Esempio n. 1
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 def __init__(self, **kwargs):
     super(retina_fpn, self).__init__(**kwargs)
     self.conv5 = conv_layer(256, 1, 1, name='P5_conv')
     self.up_pool = nn.UpSampling2D(size=(2, 2), interpolation='bilinear')
     self.conv4 = conv_layer(256, 1, 1, name='P4_conv')
     self.conv3 = conv_layer(256, 1, 1, name='P3_conv')
     self.conv6 = conv_layer(256, 3, 2, padding='same', name='P6_conv')
     self.conv7 = conv_layer(256, 3, 2, padding='same', name='P7_conv')
     self.add = nn.Add()
Esempio n. 2
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    def __init__(self, filters3x3, filters1x1, **kwargs):
        super(OSA_module, self).__init__(**kwargs)
        self.conv_layers = []

        for i in range(5):
            self.conv_layers.append(
                conv_layer(filters3x3, 3, 1, name='conv{}'.format(i + 1)))

        self.concat = nn.Concatenate(axis=-1, name='concat')
        self.conv1x1 = conv_layer(filters1x1, 1, 1, name='conv1x1')
Esempio n. 3
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    def __init__(self, **kwargs):
        super(regression, self).__init__(**kwargs)
        self.conv1 = conv_layer(256, 3, 1, 'same', name='conv1')
        self.conv2 = conv_layer(256, 3, 1, 'same', name='conv2')
        self.conv3 = conv_layer(256, 3, 1, 'same', name='conv3')
        self.conv4 = conv_layer(256, 3, 1, 'same', name='conv4')

        self.regression = nn.Conv2D(4,
                                    1,
                                    1,
                                    activation=None,
                                    name='regression')
Esempio n. 4
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    def __init__(self, out_channel, **kwargs):
        '''
        fcos classification head
        :param num_classes:
        :param kwargs:
        '''
        super(classification, self).__init__(**kwargs)
        self.conv1 = conv_layer(256, 3, 1, 'same', name='conv1')
        self.conv2 = conv_layer(256, 3, 1, 'same', name='conv2')
        self.conv3 = conv_layer(256, 3, 1, 'same', name='conv3')
        self.conv4 = conv_layer(256, 3, 1, 'same', name='conv4')

        # 加1表示center_ness
        self.classification = nn.Conv2D(out_channel,
                                        1,
                                        1,
                                        activation='sigmoid',
                                        name='classification')
Esempio n. 5
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 def __init__(self, nun_classes, **kwargs):
     '''
     model unit
     :param nun_classes: output classes
     :param kwargs:
     '''
     super(FcosHead, self).__init__(**kwargs)
     self.conv1 = conv_layer(256, 3, 1, 'same', name='conv1')
     self.conv2 = conv_layer(256, 3, 1, 'same', name='conv2')
     self.conv3 = conv_layer(256, 3, 1, 'same', name='conv3')
     self.conv4 = conv_layer(256, 3, 1, 'same', name='conv4')
     # 论文中未强调 classification和regression不能共享权重
     self.classification = nn.Conv2D(
         nun_classes + 1, 1, 1, activation='sigmoid',
         name='classification')  # num_classes: classification
     # 1: center-ness
     self.regression = nn.Conv2D(4, 1, 1,
                                 name='regression')  #linear activation
     self.concat = nn.Concatenate(axis=-1)  # stack all result
Esempio n. 6
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 def __init__(self, **kwargs):
     super(stem, self).__init__(**kwargs)
     self.conv1 = conv_layer(64, 3, 2, name='conv1')
     self.conv2 = conv_layer(64, 3, 1, name='conv2')
     self.conv3 = conv_layer(128, 3, 1, name='conv3')