# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ CenterNet evaluation script. """ import os import time import copy import json import argparse import cv2 from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval from mindspore import context from mindspore.common.tensor import Tensor from mindspore.train.serialization import load_checkpoint, load_param_into_net import mindspore.log as logger from src import COCOHP, CenterNetMultiPoseEval from src import convert_eval_format, post_process, merge_outputs from src import visual_image from src.config import dataset_config, net_config, eval_config _current_dir = os.path.dirname(os.path.realpath(__file__)) parser = argparse.ArgumentParser(description='CenterNet evaluation') parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'CPU'], help='device where the code will be implemented. (Default: Ascend)') parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--load_checkpoint_path", type=str, default="", help="Load checkpoint file path") parser.add_argument("--data_dir", type=str, default="", help="Dataset directory, " "the absolute image path is joined by the data_dir " "and the relative path in anno_path") parser.add_argument("--run_mode", type=str, default="test", help="test or validation, default is test.") parser.add_argument("--visual_image", type=str, default="false", help="Visulize the ground truth and predicted image") parser.add_argument("--enable_eval", type=str, default="true", help="Whether evaluate accuracy after prediction") parser.add_argument("--save_result_dir", type=str, default="", help="The path to save the predict results") args_opt = parser.parse_args() def predict(): ''' Predict function ''' context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target) if args_opt.device_target == "Ascend": context.set_context(device_id=args_opt.device_id) enable_nms_fp16 = True else: enable_nms_fp16 = False logger.info("Begin creating {} dataset".format(args_opt.run_mode)) coco = COCOHP(dataset_config, run_mode=args_opt.run_mode, net_opt=net_config, enable_visual_image=(args_opt.visual_image == "true"), save_path=args_opt.save_result_dir,) coco.init(args_opt.data_dir, keep_res=eval_config.keep_res) dataset = coco.create_eval_dataset() net_for_eval = CenterNetMultiPoseEval(net_config, eval_config.K, enable_nms_fp16) net_for_eval.set_train(False) param_dict = load_checkpoint(args_opt.load_checkpoint_path) load_param_into_net(net_for_eval, param_dict) # save results save_path = os.path.join(args_opt.save_result_dir, args_opt.run_mode) if not os.path.exists(save_path): os.makedirs(save_path) if args_opt.visual_image == "true": save_pred_image_path = os.path.join(save_path, "pred_image") if not os.path.exists(save_pred_image_path): os.makedirs(save_pred_image_path) save_gt_image_path = os.path.join(save_path, "gt_image") if not os.path.exists(save_gt_image_path): os.makedirs(save_gt_image_path) total_nums = dataset.get_dataset_size() print("\n========================================\n") print("Total images num: ", total_nums) print("Processing, please wait a moment.") pred_annos = {"images": [], "annotations": []} index = 0 for data in dataset.create_dict_iterator(num_epochs=1): index += 1 image = data['image'] image_id = data['image_id'].asnumpy().reshape((-1))[0] # run prediction start = time.time() detections = [] for scale in eval_config.multi_scales: images, meta = coco.pre_process_for_test(image.asnumpy(), image_id, scale) detection = net_for_eval(Tensor(images)) dets = post_process(detection.asnumpy(), meta, scale) detections.append(dets) end = time.time() print("Image {}/{} id: {} cost time {} ms".format(index, total_nums, image_id, (end - start) * 1000.)) # post-process detections = merge_outputs(detections, eval_config.soft_nms) # get prediction result pred_json = convert_eval_format(detections, image_id) gt_image_info = coco.coco.loadImgs([image_id]) for image_info in pred_json["images"]: pred_annos["images"].append(image_info) for image_anno in pred_json["annotations"]: pred_annos["annotations"].append(image_anno) if args_opt.visual_image == "true": img_file = os.path.join(coco.image_path, gt_image_info[0]['file_name']) gt_image = cv2.imread(img_file) if args_opt.run_mode != "test": annos = coco.coco.loadAnns(coco.anns[image_id]) visual_image(copy.deepcopy(gt_image), annos, save_gt_image_path) anno = copy.deepcopy(pred_json["annotations"]) visual_image(gt_image, anno, save_pred_image_path, score_threshold=eval_config.score_thresh) # save results save_path = os.path.join(args_opt.save_result_dir, args_opt.run_mode) if not os.path.exists(save_path): os.makedirs(save_path) pred_anno_file = os.path.join(save_path, '{}_pred_result.json').format(args_opt.run_mode) json.dump(pred_annos, open(pred_anno_file, 'w')) pred_res_file = os.path.join(save_path, '{}_pred_eval.json').format(args_opt.run_mode) json.dump(pred_annos["annotations"], open(pred_res_file, 'w')) if args_opt.run_mode != "test" and args_opt.enable_eval: run_eval(coco.annot_path, pred_res_file) def run_eval(gt_anno, pred_anno): """evaluation by coco api""" coco = COCO(gt_anno) coco_dets = coco.loadRes(pred_anno) coco_eval = COCOeval(coco, coco_dets, "keypoints") coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() coco_eval = COCOeval(coco, coco_dets, "bbox") coco_eval.evaluate() coco_eval.accumulate() coco_eval.summarize() if __name__ == "__main__": predict()