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99 lines
3.3 KiB
99 lines
3.3 KiB
# Copyright 2021 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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"""unet 310 infer."""
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import os
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import argparse
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import numpy as np
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from src.data_loader import create_dataset
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from src.config import cfg_unet
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from scipy.special import softmax
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class dice_coeff():
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def __init__(self):
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self.clear()
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def clear(self):
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self._dice_coeff_sum = 0
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self._samples_num = 0
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def update(self, *inputs):
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if len(inputs) != 2:
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raise ValueError('Mean dice coefficient need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
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y_pred = inputs[0]
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y = np.array(inputs[1])
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self._samples_num += y.shape[0]
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y_pred = y_pred.transpose(0, 2, 3, 1)
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y = y.transpose(0, 2, 3, 1)
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y_pred = softmax(y_pred, axis=3)
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inter = np.dot(y_pred.flatten(), y.flatten())
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union = np.dot(y_pred.flatten(), y_pred.flatten()) + np.dot(y.flatten(), y.flatten())
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single_dice_coeff = 2*float(inter)/float(union+1e-6)
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print("single dice coeff is:", single_dice_coeff)
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self._dice_coeff_sum += single_dice_coeff
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def eval(self):
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if self._samples_num == 0:
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raise RuntimeError('Total samples num must not be 0.')
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return self._dice_coeff_sum / float(self._samples_num)
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def test_net(data_dir,
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cross_valid_ind=1,
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cfg=None):
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_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False, do_crop=cfg['crop'],
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img_size=cfg['img_size'])
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labels_list = []
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for data in valid_dataset:
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labels_list.append(data[1].asnumpy())
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return labels_list
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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', '--rst_path', dest='rst_path', type=str, default='./result_Files/',
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help='infer result path')
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return parser.parse_args()
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if __name__ == '__main__':
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args = get_args()
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label_list = test_net(data_dir=args.data_url, cross_valid_ind=cfg_unet['cross_valid_ind'], cfg=cfg_unet)
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rst_path = args.rst_path
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metrics = dice_coeff()
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for j in range(len(os.listdir(rst_path))):
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file_name = rst_path + "ISBI_test_bs_1_" + str(j) + "_0" + ".bin"
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output = np.fromfile(file_name, np.float32).reshape(1, 2, 576, 576)
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label = label_list[j]
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metrics.update(output, label)
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print("Cross valid dice coeff is: ", metrics.eval())
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