mindspore/model_zoo/official/cv/unet3d/eval.py

# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# less required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================

import os
import argparse
import numpy as np
from mindspore import dtype as mstype
from mindspore import Model, context, Tensor
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.dataset import create_dataset
from src.unet3d_model import UNet3d
from src.config import config as cfg
from src.utils import create_sliding_window, CalculateDice

device_id = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)

def get_args():
    parser = argparse.ArgumentParser(description='Test the UNet3D on images and target masks')
    parser.add_argument('--data_url', dest='data_url', type=str, default='', help='image data directory')
    parser.add_argument('--seg_url', dest='seg_url', type=str, default='', help='seg data directory')
    parser.add_argument('--ckpt_path', dest='ckpt_path', type=str, default='', help='checkpoint path')
    return parser.parse_args()

def test_net(data_dir, seg_dir, ckpt_path, config=None):
    eval_dataset = create_dataset(data_path=data_dir, seg_path=seg_dir, config=config, is_training=False)
    eval_data_size = eval_dataset.get_dataset_size()
    print("train dataset length is:", eval_data_size)

    network = UNet3d(config=config)
    network.set_train(False)
    param_dict = load_checkpoint(ckpt_path)
    load_param_into_net(network, param_dict)
    model = Model(network)
    index = 0
    total_dice = 0
    for batch in eval_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
        image = batch["image"]
        seg = batch["seg"]
        print("current image shape is {}".format(image.shape), flush=True)
        sliding_window_list, slice_list = create_sliding_window(image, config.roi_size, config.overlap)
        image_size = (config.batch_size, config.num_classes) + image.shape[2:]
        output_image = np.zeros(image_size, np.float32)
        count_map = np.zeros(image_size, np.float32)
        importance_map = np.ones(config.roi_size, np.float32)
        for window, slice_ in zip(sliding_window_list, slice_list):
            window_image = Tensor(window, mstype.float32)
            pred_probs = model.predict(window_image)
            output_image[slice_] += pred_probs.asnumpy()
            count_map[slice_] += importance_map
        output_image = output_image / count_map
        dice, _ = CalculateDice(output_image, seg)
        print("The {} batch dice is {}".format(index, dice), flush=True)
        total_dice += dice
        index = index + 1
    avg_dice = total_dice / eval_data_size
    print("**********************End Eval***************************************")
    print("eval average dice is {}".format(avg_dice))

if __name__ == '__main__':
    args = get_args()
    print("Testing setting:", args)
    test_net(data_dir=args.data_url,
             seg_dir=args.seg_url,
             ckpt_path=args.ckpt_path,
             config=cfg)