SCM tf_from_tensor(SCM scm_self)
{
struct tf_tensor_t *self = get_tf_tensor(scm_self);
int type = TF_TensorType(self->tensor);
int num_dims = TF_NumDims(self->tensor);
int count = 1;
SCM scm_shape = SCM_EOL;
for (int i=num_dims - 1; i>=0; i--) {
scm_shape = scm_cons(scm_from_int(TF_Dim(self->tensor, i)), scm_shape);
count = count * TF_Dim(self->tensor, i);
};
size_t size = TF_TensorByteSize(self->tensor);
void *data;
if (type == TF_STRING) {
int64_t *offsets = TF_TensorData(self->tensor);
void *pointer = offsets + count;
size_t str_len;
data = scm_gc_malloc(sizeof(SCM) * count, "from-tensor");
SCM *result = data;
for (int i=0; i<count; i++) {
const char *str;
size_t len;
TF_StringDecode(pointer + *offsets, size - *offsets, &str, &len, status());
if (TF_GetCode(_status) != TF_OK)
scm_misc_error("from-tensor", TF_Message(_status), SCM_EOL);
*result++ = scm_from_locale_stringn(str, len);
offsets++;
};
} else {
data = scm_gc_malloc_pointerless(size, "from-tensor");
memcpy(data, TF_TensorData(self->tensor), size);
};
return scm_list_3(scm_from_int(type),
scm_shape,
scm_from_pointer(data, NULL));
}
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;
}
