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141 lines
5.5 KiB
141 lines
5.5 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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# Unless 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 Deeptext"""
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import argparse
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import os
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import time
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import numpy as np
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from src.Deeptext.deeptext_vgg16 import Deeptext_VGG16
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from src.config import config
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from src.dataset import data_to_mindrecord_byte_image, create_deeptext_dataset
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from src.utils import metrics
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from mindspore import context
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from mindspore.common import set_seed
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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set_seed(1)
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parser = argparse.ArgumentParser(description="Deeptext evaluation")
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parser.add_argument("--checkpoint_path", type=str, default='test', help="Checkpoint file path.")
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parser.add_argument("--imgs_path", type=str, required=True,
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help="Test images files paths, multiple paths can be separated by ','.")
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parser.add_argument("--annos_path", type=str, required=True,
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help="Annotations files paths of test images, multiple paths can be separated by ','.")
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parser.add_argument("--device_id", type=int, default=7, help="Device id, default is 7.")
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parser.add_argument("--mindrecord_prefix", type=str, default='Deeptext-TEST', help="Prefix of mindrecord.")
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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 deeptext_eval_test(dataset_path='', ckpt_path=''):
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"""Deeptext evaluation."""
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ds = create_deeptext_dataset(dataset_path, batch_size=config.test_batch_size,
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repeat_num=1, is_training=False)
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total = ds.get_dataset_size()
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net = Deeptext_VGG16(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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print("\n========================================\n")
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print("Processing, please wait a moment.")
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max_num = 32
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pred_data = []
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for data in ds.create_dict_iterator():
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eval_iter = eval_iter + 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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start = time.time()
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# run net
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output = net(img_data, img_metas, gt_bboxes, gt_labels, gt_num)
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gt_bboxes = gt_bboxes.asnumpy()
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gt_bboxes = gt_bboxes[gt_num.asnumpy().astype(bool), :]
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print(gt_bboxes)
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gt_labels = gt_labels.asnumpy()
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gt_labels = gt_labels[gt_num.asnumpy().astype(bool)]
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print(gt_labels)
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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] + 1
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all_mask = output[2]
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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_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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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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pred_data.append({"boxes": all_bboxes_tmp_mask,
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"labels": all_labels_tmp_mask,
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"gt_bboxes": gt_bboxes,
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"gt_labels": gt_labels})
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percent = round(eval_iter / total * 100, 2)
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print(' %s [%d/%d]' % (str(percent) + '%', eval_iter, total), end='\r')
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precisions, recalls = metrics(pred_data)
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print("\n========================================\n")
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for i in range(config.num_classes - 1):
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j = i + 1
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f1 = (2 * precisions[j] * recalls[j]) / (precisions[j] + recalls[j] + 1e-6)
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print("class {} precision is {:.2f}%, recall is {:.2f}%,"
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"F1 is {:.2f}%".format(j, precisions[j] * 100, recalls[j] * 100, f1 * 100))
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if config.use_ambigous_sample:
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break
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if __name__ == '__main__':
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prefix = args_opt.mindrecord_prefix
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config.test_images = args_opt.imgs_path
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config.test_txts = args_opt.annos_path
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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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print("CHECKING MINDRECORD FILES ...")
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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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print("Create Mindrecord. It may take some time.")
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data_to_mindrecord_byte_image(False, prefix, file_num=1)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir))
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print("CHECKING MINDRECORD FILES DONE!")
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print("Start Eval!")
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deeptext_eval_test(mindrecord_file, args_opt.checkpoint_path)
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