# 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 os 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, data_to_mindrecord_byte_image, voc_data_to_mindrecord from src.lr_schedule import get_lr from src.init_params import init_net_param, filter_checkpoint_parameter set_seed(1) def main(): parser = argparse.ArgumentParser(description="SSD 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=0.05, 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 weight parameters, default is False.") parser.add_argument("--run_platform", type=str, default="Ascend", choices=("Ascend", "GPU"), help="run platform, only support Ascend and GPU.") args_opt = parser.parse_args() if args_opt.run_platform == "Ascend": context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", 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 = args_opt.device_id % device_num else: rank = 0 device_num = 1 elif args_opt.run_platform == "GPU": context.set_context(mode=context.GRAPH_MODE, device_target="GPU", device_id=args_opt.device_id) init() 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) rank = get_rank() else: rank = 0 device_num = 1 else: raise ValueError("Unsupported platform.") print("Start create dataset!") # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is ssd.mindrecord0, 1, ... file_num. prefix = "ssd.mindrecord" mindrecord_dir = config.mindrecord_dir mindrecord_file = os.path.join(mindrecord_dir, prefix + "0") if 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): print("Create Mindrecord.") data_to_mindrecord_byte_image("coco", True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("coco_root not exits.") elif args_opt.dataset == "voc": if os.path.isdir(config.voc_dir): print("Create Mindrecord.") voc_data_to_mindrecord(mindrecord_dir, True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("voc_dir not exits.") else: if os.path.isdir(config.image_dir) and os.path.exists(config.anno_path): print("Create Mindrecord.") 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.") if not args_opt.only_create_dataset: loss_scale = float(args_opt.loss_scale) # When create MindDataset, using the fitst mindrecord file, such as ssd.mindrecord0. dataset = create_ssd_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 = 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: 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_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": 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()