You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
185 lines
9.4 KiB
185 lines
9.4 KiB
# 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
|
|
#
|
|
# 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 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, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
|
|
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_by_list
|
|
|
|
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, 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=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 ssd_model_build(args_opt):
|
|
if config.model == "ssd300":
|
|
backbone = ssd_mobilenet_v2()
|
|
ssd = SSD300(backbone=backbone, config=config)
|
|
init_net_param(ssd)
|
|
if args_opt.freeze_layer == "backbone":
|
|
for param in backbone.feature_1.trainable_params():
|
|
param.requires_grad = False
|
|
elif config.model == "ssd_mobilenet_v1_fpn":
|
|
ssd = ssd_mobilenet_v1_fpn(config=config)
|
|
init_net_param(ssd)
|
|
if config.feature_extractor_base_param != "":
|
|
param_dict = load_checkpoint(config.feature_extractor_base_param)
|
|
for x in list(param_dict.keys()):
|
|
param_dict["network.feature_extractor.mobilenet_v1." + x] = param_dict[x]
|
|
del param_dict[x]
|
|
load_param_into_net(ssd.feature_extractor.mobilenet_v1.network, param_dict)
|
|
elif config.model == "ssd_resnet50_fpn":
|
|
ssd = ssd_resnet50_fpn(config=config)
|
|
init_net_param(ssd)
|
|
if config.feature_extractor_base_param != "":
|
|
param_dict = load_checkpoint(config.feature_extractor_base_param)
|
|
for x in list(param_dict.keys()):
|
|
param_dict["network.feature_extractor.resnet." + x] = param_dict[x]
|
|
del param_dict[x]
|
|
load_param_into_net(ssd.feature_extractor.resnet, param_dict)
|
|
elif config.model == "ssd_vgg16":
|
|
ssd = ssd_vgg16(config=config)
|
|
init_net_param(ssd)
|
|
if config.feature_extractor_base_param != "":
|
|
param_dict = load_checkpoint(config.feature_extractor_base_param)
|
|
from src.vgg16 import ssd_vgg_key_mapper
|
|
for k in ssd_vgg_key_mapper:
|
|
v = ssd_vgg_key_mapper[k]
|
|
param_dict["network.backbone." + v + ".weight"] = param_dict[k + ".weight"]
|
|
del param_dict[k + ".weight"]
|
|
load_param_into_net(ssd.backbone, param_dict)
|
|
else:
|
|
raise ValueError(f'config.model: {config.model} is not supported')
|
|
return ssd
|
|
|
|
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()
|
|
if config.model == "ssd_resnet50_fpn":
|
|
context.set_auto_parallel_context(all_reduce_fusion_config=[90, 183, 279])
|
|
if config.model == "ssd_vgg16":
|
|
context.set_auto_parallel_context(all_reduce_fusion_config=[20, 41, 62])
|
|
else:
|
|
context.set_auto_parallel_context(all_reduce_fusion_config=[29, 58, 89])
|
|
rank = get_rank()
|
|
|
|
mindrecord_file = create_mindrecord(args_opt.dataset, "ssd.mindrecord", True)
|
|
|
|
if args_opt.only_create_dataset:
|
|
return
|
|
|
|
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(f"Create dataset done! dataset size is {dataset_size}")
|
|
ssd = ssd_model_build(args_opt)
|
|
if ("use_float16" in config and config.use_float16) or args_opt.run_platform == "GPU":
|
|
ssd.to_float(dtype.float16)
|
|
net = SSDWithLossCell(ssd, config)
|
|
|
|
# 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_by_list(param_dict, config.checkpoint_filter_list)
|
|
load_param_into_net(net, param_dict, True)
|
|
|
|
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))
|
|
|
|
if "use_global_norm" in config and config.use_global_norm:
|
|
opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr,
|
|
config.momentum, config.weight_decay, 1.0)
|
|
net = TrainingWrapper(net, opt, loss_scale, True)
|
|
else:
|
|
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()
|