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52 lines
2.4 KiB
52 lines
2.4 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."""
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import argparse
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from mindspore import context
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from mindspore import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.md_dataset import create_dataset
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from src.losses import OhemLoss
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from src.miou_precision import MiouPrecision
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from src.deeplabv3 import deeplabv3_resnet50
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from src.config import config
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parser = argparse.ArgumentParser(description="Deeplabv3 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('--data_url', required=True, default=None, help='Evaluation data url')
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parser.add_argument('--checkpoint_url', default=None, help='Checkpoint path')
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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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print(args_opt)
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if __name__ == "__main__":
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args_opt.crop_size = config.crop_size
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args_opt.base_size = config.crop_size
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eval_dataset = create_dataset(args_opt, args_opt.data_url, config.epoch_size, config.batch_size, usage="eval")
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net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
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infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
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decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
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fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
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param_dict = load_checkpoint(args_opt.checkpoint_url)
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load_param_into_net(net, param_dict)
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mIou = MiouPrecision(config.seg_num_classes)
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metrics = {'mIou': mIou}
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loss = OhemLoss(config.seg_num_classes, config.ignore_label)
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model = Model(net, loss, metrics=metrics)
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model.eval(eval_dataset)
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