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135 lines
5.6 KiB
135 lines
5.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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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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# less 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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# ============================================================================
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"""Evaluation for MaskRcnn"""
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import os
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import argparse
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import time
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import numpy as np
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from pycocotools.coco import COCO
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from mindspore import context, Tensor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.common import set_seed
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from src.maskrcnn_mobilenetv1.mask_rcnn_mobilenetv1 import Mask_Rcnn_Mobilenetv1
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from src.config import config
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from src.dataset import data_to_mindrecord_byte_image, create_maskrcnn_dataset
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from src.util import coco_eval, bbox2result_1image, results2json, get_seg_masks
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set_seed(1)
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parser = argparse.ArgumentParser(description="MaskRcnn evaluation")
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parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
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parser.add_argument("--ann_file", type=str, default="val.json", help="Ann file, default is val.json.")
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parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
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def MaskRcnn_eval(dataset_path, ckpt_path, ann_file):
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"""MaskRcnn evaluation."""
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ds = create_maskrcnn_dataset(dataset_path, batch_size=config.test_batch_size, is_training=False)
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net = Mask_Rcnn_Mobilenetv1(config)
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param_dict = load_checkpoint(ckpt_path)
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load_param_into_net(net, param_dict)
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net.set_train(False)
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eval_iter = 0
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total = ds.get_dataset_size()
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outputs = []
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dataset_coco = COCO(ann_file)
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print("\n========================================\n")
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print("total images num: ", total)
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print("Processing, please wait a moment.")
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max_num = 128
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for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
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img_data = data['image']
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img_metas = data['image_shape']
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gt_bboxes = data['box']
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gt_labels = data['label']
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gt_num = data['valid_num']
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gt_mask = data["mask"]
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start = time.time()
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# run net
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output = net(Tensor(img_data), Tensor(img_metas), Tensor(gt_bboxes), Tensor(gt_labels), Tensor(gt_num),
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Tensor(gt_mask))
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end = time.time()
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print("Iter {} cost time {}".format(eval_iter, end - start))
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# output
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all_bbox = output[0]
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all_label = output[1]
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all_mask = output[2]
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all_mask_fb = output[3]
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for j in range(config.test_batch_size):
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all_bbox_squee = np.squeeze(all_bbox.asnumpy()[j, :, :])
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all_label_squee = np.squeeze(all_label.asnumpy()[j, :, :])
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all_mask_squee = np.squeeze(all_mask.asnumpy()[j, :, :])
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all_mask_fb_squee = np.squeeze(all_mask_fb.asnumpy()[j, :, :, :])
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all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
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all_labels_tmp_mask = all_label_squee[all_mask_squee]
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all_mask_fb_tmp_mask = all_mask_fb_squee[all_mask_squee, :, :]
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if all_bboxes_tmp_mask.shape[0] > max_num:
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inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
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inds = inds[:max_num]
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all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
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all_labels_tmp_mask = all_labels_tmp_mask[inds]
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all_mask_fb_tmp_mask = all_mask_fb_tmp_mask[inds]
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bbox_results = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, config.num_classes)
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segm_results = get_seg_masks(all_mask_fb_tmp_mask, all_bboxes_tmp_mask, all_labels_tmp_mask, img_metas[j],
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True, config.num_classes)
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outputs.append((bbox_results, segm_results))
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eval_iter = eval_iter + 1
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eval_types = ["bbox", "segm"]
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result_files = results2json(dataset_coco, outputs, "./results.pkl")
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coco_eval(result_files, eval_types, dataset_coco, single_result=False)
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if __name__ == '__main__':
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prefix = "MaskRcnn_eval.mindrecord"
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mindrecord_dir = config.mindrecord_dir
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mindrecord_file = os.path.join(mindrecord_dir, prefix)
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if not os.path.exists(mindrecord_file):
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if not os.path.isdir(mindrecord_dir):
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os.makedirs(mindrecord_dir)
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if args_opt.dataset == "coco":
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if os.path.isdir(config.coco_root):
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print("Create Mindrecord.")
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data_to_mindrecord_byte_image("coco", False, prefix, file_num=1)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir))
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else:
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print("coco_root not exits.")
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else:
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if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
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print("Create Mindrecord.")
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data_to_mindrecord_byte_image("other", False, prefix, file_num=1)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir))
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else:
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print("IMAGE_DIR or ANNO_PATH not exits.")
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print("Start Eval!")
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MaskRcnn_eval(mindrecord_file, args_opt.checkpoint_path, args_opt.ann_file)
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