infer.py

import os
import time
import numpy as np
from eval_helper import get_nmsed_box
from eval_helper import get_dt_res
from eval_helper import draw_bounding_box_on_image
import paddle
import paddle.fluid as fluid
import reader
from utility import print_arguments, parse_args
import models.model_builder as model_builder
import models.resnet as resnet
import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params
from config import cfg


def infer():

    if '2014' in cfg.dataset:
        test_list = 'annotations/instances_val2014.json'
    elif '2017' in cfg.dataset:
        test_list = 'annotations/instances_val2017.json'

    cocoGt = COCO(os.path.join(cfg.data_dir, test_list))
    numId_to_catId_map = {i + 1: v for i, v in enumerate(cocoGt.getCatIds())}
    category_ids = cocoGt.getCatIds()
    label_list = {
        item['id']: item['name']
        for item in cocoGt.loadCats(category_ids)
    }
    label_list[0] = ['background']
    image_shape = [3, cfg.TEST.max_size, cfg.TEST.max_size]
    class_nums = cfg.class_num

    model = model_builder.FasterRCNN(
        add_conv_body_func=resnet.add_ResNet50_conv4_body,
        add_roi_box_head_func=resnet.add_ResNet_roi_conv5_head,
        use_pyreader=False,
        is_train=False)
    model.build_model(image_shape)
    rpn_rois, confs, locs = model.eval_out()
    place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
    # yapf: disable
    if cfg.pretrained_model:
        def if_exist(var):
            return os.path.exists(os.path.join(cfg.pretrained_model, var.name))
        fluid.io.load_vars(exe, cfg.pretrained_model, predicate=if_exist)
    # yapf: enable
    infer_reader = reader.infer()
    feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())

    dts_res = []
    fetch_list = [rpn_rois, confs, locs]
    data = next(infer_reader())
    im_info = [data[0][1]]
    rpn_rois_v, confs_v, locs_v = exe.run(
        fetch_list=[v.name for v in fetch_list],
        feed=feeder.feed(data),
        return_numpy=False)
    new_lod, nmsed_out = get_nmsed_box(rpn_rois_v, confs_v, locs_v, class_nums,
                                       im_info, numId_to_catId_map)
    path = os.path.join(cfg.image_path, cfg.image_name)
    draw_bounding_box_on_image(path, nmsed_out, cfg.draw_threshold, label_list)


if __name__ == '__main__':
    args = parse_args()
    print_arguments(args)
    infer()