76b060f625e037d479a2eb25462a3b3f70af5bb7,chainercv/links/model/resnet/resnet.py,ResNet,__init__,#ResNet#Any#Any#Any#Any#Any#Any#Any#,148

Before Change

        _models = self._models[arch][n_layer]
        blocks = self._blocks[n_layer]

        if n_class is None:
            if pretrained_model in _models:
                n_class = _models[pretrained_model]["n_class"]
            else:
                n_class = 1000

        if mean is None:
            if pretrained_model in _models:
                mean = _models[pretrained_model]["mean"]
            else:
                mean = _imagenet_mean
        self.mean = mean

        if initialW is None:
            initialW = initializers.HeNormal(scale=1., fan_option="fan_out")
        if "initialW" not in fc_kwargs:
            fc_kwargs["initialW"] = initializers.Normal(scale=0.01)
        if pretrained_model:
            // As a sampling process is time-consuming,
            // we employ a zero initializer for faster computation.
            initialW = initializers.constant.Zero()
            fc_kwargs["initialW"] = initializers.constant.Zero()
        kwargs = {"initialW": initialW, "stride_first": stride_first}

        super(ResNet, self).__init__()
        with self.init_scope():
            self.conv1 = Conv2DBNActiv(None, 64, 7, 2, 3, nobias=conv1_no_bias,
                                       initialW=initialW)
            self.pool1 = lambda x: F.max_pooling_2d(x, ksize=3, stride=2)
            self.res2 = ResBlock(blocks[0], None, 64, 256, 1, **kwargs)
            self.res3 = ResBlock(blocks[1], None, 128, 512, 2, **kwargs)
            self.res4 = ResBlock(blocks[2], None, 256, 1024, 2, **kwargs)
            self.res5 = ResBlock(blocks[3], None, 512, 2048, 2, **kwargs)
            self.pool5 = _global_average_pooling_2d
            self.fc6 = L.Linear(None, n_class, **fc_kwargs)
            self.prob = F.softmax

        if pretrained_model in _models:
            if not _available:
                warnings.warn("cv2 is not installed on your environment. "
                              "The scores of ResNets reported in the "
                              "README of the ChainerCV\"s classification "
                              "example are calculated using OpenCV as the "
                              "backend. With Pillow as the "
                              "backend, the scores would change.",
                              RuntimeWarning)
            path = download_model(_models[pretrained_model]["url"])
            chainer.serializers.load_npz(path, self)
        elif pretrained_model:
            chainer.serializers.load_npz(pretrained_model, self)


def _global_average_pooling_2d(x):
    n, channel, rows, cols = x.data.shape
    h = F.average_pooling_2d(x, (rows, cols), stride=1)
    h = h.reshape((n, channel))

After Change

        _models = self._models[arch][n_layer]
        blocks = self._blocks[n_layer]

        n_class, path = prepare_link_initialization(
            n_class, pretrained_model, _models, False, 1000)

        if mean is None:
            if pretrained_model in _models:
                mean = _models[pretrained_model]["mean"]
            else:
                mean = _imagenet_mean
        self.mean = mean

        if initialW is None:
            initialW = initializers.HeNormal(scale=1., fan_option="fan_out")
        if "initialW" not in fc_kwargs:
            fc_kwargs["initialW"] = initializers.Normal(scale=0.01)
        if pretrained_model:
            // As a sampling process is time-consuming,
            // we employ a zero initializer for faster computation.
            initialW = initializers.constant.Zero()
            fc_kwargs["initialW"] = initializers.constant.Zero()
        kwargs = {"initialW": initialW, "stride_first": stride_first}

        super(ResNet, self).__init__()
        with self.init_scope():
            self.conv1 = Conv2DBNActiv(None, 64, 7, 2, 3, nobias=conv1_no_bias,
                                       initialW=initialW)
            self.pool1 = lambda x: F.max_pooling_2d(x, ksize=3, stride=2)
            self.res2 = ResBlock(blocks[0], None, 64, 256, 1, **kwargs)
            self.res3 = ResBlock(blocks[1], None, 128, 512, 2, **kwargs)
            self.res4 = ResBlock(blocks[2], None, 256, 1024, 2, **kwargs)
            self.res5 = ResBlock(blocks[3], None, 512, 2048, 2, **kwargs)
            self.pool5 = _global_average_pooling_2d
            self.fc6 = L.Linear(None, n_class, **fc_kwargs)
            self.prob = F.softmax

        if path:
            chainer.serializers.load_npz(path, self)


def _global_average_pooling_2d(x):
    n, channel, rows, cols = x.data.shape
    h = F.average_pooling_2d(x, (rows, cols), stride=1)
    h = h.reshape((n, channel))

Instances