76b060f625e037d479a2eb25462a3b3f70af5bb7,chainercv/links/model/faster_rcnn/faster_rcnn_vgg.py,FasterRCNNVGG16,__init__,#FasterRCNNVGG16#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,87

Before Change

                 loc_initialW=None, score_initialW=None,
                 proposal_creator_params={}
                 ):
        if n_fg_class is None:
            if pretrained_model not in self._models:
                raise ValueError(
                    "The n_fg_class needs to be supplied as an argument")
            n_fg_class = self._models[pretrained_model]["n_fg_class"]

        if loc_initialW is None:
            loc_initialW = chainer.initializers.Normal(0.001)
        if score_initialW is None:
            score_initialW = chainer.initializers.Normal(0.01)
        if rpn_initialW is None:
            rpn_initialW = chainer.initializers.Normal(0.01)
        if vgg_initialW is None and pretrained_model:
            vgg_initialW = chainer.initializers.constant.Zero()

        extractor = VGG16(initialW=vgg_initialW)
        extractor.pick = "conv5_3"
        // Delete all layers after conv5_3.
        extractor.remove_unused()
        rpn = RegionProposalNetwork(
            512, 512,
            ratios=ratios,
            anchor_scales=anchor_scales,
            feat_stride=self.feat_stride,
            initialW=rpn_initialW,
            proposal_creator_params=proposal_creator_params,
        )
        head = VGG16RoIHead(
            n_fg_class + 1,
            roi_size=7, spatial_scale=1. / self.feat_stride,
            vgg_initialW=vgg_initialW,
            loc_initialW=loc_initialW,
            score_initialW=score_initialW
        )

        super(FasterRCNNVGG16, self).__init__(
            extractor,
            rpn,
            head,
            mean=np.array([122.7717, 115.9465, 102.9801],
                          dtype=np.float32)[:, None, None],
            min_size=min_size,
            max_size=max_size
        )

        if pretrained_model in self._models:
            path = download_model(self._models[pretrained_model]["url"])
            chainer.serializers.load_npz(path, self)
        elif pretrained_model == "imagenet":
            self._copy_imagenet_pretrained_vgg16()
        elif pretrained_model:
            chainer.serializers.load_npz(pretrained_model, self)

    def _copy_imagenet_pretrained_vgg16(self):
        pretrained_model = VGG16(pretrained_model="imagenet")
        self.extractor.conv1_1.copyparams(pretrained_model.conv1_1)
        self.extractor.conv1_2.copyparams(pretrained_model.conv1_2)

After Change

                 loc_initialW=None, score_initialW=None,
                 proposal_creator_params={}
                 ):
        n_fg_class, path = prepare_link_initialization(
            n_fg_class, pretrained_model, self._models, True)

        if loc_initialW is None:
            loc_initialW = chainer.initializers.Normal(0.001)
        if score_initialW is None:

Instances