# 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 argparse import os import paddle.fluid as fluid from ppcls.data import Reader from ppcls.utils.config import get_config from ppcls.utils.save_load import init_model, save_model from ppcls.utils import logger import program def parse_args(): parser = argparse.ArgumentParser("PaddleClas train script") parser.add_argument( '-c', '--config', type=str, default='configs/ResNet/ResNet50.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): config = get_config(args.config, overrides=args.override, show=True) # assign the place gpu_id = fluid.dygraph.parallel.Env().dev_id place = fluid.CUDAPlace(gpu_id) with fluid.dygraph.guard(place): strategy = fluid.dygraph.parallel.prepare_context() net = program.create_model(config.ARCHITECTURE, config.classes_num) net = fluid.dygraph.parallel.DataParallel(net, strategy) optimizer = program.create_optimizer( config, parameter_list=net.parameters()) # load model from checkpoint or pretrained model init_model(config, net, optimizer) train_dataloader = program.create_dataloader() train_reader = Reader(config, 'train')() train_dataloader.set_sample_list_generator(train_reader, place) if config.validate: valid_dataloader = program.create_dataloader() valid_reader = Reader(config, 'valid')() valid_dataloader.set_sample_list_generator(valid_reader, place) best_top1_acc = 0.0 # best top1 acc record for epoch_id in range(config.epochs): net.train() # 1. train with train dataset program.run(train_dataloader, config, net, optimizer, epoch_id, 'train') if fluid.dygraph.parallel.Env().local_rank == 0: # 2. validate with validate dataset if config.validate and epoch_id % config.valid_interval == 0: net.eval() top1_acc = program.run(valid_dataloader, config, net, None, epoch_id, 'valid') if top1_acc > best_top1_acc: best_top1_acc = top1_acc message = "The best top1 acc {:.5f}, in epoch: {:d}".format( best_top1_acc, epoch_id) logger.info("{:s}".format( logger.coloring(message, "RED"))) if epoch_id % config.save_interval == 0: model_path = os.path.join( config.model_save_dir, config.ARCHITECTURE["name"]) save_model(net, optimizer, model_path, "best_model_in_epoch_" + str(epoch_id)) # 3. save the persistable model if epoch_id % config.save_interval == 0: model_path = os.path.join(config.model_save_dir, config.ARCHITECTURE["name"]) save_model(net, optimizer, model_path, epoch_id) if __name__ == '__main__': args = parse_args() main(args)