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

# 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 CTPN and get checkpoint files."""
import os
import time
import argparse
import ast
import mindspore.common.dtype as mstype
from mindspore import context, Tensor
from mindspore.communication.management import init
from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, TimeMonitor
from mindspore.train import Model
from mindspore.context import ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from mindspore.nn import Momentum
from mindspore.common import set_seed
from src.ctpn import CTPN
from src.config import config, pretrain_config, finetune_config
from src.dataset import create_ctpn_dataset
from src.lr_schedule import dynamic_lr
from src.network_define import LossCallBack, LossNet, WithLossCell, TrainOneStepCell
from src.eval_utils import eval_for_ctpn, get_eval_result
from src.eval_callback import EvalCallBack

set_seed(1)

parser = argparse.ArgumentParser(description="CTPN training")
parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default: false.")
parser.add_argument("--pre_trained", type=str, default="", help="Pretrained file path.")
parser.add_argument("--device_id", type=int, default=0, help="Device id, default: 0.")
parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default: 1.")
parser.add_argument("--rank_id", type=int, default=0, help="Rank id, default: 0.")
parser.add_argument("--task_type", type=str, default="Pretraining",\
    choices=['Pretraining', 'Finetune'], help="task type, default:Pretraining")
parser.add_argument("--run_eval", type=ast.literal_eval, default=False, \
    help="Run evaluation when training, default is False.")
parser.add_argument("--save_best_ckpt", type=ast.literal_eval, default=True, \
    help="Save best checkpoint when run_eval is True, default is True.")
parser.add_argument("--eval_image_path", type=str, default="", \
    help="eval image path, when run_eval is True, eval_image_path should be set.")
parser.add_argument("--eval_dataset_path", type=str, default="", \
    help="eval dataset path, when run_eval is True, eval_dataset_path should be set.")
parser.add_argument("--eval_start_epoch", type=int, default=10, \
    help="Evaluation start epoch when run_eval is True, default is 10.")
parser.add_argument("--eval_interval", type=int, default=10, \
    help="Evaluation interval when run_eval is True, default is 10.")
args_opt = parser.parse_args()

context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id, save_graphs=True)

def apply_eval(eval_param):
    network = eval_param["eval_network"]
    eval_ds = eval_param["eval_dataset"]
    eval_image_path = eval_param["eval_image_path"]
    eval_for_ctpn(network, eval_ds, eval_image_path)
    hmean = get_eval_result()
    return hmean

if __name__ == '__main__':
    if args_opt.run_distribute:
        rank = args_opt.rank_id
        device_num = args_opt.device_num
        context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                          gradients_mean=True)
        init()
    else:
        rank = 0
        device_num = 1
    if args_opt.task_type == "Pretraining":
        print("Start to do pretraining")
        mindrecord_file = config.pretraining_dataset_file
        training_cfg = pretrain_config
    else:
        print("Start to do finetune")
        mindrecord_file = config.finetune_dataset_file
        training_cfg = finetune_config

    print("CHECKING MINDRECORD FILES ...")
    while not os.path.exists(mindrecord_file + ".db"):
        time.sleep(5)

    print("CHECKING MINDRECORD FILES DONE!")

    loss_scale = float(config.loss_scale)

    # When create MindDataset, using the fitst mindrecord file, such as ctpn_pretrain.mindrecord0.
    dataset = create_ctpn_dataset(mindrecord_file, repeat_num=1,\
        batch_size=config.batch_size, device_num=device_num, rank_id=rank)
    dataset_size = dataset.get_dataset_size()
    net = CTPN(config=config, batch_size=config.batch_size)
    net = net.set_train()

    load_path = args_opt.pre_trained
    if args_opt.task_type == "Pretraining":
        print("load backbone vgg16 ckpt {}".format(args_opt.pre_trained))
        param_dict = load_checkpoint(load_path)
        for item in list(param_dict.keys()):
            if not item.startswith('vgg16_feature_extractor'):
                param_dict.pop(item)
        load_param_into_net(net, param_dict)
    else:
        if load_path != "":
            print("load pretrain ckpt {}".format(args_opt.pre_trained))
            param_dict = load_checkpoint(load_path)
            load_param_into_net(net, param_dict)
    loss = LossNet()
    lr = Tensor(dynamic_lr(training_cfg, dataset_size), mstype.float32)
    opt = Momentum(params=net.trainable_params(), learning_rate=lr, momentum=config.momentum,\
        weight_decay=config.weight_decay, loss_scale=config.loss_scale)
    net_with_loss = WithLossCell(net, loss)
    if args_opt.run_distribute:
        net_with_grads = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale, reduce_flag=True, \
            mean=True, degree=device_num)
    else:
        net_with_grads = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale)

    time_cb = TimeMonitor(data_size=dataset_size)
    loss_cb = LossCallBack(rank_id=rank)
    cb = [time_cb, loss_cb]
    save_checkpoint_path = os.path.join(config.save_checkpoint_path, "ckpt_" + str(rank) + "/")
    if config.save_checkpoint:
        ckptconfig = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs*dataset_size,
                                      keep_checkpoint_max=config.keep_checkpoint_max)
        ckpoint_cb = ModelCheckpoint(prefix='ctpn', directory=save_checkpoint_path, config=ckptconfig)
        cb += [ckpoint_cb]
    if args_opt.run_eval:
        if args_opt.eval_dataset_path is None or (not os.path.isfile(args_opt.eval_dataset_path)):
            raise ValueError("{} is not a existing path.".format(args_opt.eval_dataset_path))
        if args_opt.eval_image_path is None or (not os.path.isdir(args_opt.eval_image_path)):
            raise ValueError("{} is not a existing path.".format(args_opt.eval_image_path))
        eval_dataset = create_ctpn_dataset(args_opt.eval_dataset_path, \
            batch_size=config.batch_size, repeat_num=1, is_training=False)
        eval_net = net
        eval_param_dict = {"eval_network": eval_net, "eval_dataset": eval_dataset, \
            "eval_image_path": args_opt.eval_image_path}
        eval_cb = EvalCallBack(apply_eval, eval_param_dict, interval=args_opt.eval_interval,
                               eval_start_epoch=args_opt.eval_start_epoch, save_best_ckpt=True,
                               ckpt_directory=save_checkpoint_path, besk_ckpt_name="best_acc.ckpt",
                               metrics_name="hmean")
        cb += [eval_cb]
    model = Model(net_with_grads)
    model.train(training_cfg.total_epoch, dataset, callbacks=cb, dataset_sink_mode=True)