# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve. # # 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import argparse import paddle.fluid as fluid import program from ppcls.data import Reader from ppcls.utils.config import get_config from ppcls.utils.save_load import init_model from paddle.fluid.incubate.fleet.collective import fleet from paddle.fluid.incubate.fleet.base import role_maker def parse_args(): parser = argparse.ArgumentParser("PaddleClas eval script") parser.add_argument( '-c', '--config', type=str, default='./configs/eval.yaml', help='config file path') parser.add_argument( '-o', '--override', action='append', default=[], help='config options to be overridden') args = parser.parse_args() return args def main(args): role = role_maker.PaddleCloudRoleMaker(is_collective=True) fleet.init(role) config = get_config(args.config, overrides=args.override, show=True) gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0)) place = fluid.CUDAPlace(gpu_id) startup_prog = fluid.Program() valid_prog = fluid.Program() valid_dataloader, valid_fetchs = program.build( config, valid_prog, startup_prog, is_train=False) valid_prog = valid_prog.clone(for_test=True) exe = fluid.Executor(place) exe.run(startup_prog) init_model(config, valid_prog, exe) valid_reader = Reader(config, 'valid')() valid_dataloader.set_sample_list_generator(valid_reader, place) compiled_valid_prog = program.compile(config, valid_prog) program.run(valid_dataloader, exe, compiled_valid_prog, valid_fetchs, 0, 'valid') if __name__ == '__main__': args = parse_args() main(args)