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149 lines
6.2 KiB
149 lines
6.2 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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import cv2
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import numpy as np
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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 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
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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 TempLoss(nn.Cell):
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"""A temp loss cell."""
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def __init__(self):
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super(TempLoss, self).__init__()
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self.identity = F.identity()
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def construct(self, logits, label):
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return self.identity(logits)
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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._iou_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('Need 2 inputs ((y_softmax, y_argmax), y), but got {}'.format(len(inputs)))
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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 = y.transpose(0, 2, 3, 1)
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b, h, w, c = y.shape
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if b != 1:
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raise ValueError('Batch size should be 1 when in evaluation.')
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y = y.reshape((h, w, c))
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if cfg_unet["eval_activate"].lower() == "softmax":
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y_softmax = np.squeeze(self._convert_data(inputs[0][0]), axis=0)
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if cfg_unet["eval_resize"]:
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y_pred = []
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for i in range(cfg_unet["num_classes"]):
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y_pred.append(cv2.resize(np.uint8(y_softmax[:, :, i] * 255), (w, h)) / 255)
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y_pred = np.stack(y_pred, axis=-1)
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else:
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y_pred = y_softmax
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elif cfg_unet["eval_activate"].lower() == "argmax":
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y_argmax = np.squeeze(self._convert_data(inputs[0][1]), axis=0)
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y_pred = []
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for i in range(cfg_unet["num_classes"]):
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if cfg_unet["eval_resize"]:
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y_pred.append(cv2.resize(np.uint8(y_argmax == i), (w, h), interpolation=cv2.INTER_NEAREST))
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else:
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y_pred.append(np.float32(y_argmax == i))
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y_pred = np.stack(y_pred, axis=-1)
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else:
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raise ValueError('config eval_activate should be softmax or argmax.')
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y_pred = y_pred.astype(np.float32)
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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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single_iou = single_dice_coeff / (2 - single_dice_coeff)
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print("single dice coeff is: {}, IOU is: {}".format(single_dice_coeff, single_iou))
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self._dice_coeff_sum += single_dice_coeff
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self._iou_sum += single_iou
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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), self._iou_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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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()})
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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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