forked from mindspore-Ecosystem/mindspore
177 lines
7.9 KiB
Python
177 lines
7.9 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 SSD and get checkpoint files."""
|
|
|
|
import os
|
|
import math
|
|
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 mindspore.model_zoo.ssd import SSD300, SSDWithLossCell, TrainingWrapper, ssd_mobilenet_v2
|
|
from config import ConfigSSD
|
|
from dataset import create_ssd_dataset, data_to_mindrecord_byte_image
|
|
|
|
|
|
def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
|
|
"""
|
|
generate learning rate array
|
|
|
|
Args:
|
|
global_step(int): total steps of the training
|
|
lr_init(float): init learning rate
|
|
lr_end(float): end learning rate
|
|
lr_max(float): max learning rate
|
|
warmup_epochs(int): number of warmup epochs
|
|
total_epochs(int): total epoch of training
|
|
steps_per_epoch(int): steps of one epoch
|
|
|
|
Returns:
|
|
np.array, learning rate array
|
|
"""
|
|
lr_each_step = []
|
|
total_steps = steps_per_epoch * total_epochs
|
|
warmup_steps = steps_per_epoch * warmup_epochs
|
|
for i in range(total_steps):
|
|
if i < warmup_steps:
|
|
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
|
|
else:
|
|
lr = lr_end + (lr_max - lr_end) * \
|
|
(1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.
|
|
if lr < 0.0:
|
|
lr = 0.0
|
|
lr_each_step.append(lr)
|
|
|
|
current_step = global_step
|
|
lr_each_step = np.array(lr_each_step).astype(np.float32)
|
|
learning_rate = lr_each_step[current_step:]
|
|
|
|
return learning_rate
|
|
|
|
|
|
def init_net_param(network, initialize_mode='XavierUniform'):
|
|
"""Init the parameters in net."""
|
|
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(initialize_mode, p.data.shape(), p.data.dtype()))
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser(description="SSD training")
|
|
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=0.25, help="Learning rate, default is 0.25.")
|
|
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=70, help="Epoch size, default is 70.")
|
|
parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.")
|
|
parser.add_argument("--checkpoint_path", type=str, default="", help="Checkpoint file path.")
|
|
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.")
|
|
args_opt = parser.parse_args()
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id)
|
|
context.set_context(enable_loop_sink=True, enable_mem_reuse=True)
|
|
|
|
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 ssd.mindrecord0, 1, ... file_num.
|
|
|
|
config = ConfigSSD()
|
|
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.")
|
|
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=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!")
|
|
|
|
ssd = SSD300(backbone=ssd_mobilenet_v2(), config=config)
|
|
net = SSDWithLossCell(ssd, config)
|
|
init_net_param(net)
|
|
|
|
# checkpoint
|
|
ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs)
|
|
ckpoint_cb = ModelCheckpoint(prefix="ssd", directory=None, config=ckpt_config)
|
|
|
|
lr = Tensor(get_lr(global_step=0, lr_init=0, lr_end=0, lr_max=args_opt.lr,
|
|
warmup_epochs=max(args_opt.epoch_size // 20, 1),
|
|
total_epochs=args_opt.epoch_size,
|
|
steps_per_epoch=dataset_size))
|
|
opt = nn.Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, 0.9, 0.0001, loss_scale)
|
|
net = TrainingWrapper(net, opt, loss_scale)
|
|
|
|
if args_opt.checkpoint_path != "":
|
|
param_dict = load_checkpoint(args_opt.checkpoint_path)
|
|
load_param_into_net(net, param_dict)
|
|
|
|
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()
|