# 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 retinanet and get checkpoint files.""" import os import argparse import ast import mindspore import mindspore.nn as nn from mindspore import context, Tensor from mindspore.communication.management import init, get_rank from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor, Callback from mindspore.train import Model from mindspore.context import ParallelMode from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.common import set_seed from src.retinanet import retinanetWithLossCell, TrainingWrapper, retinanet50, resnet50 from src.config import config from src.dataset import create_retinanet_dataset, create_mindrecord from src.lr_schedule import get_lr from src.init_params import init_net_param, filter_checkpoint_parameter set_seed(1) class Monitor(Callback): """ Monitor loss and time. Args: lr_init (numpy array): train lr Returns: None Examples: >>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy()) """ def __init__(self, lr_init=None): super(Monitor, self).__init__() self.lr_init = lr_init self.lr_init_len = len(lr_init) def step_end(self, run_context): cb_params = run_context.original_args() print("lr:[{:8.6f}]".format(self.lr_init[cb_params.cur_step_num-1]), flush=True) def main(): parser = argparse.ArgumentParser(description="retinanet training") parser.add_argument("--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create Mindrecord, default is False.") parser.add_argument("--distribute", type=ast.literal_eval, default=False, help="Run distribute, default is False.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") parser.add_argument("--lr", type=float, default=0.1, help="Learning rate, default is 0.1.") parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink.") parser.add_argument("--dataset", type=str, default="coco", help="Dataset, default is coco.") parser.add_argument("--epoch_size", type=int, default=500, help="Epoch size, default is 500.") parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.") parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained Checkpoint file path.") parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size.") parser.add_argument("--save_checkpoint_epochs", type=int, default=1, help="Save checkpoint epochs, default is 1.") parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.") parser.add_argument("--filter_weight", type=ast.literal_eval, default=False, help="Filter weight parameters, default is False.") parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend"), help="run platform, only support Ascend.") args_opt = parser.parse_args() if args_opt.run_platform == "Ascend": context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") if args_opt.distribute: if os.getenv("DEVICE_ID", "not_set").isdigit(): context.set_context(device_id=int(os.getenv("DEVICE_ID"))) init() device_num = args_opt.device_num rank = get_rank() context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, device_num=device_num) else: rank = 0 device_num = 1 context.set_context(device_id=args_opt.device_id) else: raise ValueError("Unsupported platform.") mindrecord_file = create_mindrecord(args_opt.dataset, "retinanet.mindrecord", True) if not args_opt.only_create_dataset: loss_scale = float(args_opt.loss_scale) # When create MindDataset, using the fitst mindrecord file, such as retinanet.mindrecord0. dataset = create_retinanet_dataset(mindrecord_file, repeat_num=1, batch_size=args_opt.batch_size, device_num=device_num, rank=rank) dataset_size = dataset.get_dataset_size() print("Create dataset done!") backbone = resnet50(config.num_classes) retinanet = retinanet50(backbone, config) net = retinanetWithLossCell(retinanet, config) net.to_float(mindspore.float16) init_net_param(net) if args_opt.pre_trained: if args_opt.pre_trained_epoch_size <= 0: raise KeyError("pre_trained_epoch_size must be greater than 0.") param_dict = load_checkpoint(args_opt.pre_trained) if args_opt.filter_weight: filter_checkpoint_parameter(param_dict) load_param_into_net(net, param_dict) lr = Tensor(get_lr(global_step=config.global_step, lr_init=config.lr_init, lr_end=config.lr_end_rate * args_opt.lr, lr_max=args_opt.lr, warmup_epochs1=config.warmup_epochs1, warmup_epochs2=config.warmup_epochs2, warmup_epochs3=config.warmup_epochs3, warmup_epochs4=config.warmup_epochs4, warmup_epochs5=config.warmup_epochs5, total_epochs=args_opt.epoch_size, steps_per_epoch=dataset_size)) opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, loss_scale) net = TrainingWrapper(net, opt, loss_scale) model = Model(net) print("Start train retinanet, the first epoch will be slower because of the graph compilation.") cb = [TimeMonitor(), LossMonitor()] cb += [Monitor(lr_init=lr.asnumpy())] config_ck = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs, keep_checkpoint_max=config.keep_checkpoint_max) ckpt_cb = ModelCheckpoint(prefix="retinanet", directory=config.save_checkpoint_path, config=config_ck) if args_opt.distribute: if rank == 0: cb += [ckpt_cb] model.train(args_opt.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True) else: cb += [ckpt_cb] model.train(args_opt.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True) if __name__ == '__main__': main()