forked from mindspore-Ecosystem/mindspore
114 lines
4.5 KiB
Python
114 lines
4.5 KiB
Python
# 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 SSD"""
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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 mindspore import context
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.ssd_ghostnet import SSD300, ssd_ghostnet
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from src.dataset import create_ssd_dataset, data_to_mindrecord_byte_image, voc_data_to_mindrecord
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from src.coco_eval import metrics
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from src.model_utils.config import config
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from src.model_utils.moxing_adapter import moxing_wrapper
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from src.model_utils.device_adapter import get_device_id
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def ssd_eval(dataset_path, ckpt_path):
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"""SSD evaluation."""
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batch_size = 1
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ds = create_ssd_dataset(
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dataset_path, batch_size=batch_size, repeat_num=1, is_training=False)
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net = SSD300(ssd_ghostnet(), is_training=False)
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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():
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img_id = data['img_id'].asnumpy()
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img_np = data['image']
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image_shape = data['image_shape'].asnumpy()
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output = net(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)
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print("\n========================================\n")
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print(f"mAP: {mAP}")
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@moxing_wrapper()
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def eval_net():
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"""eval net"""
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend", device_id=get_device_id())
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prefix = "ssd_eval.mindrecord"
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mindrecord_dir = os.path.join(config.data_path, "MindRecord_COCO")
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mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
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if config.dataset == "voc":
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coco_root = config.voc_root
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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 config.dataset == "coco":
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coco_root = os.path.join(config.data_path, "coco_ori")
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if os.path.isdir(coco_root):
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print("Create Mindrecord.")
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data_to_mindrecord_byte_image("coco", False, prefix)
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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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elif config.dataset == "voc":
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if os.path.isdir(config.voc_dir) and os.path.isdir(config.voc_root):
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print("Create Mindrecord.")
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voc_data_to_mindrecord(mindrecord_dir, False, prefix)
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print("Create Mindrecord Done, at {}".format(mindrecord_dir))
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else:
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print("voc_root or voc_dir 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)
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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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ssd_eval(mindrecord_file, config.checkpoint_file_path)
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if __name__ == '__main__':
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eval_net()
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