def yolo_body(inputs, num_anchors, num_classes):
    #net, endpoint = inception_v2.inception_v2(inputs)
    mobilenet = MobileNetV2(input_tensor=inputs,
                            weights='imagenet',
                            include_top=False)

    # input: 416 x 416 x 3
    # conv_pw_13_relu :13 x 13 x 1024
    # conv_pw_11_relu :26 x 26 x 512
    # conv_pw_5_relu : 52 x 52 x 256

    f1 = mobilenet.get_layer('out_relu').output
    # f1 :13 x 13 x 1024
    x, y1 = make_last_layers(f1, 512, num_anchors * (num_classes + 5))

    x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)

    f2 = mobilenet.get_layer('block_13_expand_relu').output
    # f2: 26 x 26 x 512
    x = Concatenate()([x, f2])

    x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))

    x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)

    f3 = mobilenet.get_layer('block_6_expand_relu').output
    # f3 : 52 x 52 x 256
    x = Concatenate()([x, f3])
    x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))

    return Model(inputs=inputs, outputs=[y1, y2, y3])
Example #2
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def yolo_body(inputs, num_anchors, num_classes):
    #net, endpoint = inception_v2.inception_v2(inputs)
    densenet = DenseNet121(
        input_tensor=inputs,
        weights='imagenet')  #include top can be added but will not change much

    # input: 416 x 416 x 3
    # conv_pw_13_relu :13 x 13 x 1024
    # conv_pw_11_relu :26 x 26 x 1024
    # conv_pw_5_relu : 52 x 52 x 512

    f1 = densenet.get_layer('relu').output
    # f1 :13 x 13 x 1024
    x, y1 = make_last_layers(f1, 512, num_anchors * (num_classes + 5))

    x = compose(DarknetConv2D_BN_Leaky(256, (1, 1)), UpSampling2D(2))(x)

    f2 = densenet.get_layer('pool4_relu').output
    # f2: 26 x 26 x 1024 // 512
    x = Concatenate()([x, f2])

    x, y2 = make_last_layers(x, 256, num_anchors * (num_classes + 5))

    x = compose(DarknetConv2D_BN_Leaky(128, (1, 1)), UpSampling2D(2))(x)

    f3 = densenet.get_layer('pool3_relu').output
    # f3 : 52 x 52 x 512 // 256
    x = Concatenate()([x, f3])
    x, y3 = make_last_layers(x, 128, num_anchors * (num_classes + 5))

    return Model(inputs=inputs, outputs=[y1, y2, y3])
Example #3
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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
Example #4
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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])
Example #5
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def darknet_body(x):
    '''Darknent body having 52 Convolution2D layers'''
    x = DarknetConv2D_BN_Leaky(32, (3, 3))(x)
    x = resblock_body(x, 64, 1)
    x = resblock_body(x, 128, 2)
    x = resblock_body(x, 256, 8)
    x = resblock_body(x, 512, 8)
    x = resblock_body(x, 1024, 4)
    return x
Example #6
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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])