eval.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import numpy as np
import time
import sys
import argparse
import functools
import math

import paddle
import paddle.inference as paddle_infer
from utility import add_arguments, print_arguments
import imagenet_dataset as dataset


def eval(args):
    if '2' in paddle.__version__.split('.')[0]:
        config = paddle.inference.Config(
            os.path.join(args.model_path, args.model_filename),
            os.path.join(args.model_path, args.params_filename))
    else:
        model_prefix = args.model_filename.split('.')[0]
        config = paddle.inference.Config(os.path.join(args.model_path, model_prefix))
    config.enable_mkldnn()
    config.switch_ir_optim(False)

    predictor = paddle_infer.create_predictor(config)

    input_names = predictor.get_input_names()
    input_handle = predictor.get_input_handle(input_names[0])
    output_names = predictor.get_output_names()
    output_handle = predictor.get_output_handle(output_names[0])

    val_dataset = dataset.ImageNetDataset(data_dir=args.data_dir, mode='val')
    eval_loader = paddle.io.DataLoader(
        val_dataset, batch_size=args.batch_size, drop_last=True)

    cost_time = 0.
    total_num = 0.
    correct_1_num = 0
    correct_5_num = 0
    for batch_id, data in enumerate(eval_loader()):
        img_np = np.array([tensor.numpy() for tensor in data[0]])
        label_np = np.array([tensor.numpy() for tensor in data[1]])

        input_handle.reshape(img_np.shape)
        input_handle.copy_from_cpu(img_np)

        t1 = time.time()
        predictor.run()
        t2 = time.time()
        cost_time += (t2 - t1)

        output_data = output_handle.copy_to_cpu()

        for i in range(len(label_np)):
            label = label_np[i][0]
            result = output_data[i, :]
            index = result.argsort()
            total_num += 1
            if index[-1] == label:
                correct_1_num += 1
            if label in index[-5:]:
                correct_5_num += 1

        if batch_id % 10 == 0:
            acc1 = correct_1_num / total_num
            acc5 = correct_5_num / total_num
            avg_time = cost_time / total_num
            print(
                "batch_id {}, acc1 {:.3f}, acc5 {:.3f}, avg time {:.5f} sec/img".
                format(batch_id, acc1, acc5, avg_time))

        if args.test_samples > 0 and \
            (batch_id + 1)* args.batch_size >= args.test_samples:
            break

    acc1 = correct_1_num / total_num
    acc5 = correct_5_num / total_num
    print("End test: test_acc1 {:.3f}, test_acc5 {:.5f}".format(acc1, acc5))


def main():
    parser = argparse.ArgumentParser(description=__doc__)
    add_arg = functools.partial(add_arguments, argparser=parser)
    add_arg('model_path', str, "", "The inference model path.")
    add_arg('model_filename', str, "model.pdmodel", "model filename")
    add_arg('params_filename', str, "model.pdiparams", "params filename")
    add_arg('data_dir', str, "/dataset/ILSVRC2012/",
            "The ImageNet dataset root dir.")
    add_arg('test_samples', int, -1,
            "Test samples. If set -1, use all test samples")
    add_arg('batch_size', int, 16, "Batch size.")

    args = parser.parse_args()
    print_arguments(args)

    eval(args)


if __name__ == '__main__':
    main()