# 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 sys __dir__ = os.path.dirname(os.path.abspath(__file__)) sys.path.append(__dir__) sys.path.append(os.path.abspath(os.path.join(__dir__, '../../'))) from sys import version_info import paddle from paddle.distributed import fleet from ppcls.data import Reader from ppcls.utils.config import get_config from ppcls.utils import logger from tools.static import program from save_load import init_model, save_model 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( '--vdl_dir', type=str, default=None, help='VisualDL logging directory for image.') 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) if config.get("is_distributed", True): fleet.init(is_collective=True) # assign the place use_gpu = config.get("use_gpu", True) # amp related config if 'AMP' in config: AMP_RELATED_FLAGS_SETTING = { 'FLAGS_cudnn_exhaustive_search': 1, 'FLAGS_conv_workspace_size_limit': 1500, 'FLAGS_cudnn_batchnorm_spatial_persistent': 1, 'FLAGS_max_inplace_grad_add': 8, } os.environ['FLAGS_cudnn_batchnorm_spatial_persistent'] = '1' paddle.fluid.set_flags(AMP_RELATED_FLAGS_SETTING) use_xpu = config.get("use_xpu", False) assert ( use_gpu and use_xpu ) is not True, "gpu and xpu can not be true in the same time in static mode!" if use_gpu: place = paddle.set_device('gpu') elif use_xpu: place = paddle.set_device('xpu') else: place = paddle.set_device('cpu') # startup_prog is used to do some parameter init work, # and train prog is used to hold the network startup_prog = paddle.static.Program() train_prog = paddle.static.Program() best_top1_acc = 0.0 # best top1 acc record train_fetchs, lr_scheduler, train_feeds, optimizer = program.build( config, train_prog, startup_prog, is_train=True, is_distributed=config.get("is_distributed", True)) if config.validate: valid_prog = paddle.static.Program() valid_fetchs, _, valid_feeds, _ = program.build( config, valid_prog, startup_prog, is_train=False, is_distributed=config.get("is_distributed", True)) # clone to prune some content which is irrelevant in valid_prog valid_prog = valid_prog.clone(for_test=True) # create the "Executor" with the statement of which place exe = paddle.static.Executor(place) # Parameter initialization exe.run(startup_prog) # load pretrained models or checkpoints init_model(config, train_prog, exe) if 'AMP' in config and config.AMP.get("use_pure_fp16", False): optimizer.amp_init( place, scope=paddle.static.global_scope(), test_program=valid_prog if config.validate else None) if not config.get("is_distributed", True): compiled_train_prog = program.compile( config, train_prog, loss_name=train_fetchs["loss"][0].name) else: compiled_train_prog = train_prog if not config.get('use_dali', False): train_dataloader = Reader(config, 'train', places=place)() if config.validate and paddle.distributed.get_rank() == 0: valid_dataloader = Reader(config, 'valid', places=place)() compiled_valid_prog = program.compile(config, valid_prog) else: assert use_gpu is True, "DALI only support gpu, please set use_gpu to True!" import dali train_dataloader = dali.train(config) if config.validate and paddle.distributed.get_rank() == 0: valid_dataloader = dali.val(config) compiled_valid_prog = program.compile(config, valid_prog) vdl_writer = None if args.vdl_dir: if version_info.major == 2: logger.info( "visualdl is just supported for python3, so it is disabled in python2..." ) else: from visualdl import LogWriter vdl_writer = LogWriter(args.vdl_dir) for epoch_id in range(config.epochs): # 1. train with train dataset program.run(train_dataloader, exe, compiled_train_prog, train_feeds, train_fetchs, epoch_id, 'train', config, vdl_writer, lr_scheduler) if paddle.distributed.get_rank() == 0: # 2. validate with validate dataset if config.validate and epoch_id % config.valid_interval == 0: top1_acc = program.run(valid_dataloader, exe, compiled_valid_prog, valid_feeds, valid_fetchs, epoch_id, 'valid', config) 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(train_prog, model_path, "best_model") # 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(train_prog, model_path, epoch_id) if __name__ == '__main__': paddle.enable_static() args = parse_args() main(args)