/* * @brief It is an simple inference example that runs multi-threads on a GPU. * Each thread holds it own local gradient_machine but shares the same * parameters. * If you want to run on different GPUs, you need to launch * multi-processes or set trainer_count > 1. */ void* thread_main(void* gm_ptr) { // Initialize the thread environment of Paddle. CHECK(paddle_init_thread()); paddle_gradient_machine machine = (paddle_gradient_machine)(gm_ptr); // Create input arguments. paddle_arguments in_args = paddle_arguments_create_none(); // Create input matrix. paddle_matrix mat = paddle_matrix_create(/* sample_num */ 1, /* size */ 784, /* useGPU */ true); // Create output arguments. paddle_arguments out_args = paddle_arguments_create_none(); // Create output matrix. paddle_matrix prob = paddle_matrix_create_none(); // CPU buffer to cache the input and output. paddle_real* cpu_input = (paddle_real*)malloc(784 * sizeof(paddle_real)); paddle_real* cpu_output = (paddle_real*)malloc(10 * sizeof(paddle_real)); for (int iter = 0; iter < NUM_ITER; ++iter) { // There is only one input layer of this network. CHECK(paddle_arguments_resize(in_args, 1)); CHECK(paddle_arguments_set_value(in_args, 0, mat)); for (int i = 0; i < 784; ++i) { cpu_input[i] = rand() / ((float)RAND_MAX); } CHECK(paddle_matrix_set_value(mat, cpu_input)); CHECK(paddle_gradient_machine_forward(machine, in_args, out_args, /* isTrain */ false)); CHECK(paddle_arguments_get_value(out_args, 0, prob)); CHECK(paddle_matrix_get_value(prob, cpu_output)); pthread_mutex_lock(&mutex); printf("Prob: "); for (int i = 0; i < 10; ++i) { printf("%.2f ", cpu_output[i]); } printf("\n"); pthread_mutex_unlock(&mutex); } CHECK(paddle_matrix_destroy(prob)); CHECK(paddle_arguments_destroy(out_args)); CHECK(paddle_matrix_destroy(mat)); CHECK(paddle_arguments_destroy(in_args)); CHECK(paddle_gradient_machine_destroy(machine)); free(cpu_input); free(cpu_output); return NULL; }
void* thread_main(void* gm_ptr) { paddle_gradient_machine machine = (paddle_gradient_machine)(gm_ptr); paddle_arguments in_args = paddle_arguments_create_none(); // Create input matrix. paddle_matrix mat = paddle_matrix_create(/* sample_num */ 1, /* size */ 784, /* useGPU */ false); paddle_arguments out_args = paddle_arguments_create_none(); paddle_matrix prob = paddle_matrix_create_none(); for (int iter = 0; iter < NUM_ITER; ++iter) { // There is only one input of this network. CHECK(paddle_arguments_resize(in_args, 1)); paddle_real* array; // Get First row. CHECK(paddle_matrix_get_row(mat, 0, &array)); for (int i = 0; i < 784; ++i) { array[i] = rand() / ((float)RAND_MAX); } CHECK(paddle_arguments_set_value(in_args, 0, mat)); CHECK(paddle_gradient_machine_forward(machine, in_args, out_args, /* isTrain */ false)); CHECK(paddle_arguments_get_value(out_args, 0, prob)); CHECK(paddle_matrix_get_row(prob, 0, &array)); pthread_mutex_lock(&mutex); printf("Prob: "); for (int i = 0; i < 10; ++i) { printf("%.2f ", array[i]); } printf("\n"); pthread_mutex_unlock(&mutex); } CHECK(paddle_matrix_destroy(prob)); CHECK(paddle_arguments_destroy(out_args)); CHECK(paddle_matrix_destroy(mat)); CHECK(paddle_arguments_destroy(in_args)); CHECK(paddle_gradient_machine_destroy(machine)); return NULL; }
int main() { // Initalize Paddle char* argv[] = {"--use_gpu=False"}; CHECK(paddle_init(1, (char**)argv)); // Reading config binary file. It is generated by `convert_protobin.sh` long size; void* buf = read_config(CONFIG_BIN, &size); // Create a gradient machine for inference. paddle_gradient_machine machine; CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size)); CHECK(paddle_gradient_machine_randomize_param(machine)); // Loading parameter. Uncomment the following line and change the directory. // CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, // "./some_where_to_params")); paddle_arguments in_args = paddle_arguments_create_none(); // There is only one input of this network. CHECK(paddle_arguments_resize(in_args, 1)); // Create input matrix. paddle_matrix mat = paddle_matrix_create(/* sample_num */ 1, /* size */ 784, /* useGPU */ false); srand(time(0)); paddle_real* array; // Get First row. CHECK(paddle_matrix_get_row(mat, 0, &array)); for (int i = 0; i < 784; ++i) { array[i] = rand() / ((float)RAND_MAX); } CHECK(paddle_arguments_set_value(in_args, 0, mat)); paddle_arguments out_args = paddle_arguments_create_none(); CHECK(paddle_gradient_machine_forward(machine, in_args, out_args, /* isTrain */ false)); paddle_matrix prob = paddle_matrix_create_none(); CHECK(paddle_arguments_get_value(out_args, 0, prob)); uint64_t height; uint64_t width; CHECK(paddle_matrix_get_shape(prob, &height, &width)); CHECK(paddle_matrix_get_row(prob, 0, &array)); printf("Prob: \n"); for (int i = 0; i < height * width; ++i) { printf("%.4f ", array[i]); if ((i + 1) % width == 0) { printf("\n"); } } printf("\n"); CHECK(paddle_matrix_destroy(prob)); CHECK(paddle_arguments_destroy(out_args)); CHECK(paddle_matrix_destroy(mat)); CHECK(paddle_arguments_destroy(in_args)); CHECK(paddle_gradient_machine_destroy(machine)); return 0; }
int main() { // Initalize Paddle char* argv[] = {"--use_gpu=False"}; CHECK(paddle_init(1, (char**)argv)); // Read the binary configuration file which is generated by // `convert_protobin.sh` long size; void* buf = read_config(CONFIG_BIN, &size); // Create the gradient machine for inference. paddle_gradient_machine machine; CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size)); CHECK(paddle_gradient_machine_randomize_param(machine)); // Load the trained parameters. Uncomment the following line and change the // directory as needed. // CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, // "./some_where_to_params")); paddle_arguments in_args = paddle_arguments_create_none(); // There is only one input of this network. CHECK(paddle_arguments_resize(in_args, 1)); // Create the input matrix. paddle_matrix mat = paddle_matrix_create_sparse(1, 784, 3, true, false); srand(time(0)); paddle_real* array; int colBuf[] = {9, 93, 109}; int rowBuf[] = {0, sizeof(colBuf) / sizeof(int)}; CHECK(paddle_matrix_sparse_copy_from(mat, rowBuf, sizeof(rowBuf) / sizeof(int), colBuf, sizeof(colBuf) / sizeof(int), NULL, 0)); CHECK(paddle_arguments_set_value(in_args, 0, mat)); paddle_arguments out_args = paddle_arguments_create_none(); CHECK(paddle_gradient_machine_forward(machine, in_args, out_args, /* isTrain */ false)); paddle_matrix prob = paddle_matrix_create_none(); CHECK(paddle_arguments_get_value(out_args, 0, prob)); CHECK(paddle_matrix_get_row(prob, 0, &array)); printf("Prob: "); for (int i = 0; i < 10; ++i) { printf("%.2f ", array[i]); } printf("\n"); CHECK(paddle_matrix_destroy(prob)); CHECK(paddle_arguments_destroy(out_args)); CHECK(paddle_matrix_destroy(mat)); CHECK(paddle_arguments_destroy(in_args)); CHECK(paddle_gradient_machine_destroy(machine)); return 0; }