二、訓(xùn)練

接下來回到train.py第160行，通過調(diào)用sw.train_model方法進(jìn)行訓(xùn)練：

 1     def train_model(self, max_iters):
 2         """Network training loop."""
 3         last_snapshot_iter = -1
 4         timer = Timer()
 5         model_paths = []
 6         while self.solver.iter < max_iters:
 7             # Make one SGD update
 8             timer.tic()
 9             self.solver.step(1)
10             timer.toc()
11             if self.solver.iter % (10 * self.solver_param.display) == 0:
12                 print 'speed: {:.3f}s / iter'.format(timer.average_time)
13 
14             if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0:
15                 last_snapshot_iter = self.solver.iter
16                 model_paths.append(self.snapshot())
17 
18         if last_snapshot_iter != self.solver.iter:
19             model_paths.append(self.snapshot())
20         return model_paths

方法中的self.solver.step(1)即是網(wǎng)絡(luò)進(jìn)行一次前向傳播和反向傳播。前向傳播時(shí)，數(shù)據(jù)流會(huì)從第一層流動(dòng)到最后一層，最后計(jì)算出loss，然后loss相對(duì)于各層輸入的梯度會(huì)從最后一層計(jì)算回第一層。下面逐層來介紹faster-rcnn算法的運(yùn)行過程。

2.1、input-data layer

第一層是由python代碼構(gòu)成的，其prototxt描述為：

layer {
  name: 'input-data'
  type: 'Python'
  top: 'data'
  top: 'im_info'
  top: 'gt_boxes'
  python_param {
    module: 'roi_data_layer.layer'
    layer: 'RoIDataLayer'
    param_str: "'num_classes': 2"
  }
}

從中可以看出，input-data層有三個(gè)輸出：data、im_info、gt_boxes，其實(shí)現(xiàn)為RoIDataLayer類。這一層對(duì)數(shù)據(jù)的預(yù)處理操作為：對(duì)圖片進(jìn)行長(zhǎng)寬等比例縮放，使短邊縮放至600；如果縮放后，長(zhǎng)邊的長(zhǎng)度大于1000，則以長(zhǎng)邊為基準(zhǔn)，將長(zhǎng)邊縮放至1000，短邊作相應(yīng)的等比例縮放。這一層的3個(gè)輸出分別為：

1、data：1, 3, h, w(一個(gè)batch只支持輸入一張圖)

2、im_info: im_info[0], im_info[1], im_info[2]分別為h, w, target_size/im_origin_size(縮放比例)

3、gt_boxes: (x1, y1, x2, y2, cls)

預(yù)處理部分涉及到的函數(shù)有_get_next_minibatch，get_minibatch，_get_image_blob，prep_im_for_blob，im_list_to_blob。

網(wǎng)絡(luò)在構(gòu)造過程中（即self.solver = caffe.SGDSolver(solver_prototxt)）會(huì)調(diào)用該類的setup方法：

 1 __C.TRAIN.IMS_PER_BATCH = 1
 2 __C.TRAIN.SCALES = [600]
 3 __C.TRAIN.MAX_SIZE = 1000
 4 __C.TRAIN.HAS_RPN = True
 5 __C.TRAIN.BBOX_REG = True
 6 
 7     def setup(self, bottom, top):
 8         """Setup the RoIDataLayer."""
 9 
10         # parse the layer parameter string, which must be valid YAML
11         layer_params = yaml.load(self.param_str_)
12 
13         self._num_classes = layer_params['num_classes']
14 
15         self._name_to_top_map = {}
16 
17         # data blob: holds a batch of N images, each with 3 channels
18         idx = 0
19         top[idx].reshape(cfg.TRAIN.IMS_PER_BATCH, 3,
20             max(cfg.TRAIN.SCALES), cfg.TRAIN.MAX_SIZE)
21         self._name_to_top_map['data'] = idx
22         idx += 1
23 
24         if cfg.TRAIN.HAS_RPN:
25             top[idx].reshape(1, 3)
26             self._name_to_top_map['im_info'] = idx
27             idx += 1
28 
29             top[idx].reshape(1, 4)
30             self._name_to_top_map['gt_boxes'] = idx
31             idx += 1
32         else: # not using RPN
33             # rois blob: holds R regions of interest, each is a 5-tuple
34             # (n, x1, y1, x2, y2) specifying an image batch index n and a
35             # rectangle (x1, y1, x2, y2)
36             top[idx].reshape(1, 5)
37             self._name_to_top_map['rois'] = idx
38             idx += 1
39 
40             # labels blob: R categorical labels in [0, ..., K] for K foreground
41             # classes plus background
42             top[idx].reshape(1)
43             self._name_to_top_map['labels'] = idx
44             idx += 1
45 
46             if cfg.TRAIN.BBOX_REG:
47                 # bbox_targets blob: R bounding-box regression targets with 4
48                 # targets per class
49                 top[idx].reshape(1, self._num_classes * 4)
50                 self._name_to_top_map['bbox_targets'] = idx
51                 idx += 1
52 
53                 # bbox_inside_weights blob: At most 4 targets per roi are active;
54                 # thisbinary vector sepcifies the subset of active targets
55                 top[idx].reshape(1, self._num_classes * 4)
56                 self._name_to_top_map['bbox_inside_weights'] = idx
57                 idx += 1
58 
59                 top[idx].reshape(1, self._num_classes * 4)
60                 self._name_to_top_map['bbox_outside_weights'] = idx
61                 idx += 1
62 
63         print 'RoiDataLayer: name_to_top:', self._name_to_top_map
64         assert len(top) == len(self._name_to_top_map)

主要是對(duì)輸出的shape進(jìn)行定義。要說明的是，在前向傳播的過程中，仍然會(huì)對(duì)輸出的各top的shape進(jìn)行重定義，并且二者定義的shape往往都是不同的。

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