/*
* @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;
}
TEST(GradientMachine, testPredict) {
//! TODO(yuyang18): Test GPU Code.
paddle::TrainerConfigHelper config("./test_predict_network.py");
std::string buffer;
ASSERT_TRUE(config.getModelConfig().SerializeToString(&buffer));
paddle_gradient_machine machine;
ASSERT_EQ(kPD_NO_ERROR,
paddle_gradient_machine_create_for_inference(
&machine, &buffer[0], (int)buffer.size()));
std::unique_ptr<paddle::GradientMachine> gm(
paddle::GradientMachine::create(config.getModelConfig()));
ASSERT_NE(nullptr, gm);
gm->randParameters();
gm->saveParameters("./");
ASSERT_EQ(kPD_NO_ERROR,
paddle_gradient_machine_load_parameter_from_disk(machine, "./"));
paddle_gradient_machine machineSlave;
ASSERT_EQ(kPD_NO_ERROR,
paddle_gradient_machine_create_shared_param(
machine, &buffer[0], (int)buffer.size(), &machineSlave));
std::swap(machineSlave, machine);
paddle_arguments outArgs = paddle_arguments_create_none();
paddle_arguments inArgs = paddle_arguments_create_none();
ASSERT_EQ(kPD_NO_ERROR, paddle_arguments_resize(inArgs, 1));
paddle_matrix mat = paddle_matrix_create(1, 100, false);
static_assert(std::is_same<paddle_real, paddle::real>::value, "");
auto data = randomBuffer(100);
paddle_real* rowPtr;
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_get_row(mat, 0, &rowPtr));
memcpy(rowPtr, data.data(), data.size() * sizeof(paddle_real));
ASSERT_EQ(kPD_NO_ERROR, paddle_arguments_set_value(inArgs, 0, mat));
ASSERT_EQ(kPD_NO_ERROR,
paddle_gradient_machine_forward(machine, inArgs, outArgs, false));
uint64_t sz;
ASSERT_EQ(kPD_NO_ERROR, paddle_arguments_get_size(outArgs, &sz));
ASSERT_EQ(1UL, sz);
ASSERT_EQ(kPD_NO_ERROR, paddle_arguments_get_value(outArgs, 0, mat));
std::vector<paddle::Argument> paddleInArgs;
std::vector<paddle::Argument> paddleOutArgs;
paddleInArgs.resize(1);
paddleInArgs[0].value =
paddle::Matrix::create(data.data(), 1, 100, false, false);
gm->forward(paddleInArgs, &paddleOutArgs, paddle::PASS_TEST);
auto matPaddle = paddleOutArgs[0].value;
uint64_t height, width;
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_get_shape(mat, &height, &width));
ASSERT_EQ(matPaddle->getHeight(), height);
ASSERT_EQ(matPaddle->getWidth(), width);
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_get_row(mat, 0, &rowPtr));
for (size_t i = 0; i < width; ++i) {
ASSERT_NEAR(matPaddle->getData()[i], rowPtr[i], 1e-5);
}
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_destroy(mat));
ASSERT_EQ(kPD_NO_ERROR, paddle_arguments_destroy(inArgs));
ASSERT_EQ(kPD_NO_ERROR, paddle_arguments_destroy(outArgs));
std::swap(machineSlave, machine);
ASSERT_EQ(kPD_NO_ERROR, paddle_gradient_machine_destroy(machineSlave));
ASSERT_EQ(kPD_NO_ERROR, paddle_gradient_machine_destroy(machine));
}
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;
}