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117 lines
4.3 KiB
117 lines
4.3 KiB
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include <paddle/capi.h>
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#include <time.h>
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#include "../common/common.h"
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// Modify this path as needed.
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#define CONFIG_BIN "./trainer_config.bin"
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// Modify this path as needed.
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// This demo assumes that merged model is not used, then this path is the
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// directory storing all the trained parameters.
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// If the model is trained by PaddlePaddle V2 API, the model is saved as
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// a compressed file. You need to uncompress the compressed file first.
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#define MODEL_PATH "models/pass_4"
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int main() {
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// Initalize the PaddlePaddle runtime environment.
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char* argv[] = {"--use_gpu=False"};
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CHECK(paddle_init(1, (char**)argv));
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// Read the binary configuration file generated by `convert_protobin.sh`
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long size;
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void* buf = read_config(CONFIG_BIN, &size);
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// Create the gradient machine for inference.
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paddle_gradient_machine machine;
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CHECK(paddle_gradient_machine_create_for_inference(&machine, buf, (int)size));
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// Load the trained model. Modify the parameter MODEL_PATH to set the correct
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// path of the trained model.
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CHECK(paddle_gradient_machine_load_parameter_from_disk(machine, MODEL_PATH));
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// Inputs and outputs of the network are organized as paddle_arguments object
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// in C-API. In the comments below, "argument" specifically means one input of
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// the neural network in PaddlePaddle C-API.
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paddle_arguments in_args = paddle_arguments_create_none();
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// There is only one data layer in this demo MNIST network, invoke this
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// function to create one argument.
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CHECK(paddle_arguments_resize(in_args, 1));
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// Each argument needs one matrix or one ivector (integer vector, for sparse
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// index input, usually used in NLP task) to holds the real input data.
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// In the comments below, "matrix" specifically means the object needed by
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// argument to hold the data. Here we create the matrix for the above created
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// agument to store the testing samples.
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paddle_matrix mat =
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paddle_matrix_create(/* height = batch size */ 1,
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/* width = dimensionality of the data layer */ 784,
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/* whether to use GPU */ false);
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paddle_real* array;
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// Get the pointer pointing to the start address of the first row of the
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// created matrix.
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CHECK(paddle_matrix_get_row(mat, 0, &array));
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// Fill the matrix with a randomly generated test sample.
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srand(time(0));
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for (int i = 0; i < 784; ++i) {
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array[i] = rand() / ((float)RAND_MAX);
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}
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// Assign the matrix to the argument.
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CHECK(paddle_arguments_set_value(in_args, 0, mat));
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// Create the output argument.
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paddle_arguments out_args = paddle_arguments_create_none();
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// Invoke the forward computation.
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CHECK(paddle_gradient_machine_forward(machine,
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in_args,
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out_args,
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/* is train taks or not */ false));
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// Create the matrix to hold the forward result of the neural network.
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paddle_matrix prob = paddle_matrix_create_none();
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// Access the matrix of the output argument, the predicted result is stored in
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// which.
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CHECK(paddle_arguments_get_value(out_args, 0, prob));
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uint64_t height;
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uint64_t width;
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CHECK(paddle_matrix_get_shape(prob, &height, &width));
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CHECK(paddle_matrix_get_row(prob, 0, &array));
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printf("Prob: \n");
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for (int i = 0; i < height * width; ++i) {
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printf("%.4f ", array[i]);
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if ((i + 1) % width == 0) {
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printf("\n");
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}
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}
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printf("\n");
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// The cleaning up.
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CHECK(paddle_matrix_destroy(prob));
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CHECK(paddle_arguments_destroy(out_args));
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CHECK(paddle_matrix_destroy(mat));
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CHECK(paddle_arguments_destroy(in_args));
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CHECK(paddle_gradient_machine_destroy(machine));
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return 0;
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}
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