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80 lines
3.6 KiB
80 lines
3.6 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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import os
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import argparse
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import logging
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from mindspore import context, Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.data_loader import create_dataset, create_cell_nuclei_dataset
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from src.unet_medical import UNetMedical
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from src.unet_nested import NestedUNet, UNet
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from src.config import cfg_unet
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from src.utils import UnetEval, TempLoss, dice_coeff
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
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def test_net(data_dir,
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ckpt_path,
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cross_valid_ind=1,
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cfg=None):
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if cfg['model'] == 'unet_medical':
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net = UNetMedical(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
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elif cfg['model'] == 'unet_nested':
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net = NestedUNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'], use_deconv=cfg['use_deconv'],
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use_bn=cfg['use_bn'], use_ds=False)
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elif cfg['model'] == 'unet_simple':
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net = UNet(in_channel=cfg['num_channels'], n_class=cfg['num_classes'])
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else:
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raise ValueError("Unsupported model: {}".format(cfg['model']))
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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 = UnetEval(net)
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if 'dataset' in cfg and cfg['dataset'] == "Cell_nuclei":
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valid_dataset = create_cell_nuclei_dataset(data_dir, cfg['img_size'], 1, 1, is_train=False,
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eval_resize=cfg["eval_resize"], split=0.8)
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else:
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_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False,
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do_crop=cfg['crop'], img_size=cfg['img_size'])
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model = Model(net, loss_fn=TempLoss(), metrics={"dice_coeff": dice_coeff(cfg_unet)})
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print("============== Starting Evaluating ============")
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eval_score = model.eval(valid_dataset, dataset_sink_mode=False)["dice_coeff"]
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print("============== Cross valid dice coeff is:", eval_score[0])
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print("============== Cross valid IOU is:", eval_score[1])
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def get_args():
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parser = argparse.ArgumentParser(description='Test the UNet on images and target masks',
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument('-d', '--data_url', dest='data_url', type=str, default='data/',
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help='data directory')
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parser.add_argument('-p', '--ckpt_path', dest='ckpt_path', type=str, default='ckpt_unet_medical_adam-1_600.ckpt',
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help='checkpoint path')
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return parser.parse_args()
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if __name__ == '__main__':
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logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
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args = get_args()
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print("Testing setting:", args)
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test_net(data_dir=args.data_url,
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ckpt_path=args.ckpt_path,
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cross_valid_ind=cfg_unet['cross_valid_ind'],
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cfg=cfg_unet)
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