SCM tf_run(SCM scm_session, SCM scm_input, SCM scm_output)
{
SCM retval;
if (scm_is_true(scm_list_p(scm_output))) {
struct tf_session_t *session = get_tf_session(scm_session);
int ninputs = scm_ilength(scm_input);
TF_Output *inputs = scm_gc_malloc(sizeof(TF_Output) * ninputs, "tf-run");
TF_Tensor **input_values = scm_gc_malloc(sizeof(TF_Tensor *) * ninputs, "tf-run");
for (int i=0; i<ninputs; i++) {
memcpy(&inputs[i], &get_tf_output(scm_caar(scm_input))->output, sizeof(TF_Output));
input_values[i] = get_tf_tensor(scm_cdar(scm_input))->tensor;
scm_input = scm_cdr(scm_input);
};
int noutputs = scm_ilength(scm_output);
TF_Output *output = scm_gc_malloc(sizeof(TF_Output) * noutputs, "tf-run");
TF_Tensor **output_values = scm_gc_malloc(sizeof(TF_Tensor *) * noutputs, "tf-run");
for (int i=0; i<noutputs; i++) {
output[i] = get_tf_output(scm_car(scm_output))->output;
scm_output = scm_cdr(scm_output);
};
TF_SessionRun(session->session, NULL, inputs, input_values, ninputs, output, output_values, noutputs, NULL, 0, NULL, status());
if (TF_GetCode(_status) != TF_OK)
scm_misc_error("tf-run", TF_Message(_status), SCM_EOL);
retval = SCM_EOL;
for (int i=noutputs-1; i>=0; i--) {
SCM element;
struct tf_tensor_t *result = (struct tf_tensor_t *)scm_gc_calloc(sizeof(struct tf_tensor_t), "make-tensor");
SCM_NEWSMOB(element, tf_tensor_tag, result);
result->tensor = output_values[i];
retval = scm_cons(element, retval);
};
} else
retval = scm_car(tf_run(scm_session, scm_input, scm_list_1(scm_output)));
return retval;
}
DNNReturnType ff_dnn_execute_model_tf(const DNNModel *model)
{
TFModel *tf_model = (TFModel *)model->model;
TF_Tensor *output_tensor;
TF_SessionRun(tf_model->session, NULL,
&tf_model->input, &tf_model->input_tensor, 1,
&tf_model->output, &output_tensor, 1,
NULL, 0, NULL, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
else{
memcpy(tf_model->output_data->data, TF_TensorData(output_tensor),
tf_model->output_data->height * tf_model->output_data->width *
tf_model->output_data->channels * sizeof(float));
TF_DeleteTensor(output_tensor);
return DNN_SUCCESS;
}
}
static DNNReturnType set_input_output_tf(void *model, DNNData *input, DNNData *output)
{
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;
// Input operation should be named 'x'
tf_model->input.oper = TF_GraphOperationByName(tf_model->graph, "x");
if (!tf_model->input.oper){
return DNN_ERROR;
}
tf_model->input.index = 0;
if (tf_model->input_tensor){
TF_DeleteTensor(tf_model->input_tensor);
}
tf_model->input_tensor = TF_AllocateTensor(TF_FLOAT, input_dims, 4,
input_dims[1] * input_dims[2] * input_dims[3] * sizeof(float));
if (!tf_model->input_tensor){
return DNN_ERROR;
}
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");
if (!tf_model->output.oper){
return DNN_ERROR;
}
tf_model->output.index = 0;
if (tf_model->session){
TF_CloseSession(tf_model->session, tf_model->status);
TF_DeleteSession(tf_model->session, tf_model->status);
}
sess_opts = TF_NewSessionOptions();
tf_model->session = TF_NewSession(tf_model->graph, sess_opts, tf_model->status);
TF_DeleteSessionOptions(sess_opts);
if (TF_GetCode(tf_model->status) != TF_OK)
{
return DNN_ERROR;
}
// Run initialization operation with name "init" if it is present in graph
if (init_op){
TF_SessionRun(tf_model->session, NULL,
NULL, NULL, 0,
NULL, NULL, 0,
&init_op, 1, NULL, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK)
{
return DNN_ERROR;
}
}
// Execute network to get output height, width and number of channels
TF_SessionRun(tf_model->session, NULL,
&tf_model->input, &tf_model->input_tensor, 1,
&tf_model->output, &output_tensor, 1,
NULL, 0, NULL, tf_model->status);
if (TF_GetCode(tf_model->status) != TF_OK){
return DNN_ERROR;
}
else{
output->height = TF_Dim(output_tensor, 1);
output->width = TF_Dim(output_tensor, 2);
output->channels = TF_Dim(output_tensor, 3);
output->data = av_malloc(output->height * output->width * output->channels * sizeof(float));
if (!output->data){
return DNN_ERROR;
}
tf_model->output_data = output;
TF_DeleteTensor(output_tensor);
}
return DNN_SUCCESS;
}
