# 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 import paddle from paddleseg.cvlibs import manager, Config from core import evaluate from paddleseg.utils import get_sys_env, logger, utils from datasets import tusimple def get_test_config(cfg, args): test_config = cfg.test_config return test_config def parse_args(): parser = argparse.ArgumentParser(description='Model evaluation') # params of evaluate parser.add_argument( "--config", dest="cfg", help="The config file.", default=None, type=str) parser.add_argument( '--model_path', dest='model_path', help='The path of model for evaluation', type=str, default=None) parser.add_argument( '--num_workers', dest='num_workers', help='Num workers for data loader', type=int, default=0) parser.add_argument( '--data_format', dest='data_format', help='Data format that specifies the layout of input. It can be "NCHW" or "NHWC". Default: "NCHW".', type=str, default='NCHW') parser.add_argument( '--is_view', dest='is_view', help='Whether to visualize results.', type=str, default=False) parser.add_argument( '--save_dir', dest='save_dir', help='The directory for saving the predicted results', type=str, default='./output/result') return parser.parse_args() def main(args): env_info = get_sys_env() place = 'gpu' if env_info['Paddle compiled with cuda'] and env_info[ 'GPUs used'] else 'cpu' paddle.set_device(place) if not args.cfg: raise RuntimeError('No configuration file specified.') cfg = Config(args.cfg) cfg.check_sync_info() # Only support for the DeepLabv3+ model if args.data_format == 'NHWC': if cfg.dic['model']['type'] != 'DeepLabV3P': raise ValueError( 'The "NHWC" data format only support the DeepLabV3P model!') cfg.dic['model']['data_format'] = args.data_format cfg.dic['model']['backbone']['data_format'] = args.data_format loss_len = len(cfg.dic['loss']['types']) for i in range(loss_len): cfg.dic['loss']['types'][i]['data_format'] = args.data_format val_dataset = cfg.val_dataset if val_dataset is None: raise RuntimeError( 'The verification dataset is not specified in the configuration file.' ) elif len(val_dataset) == 0: raise ValueError( 'The length of val_dataset is 0. Please check if your dataset is valid' ) msg = '\n---------------Config Information---------------\n' msg += str(cfg) msg += '------------------------------------------------' logger.info(msg) model = cfg.model if args.model_path: utils.load_entire_model(model, args.model_path) logger.info('Loaded trained params of model successfully') test_config = get_test_config(cfg, args) evaluate( model, val_dataset, num_workers=args.num_workers, is_view=args.is_view, save_dir=args.save_dir, **test_config) if __name__ == '__main__': args = parse_args() main(args)