about lib/fast_rcnn/train.py

[foralliance]

@YuwenXiong
@oh233

Can you simply explain the content of the snapshot section

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_faster_rcnn = (cfg.TRAIN.BBOX_REG and
                             cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
                             net.params.has_key('bbox_pred'))

        scale_bbox_params_rfcn = (cfg.TRAIN.BBOX_REG and
                             cfg.TRAIN.BBOX_NORMALIZE_TARGETS and
                             net.params.has_key('rfcn_bbox'))

        scale_bbox_params_rpn = (cfg.TRAIN.RPN_NORMALIZE_TARGETS and
                                 net.params.has_key('rpn_bbox_pred'))

        if scale_bbox_params_faster_rcnn:
            # 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)

        if scale_bbox_params_rpn:
            rpn_orig_0 = net.params['rpn_bbox_pred'][0].data.copy()
            rpn_orig_1 = net.params['rpn_bbox_pred'][1].data.copy()
            num_anchor = rpn_orig_0.shape[0] / 4
            # scale and shift with bbox reg unnormalization; then save snapshot
            self.rpn_means = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_MEANS),
                                      num_anchor)
            self.rpn_stds = np.tile(np.asarray(cfg.TRAIN.RPN_NORMALIZE_STDS),
                                     num_anchor)
            net.params['rpn_bbox_pred'][0].data[...] = \
                (net.params['rpn_bbox_pred'][0].data *
                 self.rpn_stds[:, np.newaxis, np.newaxis, np.newaxis])
            net.params['rpn_bbox_pred'][1].data[...] = \
                (net.params['rpn_bbox_pred'][1].data *
                 self.rpn_stds + self.rpn_means)
          ..................

many many thanks !!