示例#1
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def tiny_yolo_body(inputs, num_anchors, num_classes):
    '''Create Tiny YOLO_v3 model CNN body in keras.'''
    x1 = compose(
            DarknetConv2D_BN_Leaky(16, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(32, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(64, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(128, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(256, (3,3)))(inputs)
    x2 = compose(
            MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='same'),
            DarknetConv2D_BN_Leaky(512, (3,3)),
            MaxPooling2D(pool_size=(2,2), strides=(1,1), padding='same'),
            DarknetConv2D_BN_Leaky(1024, (3,3)),
            DarknetConv2D_BN_Leaky(256, (1,1)))(x1)
    y1 = compose(
            DarknetConv2D_BN_Leaky(512, (3,3)),
            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))(x2)

    x2 = compose(
            DarknetConv2D_BN_Leaky(128, (1,1)),
            UpSampling2D(2))(x2)
    y2 = compose(
            Concatenate(),
            DarknetConv2D_BN_Leaky(256, (3,3)),
            DarknetConv2D(num_anchors*(num_classes+5), (1,1)))([x2,x1])

    return Model(inputs, [y1,y2])
示例#2
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def make_last_layers(x, num_filters, out_filters):
    '''6 Conv2D_BN_Leaky layers followed by a Conv2D_linear layer'''
    x = compose(DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
                DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
                DarknetConv2D_BN_Leaky(num_filters, (1, 1)),
                DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
                DarknetConv2D_BN_Leaky(num_filters, (1, 1)))(x)
    y = compose(DarknetConv2D_BN_Leaky(num_filters * 2, (3, 3)),
                DarknetConv2D(out_filters, (1, 1)))(x)
    return x, y
示例#3
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def yolo_body(inputs, num_anchors, num_classes):
    """Create YOLO_V3 model CNN body in Keras."""
    darknet = Model(inputs, darknet_body(inputs))
    x, y1 = make_last_layers(darknet.output, 512,
                             num_anchors * (num_classes + 5))

    x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)
    x = Concatenate()([x, darknet.layers[152].output])
    x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))

    x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)
    x = Concatenate()([x, darknet.layers[92].output])
    x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))

    return Model(inputs, [y1, y2, y3])
示例#4
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def DarknetConv2D_BN_Leaky(*args, **kwargs):
    """Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""
    no_bias_kwargs = {'use_bias': False}
    no_bias_kwargs.update(kwargs)
    return compose(
        DarknetConv2D(*args, **no_bias_kwargs),
        BatchNormalization(),
        LeakyReLU(alpha=0.1))
示例#5
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def resblock_body(x, num_filters, num_blocks):
    '''A series of resblocks starting with a downsampling Convolution2D'''
    # Darknet uses left and top padding instead of 'same' mode
    x = ZeroPadding2D(((1, 0), (1, 0)))(x)
    x = DarknetConv2D_BN_Leaky(num_filters, (3, 3), strides=(2, 2))(x)
    for i in range(num_blocks):
        y = compose(DarknetConv2D_BN_Leaky(num_filters // 2, (1, 1)),
                    DarknetConv2D_BN_Leaky(num_filters, (3, 3)))(x)
        x = Add()([x, y])
    return x