# Copyright 2021 Huawei Technologies Co., Ltd # # 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. # ============================================================================ """train resnet.""" import os import argparse import ast from mindspore import context from mindspore import Tensor from mindspore.nn.optim.momentum import Momentum from mindspore.train.model import Model from mindspore.context import ParallelMode from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.communication.management import init, get_rank from mindspore.common import set_seed import mindspore.nn as nn import mindspore.common.initializer as weight_init from src.lr_generator import get_lr from src.CrossEntropySmooth import CrossEntropySmooth from src.resnet import resnet152 as resnet from src.config import config5 as config from src.dataset import create_dataset2 as create_dataset # imagenet2012 parser = argparse.ArgumentParser(description='Image classification--resnet152') parser.add_argument('--data_url', type=str, default=None, help='Dataset path') parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute') parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path') parser.add_argument('--rank', type=int, default=0, help='local rank of distributed') parser.add_argument('--is_save_on_master', type=ast.literal_eval, default=True, help='save ckpt on master or all rank') args_opt = parser.parse_args() set_seed(1) if __name__ == '__main__': ckpt_save_dir = config.save_checkpoint_path # init context print(args_opt.run_distribute) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) if args_opt.run_distribute: device_id = int(os.getenv('DEVICE_ID')) rank_size = int(os.environ.get("RANK_SIZE", 1)) print(rank_size) device_num = rank_size context.set_context(device_id=device_id, enable_auto_mixed_precision=True) context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, all_reduce_fusion_config=[180, 313]) init() args_opt.rank = get_rank() print(args_opt.rank) # select for master rank save ckpt or all rank save, compatible for model parallel args_opt.rank_save_ckpt_flag = 0 if args_opt.is_save_on_master: if args_opt.rank == 0: args_opt.rank_save_ckpt_flag = 1 else: args_opt.rank_save_ckpt_flag = 1 local_data_path = args_opt.data_url local_data_path = args_opt.data_url print('Download data:') # create dataset dataset = create_dataset(dataset_path=local_data_path, do_train=True, repeat_num=1, batch_size=config.batch_size, target="Ascend", distribute=args_opt.run_distribute) step_size = dataset.get_dataset_size() print("step"+str(step_size)) # define net net = resnet(class_num=config.class_num) # init weight if args_opt.pre_trained: param_dict = load_checkpoint(args_opt.pre_trained) load_param_into_net(net, param_dict) else: for _, cell in net.cells_and_names(): if isinstance(cell, nn.Conv2d): cell.weight.set_data(weight_init.initializer(weight_init.HeUniform(), cell.weight.shape, cell.weight.dtype)) if isinstance(cell, nn.Dense): cell.weight.set_data(weight_init.initializer(weight_init.HeNormal(), cell.weight.shape, cell.weight.dtype)) # init lr lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max, warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size, lr_decay_mode=config.lr_decay_mode) lr = Tensor(lr) # define opt decayed_params = [] no_decayed_params = [] for param in net.trainable_params(): if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name: decayed_params.append(param) else: no_decayed_params.append(param) group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay}, {'params': no_decayed_params}, {'order_params': net.trainable_params()}] opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale) # define loss, model if not config.use_label_smooth: config.label_smooth_factor = 0.0 loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=config.label_smooth_factor, num_classes=config.class_num) loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'top_1_accuracy', 'top_5_accuracy'}, amp_level="O3", keep_batchnorm_fp32=False) # define callbacks time_cb = TimeMonitor(data_size=step_size) loss_cb = LossMonitor() cb = [time_cb, loss_cb] if config.save_checkpoint: if args_opt.rank_save_ckpt_flag: config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size, keep_checkpoint_max=config.keep_checkpoint_max) ckpt_cb = ModelCheckpoint(prefix="resnet152", directory=ckpt_save_dir, config=config_ck) cb += [ckpt_cb] # train model dataset_sink_mode = True print(dataset.get_dataset_size()) model.train(config.epoch_size, dataset, callbacks=cb, sink_size=dataset.get_dataset_size(), dataset_sink_mode=dataset_sink_mode)