forked from OSSInnovation/mindspore
163 lines
8.3 KiB
Python
163 lines
8.3 KiB
Python
# 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 YOLOv3 example ########################
|
|
train YOLOv3 and get network model files(.ckpt) :
|
|
python train.py --image_dir /data --anno_path /data/coco/train_coco.txt --mindrecord_dir=/data/Mindrecord_train
|
|
|
|
If the mindrecord_dir is empty, it wil generate mindrecord file by image_dir and anno_path.
|
|
Note if mindrecord_dir isn't empty, it will use mindrecord_dir rather than image_dir and anno_path.
|
|
"""
|
|
|
|
import os
|
|
import argparse
|
|
import numpy as np
|
|
import mindspore.nn as nn
|
|
from mindspore import context, Tensor
|
|
from mindspore.communication.management import init
|
|
from mindspore.train.callback import CheckpointConfig, ModelCheckpoint, LossMonitor, TimeMonitor
|
|
from mindspore.train import Model, ParallelMode
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
from mindspore.common.initializer import initializer
|
|
|
|
from src.yolov3 import yolov3_resnet18, YoloWithLossCell, TrainingWrapper
|
|
from src.dataset import create_yolo_dataset, data_to_mindrecord_byte_image
|
|
from src.config import ConfigYOLOV3ResNet18
|
|
|
|
|
|
def get_lr(learning_rate, start_step, global_step, decay_step, decay_rate, steps=False):
|
|
"""Set learning rate."""
|
|
lr_each_step = []
|
|
for i in range(global_step):
|
|
if steps:
|
|
lr_each_step.append(learning_rate * (decay_rate ** (i // decay_step)))
|
|
else:
|
|
lr_each_step.append(learning_rate * (decay_rate ** (i / decay_step)))
|
|
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
|
lr_each_step = lr_each_step[start_step:]
|
|
return lr_each_step
|
|
|
|
|
|
def init_net_param(network, init_value='ones'):
|
|
"""Init:wq the parameters in network."""
|
|
params = network.trainable_params()
|
|
for p in params:
|
|
if isinstance(p.data, Tensor) and 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
|
|
p.set_parameter_data(initializer(init_value, p.data.shape, p.data.dtype))
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="YOLOv3 train")
|
|
parser.add_argument("--only_create_dataset", type=bool, default=False, help="If set it true, only create "
|
|
"Mindrecord, default is false.")
|
|
parser.add_argument("--distribute", type=bool, 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=0.001, help="Learning rate, default is 0.001.")
|
|
parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink")
|
|
parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10")
|
|
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=5, help="Save checkpoint epochs, default is 5.")
|
|
parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.")
|
|
parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord_train",
|
|
help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by"
|
|
"image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir "
|
|
"rather than image_dir and anno_path. Default is ./Mindrecord_train")
|
|
parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, "
|
|
"the absolute image path is joined by the image_dir "
|
|
"and the relative path in anno_path")
|
|
parser.add_argument("--anno_path", type=str, default="", help="Annotation path.")
|
|
args_opt = parser.parse_args()
|
|
|
|
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, mirror_mean=True,
|
|
device_num=device_num)
|
|
init()
|
|
rank = args_opt.device_id % device_num
|
|
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 yolo.mindrecord0, 1, ... file_num.
|
|
if not os.path.isdir(args_opt.mindrecord_dir):
|
|
os.makedirs(args_opt.mindrecord_dir)
|
|
|
|
prefix = "yolo.mindrecord"
|
|
mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0")
|
|
if not os.path.exists(mindrecord_file):
|
|
if os.path.isdir(args_opt.image_dir) and os.path.exists(args_opt.anno_path):
|
|
print("Create Mindrecord.")
|
|
data_to_mindrecord_byte_image(args_opt.image_dir,
|
|
args_opt.anno_path,
|
|
args_opt.mindrecord_dir,
|
|
prefix=prefix,
|
|
file_num=8)
|
|
print("Create Mindrecord Done, at {}".format(args_opt.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 yolo.mindrecord0.
|
|
dataset = create_yolo_dataset(mindrecord_file, repeat_num=args_opt.epoch_size,
|
|
batch_size=args_opt.batch_size, device_num=device_num, rank=rank)
|
|
dataset_size = dataset.get_dataset_size()
|
|
print("Create dataset done!")
|
|
|
|
net = yolov3_resnet18(ConfigYOLOV3ResNet18())
|
|
net = YoloWithLossCell(net, ConfigYOLOV3ResNet18())
|
|
init_net_param(net, "XavierUniform")
|
|
|
|
# checkpoint
|
|
ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
|
|
ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, 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)
|
|
load_param_into_net(net, param_dict)
|
|
total_epoch_size = 60
|
|
if args_opt.distribute:
|
|
total_epoch_size = 160
|
|
lr = Tensor(get_lr(learning_rate=args_opt.lr, start_step=args_opt.pre_trained_epoch_size * dataset_size,
|
|
global_step=total_epoch_size * dataset_size,
|
|
decay_step=1000, decay_rate=0.95, steps=True))
|
|
opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=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 YOLOv3, 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()
|