SCM make_tensor(SCM scm_type, SCM scm_shape, SCM scm_size, SCM scm_source) { SCM retval; struct tf_tensor_t *self = (struct tf_tensor_t *)scm_gc_calloc(sizeof(struct tf_tensor_t), "make-tensor"); SCM_NEWSMOB(retval, tf_tensor_tag, self); int type = scm_to_int(scm_type); int num_dims = scm_to_int(scm_length(scm_shape)); int64_t *dims = scm_gc_malloc_pointerless(sizeof(int64_t) * num_dims, "make-tensor"); int count = 1; for (int i=0; i<num_dims; i++) { dims[i] = scm_to_int(scm_car(scm_shape)); count = count * dims[i]; scm_shape = scm_cdr(scm_shape); }; if (type == TF_STRING) { SCM* pointer = scm_to_pointer(scm_source); size_t encoded_size = 0; for (int i=0; i<count; i++) { encoded_size += TF_StringEncodedSize(scm_c_string_length(*pointer)) + 8; pointer++; }; self->tensor = TF_AllocateTensor(type, dims, num_dims, encoded_size); int64_t *offsets = TF_TensorData(self->tensor); int offset = 0; void *result = offsets + count; pointer = scm_to_pointer(scm_source); encoded_size = encoded_size - count * sizeof(int64_t); for (int i=0; i<count; i++) { char *str = scm_to_locale_string(*pointer); int len = TF_StringEncodedSize(scm_c_string_length(*pointer)); *offsets++ = offset; TF_StringEncode(str, scm_c_string_length(*pointer), result, encoded_size, status()); free(str); if (TF_GetCode(_status) != TF_OK) scm_misc_error("make-tensor", TF_Message(_status), SCM_EOL); offset += len; encoded_size -= len; result += len; pointer++; }; } else { self->tensor = TF_AllocateTensor(type, dims, num_dims, scm_to_int(scm_size)); memcpy(TF_TensorData(self->tensor), scm_to_pointer(scm_source), scm_to_int(scm_size)); }; return retval; }
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 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_Output input; 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[0] = 0; pads[1] = 0; pads[2] = pad; pads[3] = pad; pads[4] = pad; pads[5] = pad; pads[6] = 0; pads[7] = 0; TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } op_desc = TF_NewOperation(tf_model->graph, "MirrorPad", "mirror_pad"); input.oper = input_op; input.index = 0; TF_AddInput(op_desc, input); input.oper = op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); TF_SetAttrType(op_desc, "Tpaddings", TF_INT32); TF_SetAttrString(op_desc, "mode", "SYMMETRIC", 9); op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } return op; }
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 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; size_t len; op_desc = TF_NewOperation(tf_model->graph, "Const", name); TF_SetAttrType(op_desc, "dtype", TF_FLOAT); len = sizeof(float); for (dim = 0; dim < dims_len; ++dim){ len *= dims[dim]; } tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, len); memcpy(TF_TensorData(tensor), values, len); TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } return TF_FinishOperation(op_desc, tf_model->status); }
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; }
static DNNReturnType add_conv_layer(TFModel *tf_model, TF_Operation *transpose_op, TF_Operation **cur_op, ConvolutionalParams* params, const int layer) { TF_Operation *op; TF_OperationDescription *op_desc; TF_Output input; int64_t strides[] = {1, 1, 1, 1}; TF_Tensor *tensor; int64_t dims[4]; int dims_len; char name_buffer[NAME_BUFFER_SIZE]; int32_t size; size = params->input_num * params->output_num * params->kernel_size * params->kernel_size; input.index = 0; snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_kernel%d", layer); op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer); TF_SetAttrType(op_desc, "dtype", TF_FLOAT); dims[0] = params->output_num; dims[1] = params->kernel_size; dims[2] = params->kernel_size; dims[3] = params->input_num; dims_len = 4; tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, size * sizeof(float)); memcpy(TF_TensorData(tensor), params->kernel, size * sizeof(float)); TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } snprintf(name_buffer, NAME_BUFFER_SIZE, "transpose%d", layer); op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer); input.oper = op; TF_AddInput(op_desc, input); input.oper = transpose_op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); TF_SetAttrType(op_desc, "Tperm", TF_INT32); op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } snprintf(name_buffer, NAME_BUFFER_SIZE, "conv2d%d", layer); op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer); input.oper = *cur_op; TF_AddInput(op_desc, input); input.oper = op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); TF_SetAttrIntList(op_desc, "strides", strides, 4); TF_SetAttrString(op_desc, "padding", "VALID", 5); *cur_op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } snprintf(name_buffer, NAME_BUFFER_SIZE, "conv_biases%d", layer); op_desc = TF_NewOperation(tf_model->graph, "Const", name_buffer); TF_SetAttrType(op_desc, "dtype", TF_FLOAT); dims[0] = params->output_num; dims_len = 1; tensor = TF_AllocateTensor(TF_FLOAT, dims, dims_len, params->output_num * sizeof(float)); memcpy(TF_TensorData(tensor), params->biases, params->output_num * sizeof(float)); TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } snprintf(name_buffer, NAME_BUFFER_SIZE, "bias_add%d", layer); op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer); input.oper = *cur_op; TF_AddInput(op_desc, input); input.oper = op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); *cur_op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } snprintf(name_buffer, NAME_BUFFER_SIZE, "activation%d", layer); switch (params->activation){ case RELU: op_desc = TF_NewOperation(tf_model->graph, "Relu", name_buffer); break; case TANH: op_desc = TF_NewOperation(tf_model->graph, "Tanh", name_buffer); break; case SIGMOID: op_desc = TF_NewOperation(tf_model->graph, "Sigmoid", name_buffer); break; default: return DNN_ERROR; } input.oper = *cur_op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); *cur_op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return DNN_ERROR; } return DNN_SUCCESS; }
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_Output input; int64_t strides[] = {1, 1, 1, 1}; int32_t *transpose_perm; TF_Tensor *tensor; int64_t transpose_perm_shape[] = {4}; #define NAME_BUFF_SIZE 256 char name_buffer[NAME_BUFF_SIZE]; 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[0] = 1; transpose_perm[1] = 2; transpose_perm[2] = 3; transpose_perm[3] = 0; TF_SetAttrTensor(op_desc, "value", tensor, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } transpose_op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } input.index = 0; for (i = 0; i < layers_num; ++i){ snprintf(name_buffer, NAME_BUFF_SIZE, "conv_kernel%d", i); op = add_const_op(tf_model, consts[i << 1], consts_dims[i << 1], consts_dims_len[i << 1], name_buffer); if (TF_GetCode(tf_model->status) != TF_OK || op == NULL){ return NULL; } snprintf(name_buffer, NAME_BUFF_SIZE, "transpose%d", i); op_desc = TF_NewOperation(tf_model->graph, "Transpose", name_buffer); input.oper = op; TF_AddInput(op_desc, input); input.oper = transpose_op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); TF_SetAttrType(op_desc, "Tperm", TF_INT32); op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } snprintf(name_buffer, NAME_BUFF_SIZE, "conv2d%d", i); op_desc = TF_NewOperation(tf_model->graph, "Conv2D", name_buffer); input.oper = input_op; TF_AddInput(op_desc, input); input.oper = op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); TF_SetAttrIntList(op_desc, "strides", strides, 4); TF_SetAttrString(op_desc, "padding", "VALID", 5); input_op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } snprintf(name_buffer, NAME_BUFF_SIZE, "conv_biases%d", i); op = add_const_op(tf_model, consts[(i << 1) + 1], consts_dims[(i << 1) + 1], consts_dims_len[(i << 1) + 1], name_buffer); if (TF_GetCode(tf_model->status) != TF_OK || op == NULL){ return NULL; } snprintf(name_buffer, NAME_BUFF_SIZE, "bias_add%d", i); op_desc = TF_NewOperation(tf_model->graph, "BiasAdd", name_buffer); input.oper = input_op; TF_AddInput(op_desc, input); input.oper = op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); input_op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } snprintf(name_buffer, NAME_BUFF_SIZE, "activation%d", i); op_desc = TF_NewOperation(tf_model->graph, activations[i], name_buffer); input.oper = input_op; TF_AddInput(op_desc, input); TF_SetAttrType(op_desc, "T", TF_FLOAT); input_op = TF_FinishOperation(op_desc, tf_model->status); if (TF_GetCode(tf_model->status) != TF_OK){ return NULL; } } return input_op; }