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

# Copyright 2020-2021 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 FasterRcnn and get checkpoint files."""

import os
import time
import argparse
import ast
import numpy as np

import mindspore.common.dtype as mstype
from mindspore import context, Tensor, Parameter
from mindspore.communication.management import init, get_rank, get_group_size
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 SGD
from mindspore.common import set_seed

from src.FasterRcnn.faster_rcnn_r50 import Faster_Rcnn_Resnet50
from src.network_define import LossCallBack, WithLossCell, TrainOneStepCell, LossNet
from src.config import config
from src.dataset import data_to_mindrecord_byte_image, create_fasterrcnn_dataset
from src.lr_schedule import dynamic_lr

set_seed(1)

parser = argparse.ArgumentParser(description="FasterRcnn training")
parser.add_argument("--run_distribute", type=ast.literal_eval, default=False, help="Run distribute, default: false.")
parser.add_argument("--dataset", type=str, default="coco", help="Dataset name, default: coco.")
parser.add_argument("--pre_trained", type=str, default="", help="Pretrained file path.")
parser.add_argument("--device_target", type=str, default="Ascend",
                    help="device where the code will be implemented, default is Ascend")
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.")
args_opt = parser.parse_args()

context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args_opt.device_id)

if __name__ == '__main__':
    if args_opt.run_distribute:
        if args_opt.device_target == "Ascend":
            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:
            init("nccl")
            context.reset_auto_parallel_context()
            rank = get_rank()
            device_num = get_group_size()
            context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
                                              gradients_mean=True)
    else:
        rank = 0
        device_num = 1

    print("Start create dataset!")

    # It will generate mindrecord file in args_opt.mindrecord_dir,
    # and the file name is FasterRcnn.mindrecord0, 1, ... file_num.
    prefix = "FasterRcnn.mindrecord"
    mindrecord_dir = config.mindrecord_dir
    mindrecord_file = os.path.join(mindrecord_dir, prefix + "0")
    print("CHECKING MINDRECORD FILES ...")

    if rank == 0 and not os.path.exists(mindrecord_file):
        if not os.path.isdir(mindrecord_dir):
            os.makedirs(mindrecord_dir)
        if args_opt.dataset == "coco":
            if os.path.isdir(config.coco_root):
                if not os.path.exists(config.coco_root):
                    print("Please make sure config:coco_root is valid.")
                    raise ValueError(config.coco_root)
                print("Create Mindrecord. It may take some time.")
                data_to_mindrecord_byte_image("coco", True, prefix)
                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
            else:
                print("coco_root not exits.")
        else:
            if os.path.isdir(config.image_dir) and os.path.exists(config.anno_path):
                if not os.path.exists(config.image_dir):
                    print("Please make sure config:image_dir is valid.")
                    raise ValueError(config.image_dir)
                print("Create Mindrecord. It may take some time.")
                data_to_mindrecord_byte_image("other", True, prefix)
                print("Create Mindrecord Done, at {}".format(mindrecord_dir))
            else:
                print("image_dir or anno_path not exits.")

    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 FasterRcnn.mindrecord0.
    dataset = create_fasterrcnn_dataset(mindrecord_file, batch_size=config.batch_size,
                                        device_num=device_num, rank_id=rank)

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

    net = Faster_Rcnn_Resnet50(config=config)
    net = net.set_train()

    load_path = args_opt.pre_trained
    if load_path != "":
        param_dict = load_checkpoint(load_path)

        key_mapping = {'down_sample_layer.1.beta': 'bn_down_sample.beta',
                       'down_sample_layer.1.gamma': 'bn_down_sample.gamma',
                       'down_sample_layer.0.weight': 'conv_down_sample.weight',
                       'down_sample_layer.1.moving_mean': 'bn_down_sample.moving_mean',
                       'down_sample_layer.1.moving_variance': 'bn_down_sample.moving_variance',
                       }
        for oldkey in list(param_dict.keys()):
            if not oldkey.startswith(('backbone', 'end_point', 'global_step', 'learning_rate', 'moments', 'momentum')):
                data = param_dict.pop(oldkey)
                newkey = 'backbone.' + oldkey
                param_dict[newkey] = data
                oldkey = newkey
            for k, v in key_mapping.items():
                if k in oldkey:
                    newkey = oldkey.replace(k, v)
                    param_dict[newkey] = param_dict.pop(oldkey)
                    break

        for item in list(param_dict.keys()):
            if not item.startswith('backbone'):
                param_dict.pop(item)

        for key, value in param_dict.items():
            tensor = value.asnumpy().astype(np.float32)
            param_dict[key] = Parameter(tensor, key)
        load_param_into_net(net, param_dict)

    loss = LossNet()
    lr = Tensor(dynamic_lr(config, dataset_size), mstype.float32)

    opt = SGD(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 = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale, reduce_flag=True,
                               mean=True, degree=device_num)
    else:
        net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale)

    time_cb = TimeMonitor(data_size=dataset_size)
    loss_cb = LossCallBack(rank_id=rank)
    cb = [time_cb, loss_cb]
    if config.save_checkpoint:
        ckptconfig = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * dataset_size,
                                      keep_checkpoint_max=config.keep_checkpoint_max)
        save_checkpoint_path = os.path.join(config.save_checkpoint_path, "ckpt_" + str(rank) + "/")
        ckpoint_cb = ModelCheckpoint(prefix='faster_rcnn', directory=save_checkpoint_path, config=ckptconfig)
        cb += [ckpoint_cb]

    model = Model(net)
    model.train(config.epoch_size, dataset, callbacks=cb)