Const", "pads");
>> > TF_SetAttrType(op_desc, "dtype", TF_INT32);
>> > tensor = TF_AllocateTensor(TF_INT32, pads_shape, 2, 4 * 2 *
>> sizeof(int32_t));
>> > - pads = (int32_t*)TF_TensorData(tensor);
>> > + pads = (int32_t *)TF_TensorData(tensor);
>> > pads[0] = 0; pads[1] = 0;
>> > pads[2] = pad; pads[3] = pad;
>> > pads[4] = pad; pads[5] = pad;
>> > @@ -246,11 +246,11 @@ static TF_Operation* add_pad_op(TFModel* tf_model,
>> TF_Operation* input_op, int32
>> > return op;
>> > }
>> >
>> > -static TF_Operation* add_const_op(TFModel* tf_model, const float*
>> values, const int64_t* dims, int dims_len, const char* name)
>> > +static TF_Operation *add_const_op(TFModel *tf_model, const float
>> *values, const int64_t *dims, int dims_len, const char *name)
>> > {
>> > int dim;
>> > - TF_OperationDescription* op_desc;
>> > - TF_Tensor* tensor;
>> > + TF_OperationDescription *op_desc;
>> > + TF_Tensor *tensor;
>> > size_t len;
>> >
>> > op_desc = TF_NewOperation(tf_model->graph, "Const", name);
>> > @@ -269,25 +269,25 @@ static TF_Operation* add_const_op(TFModel*
>> tf_model, const float* values, const
>> > return TF_FinishOperation(op_desc, tf_model->status);
>> > }
>> >
>> > -static TF_Operation* add_conv_layers(TFModel* tf_model, const float**
>> consts, const int64_t** consts_dims,
>> > - const int* consts_dims_len, const
>> char** activations,
>> > - TF_Operation* input_op, int
>> layers_num)
>> > +static TF_Operation* add_conv_layers(TFModel *tf_model, const float
>> **consts, const int64_t **consts_dims,
>> > + const int *consts_dims_len, const
>> char **activations,
>> > + TF_Operation *input_op, int
>> layers_num)
>> > {
>> > int i;
>> > - TF_OperationDescription* op_desc;
>> > - TF_Operation* op;
>> > - TF_Operation* transpose_op;
>> > + TF_OperationDescription *op_desc;
>> > + TF_Operation *op;
>> > + TF_Operation *transpose_op;
>> > TF_Output input;
>> > int64_t strides[] = {1, 1, 1, 1};
>> > - int32_t* transpose_perm;
>> > - TF_Tensor* tensor;
>> > + int32_t *transpose_perm;
>> > + TF_Tensor *tensor;
>> > int64_t transpose_perm_shape[] = {4};
>> > char name_buffer[256];
>> >
>> > op_desc = TF_NewOperation(tf_model->graph, "Const",
>> "transpose_perm");
>> > TF_SetAttrType(op_desc, "dtype", TF_INT32);
>> > tensor = TF_AllocateTensor(TF_INT32, transpose_perm_shape, 1, 4 *
>> sizeof(int32_t));
>> > - transpose_perm = (int32_t*)TF_TensorData(tensor);
>> > + transpose_perm = (int32_t *)TF_TensorData(tensor);
>> > transpose_perm[0] = 1;
>> > transpose_perm[1] = 2;
>> > transpose_perm[2] = 3;
>> > @@ -368,13 +368,13 @@ static TF_Operation* add_conv_layers(TFModel*
>> tf_model, const float** consts, co
>> > return input_op;
>> > }
>> >
>> > -DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
>> > +DNNModel *ff_dnn_load_default_model_tf(DNNDefaultModel model_type)
>> > {
>> > - DNNModel* model = NULL;
>> > - TFModel* tf_model = NULL;
>> > - TF_OperationDescription* op_desc;
>> > - TF_Operation* op;
>> > - TF_Operation* const_ops_buffer[6];
>> > + DNNModel *model = NULL;
>> > + TFModel *tf_model = NULL;
>> > + TF_OperationDescription *op_desc;
>> > + TF_Operation *op;
>> > + TF_Operation *const_ops_buffer[6];
>> > TF_Output input;
>> > int64_t input_shape[] = {1, -1, -1, 1};
>> >
>> > @@ -460,16 +460,16 @@ DNNModel*
>> > ff_dnn_load_default_model_tf(DNNDefaultModel
>> model_type)
>> > CLEANUP_ON_ERROR(tf_model, model);
>> > }
>> >
>> > - model->model = (void*)tf_model;
>> > + model->model = (void *)tf_model;
>> > model->set_input_output = &set_input_output_tf;
>> >
>> > return model;
>> > }
>> >
>> > -DNNReturnType ff_dnn_execute_model_tf(const DNNModel* model)
>> > +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
>> > {
>> > - TFModel* tf_model = (TFModel*)model->model;
>> > - TF_Tensor* output_tensor;
>> > + TFModel *tf_model = (TFModel *)model->model;
>> > + TF_Tensor *output_tensor;
>> >
>> > TF_SessionRun(tf_model->session, NULL,
>> > &tf_model->input, &tf_model->input_tensor, 1,
>> > @@ -489,12 +489,12 @@ DNNReturnType ff_dnn_execute_model_tf(const
>> DNNModel* model)
>> > }
>> > }
>> >
>> > -void ff_dnn_free_model_tf(DNNModel** model)
>> > +void ff_dnn_free_model_tf(DNNModel **model)
>> > {
>> > - TFModel* tf_model;
>> > + TFModel *tf_model;
>> >
>> > if (*model){
>> > - tf_model = (TFModel*)(*model)->model;
>> > + tf_model = (TFModel *)(*model)->model;
>> > if (tf_model->graph){
>> > TF_DeleteGraph(tf_model->graph);
>> > }
>> > diff --git a/libavfilter/dnn_backend_tf.h b/libavfilter/dnn_backend_tf.h
>> > index 08e4a568b3..357a82d948 100644
>> > --- a/libavfilter/dnn_backend_tf.h
>> > +++ b/libavfilter/dnn_backend_tf.h
>> > @@ -29,12 +29,12 @@
>> >
>> > #include "dnn_interface.h"
>> >
>> > -DNNModel* ff_dnn_load_model_tf(const char* model_filename);
>> > +DNNModel *ff_dnn_load_model_tf(const char *model_filename);
>> >
>> > -DNNModel* ff_dnn_load_default_model_tf(DNNDefaultModel model_type);
>> > +DNNModel *ff_dnn_load_default_model_tf(DNNDefaultModel model_type);
>> >
>> > -DNNReturnType ff_dnn_execute_model_tf(const DNNModel* model);
>> > +DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model);
>> >
>> > -void ff_dnn_free_model_tf(DNNModel** model);
>> > +void ff_dnn_free_model_tf(DNNModel **model);
>> >
>> > #endif
>> > diff --git a/libavfilter/dnn_espcn.h b/libavfilter/dnn_espcn.h
>> > index 315ecf031d..a0dd61cd0d 100644
>> > --- a/libavfilter/dnn_espcn.h
>> > +++ b/libavfilter/dnn_espcn.h
>> > @@ -5398,7 +5398,7 @@ static const long int espcn_conv3_bias_dims[] = {
>> > 4
>> > };
>> >
>> > -static const float* espcn_consts[] = {
>> > +static const float *espcn_consts[] = {
>> > espcn_conv1_kernel,
>> > espcn_conv1_bias,
>> > espcn_conv2_kernel,
>> > @@ -5407,7 +5407,7 @@ static const float* espcn_consts[] = {
>> > espcn_conv3_bias
>> > };
>> >
>> > -static const long int* espcn_consts_dims[] = {
>> > +static const long int *espcn_consts_dims[] = {
>> > espcn_conv1_kernel_dims,
>> > espcn_conv1_bias_dims,
>> > espcn_conv2_kernel_dims,
>> > @@ -5429,7 +5429,7 @@ static const char espcn_tanh[] = "Tanh";
>> >
>> > static const char espcn_sigmoid[] = "Sigmoid";
>> >
>> > -static const char* espcn_activations[] = {
>> > +static const char *espcn_activations[] = {
>> > espcn_tanh,
>> > espcn_tanh,
>> > espcn_sigmoid
>> > diff --git a/libavfilter/dnn_interface.c b/libavfilter/dnn_interface.c
>> > index 87c90526be..ca7d6d1ea5 100644
>> > --- a/libavfilter/dnn_interface.c
>> > +++ b/libavfilter/dnn_interface.c
>> > @@ -28,9 +28,9 @@
>> > #include "dnn_backend_tf.h"
>> > #include "libavutil/mem.h"
>> >
>> > -DNNModule* ff_get_dnn_module(DNNBackendType backend_type)
>> > +DNNModule *ff_get_dnn_module(DNNBackendType backend_type)
>> > {
>> > - DNNModule* dnn_module;
>> > + DNNModule *dnn_module;
>> >
>> > dnn_module = av_malloc(sizeof(DNNModule));
>> > if(!dnn_module){
>> > diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h
>> > index 6b820d1d5b..a69717ae62 100644
>> > --- a/libavfilter/dnn_interface.h
>> > +++ b/libavfilter/dnn_interface.h
>> > @@ -33,31 +33,31 @@ typedef enum {DNN_NATIVE, DNN_TF} DNNBackendType;
>> > typedef enum {DNN_SRCNN, DNN_ESPCN} DNNDefaultModel;
>> >
>> > typedef struct DNNData{
>> > - float* data;
>> > + float *data;
>> > int width, height, channels;
>> > } DNNData;
>> >
>> > typedef struct DNNModel{
>> > // Stores model that can be different for different backends.
>> > - void* model;
>> > + void *model;
>> > // Sets model input and output, while allocating additional memory
>> for intermediate calculations.
>> > // Should be called at least once before model execution.
>> > - DNNReturnType (*set_input_output)(void* model, DNNData* input,
>> DNNData* output);
>> > + DNNReturnType (*set_input_output)(void *model, DNNData *input,
>> DNNData *output);
>> > } DNNModel;
>> >
>> > // Stores pointers to functions for loading, executing, freeing DNN
>> models for one of the backends.
>> > typedef struct DNNModule{
>> > // Loads model and parameters from given file. Returns NULL if it
>> is not possible.
>> > - DNNModel* (*load_model)(const char* model_filename);
>> > + DNNModel *(*load_model)(const char *model_filename);
>> > // Loads one of the default models
>> > - DNNModel* (*load_default_model)(DNNDefaultModel model_type);
>> > + DNNModel *(*load_default_model)(DNNDefaultModel model_type);
>> > // Executes model with specified input and output. Returns
>> DNN_ERROR otherwise.
>> > - DNNReturnType (*execute_model)(const DNNModel* model);
>> > + DNNReturnType (*execute_model)(const DNNModel *model);
>> > // Frees memory allocated for model.
>> > - void (*free_model)(DNNModel** model);
>> > + void (*free_model)(DNNModel **model);
>> > } DNNModule;
>> >
>> > // Initializes DNNModule depending on chosen backend.
>> > -DNNModule* ff_get_dnn_module(DNNBackendType backend_type);
>> > +DNNModule *ff_get_dnn_module(DNNBackendType backend_type);
>> >
>> > #endif
>> > diff --git a/libavfilter/dnn_srcnn.h b/libavfilter/dnn_srcnn.h
>> > index 7ec11654b3..26143654b8 100644
>> > --- a/libavfilter/dnn_srcnn.h
>> > +++ b/libavfilter/dnn_srcnn.h
>> > @@ -2110,7 +2110,7 @@ static const long int srcnn_conv3_bias_dims[] = {
>> > 1
>> > };
>> >
>> > -static const float* srcnn_consts[] = {
>> > +static const float *srcnn_consts[] = {
>> > srcnn_conv1_kernel,
>> > srcnn_conv1_bias,
>> > srcnn_conv2_kernel,
>> > @@ -2119,7 +2119,7 @@ static const float* srcnn_consts[] = {
>> > srcnn_conv3_bias
>> > };
>> >
>> > -static const long int* srcnn_consts_dims[] = {
>> > +static const long int *srcnn_consts_dims[] = {
>> > srcnn_conv1_kernel_dims,
>> > srcnn_conv1_bias_dims,
>> > srcnn_conv2_kernel_dims,
>> > @@ -2139,7 +2139,7 @@ static const int srcnn_consts_dims_len[] = {
>> >
>> > static const char srcnn_relu[] = "Relu";
>> >
>> > -static const char* srcnn_activations[] = {
>> > +static const char *srcnn_activations[] = {
>> > srcnn_relu,
>> > srcnn_relu,
>> > srcnn_relu
>> > diff --git a/libavfilter/vf_sr.c b/libavfilter/vf_sr.c
>> > index f3ca9a09a8..944a0e28e7 100644
>> > --- a/libavfilter/vf_sr.c
>> > +++ b/libavfilter/vf_sr.c
>> > @@ -39,13 +39,13 @@ typedef struct SRContext {
>> > const AVClass *class;
>> >
>> > SRModel model_type;
>> > - char* model_filename;
>> > + char *model_filename;
>> > DNNBackendType backend_type;
>> > - DNNModule* dnn_module;
>> > - DNNModel* model;
>> > + DNNModule *dnn_module;
>> > + DNNModel *model;
>> > DNNData input, output;
>> > int scale_factor;
>> > - struct SwsContext* sws_context;
>> > + struct SwsContext *sws_context;
>> > int sws_slice_h;
>> > } SRContext;
>> >
>> > @@ -67,9 +67,9 @@ static const AVOption sr_options[] = {
>> >
>> > AVFILTER_DEFINE_CLASS(sr);
>> >
>> > -static av_cold int init(AVFilterContext* context)
>> > +static av_cold int init(AVFilterContext *context)
>> > {
>> > - SRContext* sr_context = context->priv;
>> > + SRContext *sr_context = context->priv;
>> >
>> > sr_context->dnn_module = ff_get_dnn_module(sr_context->
>> backend_type);
>> > if (!sr_context->dnn_module){
>> > @@ -98,12 +98,12 @@ static av_cold int init(AVFilterContext* context)
>> > return 0;
>> > }
>> >
>> > -static int query_formats(AVFilterContext* context)
>> > +static int query_formats(AVFilterContext *context)
>> > {
>> > const enum AVPixelFormat pixel_formats[] = {AV_PIX_FMT_YUV420P,
>> AV_PIX_FMT_YUV422P, AV_PIX_FMT_YUV444P,
>> > AV_PIX_FMT_YUV410P,
>> AV_PIX_FMT_YUV411P, AV_PIX_FMT_GRAY8,
>> > AV_PIX_FMT_NONE};
>> > - AVFilterFormats* formats_list;
>> > + AVFilterFormats *formats_list;
>> >
>> > formats_list = ff_make_format_list(pixel_formats);
>> > if (!formats_list){
>> > @@ -113,11 +113,11 @@ static int query_formats(AVFilterContext* context)
>> > return ff_set_common_formats(context, formats_list);
>> > }
>> >
>> > -static int config_props(AVFilterLink* inlink)
>> > +static int config_props(AVFilterLink *inlink)
>> > {
>> > - AVFilterContext* context = inlink->dst;
>> > - SRContext* sr_context = context->priv;
>> > - AVFilterLink* outlink = context->outputs[0];
>> > + AVFilterContext *context = inlink->dst;
>> > + SRContext *sr_context = context->priv;
>> > + AVFilterLink *outlink = context->outputs[0];
>> > DNNReturnType result;
>> > int sws_src_h, sws_src_w, sws_dst_h, sws_dst_w;
>> >
>> > @@ -202,18 +202,18 @@ static int config_props(AVFilterLink* inlink)
>> > }
>> >
>> > typedef struct ThreadData{
>> > - uint8_t* data;
>> > + uint8_t *data;
>> > int data_linesize, height, width;
>> > } ThreadData;
>> >
>> > -static int uint8_to_float(AVFilterContext* context, void* arg, int
>> jobnr, int nb_jobs)
>> > +static int uint8_to_float(AVFilterContext *context, void *arg, int
>> jobnr, int nb_jobs)
>> > {
>> > - SRContext* sr_context = context->priv;
>> > - const ThreadData* td = arg;
>> > + SRContext *sr_context = context->priv;
>> > + const ThreadData *td = arg;
>> > const int slice_start = (td->height * jobnr ) / nb_jobs;
>> > const int slice_end = (td->height * (jobnr + 1)) / nb_jobs;
>> > - const uint8_t* src = td->data + slice_start * td->data_linesize;
>> > - float* dst = sr_context->input.data + slice_start * td->width;
>> > + const uint8_t *src = td->data + slice_start * td->data_linesize;
>> > + float *dst = sr_context->input.data + slice_start * td->width;
>> > int y, x;
>> >
>> > for (y = slice_start; y < slice_end; ++y){
>> > @@ -227,14 +227,14 @@ static int uint8_to_float(AVFilterContext*
>> context, void* arg, int jobnr, int nb
>> > return 0;
>> > }
>> >
>> > -static int float_to_uint8(AVFilterContext* context, void* arg, int
>> jobnr, int nb_jobs)
>> > +static int float_to_uint8(AVFilterContext *context, void *arg, int
>> jobnr, int nb_jobs)
>> > {
>> > - SRContext* sr_context = context->priv;
>> > - const ThreadData* td = arg;
>> > + SRContext *sr_context = context->priv;
>> > + const ThreadData *td = arg;
>> > const int slice_start = (td->height * jobnr ) / nb_jobs;
>> > const int slice_end = (td->height * (jobnr + 1)) / nb_jobs;
>> > - const float* src = sr_context->output.data + slice_start *
>> td->width;
>> > - uint8_t* dst = td->data + slice_start * td->data_linesize;
>> > + const float *src = sr_context->output.data + slice_start *
>> td->width;
>> > + uint8_t *dst = td->data + slice_start * td->data_linesize;
>> > int y, x;
>> >
>> > for (y = slice_start; y < slice_end; ++y){
>> > @@ -248,12 +248,12 @@ static int float_to_uint8(AVFilterContext*
>> context, void* arg, int jobnr, int nb
>> > return 0;
>> > }
>> >
>> > -static int filter_frame(AVFilterLink* inlink, AVFrame* in)
>> > +static int filter_frame(AVFilterLink *inlink, AVFrame *in)
>> > {
>> > - AVFilterContext* context = inlink->dst;
>> > - SRContext* sr_context = context->priv;
>> > - AVFilterLink* outlink = context->outputs[0];
>> > - AVFrame* out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
>> > + AVFilterContext *context = inlink->dst;
>> > + SRContext *sr_context = context->priv;
>> > + AVFilterLink *outlink = context->outputs[0];
>> > + AVFrame *out = ff_get_video_buffer(outlink, outlink->w, outlink->h);
>> > ThreadData td;
>> > int nb_threads;
>> > DNNReturnType dnn_result;
>> > @@ -307,9 +307,9 @@ static int filter_frame(AVFilterLink* inlink,
>> AVFrame* in)
>> > return ff_filter_frame(outlink, out);
>> > }
>> >
>> > -static av_cold void uninit(AVFilterContext* context)
>> > +static av_cold void uninit(AVFilterContext *context)
>> > {
>> > - SRContext* sr_context = context->priv;
>> > + SRContext *sr_context = context->priv;
>> >
>> > if (sr_context->dnn_module){
>> > (sr_context->dnn_module->free_model)(&sr_context->model);
>> > --
>> > 2.14.1
>> >
>> > _______________________________________________
>> > ffmpeg-devel mailing list
>> > ffmpeg-devel@ffmpeg.org
>> > http://ffmpeg.org/mailman/listinfo/ffmpeg-devel
>>
>> LGTM.
>> I intend to push it by tomorrow.
>>
>
> _______________________________________________
> ffmpeg-devel mailing list
> ffmpeg-devel@ffmpeg.org
> http://ffmpeg.org/mailman/listinfo/ffmpeg-devel
>
Pushed.
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