/*
* @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;
}
