對中間過程進行調試
阿新 • • 發佈：2017-09-16
調試 del caffe roc 生成 director set multi target
這是觀測stage2生成的rpn情況的代碼：
#!/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, osimport 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‘]), rpn_model_path = ‘/home/bnrc/py-faster-rcnn1/output/faster_rcnn_alt_opt/voc_2007_trainval/zf_fast_rcnn_stage2_iter_40000.caffemodel‘, #修改部分 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)
實際上就是根據train部分改的，要想可視化，還需要generate.py中將imdb_proposals函數更改：
if 0: dets = np.hstack((imdb_boxes[i], scores))
將0改為1 就可以了
對中間過程進行調試