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- # Copyright 2020-2021 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.
- # ============================================================================
-
- """Evaluation for FasterRcnn"""
- import os
- import time
- import numpy as np
- from pycocotools.coco import COCO
- import mindspore.common.dtype as mstype
- from mindspore import context
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.common import set_seed, Parameter
-
- from src.dataset import data_to_mindrecord_byte_image, create_fasterrcnn_dataset
- from src.util import coco_eval, bbox2result_1image, results2json
- from src.model_utils.config import config
- from src.model_utils.moxing_adapter import moxing_wrapper
- from src.model_utils.device_adapter import get_device_id
-
-
- set_seed(1)
- context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
-
- if config.backbone in ("resnet_v1.5_50", "resnet_v1_101", "resnet_v1_152"):
- from src.FasterRcnn.faster_rcnn_resnet import Faster_Rcnn_Resnet
- elif config.backbone == "resnet_v1_50":
- from src.FasterRcnn.faster_rcnn_resnet50v1 import Faster_Rcnn_Resnet
-
- def fasterrcnn_eval(dataset_path, ckpt_path, ann_file):
- """FasterRcnn evaluation."""
- ds = create_fasterrcnn_dataset(config, dataset_path, batch_size=config.test_batch_size, is_training=False)
- net = Faster_Rcnn_Resnet(config)
- param_dict = load_checkpoint(ckpt_path)
- if config.device_target == "GPU":
- for key, value in param_dict.items():
- tensor = value.asnumpy().astype(np.float32)
- param_dict[key] = Parameter(tensor, key)
- load_param_into_net(net, param_dict)
-
- net.set_train(False)
- device_type = "Ascend" if context.get_context("device_target") == "Ascend" else "Others"
- if device_type == "Ascend":
- net.to_float(mstype.float16)
-
- eval_iter = 0
- total = ds.get_dataset_size()
- outputs = []
- dataset_coco = COCO(ann_file)
-
- print("\n========================================\n")
- print("total images num: ", total)
- print("Processing, please wait a moment.")
- max_num = 128
- for data in ds.create_dict_iterator(num_epochs=1):
- eval_iter = eval_iter + 1
-
- img_data = data['image']
- img_metas = data['image_shape']
- gt_bboxes = data['box']
- gt_labels = data['label']
- gt_num = data['valid_num']
-
- start = time.time()
- # run net
- output = net(img_data, img_metas, gt_bboxes, gt_labels, gt_num)
- end = time.time()
- print("Iter {} cost time {}".format(eval_iter, end - start))
-
- # output
- all_bbox = output[0]
- all_label = output[1]
- all_mask = output[2]
-
- for j in range(config.test_batch_size):
- all_bbox_squee = np.squeeze(all_bbox.asnumpy()[j, :, :])
- all_label_squee = np.squeeze(all_label.asnumpy()[j, :, :])
- all_mask_squee = np.squeeze(all_mask.asnumpy()[j, :, :])
-
- all_bboxes_tmp_mask = all_bbox_squee[all_mask_squee, :]
- all_labels_tmp_mask = all_label_squee[all_mask_squee]
-
- if all_bboxes_tmp_mask.shape[0] > max_num:
- inds = np.argsort(-all_bboxes_tmp_mask[:, -1])
- inds = inds[:max_num]
- all_bboxes_tmp_mask = all_bboxes_tmp_mask[inds]
- all_labels_tmp_mask = all_labels_tmp_mask[inds]
-
- outputs_tmp = bbox2result_1image(all_bboxes_tmp_mask, all_labels_tmp_mask, config.num_classes)
-
- outputs.append(outputs_tmp)
-
- eval_types = ["bbox"]
- result_files = results2json(dataset_coco, outputs, "./results.pkl")
-
- coco_eval(result_files, eval_types, dataset_coco, single_result=True)
-
-
- def modelarts_pre_process():
- pass
- # config.ckpt_path = os.path.join(config.output_path, str(get_rank_id()), config.checkpoint_path)
-
- @moxing_wrapper(pre_process=modelarts_pre_process)
- def eval_fasterrcnn():
- """ eval_fasterrcnn """
- prefix = "FasterRcnn_eval.mindrecord"
- mindrecord_dir = config.mindrecord_dir
- mindrecord_file = os.path.join(mindrecord_dir, prefix)
- print("CHECKING MINDRECORD FILES ...")
-
- if not os.path.exists(mindrecord_file):
- if not os.path.isdir(mindrecord_dir):
- os.makedirs(mindrecord_dir)
- if config.dataset == "coco":
- if os.path.isdir(config.coco_root):
- print("Create Mindrecord. It may take some time.")
- data_to_mindrecord_byte_image(config, "coco", False, prefix, file_num=1)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("coco_root not exits.")
- else:
- if os.path.isdir(config.IMAGE_DIR) and os.path.exists(config.ANNO_PATH):
- print("Create Mindrecord. It may take some time.")
- data_to_mindrecord_byte_image(config, "other", False, prefix, file_num=1)
- print("Create Mindrecord Done, at {}".format(mindrecord_dir))
- else:
- print("IMAGE_DIR or ANNO_PATH not exits.")
-
- print("CHECKING MINDRECORD FILES DONE!")
- print("Start Eval!")
- fasterrcnn_eval(mindrecord_file, config.checkpoint_path, config.ann_file)
-
- if __name__ == '__main__':
- eval_fasterrcnn()
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