嵌入式培訓
SSD算法是object detection領域是一個比較經典的算法，github上面就有一個寫得較好的MXNet版本的實現(xiàn)代碼，目前該代碼也已經并入MXNet官方github。想要本地實現(xiàn)可以本文中的介紹：SSD算法的MXNet實現(xiàn)。
接下來這一系列就是小編想大家介紹的該代碼中關于實現(xiàn)SSD算法中的一些細節(jié)，也會涉及部分Python語言的巧妙代碼，以訓練模型為切入口展開介紹，按順序閱讀，詳細注釋已經在代碼中給出。這一篇文章介紹訓練模型的入口代碼：train.py腳本，該腳本主要包含一些參數(shù)設置和主函數(shù)。
import argparse
import tools.find_mxnet
import mxnet as mx
import os
import sys
from train.train_net import train_net
def parse_args():
parser = argparse.ArgumentParser(description='Train a Single-shot detection network')
parser.add_argument('--train-path', dest='train_path', help='train record to use',
default=os.path.join(os.getcwd(), 'data', 'train.rec'), type=str)
parser.add_argument('--train-list', dest='train_list', help='train list to use',
default="", type=str)
parser.add_argument('--val-path', dest='val_path', help='validation record to use',
default=os.path.join(os.getcwd(), 'data', 'val.rec'), type=str)
parser.add_argument('--val-list', dest='val_list', help='validation list to use',
default="", type=str)
parser.add_argument('--network', dest='network', type=str, default='vgg16_reduced',
help='which network to use')
parser.add_argument('--batch-size', dest='batch_size', type=int, default=32,
help='training batch size')
parser.add_argument('--resume', dest='resume', type=int, default=-1,
help='resume training from epoch n')
parser.add_argument('--finetune', dest='finetune', type=int, default=-1,
help='finetune from epoch n, rename the model before doing this')
parser.add_argument('--pretrained', dest='pretrained', help='pretrained model prefix',
default=os.path.join(os.getcwd(), 'model', 'vgg16_reduced'), type=str)
parser.add_argument('--epoch', dest='epoch', help='epoch of pretrained model',
default=1, type=int)
parser.add_argument('--prefix', dest='prefix', help='new model prefix',
default=os.path.join(os.getcwd(), 'model', 'ssd'), type=str)
parser.add_argument('--gpus', dest='gpus', help='GPU devices to train with',
default='0', type=str)
parser.add_argument('--begin-epoch', dest='begin_epoch', help='begin epoch of training',
default=0, type=int)
parser.add_argument('--end-epoch', dest='end_epoch', help='end epoch of training',
default=240, type=int)
parser.add_argument('--frequent', dest='frequent', help='frequency of logging',
default=20, type=int)
parser.add_argument('--data-shape', dest='data_shape', type=int, default=300,
help='set image shape')
parser.add_argument('--label-width', dest='label_width', type=int, default=350,
help='force padding label width to sync across train and validation')
parser.add_argument('--lr', dest='learning_rate', type=float, default=0.004,
help='learning rate')
parser.add_argument('--momentum', dest='momentum', type=float, default=0.9,
help='momentum')
parser.add_argument('--wd', dest='weight_decay', type=float, default=0.0005,
help='weight decay')
parser.add_argument('--mean-r', dest='mean_r', type=float, default=123,
help='red mean value')
parser.add_argument('--mean-g', dest='mean_g', type=float, default=117,
help='green mean value')
parser.add_argument('--mean-b', dest='mean_b', type=float, default=104,
help='blue mean value')
parser.add_argument('--lr-steps', dest='lr_refactor_step', type=str, default='80, 160',
help='refactor learning rate at specified epochs')
parser.add_argument('--lr-factor', dest='lr_refactor_ratio', type=str, default=0.1,
help='ratio to refactor learning rate')
parser.add_argument('--freeze', dest='freeze_pattern', type=str, default="^(conv1_|conv2_).*",
help='freeze layer pattern')
parser.add_argument('--log', dest='log_file', type=str, default="train.log",
help='save training log to file')
parser.add_argument('--monitor', dest='monitor', type=int, default=0,
help='log network parameters every N iters if larger than 0')
parser.add_argument('--pattern', dest='monitor_pattern', type=str, default=".*",
help='monitor parameter pattern, as regex')
parser.add_argument('--num-class', dest='num_class', type=int, default=20,
help='number of classes')
parser.add_argument('--num-example', dest='num_example', type=int, default=16551,
help='number of image examples')
parser.add_argument('--class-names', dest='class_names', type=str,
default='aeroplane, bicycle, bird, boat, bottle, bus,
car, cat, chair, cow, diningtable, dog, horse, motorbike,
person, pottedplant, sheep, sofa, train, tvmonitor',
help='string of comma separated names, or text filename')
parser.add_argument('--nms', dest='nms_thresh', type=float, default=0.45,
help='non-maximum suppression threshold')
parser.add_argument('--overlap', dest='overlap_thresh', type=float, default=0.5,
help='evaluation overlap threshold')
parser.add_argument('--force', dest='force_nms', type=bool, default=False,
help='force non-maximum suppression on different class')
parser.add_argument('--use-difficult', dest='use_difficult', type=bool, default=False,
help='use difficult ground-truths in evaluation')
parser.add_argument('--voc07', dest='use_voc07_metric', type=bool, default=True,
help='use PASCAL VOC 07 11-point metric')
args = parser.parse_args()
return args
def parse_class_names(args):
""" parse # classes and class_names if applicable """
num_class = args.num_class
if len(args.class_names) > 0:
if os.path.isfile(args.class_names):
# try to open it to read class names
with open(args.class_names, 'r') as f:
class_names = [l.strip() for l in f.readlines()]
else:
class_names = [c.strip() for c in args.class_names.split(',')]
assert len(class_names) == num_class, str(len(class_names))
for name in class_names:
assert len(name) > 0
else:
class_names = None
return class_names
if __name__ == '__main__':
args = parse_args()
# context list
ctx = [mx.gpu(int(i)) for i in args.gpus.split(',') if i.strip()]
ctx = [mx.cpu()] if not ctx else ctx
# class names if applicable
class_names = parse_class_names(args)
# start training
train_net(args.network, args.train_path, # train_path是訓練數(shù)據的.rec格式的路徑
args.num_class, args.batch_size,
args.data_shape, [args.mean_r, args.mean_g, args.mean_b],
# resume如果大于0，表示從之前保存的檢測模型開始訓練，如果設置為-1，表示重新訓練檢測模型。
args.resume, args.finetune, args.pretrained,
args.epoch, args.prefix, ctx, args.begin_epoch, args.end_epoch,
# frequent就是模型每訓練多少個batch顯示訓練結果，momentum是和梯度下降相關的參數(shù)，weight_decay是正則項的系數(shù)
args.frequent, args.learning_rate, args.momentum, args.weight_decay,
args.lr_refactor_step, args.lr_refactor_ratio, # 這兩個都是學習率策略的參數(shù)
val_path=args.val_path,
num_example=args.num_example,
class_names=class_names, # object detection問題需要標出object的label，對應這邊的class_names
label_pad_width=args.label_width,
# str，默認是“^(conv1_|conv2_).*”，是一個正則表達式，簡單講就是將conv1_開頭和conv2_開頭的參數(shù)都固定，
# 比如conv1_1_weight，conv1_1_bias等，在模型更新過程中參數(shù)不變。
freeze_layer_pattern=args.freeze_pattern,
iter_monitor=args.monitor,
monitor_pattern=args.monitor_pattern,
# 訓練模型時候得到的log文件存放的路徑和命名，代碼中默認是叫train.log，放在當前目錄下
log_file=args.log_file,
# nms是去掉重復框的一個操作，這個nms_thresh是一個閾值，表示高于這個閾值就說明重復太厲害了，需要去掉
nms_thresh=args.nms_thresh,
force_nms=args.force_nms,
ovp_thresh=args.overlap_thresh,
# 在evaluation的時候用不同的ground-truths
use_difficult=args.use_difficult,
voc07_metric=args.use_voc07_metric)
從上面的代碼可以看出，主要的訓練代碼是通過train_net.py腳本的train_net函數(shù)進行的，因此接下來就一起來看看train_net.py腳本的內容吧
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