faster rcnn源码解读（三）train_faster_rcnn_alt_opt.py

转载自：faster rcnn源码解读（三）train_faster_rcnn_alt_opt.py - 野孩子的专栏 - 博客频道 - CSDN.NET

http://blog.csdn.net/u010668907/article/details/51945320

faster用python版本的https://github.com/rbgirshick/py-faster-rcnn

train_faster_rcnn_alt_opt.py源码在https://github.com/rbgirshick/py-faster-rcnn/blob/master/tools/train_faster_rcnn_alt_opt.py

faster rcnn训练的开始是：faster_rcnn_alt_opt.sh。下面命令是训练的，还有它的参数说明。

1.调用最初脚本的说明

cd $FRCN_ROOT

# ./experiments/scripts/faster_rcnn_alt_opt.sh  GPU  NET  DATASET [options args to {train,test}_net.py]

# GPU_ID is the GPU you want to train on

# NET in {ZF, VGG_CNN_M_1024, VGG16} is the network arch to use

# DATASET is only pascal_voc for now

train_faster_rcnn_alt_opt.py的源码：

 #!/usr/bin/env python

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

 """Train a Faster R-CNN network using alternating optimization.
 This tool implements the alternating optimization algorithm described in our
 NIPS 2015 paper ("Faster R-CNN: Towards Real-time Object Detection with Region
 Proposal Networks." Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun.)
 """

 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
 from rpn.generate import imdb_proposals
 import argparse
 import pprint
 import numpy as np
 import sys, os
 import multiprocessing as mp
 import cPickle
 import shutil

 def parse_args():
     """
     Parse input arguments
     """
     parser = argparse.ArgumentParser(description='Train a Faster R-CNN network')
     parser.add_argument('--gpu', dest='gpu_id',
                         help='GPU device id to use [0]',
                         default=0, type=int)
     parser.add_argument('--net_name', dest='net_name',
                         help='network name (e.g., "ZF")',
                         default=None, type=str)
     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('--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 get_roidb(imdb_name, rpn_file=None):
     imdb = get_imdb(imdb_name)
     print 'Loaded dataset `{:s}` for training'.format(imdb.name)
     imdb.set_proposal_method(cfg.TRAIN.PROPOSAL_METHOD)
     print 'Set proposal method: {:s}'.format(cfg.TRAIN.PROPOSAL_METHOD)
     if rpn_file is not None:
         imdb.config['rpn_file'] = rpn_file
     roidb = get_training_roidb(imdb)
     return roidb, imdb

 def get_solvers(net_name):
     # Faster R-CNN Alternating Optimization
     n = 'faster_rcnn_alt_opt'
     # Solver for each training stage
     solvers = [[net_name, n, 'stage1_rpn_solver60k80k.pt'],
                [net_name, n, 'stage1_fast_rcnn_solver30k40k.pt'],
                [net_name, n, 'stage2_rpn_solver60k80k.pt'],
                [net_name, n, 'stage2_fast_rcnn_solver30k40k.pt']]
     solvers = [os.path.join(cfg.MODELS_DIR, *s) for s in solvers]
     # Iterations for each training stage
     max_iters = [80000, 40000, 80000, 40000]
     # max_iters = [100, 100, 100, 100]
     # Test prototxt for the RPN
     rpn_test_prototxt = os.path.join(
         cfg.MODELS_DIR, net_name, n, 'rpn_test.pt')
     return solvers, max_iters, rpn_test_prototxt

 # ------------------------------------------------------------------------------
 # Pycaffe doesn't reliably free GPU memory when instantiated nets are discarded
 # (e.g. "del net" in Python code). To work around this issue, each training
 # stage is executed in a separate process using multiprocessing.Process.
 # ------------------------------------------------------------------------------

 def _init_caffe(cfg):
     """Initialize pycaffe in a training process.
     """

     import caffe
     # 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(cfg.GPU_ID)

 def train_rpn(queue=None, imdb_name=None, init_model=None, solver=None,
               max_iters=None, cfg=None):
     """Train a Region Proposal Network in a separate training process.
     """

     # Not using any proposals, just ground-truth boxes
     cfg.TRAIN.HAS_RPN = True
     cfg.TRAIN.BBOX_REG = False  # applies only to Fast R-CNN bbox regression
     cfg.TRAIN.PROPOSAL_METHOD = 'gt'
     cfg.TRAIN.IMS_PER_BATCH = 1
     print 'Init model: {}'.format(init_model)
     print('Using config:')
     pprint.pprint(cfg)

     import caffe
     _init_caffe(cfg)

     roidb, imdb = get_roidb(imdb_name)
     print 'roidb len: {}'.format(len(roidb))
     output_dir = get_output_dir(imdb)
     print 'Output will be saved to `{:s}`'.format(output_dir)

     model_paths = train_net(solver, roidb, output_dir,
                             pretrained_model=init_model,
                             max_iters=max_iters)
     # Cleanup all but the final model
     for i in model_paths[:-1]:
         os.remove(i)
     rpn_model_path = model_paths[-1]
     # Send final model path through the multiprocessing queue
     queue.put({'model_path': rpn_model_path})

 def rpn_generate(queue=None, imdb_name=None, rpn_model_path=None, cfg=None,
                  rpn_test_prototxt=None):
     """Use a trained RPN to generate proposals.
     """

     cfg.TEST.RPN_PRE_NMS_TOP_N = -1     # no pre NMS filtering
     cfg.TEST.RPN_POST_NMS_TOP_N = 2000  # limit top boxes after NMS
     print 'RPN model: {}'.format(rpn_model_path)
     print('Using config:')
     pprint.pprint(cfg)

     import caffe
     _init_caffe(cfg)

     # NOTE: the matlab implementation computes proposals on flipped images, too.
     # We compute them on the image once and then flip the already computed
     # proposals. This might cause a minor loss in mAP (less proposal jittering).
     imdb = get_imdb(imdb_name)
     print 'Loaded dataset `{:s}` for proposal generation'.format(imdb.name)

     # Load RPN and configure output directory
     rpn_net = caffe.Net(rpn_test_prototxt, rpn_model_path, caffe.TEST)
     output_dir = get_output_dir(imdb)
     print 'Output will be saved to `{:s}`'.format(output_dir)
     # Generate proposals on the imdb
     rpn_proposals = imdb_proposals(rpn_net, imdb)
     # Write proposals to disk and send the proposal file path through the
     # multiprocessing queue
     rpn_net_name = os.path.splitext(os.path.basename(rpn_model_path))[0]
     rpn_proposals_path = os.path.join(
         output_dir, rpn_net_name + '_proposals.pkl')
     with open(rpn_proposals_path, 'wb') as f:
         cPickle.dump(rpn_proposals, f, cPickle.HIGHEST_PROTOCOL)
     print 'Wrote RPN proposals to {}'.format(rpn_proposals_path)
     queue.put({'proposal_path': rpn_proposals_path})

 def train_fast_rcnn(queue=None, imdb_name=None, init_model=None, solver=None,
                     max_iters=None, cfg=None, rpn_file=None):
     """Train a Fast R-CNN using proposals generated by an RPN.
     """

     cfg.TRAIN.HAS_RPN = False           # not generating prosals on-the-fly
     cfg.TRAIN.PROPOSAL_METHOD = 'rpn'   # use pre-computed RPN proposals instead
     cfg.TRAIN.IMS_PER_BATCH = 2
     print 'Init model: {}'.format(init_model)
     print 'RPN proposals: {}'.format(rpn_file)
     print('Using config:')
     pprint.pprint(cfg)

     import caffe
     _init_caffe(cfg)

     roidb, imdb = get_roidb(imdb_name, rpn_file=rpn_file)
     output_dir = get_output_dir(imdb)
     print 'Output will be saved to `{:s}`'.format(output_dir)
     # Train Fast R-CNN
     model_paths = train_net(solver, roidb, output_dir,
                             pretrained_model=init_model,
                             max_iters=max_iters)
     # Cleanup all but the final model
     for i in model_paths[:-1]:
         os.remove(i)
     fast_rcnn_model_path = model_paths[-1]
     # Send Fast R-CNN model path over the multiprocessing queue
     queue.put({'model_path': fast_rcnn_model_path})

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

     print('Called with args:')
     print(args)

     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

     # --------------------------------------------------------------------------
     # Pycaffe doesn't reliably free GPU memory when instantiated nets are
     # discarded (e.g. "del net" in Python code). To work around this issue, each
     # training stage is executed in a separate process using
     # multiprocessing.Process.
     # --------------------------------------------------------------------------

     # queue for communicated results between processes
     mp_queue = mp.Queue()
     # solves, iters, etc. for each training stage
     solvers, max_iters, rpn_test_prototxt = get_solvers(args.net_name)

     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
     print 'Stage 1 RPN, init from ImageNet model'
     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'

     cfg.TRAIN.SNAPSHOT_INFIX = 'stage1'
     mp_kwargs = dict(
             queue=mp_queue,
             imdb_name=args.imdb_name,
             init_model=args.pretrained_model,
             solver=solvers[0],
             max_iters=max_iters[0],
             cfg=cfg)
     p = mp.Process(target=train_rpn, kwargs=mp_kwargs)
     p.start()
     rpn_stage1_out = mp_queue.get()
     p.join()

     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
     print 'Stage 1 RPN, generate proposals'
     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'

     mp_kwargs = dict(
             queue=mp_queue,
             imdb_name=args.imdb_name,
             rpn_model_path=str(rpn_stage1_out['model_path']),
             cfg=cfg,
             rpn_test_prototxt=rpn_test_prototxt)
     p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
     p.start()
     rpn_stage1_out['proposal_path'] = mp_queue.get()['proposal_path']
     p.join()

     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
     print 'Stage 1 Fast R-CNN using RPN proposals, init from ImageNet model'
     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'

     cfg.TRAIN.SNAPSHOT_INFIX = 'stage1'
     mp_kwargs = dict(
             queue=mp_queue,
             imdb_name=args.imdb_name,
             init_model=args.pretrained_model,
             solver=solvers[1],
             max_iters=max_iters[1],
             cfg=cfg,
             rpn_file=rpn_stage1_out['proposal_path'])
     p = mp.Process(target=train_fast_rcnn, kwargs=mp_kwargs)
     p.start()
     fast_rcnn_stage1_out = mp_queue.get()
     p.join()

     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
     print 'Stage 2 RPN, init from stage 1 Fast R-CNN model'
     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'

     cfg.TRAIN.SNAPSHOT_INFIX = 'stage2'
     mp_kwargs = dict(
             queue=mp_queue,
             imdb_name=args.imdb_name,
             init_model=str(fast_rcnn_stage1_out['model_path']),
             solver=solvers[2],
             max_iters=max_iters[2],
             cfg=cfg)
     p = mp.Process(target=train_rpn, kwargs=mp_kwargs)
     p.start()
     rpn_stage2_out = mp_queue.get()
     p.join()

     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
     print 'Stage 2 RPN, generate proposals'
     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'

     mp_kwargs = dict(
             queue=mp_queue,
             imdb_name=args.imdb_name,
             rpn_model_path=str(rpn_stage2_out['model_path']),
             cfg=cfg,
             rpn_test_prototxt=rpn_test_prototxt)
     p = mp.Process(target=rpn_generate, kwargs=mp_kwargs)
     p.start()
     rpn_stage2_out['proposal_path'] = mp_queue.get()['proposal_path']
     p.join()

     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
     print 'Stage 2 Fast R-CNN, init from stage 2 RPN R-CNN model'
     print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'

     cfg.TRAIN.SNAPSHOT_INFIX = 'stage2'
     mp_kwargs = dict(
             queue=mp_queue,
             imdb_name=args.imdb_name,
             init_model=str(rpn_stage2_out['model_path']),
             solver=solvers[3],
             max_iters=max_iters[3],
             cfg=cfg,
             rpn_file=rpn_stage2_out['proposal_path'])
     p = mp.Process(target=train_fast_rcnn, kwargs=mp_kwargs)
     p.start()
     fast_rcnn_stage2_out = mp_queue.get()
     p.join()

     # Create final model (just a copy of the last stage)
     final_path = os.path.join(
             os.path.dirname(fast_rcnn_stage2_out['model_path']),
             args.net_name + '_faster_rcnn_final.caffemodel')
     print 'cp {} -> {}'.format(
             fast_rcnn_stage2_out['model_path'], final_path)
     shutil.copy(fast_rcnn_stage2_out['model_path'], final_path)
     print 'Final model: {}'.format(final_path)

2. train_faster_rcnn_alt_opt.py的部分参数说明

net_name:      {ZF, VGG_CNN_M_1024, VGG16}

pretrained_model:      data/imagenet_models/${net_name}.v2.caffemodel

cfg_file:     experiments/cfgs/faster_rcnn_alt_opt.yml

imdb_name:     "voc_2007_trainval" or "voc_2007_test"

 

cfg.TRAIN.HAS_RPN = True表示用xml提供的propoal

cfg是配置文件，它的默认值放在上面的cfg_file里，其他还可以自己写配置文件之后与默认配置文件融合。

  2.1 net_name是用get_solvers()找到网络。还要用到cfg的参数MODELS_DIR，

    例子是join（MODELS_DIR， net_name， 'faster_rcnn_alt_opt', 'stage1_rpn_solver60k80k.pt'）

  2.2 imdb_name在factory中被拆成‘2007’（year）和‘trainval’/‘test’（split）到类pascal_voc中产生相应的imdb

  2.3 整个step的大致流程：

(ImageNet model)->stage1_rpn_train->rpn_test

                                                                           |(proposal_path)

                    (ImageNetmodel)->stage1_fast_rcnn_train-> stage2_rpn_train-> rpn_test-> stage2_fast_rcnn_train

  2.4 数据imdb和roidb

  roidb原本是imdb的一个属性，但imdb其实是为了计算roidb存在的，他所有的其他属性和方法都是为了计算roidb