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99 lines
4.1 KiB
99 lines
4.1 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 SSD"""
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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 mindspore import context, Tensor
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn
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from src.dataset import create_ssd_dataset, create_mindrecord
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from src.config import config
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from src.eval_utils import metrics
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from src.box_utils import default_boxes
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def ssd_eval(dataset_path, ckpt_path, anno_json):
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"""SSD evaluation."""
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batch_size = 1
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ds = create_ssd_dataset(dataset_path, batch_size=batch_size, repeat_num=1,
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is_training=False, use_multiprocessing=False)
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if config.model == "ssd300":
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net = SSD300(ssd_mobilenet_v2(), config, is_training=False)
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else:
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net = ssd_mobilenet_v1_fpn(config=config)
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net = SsdInferWithDecoder(net, Tensor(default_boxes), config)
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print("Load Checkpoint!")
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param_dict = load_checkpoint(ckpt_path)
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net.init_parameters_data()
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load_param_into_net(net, param_dict)
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net.set_train(False)
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i = batch_size
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total = ds.get_dataset_size() * batch_size
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start = time.time()
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pred_data = []
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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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for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
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img_id = data['img_id']
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img_np = data['image']
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image_shape = data['image_shape']
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output = net(Tensor(img_np))
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for batch_idx in range(img_np.shape[0]):
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pred_data.append({"boxes": output[0].asnumpy()[batch_idx],
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"box_scores": output[1].asnumpy()[batch_idx],
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"img_id": int(np.squeeze(img_id[batch_idx])),
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"image_shape": image_shape[batch_idx]})
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percent = round(i / total * 100., 2)
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print(f' {str(percent)} [{i}/{total}]', end='\r')
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i += batch_size
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cost_time = int((time.time() - start) * 1000)
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print(f' 100% [{total}/{total}] cost {cost_time} ms')
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mAP = metrics(pred_data, anno_json)
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print("\n========================================\n")
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print(f"mAP: {mAP}")
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def get_eval_args():
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parser = argparse.ArgumentParser(description='SSD evaluation')
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parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
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parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.")
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parser.add_argument("--checkpoint_path", type=str, required=True, help="Checkpoint file path.")
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parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
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help="run platform, support Ascend ,GPU and CPU.")
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return parser.parse_args()
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if __name__ == '__main__':
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args_opt = get_eval_args()
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if args_opt.dataset == "coco":
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json_path = os.path.join(config.coco_root, config.instances_set.format(config.val_data_type))
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elif args_opt.dataset == "voc":
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json_path = os.path.join(config.voc_root, config.voc_json)
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else:
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raise ValueError('SSD eval only supprt dataset mode is coco and voc!')
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.run_platform, device_id=args_opt.device_id)
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mindrecord_file = create_mindrecord(args_opt.dataset, "ssd_eval.mindrecord", False)
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print("Start Eval!")
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ssd_eval(mindrecord_file, args_opt.checkpoint_path, json_path)
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