faster rcnn源码解读（五）之layer（网络里的input-data）
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faster rcnn源码解读（五）之layer（网络里的input-data）
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#?--------------------------------------------------------?? #?Fast?R-CNN?? #?Copyright?(c)?2015?Microsoft?? #?Licensed?under?The?MIT?License?[see?LICENSE?for?details]?? #?Written?by?Ross?Girshick?? #?--------------------------------------------------------?? ?? """The?data?layer?used?during?training?to?train?a?Fast?R-CNN?network.? ? RoIDataLayer?implements?a?Caffe?Python?layer.? """?? ?? import?caffe?? from?fast_rcnn.config?import?cfg?? from?roi_data_layer.minibatch?import?get_minibatch?? import?numpy?as?np?? import?yaml?? from?multiprocessing?import?Process,?Queue?? ?? class?RoIDataLayer(caffe.Layer):?? ????"""Fast?R-CNN?data?layer?used?for?training."""?? ?? ????def?_shuffle_roidb_inds(self):?? ????????"""Randomly?permute?the?training?roidb."""?? ????????if?cfg.TRAIN.ASPECT_GROUPING:?? ????????????widths?=?np.array([r['width']?for?r?in?self._roidb])?? ????????????heights?=?np.array([r['height']?for?r?in?self._roidb])?? ????????????horz?=?(widths?>=?heights)?? ????????????vert?=?np.logical_not(horz)?? ????????????horz_inds?=?np.where(horz)[0]?? ????????????vert_inds?=?np.where(vert)[0]?? ????????????inds?=?np.hstack((?? ????????????????np.random.permutation(horz_inds),?? ????????????????np.random.permutation(vert_inds)))?? ????????????inds?=?np.reshape(inds,?(-1,?2))?? ????????????row_perm?=?np.random.permutation(np.arange(inds.shape[0]))?? ????????????inds?=?np.reshape(inds[row_perm,?:],?(-1,))?? ????????????self._perm?=?inds#把roidb的索引打亂，造成的shuffle，打亂的索引存儲的地方?? ????????else:?? ????????????self._perm?=?np.random.permutation(np.arange(len(self._roidb)))?? ????????self._cur?=?0?? ?? ????def?_get_next_minibatch_inds(self):?? ????????"""Return?the?roidb?indices?for?the?next?minibatch."""?? ????????if?self._cur?+?cfg.TRAIN.IMS_PER_BATCH?>=?len(self._roidb):?? ????????????self._shuffle_roidb_inds()?? ?? ????????db_inds?=?self._perm[self._cur:self._cur?+?cfg.TRAIN.IMS_PER_BATCH]?? ????????self._cur?+=?cfg.TRAIN.IMS_PER_BATCH#相當于一個指向_perm的指針，每次取走圖片后，他會跟著變化#cfg.TRAIN.IMS_PER_BATCH：?（猜測，每次取圖片的數量）?? ????????return?db_inds#本次取得圖片的索引?? ?? ????def?_get_next_minibatch(self):#取得本次圖片的索引，即db_inds?? ????????"""Return?the?blobs?to?be?used?for?the?next?minibatch.? ? ????????If?cfg.TRAIN.USE_PREFETCH?is?True,?then?blobs?will?be?computed?in?a? ????????separate?process?and?made?available?through?self._blob_queue.? ????????"""?? ????????if?cfg.TRAIN.USE_PREFETCH:?? ????????????return?self._blob_queue.get()?? ????????else:?? ????????????db_inds?=?self._get_next_minibatch_inds()?? ????????????minibatch_db?=?[self._roidb[i]?for?i?in?db_inds]#本次的roidb?? ????????????return?get_minibatch(minibatch_db,?self._num_classes)?? ?? ????def?set_roidb(self,?roidb):?? ????????"""Set?the?roidb?to?be?used?by?this?layer?during?training."""?? ????????self._roidb?=?roidb?? ????????self._shuffle_roidb_inds()?? ????????if?cfg.TRAIN.USE_PREFETCH:?? ????????????self._blob_queue?=?Queue(10)?? ????????????self._prefetch_process?=?BlobFetcher(self._blob_queue,?? ?????????????????????????????????????????????????self._roidb,?? ?????????????????????????????????????????????????self._num_classes)?? ????????????self._prefetch_process.start()?? ????????????#?Terminate?the?child?process?when?the?parent?exists?? ????????????def?cleanup():?? ????????????????print?'Terminating?BlobFetcher'?? ????????????????self._prefetch_process.terminate()?? ????????????????self._prefetch_process.join()?? ????????????import?atexit?? ????????????atexit.register(cleanup)?? ?? ????def?setup(self,?bottom,?top):?? ????????"""Setup?the?RoIDataLayer."""?? ?? ????????#?parse?the?layer?parameter?string,?which?must?be?valid?YAML?? ????????layer_params?=?yaml.load(self.param_str_)?? ?? ????????self._num_classes?=?layer_params['num_classes']#網絡里的類別數值21?? ?? ????????self._name_to_top_map?=?{}#{'gt_boxes':?2,?'data':?0,?'im_info':?1}字典的value值是top的對應索引?? ?? ????????#?data?blob:?holds?a?batch?of?N?images,?each?with?3?channels?? ????????idx?=?0?? ????????top[idx].reshape(cfg.TRAIN.IMS_PER_BATCH,?3,?? ????????????max(cfg.TRAIN.SCALES),?cfg.TRAIN.MAX_SIZE)?? ????????self._name_to_top_map['data']?=?idx?? ????????idx?+=?1?? ?? ????????if?cfg.TRAIN.HAS_RPN:?? ????????????top[idx].reshape(1,?3)?? ????????????self._name_to_top_map['im_info']?=?idx?? ????????????idx?+=?1?? ?? ????????????top[idx].reshape(1,?4)?? ????????????self._name_to_top_map['gt_boxes']?=?idx?? ????????????idx?+=?1?? ????????else:?#?not?using?RPN?? ????????????#?rois?blob:?holds?R?regions?of?interest,?each?is?a?5-tuple?? ????????????#?(n,?x1,?y1,?x2,?y2)?specifying?an?image?batch?index?n?and?a?? ????????????#?rectangle?(x1,?y1,?x2,?y2)?? ????????????top[idx].reshape(1,?5)?? ????????????self._name_to_top_map['rois']?=?idx?? ????????????idx?+=?1?? ?? ????????????#?labels?blob:?R?categorical?labels?in?[0,?...,?K]?for?K?foreground?? ????????????#?classes?plus?background?? ????????????top[idx].reshape(1)?? ????????????self._name_to_top_map['labels']?=?idx?? ????????????idx?+=?1?? ?? ????????????if?cfg.TRAIN.BBOX_REG:?? ????????????????#?bbox_targets?blob:?R?bounding-box?regression?targets?with?4?? ????????????????#?targets?per?class?? ????????????????top[idx].reshape(1,?self._num_classes?*?4)?? ????????????????self._name_to_top_map['bbox_targets']?=?idx?? ????????????????idx?+=?1?? ?? ????????????????#?bbox_inside_weights?blob:?At?most?4?targets?per?roi?are?active;?? ????????????????#?thisbinary?vector?sepcifies?the?subset?of?active?targets?? ????????????????top[idx].reshape(1,?self._num_classes?*?4)?? ????????????????self._name_to_top_map['bbox_inside_weights']?=?idx?? ????????????????idx?+=?1?? ?? ????????????????top[idx].reshape(1,?self._num_classes?*?4)?? ????????????????self._name_to_top_map['bbox_outside_weights']?=?idx?? ????????????????idx?+=?1?? ?? ????????print?'RoiDataLayer:?name_to_top:',?self._name_to_top_map?? ????????assert?len(top)?==?len(self._name_to_top_map)?? ?? ????def?forward(self,?bottom,?top):?? ????????"""Get?blobs?and?copy?them?into?this?layer's?top?blob?vector."""?? ????????blobs?=?self._get_next_minibatch()?? ?? ????????for?blob_name,?blob?in?blobs.iteritems():?? ????????????top_ind?=?self._name_to_top_map[blob_name]?? ????????????#?Reshape?net's?input?blobs?? ????????????top[top_ind].reshape(*(blob.shape))?? ????????????#?Copy?data?into?net's?input?blobs?? ????????????top[top_ind].data[...]?=?blob.astype(np.float32,?copy=False)?? ?? ????def?backward(self,?top,?propagate_down,?bottom):?? ????????"""This?layer?does?not?propagate?gradients."""?? ????????pass?? ?? ????def?reshape(self,?bottom,?top):?? ????????"""Reshaping?happens?during?the?call?to?forward."""?? ????????pass?? ?? class?BlobFetcher(Process):?? ????"""Experimental?class?for?prefetching?blobs?in?a?separate?process."""?? ????def?__init__(self,?queue,?roidb,?num_classes):?? ????????super(BlobFetcher,?self).__init__()?? ????????self._queue?=?queue?? ????????self._roidb?=?roidb?? ????????self._num_classes?=?num_classes?? ????????self._perm?=?None?? ????????self._cur?=?0?? ????????self._shuffle_roidb_inds()?? ????????#?fix?the?random?seed?for?reproducibility?? ????????np.random.seed(cfg.RNG_SEED)?? ?? ????def?_shuffle_roidb_inds(self):?? ????????"""Randomly?permute?the?training?roidb."""?? ????????#?TODO(rbg):?remove?duplicated?code?? ????????self._perm?=?np.random.permutation(np.arange(len(self._roidb)))?? ????????self._cur?=?0?? ?? ????def?_get_next_minibatch_inds(self):?? ????????"""Return?the?roidb?indices?for?the?next?minibatch."""?? ????????#?TODO(rbg):?remove?duplicated?code?? ????????if?self._cur?+?cfg.TRAIN.IMS_PER_BATCH?>=?len(self._roidb):?? ????????????self._shuffle_roidb_inds()?? ?? ????????db_inds?=?self._perm[self._cur:self._cur?+?cfg.TRAIN.IMS_PER_BATCH]?? ????????self._cur?+=?cfg.TRAIN.IMS_PER_BATCH?? ????????return?db_inds?? ?? ????def?run(self):?? ????????print?'BlobFetcher?started'?? ????????while?True:?? ????????????db_inds?=?self._get_next_minibatch_inds()?? ????????????minibatch_db?=?[self._roidb[i]?for?i?in?db_inds]?? ????????????blobs?=?get_minibatch(minibatch_db,?self._num_classes)?? ????????????self._queue.put(blobs)??
下面的roidb都只是一次batch的
http://blog.csdn.net/u010668907/article/details/51945844
faster rcnn用python版本的https://github.com/rbgirshick/py-faster-rcnn
layer源碼地址：https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/roi_data_layer/layer.py
源碼：
[python]?view plaincopy print?
3.1 setup在caffe.SGDSolver時調用；setup的top（list猜測是c++的vector）的每個項是caffe._caffe.Blob
（猜測，輸出的Top shape就是上面的top,在setup中被shape；top[0],1 3 [600] 1000;top[1],1 3;top[2], 1 4)（疑問，在forward中blob的數據shape被重置，有時大小甚至會不定）
??3.2 name_to_top: {'gt_boxes': 2, 'data': 0, 'im_info': 1}字典的value值是top的對應索引
??3.3 solver.step(1)會調用layer的reshape、forward
??3.4 self._perm： 把roidb的索引打亂，造成圖片的shuffle，打亂的索引存儲的地方
??3.5 cfg.TRAIN.IMS_PER_BATCH： （猜測，每次取圖片的數量）
??3.6 self._cur： 相當于一個指向_perm的指針，每次取走圖片后，他會跟著變化
??3.7 db_inds： 本次取得圖片的索引
??3.8 def _get_next_minibatch_inds(self)： 取得本次圖片的索引，即db_inds
??3.9 minibatch_db： 本次的roidb
??3.10 _num_classes： 網絡里的類別數值21
??3.11 forward（）： 得到blob并處理放進top
solver.step(1)-》reshape-》forward-》_get_next_minbatch-》_get_next_minbatch_inds-》(前面在layers里,現在進入minibatch組建真正的blob)get_minibatch總結
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