Faster Rcnn 代码解读之 train_val.py

最新推荐文章于 2022-12-14 23:56:24 发布

多一些不为什么的坚持

最新推荐文章于 2022-12-14 23:56:24 发布

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分类专栏： FasterRcnn

本文链接：https://blog.youkuaiyun.com/qq_33193309/article/details/99605937

版权

本文详细解读了Faster R-CNN在训练过程中的核心代码train_val.py，涵盖了目标检测模型的训练流程，包括数据预处理、网络架构、损失计算和反向传播等关键步骤。

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# --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Xinlei Chen and Zheqi He
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from model.config import cfg
import roi_data_layer.roidb as rdl_roidb
from roi_data_layer.layer import RoIDataLayer
from utils.timer import Timer

try:
    import cPickle as pickle
except ImportError:
    import pickle
import numpy as np
import os
import sys
import glob
import time

import tensorflow as tf
from tensorflow.python import pywrap_tensorflow


# Slover的封装类，包含和训练有关的属性和方法
class SolverWrapper(object):
    """
      A wrapper class for the training process
    """

    def __init__(self, sess, network, imdb, roidb, valroidb, output_dir, tbdir, pretrained_model=None):
        self.net = network  # network类的实例
        self.imdb = imdb  # imdb类的实例
        self.roidb = roidb  # roidb字典
        self.valroidb = valroidb  # 验证roidb字典
        self.output_dir = output_dir  # 模型保存路径
        self.tbdir = tbdir  # tensorboard保存路径
        # Simply put '_val' at the end to save the summaries from the validation set
        self.tbvaldir = tbdir + '_val'  # 验证过程的tensorboard保存路径
        if not os.path.exists(self.tbvaldir):
            os.makedirs(self.tbvaldir)
        self.pretrained_model = pretrained_model  # 预训练权重的路径

    # 保存快照，包括模型权重的ckpt文件和训练参数的pkl文件
    def snapshot(self, sess, iter):

        net = self.net

        if not os.path.exists(self.output_dir):
            os.makedirs(self.output_dir)

        # Store the model snapshot
        # 保存模型权重 例：shufflenetv2_faster_rcnn_iter_10000.cpkt等三个文件
        # SNAPSHOT_PREFIX在yml文件中配置 例：'shufflenetv2_faster_rcnn'
        filename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.ckpt'
        filename = os.path.join(self.output_dir, filename)
        self.saver.save(sess, filename)
        print('Wrote snapshot to: {:s}'.format(filename))

        # Also store some meta information, random state, etc.
        # 保存随机数状态等训练过程参数
        nfilename = cfg.TRAIN.SNAPSHOT_PREFIX + '_iter_{:d}'.format(iter) + '.pkl'
        nfilename = os.path.join(self.output_dir, nfilename)
        # current state of numpy random
        # 保存随机数种子
        st0 = np.random.get_state()
        # current position in the database
        # 保存当前图片序号
        cur = self.data_layer._cur
        # current shuffled indexes of the database
        # 保存打乱后的图片序号列表
        perm = self.data_layer._perm
        # current position in the validation database
        # 验证过程，同上
        cur_val = self.data_layer_val._cur
        # current shuffled indexes of the validation database
        perm_val = self.data_layer_val._perm

        # Dump the meta info
        # 写入 例：shufflenetv2_faster_rcnn_iter_10000.pkl
        with open(nfilename, 'wb') as fid:
            pickle.dump(st0, fid, pickle.HIGHEST_PROTOCOL)
            pickle.dump(cur, fid, pickle.HIGHEST_PROTOCOL)
            pickle.dump(perm, fid, pickle.HIGHEST_PROTOCOL)
            pickle.dump(cur_val, fid, pickle.HIGHEST_PROTOCOL)
            pickle.dump(perm_val, fid, pickle.HIGHEST_PROTOCOL)
            pickle.dump(iter, fid, pickle.HIGHEST_PROTOCOL)

        return filename, nfilename

    # 载入快照
    def from_snapshot(self, sess, sfile, nfile):

        print('Restoring model snapshots from {:s}'.format(sfile))
        self.saver.restore(sess, sfile)
        print('Restored.')
        # Needs to restore the other hyper-parameters/states for training, (TODO xinlei) I have
        # tried my best to find the random states so that it can be recovered exactly
        # However the Tensorflow state

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