#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # #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 # #Unless 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 argparse import os # 选择使用0号卡 os.environ['CUDA_VISIBLE_DEVICES'] = '0' from paddlex.seg import transforms import paddlex as pdx def train(model_dir, sensitivities_file, eval_metric_loss): # 下载和解压视盘分割数据集 optic_dataset = 'https://bj.bcebos.com/paddlex/datasets/optic_disc_seg.tar.gz' pdx.utils.download_and_decompress(optic_dataset, path='./') # 定义训练和验证时的transforms train_transforms = transforms.Compose([ transforms.RandomHorizontalFlip(), transforms.ResizeRangeScaling(), transforms.RandomPaddingCrop(crop_size=512), transforms.Normalize() ]) eval_transforms = transforms.Compose([ transforms.ResizeByLong(long_size=512), transforms.Padding(target_size=512), transforms.Normalize() ]) # 定义训练和验证所用的数据集 train_dataset = pdx.datasets.SegDataset( data_dir='optic_disc_seg', file_list='optic_disc_seg/train_list.txt', label_list='optic_disc_seg/labels.txt', transforms=train_transforms, shuffle=True) eval_dataset = pdx.datasets.SegDataset( data_dir='optic_disc_seg', file_list='optic_disc_seg/val_list.txt', label_list='optic_disc_seg/labels.txt', transforms=eval_transforms) if model_dir is None: # 使用coco数据集上的预训练权重 pretrain_weights = "COCO" else: assert os.path.isdir(model_dir), "Path {} is not a directory".format( model_dir) pretrain_weights = model_dir save_dir = "output/unet" if sensitivities_file is not None: if sensitivities_file != 'DEFAULT': assert os.path.exists( sensitivities_file), "Path {} not exist".format( sensitivities_file) save_dir = "output/unet_prune" num_classes = len(train_dataset.labels) model = pdx.seg.UNet(num_classes=num_classes) model.train( num_epochs=20, train_dataset=train_dataset, train_batch_size=4, eval_dataset=eval_dataset, learning_rate=0.01, pretrain_weights=pretrain_weights, save_dir=save_dir, use_vdl=True, sensitivities_file=sensitivities_file, eval_metric_loss=eval_metric_loss) if __name__ == '__main__': parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--model_dir", default=None, type=str, help="The model path.") parser.add_argument( "--sensitivities_file", default=None, type=str, help="The sensitivities file path.") parser.add_argument( "--eval_metric_loss", default=0.05, type=float, help="The loss threshold.") args = parser.parse_args() train(args.model_dir, args.sensitivities_file, args.eval_metric_loss)