SCM tf_set_attr_tensor(SCM scm_description, SCM scm_name, SCM scm_value) { struct tf_description_t *self = get_tf_description(scm_description); struct tf_tensor_t *value = get_tf_tensor(scm_value); char *name = scm_to_locale_string(scm_name); TF_SetAttrTensor(self->description, name, value->tensor, status()); free(name); if (TF_GetCode(_status) != TF_OK) scm_misc_error("tf-set-attr-tensor", TF_Message(_status), SCM_EOL); return SCM_UNDEFINED; }
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; }
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 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; }