Example #1
0
    def __init__(self,
                 depth=18,
                 init_plane=64,
                 out_plane=None,
                 stage=4,
                 num_class=10,
                 small_input=True,
                 doublechannel=None,
                 downsample=None):
        """Create layers.

        :param num_reps: number of layers
        :type num_reqs: int
        :param items: channel and stride of every layer
        :type items: dict
        :param num_class: number of class
        :type num_class: int
        """
        super(ResNet, self).__init__()
        self.backbone = ResNetGeneral(small_input, init_plane, depth, stage,
                                      doublechannel, downsample)
        self.adaptiveAvgPool2d = AdaptiveAvgPool2d(output_size=(1, 1))
        self.view = View()
        out_plane = out_plane or self.backbone.output_channel
        self.head = Linear(in_features=out_plane, out_features=num_class)
Example #2
0
    def __init__(self, **descript):
        """Create layers."""
        super().__init__()
        # state_dim = descript.get("state_dim")
        action_dim = descript.get("action_dim")

        self.lambda1 = Lambda(lambda x: tf.cast(x, dtype='float32') / 255.)
        self.conv1 = Conv2d(in_channels=4,
                            out_channels=32,
                            kernel_size=8,
                            stride=4,
                            bias=False)
        self.ac1 = Relu()
        self.conv2 = Conv2d(in_channels=32,
                            out_channels=64,
                            kernel_size=4,
                            stride=2,
                            bias=False)
        self.ac2 = Relu()
        self.conv3 = Conv2d(in_channels=64,
                            out_channels=64,
                            kernel_size=3,
                            stride=1,
                            bias=False)
        self.ac3 = Relu()
        self.view = View()
        self.fc1 = Linear(64, 256)
        self.ac4 = Relu()
        self.fc2 = Linear(256, action_dim)
Example #3
0
    def __init__(self,
                 code=None,
                 depth=18,
                 base_channel=64,
                 out_plane=2048,
                 stage=4,
                 num_class=1000,
                 small_input=True,
                 block='BasicBlock',
                 pretrained_arch=None,
                 pretrained=None):
        """Create layers.

        :param num_reps: number of layers
        :type num_reqs: int
        :param items: channel and stride of every layer
        :type items: dict
        :param num_class: number of class
        :type num_class: int
        """
        super(FasterBackbone, self).__init__()
        self.backbone = SpResNetDet(depth=depth,
                                    block=block,
                                    code=code,
                                    pretrained=pretrained,
                                    pretrained_arch=pretrained_arch)
        self.adaptiveAvgPool2d = AdaptiveAvgPool2d(output_size=(1, 1))
        self.view = View()
        out_plane = out_plane or self.backbone.out_channels
        self.head = Linear(in_features=out_plane, out_features=num_class)