From f4b3c0e55c84434eb897c2a4a1179cb1d202c52c Mon Sep 17 00:00:00 2001 From: "Guo, Yejun" Date: Mon, 21 Oct 2019 20:38:17 +0800 Subject: [PATCH] avfilter/dnn: add a new interface to query dnn model's input info to support dnn networks more general, we need to know the input info of the dnn model. background: The data type of dnn model's input could be float32, uint8 or fp16, etc. And the w/h of input image could be fixed or variable. Signed-off-by: Guo, Yejun Signed-off-by: Pedro Arthur --- libavfilter/dnn/dnn_backend_native.c | 24 ++++++++++++++++++++- libavfilter/dnn/dnn_backend_tf.c | 32 ++++++++++++++++++++++++++++ libavfilter/dnn_interface.h | 3 +++ 3 files changed, 58 insertions(+), 1 deletion(-) diff --git a/libavfilter/dnn/dnn_backend_native.c b/libavfilter/dnn/dnn_backend_native.c index add1db42cf..94634b3065 100644 --- a/libavfilter/dnn/dnn_backend_native.c +++ b/libavfilter/dnn/dnn_backend_native.c @@ -28,6 +28,28 @@ #include "dnn_backend_native_layer_conv2d.h" #include "dnn_backend_native_layers.h" +static DNNReturnType get_input_native(void *model, DNNData *input, const char *input_name) +{ + ConvolutionalNetwork *network = (ConvolutionalNetwork *)model; + + for (int i = 0; i < network->operands_num; ++i) { + DnnOperand *oprd = &network->operands[i]; + if (strcmp(oprd->name, input_name) == 0) { + if (oprd->type != DOT_INPUT) + return DNN_ERROR; + input->dt = oprd->data_type; + av_assert0(oprd->dims[0] == 1); + input->height = oprd->dims[1]; + input->width = oprd->dims[2]; + input->channels = oprd->dims[3]; + return DNN_SUCCESS; + } + } + + // do not find the input operand + return DNN_ERROR; +} + static DNNReturnType set_input_output_native(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output) { ConvolutionalNetwork *network = (ConvolutionalNetwork *)model; @@ -37,7 +59,6 @@ static DNNReturnType set_input_output_native(void *model, DNNData *input, const return DNN_ERROR; /* inputs */ - av_assert0(input->dt == DNN_FLOAT); for (int i = 0; i < network->operands_num; ++i) { oprd = &network->operands[i]; if (strcmp(oprd->name, input_name) == 0) { @@ -234,6 +255,7 @@ DNNModel *ff_dnn_load_model_native(const char *model_filename) } model->set_input_output = &set_input_output_native; + model->get_input = &get_input_native; return model; } diff --git a/libavfilter/dnn/dnn_backend_tf.c b/libavfilter/dnn/dnn_backend_tf.c index ed91d0500d..a921667424 100644 --- a/libavfilter/dnn/dnn_backend_tf.c +++ b/libavfilter/dnn/dnn_backend_tf.c @@ -105,6 +105,37 @@ static TF_Tensor *allocate_input_tensor(const DNNData *input) input_dims[1] * input_dims[2] * input_dims[3] * size); } +static DNNReturnType get_input_tf(void *model, DNNData *input, const char *input_name) +{ + TFModel *tf_model = (TFModel *)model; + TF_Status *status; + int64_t dims[4]; + + TF_Output tf_output; + tf_output.oper = TF_GraphOperationByName(tf_model->graph, input_name); + if (!tf_output.oper) + return DNN_ERROR; + + tf_output.index = 0; + input->dt = TF_OperationOutputType(tf_output); + + status = TF_NewStatus(); + TF_GraphGetTensorShape(tf_model->graph, tf_output, dims, 4, status); + if (TF_GetCode(status) != TF_OK){ + TF_DeleteStatus(status); + return DNN_ERROR; + } + TF_DeleteStatus(status); + + // currently only NHWC is supported + av_assert0(dims[0] == 1); + input->height = dims[1]; + input->width = dims[2]; + input->channels = dims[3]; + + return DNN_SUCCESS; +} + static DNNReturnType set_input_output_tf(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output) { TFModel *tf_model = (TFModel *)model; @@ -568,6 +599,7 @@ DNNModel *ff_dnn_load_model_tf(const char *model_filename) model->model = (void *)tf_model; model->set_input_output = &set_input_output_tf; + model->get_input = &get_input_tf; return model; } diff --git a/libavfilter/dnn_interface.h b/libavfilter/dnn_interface.h index fdefcb708b..b20e5c8fab 100644 --- a/libavfilter/dnn_interface.h +++ b/libavfilter/dnn_interface.h @@ -43,6 +43,9 @@ typedef struct DNNData{ typedef struct DNNModel{ // Stores model that can be different for different backends. void *model; + // Gets model input information + // Just reuse struct DNNData here, actually the DNNData.data field is not needed. + DNNReturnType (*get_input)(void *model, DNNData *input, const char *input_name); // Sets model input and output. // Should be called at least once before model execution. DNNReturnType (*set_input_output)(void *model, DNNData *input, const char *input_name, const char **output_names, uint32_t nb_output);