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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
- from collections import defaultdict
-
- import numpy as np
- from pycocotools.coco import COCO
- import mindspore as ms
- from mindspore.common import set_seed, Parameter
-
- from src.dataset import data_to_mindrecord_byte_image, create_fasterrcnn_dataset, parse_json_annos_from_txt
- 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
- from src.FasterRcnn.faster_rcnn import Faster_Rcnn
- ms.context.set_context(max_call_depth=2000)
-
-
- def modelarts_pre_process():
- '''modelarts pre process function.'''
- def unzip(zip_file, save_dir):
- import zipfile
- s_time = time.time()
- if not os.path.exists(os.path.join(save_dir, config.modelarts_dataset_unzip_name)):
- zip_isexist = zipfile.is_zipfile(zip_file)
- if zip_isexist:
- fz = zipfile.ZipFile(zip_file, 'r')
- data_num = len(fz.namelist())
- print("Extract Start...")
- print("unzip file num: {}".format(data_num))
- data_print = int(data_num / 100) if data_num > 100 else 1
- i = 0
- for file in fz.namelist():
- if i % data_print == 0:
- print("unzip percent: {}%".format(int(i * 100 / data_num)), flush=True)
- i += 1
- fz.extract(file, save_dir)
- print("cost time: {}min:{}s.".format(int((time.time() - s_time) / 60),
- int(int(time.time() - s_time) % 60)))
- print("Extract Done.")
- else:
- print("This is not zip.")
- else:
- print("Zip has been extracted.")
-
- if config.need_modelarts_dataset_unzip:
- zip_file_1 = os.path.join(config.data_path, config.modelarts_dataset_unzip_name + ".zip")
- save_dir_1 = os.path.join(config.data_path)
- files = os.listdir(config.data_path)
- for f in files:
- print(f)
-
- print("Zip file path: ", zip_file_1)
- print("Unzip file save dir: ", save_dir_1)
- unzip(zip_file_1, save_dir_1)
- print("===Finish extract data synchronization===")
- print("Finish sync unzip data from {} to {}.".format(zip_file_1, save_dir_1))
- config.checkpoint_path = config.output_path
-
- @moxing_wrapper(pre_process=modelarts_pre_process)
- def fasterrcnn_eval(dataset_path, ckpt_path, anno_path):
- """FasterRcnn evaluation."""
- if config.enable_modelarts:
- print("ckpt:")
- # ckpt_path = os.path.join(config.load_path, config.load_path[0])
- ckpt_path = "/cache/checkpoint_path/hrnet.ckpt"
- print(ckpt_path)
- anno_path = config.data_path + "/COCO2017/annotations/instances_val2017.json"
- if not os.path.isfile(ckpt_path):
- raise RuntimeError("CheckPoint file {} is not valid.".format(ckpt_path))
- ds = create_fasterrcnn_dataset(config, dataset_path, batch_size=config.test_batch_size, is_training=False)
- net = Faster_Rcnn(config)
-
- try:
- param_dict = ms.load_checkpoint(ckpt_path)
- except RuntimeError as ex:
- ex = str(ex)
- print("Traceback:\n", ex, flush=True)
- if "reg_scores.weight" in ex:
- exit("[ERROR] The loss calculation of faster_rcnn has been updated. "
- "If the training is on an old version, please set `without_bg_loss` to False.")
-
- # in previous version of code there was a typo in layer name 'fpn_neck': it was 'fpn_ncek'
- # in order to make backward compatibility with checkpoints created with that typo
- # we need to manually check and rename that layer in param_dict
- for key, value in param_dict.items():
- if key.startswith('fpn_ncek'):
- new_key = key.replace('fpn_ncek', 'fpn_neck')
- param_dict[new_key] = param_dict.pop(key)
- print(f"param_dict fixed typo: {key} renamed to {new_key}")
-
- if config.device_target == "GPU":
- for key, value in param_dict.items():
- tensor = value.asnumpy().astype(np.float32)
- param_dict[key] = Parameter(tensor, key)
- print(param_dict.keys())
- ms.load_param_into_net(net, param_dict)
-
- net.set_train(False)
- device_type = "Ascend" if ms.get_context("device_target") == "Ascend" else "Others"
- if device_type == "Ascend":
- net.to_float(ms.float16)
-
- eval_iter = 0
- total = ds.get_dataset_size()
- outputs = []
-
- if config.dataset != "coco":
- dataset_coco = COCO()
- dataset_coco.dataset, dataset_coco.anns, dataset_coco.cats, dataset_coco.imgs = dict(), dict(), dict(), dict()
- dataset_coco.imgToAnns, dataset_coco.catToImgs = defaultdict(list), defaultdict(list)
- dataset_coco.dataset = parse_json_annos_from_txt(anno_path, config)
- dataset_coco.createIndex()
- else:
- dataset_coco = COCO(anno_path)
-
- print("\n========================================\n")
- print("total images num: ", total)
- print("Processing, please wait a moment.")
- max_num = config.num_gts
- 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)
- print("\nEvaluation done!")
-
-
- def modelarts_pre_process():
- pass
-
-
- @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!")
- start_time = time.time()
- fasterrcnn_eval(mindrecord_file, config.checkpoint_path, config.anno_path)
- end_time = time.time()
- total_time = end_time - start_time
- print("\nDone!\nTime taken: {:.2f} seconds".format(total_time))
-
- # config.eval_result_path = os.path.abspath("./eval_result")
-
-
-
- if __name__ == '__main__':
- set_seed(1)
- ms.set_context(mode=ms.GRAPH_MODE, device_target=config.device_target, device_id=get_device_id())
-
- eval_fasterrcnn()
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