Faster RCNN train.py

定义了一个类：class SolverWrapper

class SolverWrapper(object):
    """A simple wrapper around Caffe's solver.
    This wrapper gives us control over he 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
        #cfg.TRAIN.HAS_RPN: True --->  只有在训练rpn是TRAIN.HAS_RPN才为ture
        #cfg.TRAIN.BBOX_REG: False ---> 只有在训练rpn的时候才为False
        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

        if cfg.TRAIN.BBOX_REG:
            print 'Computing bounding-box regression targets...'
            self.bbox_means, self.bbox_stds = \
                    #add_bbox_regression_targets主要是为roidb添加一些有用的信息，比如bbox_targets mean std 
                    rdl_roidb.add_bbox_regression_targets(roidb)
            print 'done'
        #新建一个sgdsolver示例，此过程会：调用底层SGDSolver的构造函数 --> creating Net --> Init各层 直至“Network initialization done.”，直至“Solver scaffolding done.”
        self.solver = caffe.SGDSolver(solver_prototxt)
        if pretrained_model is not None:
            print ('Loading pretrained model '
                   'weights from {:s}').format(pretrained_model)
              #加载已经训练好的网络，这是fine-tuning所需要调用的代码，同时，solver.net.copy_from会调用底层的Net<Dtype>::CopyTrainedLayersFrom方法
            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)

        # 调用later.py中的set_roidb方法，为网络的第一层（RoIDataLayer）设置roidb同时打乱顺序
        # 这样做是必然的，毕竟类imdb或者pascal_voc的实例中的roidb必须要传到layer中，网络才能继续向前传播；
        # 在RoIDataLayer的foward方法中，就是将RoIDataLayer实例的_roidb拷贝给RoIDataLayer的top blob
        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.
        """
        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
            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)

        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)

        net.save(str(filename))
        print 'Wrote snapshot to: {:s}'.format(filename)

        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()
            #step（1）迭代一次，调用底层的Solver::Step()方法
            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)

            # iter是一个Caffe.Solver的一个属性，调用底层的Solver<Dtype>::iter方法
            if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0:
                last_snapshot_iter = self.solver.iter
                model_paths.append(self.snapshot())

        if last_snapshot_iter != self.solver.iter:
            model_paths.append(self.snapshot())
        return model_paths