pyfasterrcnn代码阅读1train_net.py & train.py

# train_net.py
#!/usr/bin/env python

# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------

"""Train a Fast R-CNN network on a region of interest database."""

import _init_paths
from fast_rcnn.train import get_training_roidb, train_net
from fast_rcnn.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from datasets.factory import get_imdb
import datasets.imdb
import caffe
import argparse
import pprint
import numpy as np
import sys

def parse_args():
# 运行时命令行参数
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
    parser.add_argument('--gpu', dest='gpu_id',
                        help='GPU device id to use [0]',
                        default=0, type=int)
    parser.add_argument('--solver', dest='solver',
                        help='solver prototxt',
                        default=None, type=str)
    parser.add_argument('--iters', dest='max_iters',
                        help='number of iterations to train',
                        default=40000, type=int)
    parser.add_argument('--weights', dest='pretrained_model',
                        help='initialize with pretrained model weights',
                        default=None, type=str)
    parser.add_argument('--cfg', dest='cfg_file',
                        help='optional config file',
                        default=None, type=str)
    parser.add_argument('--imdb', dest='imdb_name',
                        help='dataset to train on',
                        default='voc_2007_trainval', type=str)
    parser.add_argument('--rand', dest='randomize',
                        help='randomize (do not use a fixed seed)',
                        action='store_true')
    parser.add_argument('--set', dest='set_cfgs',
                        help='set config keys', default=None,
                        nargs=argparse.REMAINDER)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args

def combined_roidb(imdb_names):
# 融合roidb，roidb来自于数据集（实验可能用到多个），所以需要combine多个数据集的roidb
# 
    def get_roidb(imdb_name):
        imdb = get_imdb(imdb_name)
        print 'Loaded dataset `{:s}` for training'.format(imdb.name)
        # 设置proposal方法，这里是selective search（config.py）
        imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
        print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
　　　　　# 得到用于训练的roidb,定义在train.py,进行了水平翻转，以及为原始roidb添加了一些说明性的属性
        roidb = get_training_roidb(imdb)
        return roidb

    roidbs = [get_roidb(s) for s in imdb_names.split('+')]
    roidb = roidbs[0]
    # 这里进行combine roidb
    if len(roidbs) > 1:
        for r in roidbs[1:]:
            roidb.extend(r)
        imdb = datasets.imdb.imdb(imdb_names)
    else:
        # get_imdb方法定义在dataset/factory.py,通过名字得到imdb
        imdb = get_imdb(imdb_names)
    return imdb, roidb

if __name__ == '__main__':
    args = parse_args()

    print('Called with args:')
    print(args)
    #定义在config.py，见py-faster-rcnn代码阅读2-config.py 
    if args.cfg_file is not None:
        cfg_from_file(args.cfg_file)
    if args.set_cfgs is not None:
        cfg_from_list(args.set_cfgs)

    cfg.GPU_ID = args.gpu_id

    print('Using config:')
    pprint.pprint(cfg)

    if not args.randomize:
        # fix the random seeds (numpy and caffe) for reproducibility
        np.random.seed(cfg.RNG_SEED)
        caffe.set_random_seed(cfg.RNG_SEED)

    # set up caffe
    caffe.set_mode_gpu()
    caffe.set_device(args.gpu_id)

    imdb, roidb = combined_roidb(args.imdb_name)
    print '{:d} roidb entries'.format(len(roidb))

    output_dir = get_output_dir(imdb)
    print 'Output will be saved to `{:s}`'.format(output_dir)
    #定义在train.py
    train_net(args.solver, roidb, output_dir,
              pretrained_model=args.pretrained_model,
              max_iters=args.max_iters)

# train.py

"""Train a Fast R-CNN network."""

import caffe
from fast_rcnn.config import cfg
import roi_data_layer.roidb as rdl_roidb
from utils.timer import Timer
import numpy as np
import os

from caffe.proto import caffe_pb2
import google.protobuf as pb2

class SolverWrapper(object):
    # 对caffe solver的简单封装，封装允许我们控制snapshot过程，用于去除归一化学习到的bb回归权重
    """A simple wrapper around Caffe's solver.
    This wrapper gives us control over the snapshotting process, which we
    use to unnormalize the learned bounding-box regression weights.
    """

    def __init__(self, solver_prototxt, roidb, output_dir,
                 pretrained_model=None):
        """Initialize the SolverWrapper."""
        self.output_dir = output_dir

        if (cfg.TRAIN.HAS_RPN and cfg.TRAIN.BBOX_REG and
            cfg.TRAIN.BBOX_NORMALIZE_TARGETS):
            # RPN can only use precomputed normalization because there are no
            # fixed statistics to compute a priori

            assert cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED
        # 求bb回归器的target,返回box的均值和标准差
        if cfg.TRAIN.BBOX_REG:
            print 'Computing bounding-box regression targets...'
            self.bbox_means, self.bbox_stds = \
                    rdl_roidb.add_bbox_regression_targets(roidb)
            print 'done'
        # solver，copy预训练模型的权重，set roidb
        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
            self.solver.net.copy_from(pretrained_model)

        self.solver_param = caffe_pb2.SolverParameter()
        with open(solver_prototxt, 'rt') as f:
            pb2.text_format.Merge(f.read(), self.solver_param)

        self.solver.net.layers[0].set_roidb(roidb)

    def snapshot(self):
        """Take a snapshot of the network after unnormalizing the learned
        bounding-box regression weights. This enables easy use at test-time.
        """
        # 给snapshot的bb预测参数做去归一化
        net = self.solver.net

        scale_bbox_params = (cfg.TRAIN.BBOX_REG and
                             cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
                             net.params.has_key('bbox_pred'))

        if scale_bbox_params:
            # save original values
            orig_0 = net.params['bbox_pred'][0].data.copy()
            orig_1 = net.params['bbox_pred'][1].data.copy()

            # scale and shift with bbox reg unnormalization; then save snapshot
            # 去除归一化，乘标准差，加上均值
            net.params['bbox_pred'][0].data[...] = \
                    (net.params['bbox_pred'][0].data *
                     self.bbox_stds[:, np.newaxis])
            net.params['bbox_pred'][1].data[...] = \
                    (net.params['bbox_pred'][1].data *
                     self.bbox_stds + self.bbox_means)
        # 给snapshot命名
        infix = ('_' + cfg.TRAIN.SNAPSHOT_INFIX
                 if cfg.TRAIN.SNAPSHOT_INFIX != '' else '')
        filename = (self.solver_param.snapshot_prefix + infix +
                    '_iter_{:d}'.format(self.solver.iter) + '.caffemodel')
        filename = os.path.join(self.output_dir, filename)
        # 存snapshot
        net.save(str(filename))
        print 'Wrote snapshot to: {:s}'.format(filename)

        # 只是存入的snapshot的bb参数做了去归一化用于测试，但是训练部分仍需要保持归一化的状态
        if scale_bbox_params:
            # restore net to original state
            net.params['bbox_pred'][0].data[...] = orig_0
            net.params['bbox_pred'][1].data[...] = orig_1
        return filename

    def train_model(self, max_iters):
        """Network training loop."""
        last_snapshot_iter = -1
        timer = Timer()
        model_paths = []
        while self.solver.iter < max_iters:
            # Make one SGD update
            timer.tic()
            self.solver.step(1)
            timer.toc()
            if self.solver.iter % (10 * self.solver_param.display) == 0:
                print 'speed: {:.3f}s / iter'.format(timer.average_time)
            # 达到预设次数保存snapshot
            if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0:
                last_snapshot_iter = self.solver.iter
                model_paths.append(self.snapshot())
        # 整体迭代完成后也要存snapshot
        if last_snapshot_iter != self.solver.iter:
            model_paths.append(self.snapshot())
        return model_paths

def get_training_roidb(imdb):
    """Returns a roidb (Region of Interest database) for use in training."""
    if cfg.TRAIN.USE_FLIPPED:
        print 'Appending horizontally-flipped training examples...'
        # 水平翻转，得到更多的roidb
        imdb.append_flipped_images()
        print 'done'

    print 'Preparing training data...'
    # 为原始数据集的roidb添加一些说明性的属性，max-overlap,max-classes...
    rdl_roidb.prepare_roidb(imdb)
    print 'done'

    return imdb.roidb

def filter_roidb(roidb):
    """Remove roidb entries that have no usable RoIs."""
    # 删掉没用的RoIs, 有效的图片必须各有前景和背景ROI
    def is_valid(entry):
        # Valid images have:
        #   (1) At least one foreground RoI OR
        #   (2) At least one background RoI
        overlaps = entry['max_overlaps']
        # find boxes with sufficient overlap
        fg_inds = np.where(overlaps >= cfg.TRAIN.FG_THRESH)[0]
        # Select background RoIs as those within [BG_THRESH_LO, BG_THRESH_HI)
        bg_inds = np.where((overlaps < cfg.TRAIN.BG_THRESH_HI) &
                           (overlaps >= cfg.TRAIN.BG_THRESH_LO))[0]
        # image is only valid if such boxes exist
        valid = len(fg_inds) > 0 or len(bg_inds) > 0
        return valid

    num = len(roidb)
    filtered_roidb = [entry for entry in roidb if is_valid(entry)]
    num_after = len(filtered_roidb)
    print 'Filtered {} roidb entries: {} -> {}'.format(num - num_after,
                                                       num, num_after)
    return filtered_roidb

def train_net(solver_prototxt, roidb, output_dir,
              pretrained_model=None, max_iters=40000):
    """Train a Fast R-CNN network."""
    # 训练Fast R-CNN
    roidb = filter_roidb(roidb)
    sw = SolverWrapper(solver_prototxt, roidb, output_dir,
                       pretrained_model=pretrained_model)

    print 'Solving...'
    # 训练...
    model_paths = sw.train_model(max_iters)
    print 'done solving'
    return model_paths