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124 lines
4.6 KiB
124 lines
4.6 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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# 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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import os
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import argparse
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import logging
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import numpy as np
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import mindspore
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import mindspore.nn as nn
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import mindspore.ops.operations as F
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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 mindspore.nn.loss.loss import _Loss
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from src.data_loader import create_dataset
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from src.unet import UNet
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from src.config import cfg_unet
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from scipy.special import softmax
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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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class CrossEntropyWithLogits(_Loss):
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def __init__(self):
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super(CrossEntropyWithLogits, self).__init__()
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self.transpose_fn = F.Transpose()
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self.reshape_fn = F.Reshape()
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self.softmax_cross_entropy_loss = nn.SoftmaxCrossEntropyWithLogits()
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self.cast = F.Cast()
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def construct(self, logits, label):
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# NCHW->NHWC
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logits = self.transpose_fn(logits, (0, 2, 3, 1))
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logits = self.cast(logits, mindspore.float32)
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label = self.transpose_fn(label, (0, 2, 3, 1))
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loss = self.reduce_mean(self.softmax_cross_entropy_loss(self.reshape_fn(logits, (-1, 2)),
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self.reshape_fn(label, (-1, 2))))
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return self.get_loss(loss)
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class dice_coeff(nn.Metric):
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def __init__(self):
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super(dice_coeff, self).__init__()
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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 coeffcient need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
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y_pred = self._convert_data(inputs[0])
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y = self._convert_data(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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ckpt_path,
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cross_valid_ind=1,
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cfg=None):
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net = UNet(n_channels=cfg['num_channels'], n_classes=cfg['num_classes'])
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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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criterion = CrossEntropyWithLogits()
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_, valid_dataset = create_dataset(data_dir, 1, 1, False, cross_valid_ind, False)
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model = Model(net, loss_fn=criterion, metrics={"dice_coeff": dice_coeff()})
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print("============== Starting Evaluating ============")
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dice_score = model.eval(valid_dataset, dataset_sink_mode=False)
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print("============== Cross valid dice coeff is:", dice_score)
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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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