# 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__, '../../'))) import paddle from paddle.distributed import fleet from visualdl import LogWriter from ppcls.data import build_dataloader from ppcls.utils.config import get_config, print_config from ppcls.utils import logger from ppcls.utils.logger import init_logger from ppcls.static.save_load import init_model, save_model from ppcls.static 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( '-p', '--profiler_options', type=str, default=None, help='The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\".' ) parser.add_argument( '-o', '--override', action='append', default=[], help='config options to be overridden') args = parser.parse_args() return args def main(args): """ all the config of training paradigm should be in config["Global"] """ config = get_config(args.config, overrides=args.override, show=False) global_config = config["Global"] mode = "train" log_file = os.path.join(global_config['output_dir'], config["Arch"]["name"], f"{mode}.log") init_logger(log_file=log_file) print_config(config) if global_config.get("is_distributed", True): fleet.init(is_collective=True) # assign the device assert global_config[ "device"] in ["cpu", "gpu", "xpu", "npu", "mlu", "ascend"] device = paddle.set_device(global_config["device"]) # 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.set_flags(AMP_RELATED_FLAGS_SETTING) # visualDL vdl_writer = None if global_config["use_visualdl"]: vdl_dir = os.path.join(global_config["output_dir"], "vdl") vdl_writer = LogWriter(vdl_dir) # build dataloader eval_dataloader = None use_dali = global_config.get('use_dali', False) class_num = config["Arch"].get("class_num", None) config["DataLoader"].update({"class_num": class_num}) train_dataloader = build_dataloader(config, "Train") if global_config["eval_during_train"]: eval_dataloader = build_dataloader(config, "Eval") step_each_epoch = len(train_dataloader) # 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, class_num, step_each_epoch=step_each_epoch, is_train=True, is_distributed=global_config.get("is_distributed", True)) if global_config["eval_during_train"]: eval_prog = paddle.static.Program() eval_fetchs, _, eval_feeds, _ = program.build( config, eval_prog, startup_prog, is_train=False, is_distributed=global_config.get("is_distributed", True)) # clone to prune some content which is irrelevant in eval_prog eval_prog = eval_prog.clone(for_test=True) # create the "Executor" with the statement of which device exe = paddle.static.Executor(device) # Parameter initialization exe.run(startup_prog) # load pretrained models or checkpoints init_model(global_config, train_prog, exe) if 'AMP' in config: if config["AMP"].get("level", "O1").upper() == "O2": use_fp16_test = True msg = "Only support FP16 evaluation when AMP O2 is enabled." logger.warning(msg) elif "use_fp16_test" in config["AMP"]: use_fp16_test = config["AMP"].get["use_fp16_test"] else: use_fp16_test = False optimizer.amp_init( device, scope=paddle.static.global_scope(), test_program=eval_prog if global_config["eval_during_train"] else None, use_fp16_test=use_fp16_test) if not global_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 eval_dataloader is not None: if not global_config.get("is_distributed", True): compiled_eval_prog = program.compile(config, eval_prog) else: compiled_eval_prog = eval_prog for epoch_id in range(global_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, args.profiler_options) # 2. evaluate with eval dataset if global_config["eval_during_train"] and epoch_id % global_config[ "eval_interval"] == 0: top1_acc = program.run(eval_dataloader, exe, compiled_eval_prog, eval_feeds, eval_fetchs, epoch_id, "eval", 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(message) if epoch_id % global_config["save_interval"] == 0: model_path = os.path.join(global_config["output_dir"], config["Arch"]["name"]) save_model(train_prog, model_path, "best_model") # 3. save the persistable model if epoch_id % global_config["save_interval"] == 0: model_path = os.path.join(global_config["output_dir"], config["Arch"]["name"]) save_model(train_prog, model_path, epoch_id) if __name__ == '__main__': paddle.enable_static() args = parse_args() main(args)