mindspore/model_zoo/official/cv/ssd/train.py

# Copyright 2020 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
#
# less 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 SSD and get checkpoint files."""

import argparse
import ast
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
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, dtype
from src.ssd import SSD300, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2
from src.config import config
from src.dataset import create_ssd_dataset, create_mindrecord
from src.lr_schedule import get_lr
from src.init_params import init_net_param, filter_checkpoint_parameter

set_seed(1)

def get_args():
    parser = argparse.ArgumentParser(description="SSD training")
    parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"),
                        help="run platform, support Ascend, GPU and CPU.")
    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=0.05, help="Learning rate, default is 0.05.")
    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, defalut 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=10, help="Save checkpoint epochs, default is 10.")
    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 head weight parameters, default is False.")
    parser.add_argument('--freeze_layer', type=str, default="none", choices=["none", "backbone"],
                        help="freeze the weights of network, support freeze the backbone's weights, "
                             "default is not freezing.")
    args_opt = parser.parse_args()
    return args_opt

def main():
    args_opt = get_args()
    rank = 0
    device_num = 1
    if args_opt.run_platform == "CPU":
        context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    else:
        context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.run_platform, device_id=args_opt.device_id)
        if args_opt.distribute:
            device_num = args_opt.device_num
            context.reset_auto_parallel_context()
            context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True,
                                              device_num=device_num)
            init()
            rank = get_rank()

    mindrecord_file = create_mindrecord(args_opt.dataset, "ssd.mindrecord", True)
    if not args_opt.only_create_dataset:
        loss_scale = float(args_opt.loss_scale)
        if args_opt.run_platform == "CPU":
            loss_scale = 1.0

        # When create MindDataset, using the fitst mindrecord file, such as ssd.mindrecord0.
        use_multiprocessing = (args_opt.run_platform != "CPU")
        dataset = create_ssd_dataset(mindrecord_file, repeat_num=1, batch_size=args_opt.batch_size,
                                     device_num=device_num, rank=rank, use_multiprocessing=use_multiprocessing)

        dataset_size = dataset.get_dataset_size()
        print("Create dataset done!")

        backbone = ssd_mobilenet_v2()
        ssd = SSD300(backbone=backbone, config=config)
        if args_opt.run_platform == "GPU":
            ssd.to_float(dtype.float16)
        net = SSDWithLossCell(ssd, config)
        init_net_param(net)

        # checkpoint
        ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
        save_ckpt_path = './ckpt_' + str(rank) + '/'
        ckpoint_cb = ModelCheckpoint(prefix="ssd", directory=save_ckpt_path, config=ckpt_config)

        if args_opt.pre_trained:
            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)

        if args_opt.freeze_layer == "backbone":
            for param in backbone.feature_1.trainable_params():
                param.requires_grad = False

        lr = Tensor(get_lr(global_step=args_opt.pre_trained_epoch_size * dataset_size,
                           lr_init=config.lr_init, lr_end=config.lr_end_rate * args_opt.lr, lr_max=args_opt.lr,
                           warmup_epochs=config.warmup_epochs,
                           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)

        callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb]
        model = Model(net)
        dataset_sink_mode = False
        if args_opt.mode == "sink" and args_opt.run_platform != "CPU":
            print("In sink mode, one epoch return a loss.")
            dataset_sink_mode = True
        print("Start train SSD, the first epoch will be slower because of the graph compilation.")
        model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)

if __name__ == '__main__':
    main()