from functools import reduce

# from keras import layers
# from keras import initializers
# from keras import models
# from keras_ import EfficientNetB0, EfficientNetB1, EfficientNetB2
# from keras_ import EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6

from tensorflow.keras import layers
from tensorflow.keras import initializers
from tensorflow.keras import models
from .tfkeras import EfficientNetB0, EfficientNetB1, EfficientNetB2
from .tfkeras import EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6

from .layers import ClipBoxes, RegressBoxes, FilterDetections, wBiFPNAdd, BatchNormalization
from .initializers import PriorProbability

w_bifpns = [64, 88, 112, 160, 224, 288, 384]
image_sizes = [512, 640, 768, 896, 1024, 1280, 1408]
backbones = [EfficientNetB0, EfficientNetB1, EfficientNetB2,
             EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6]


def DepthwiseConvBlock(kernel_size, strides, name, freeze_bn=False):
    f1 = layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides, padding='same',
                                use_bias=False, name='{}_dconv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)), (f1, f2, f3))


def ConvBlock(num_channels, kernel_size, strides, name, freeze_bn=False):
    f1 = layers.Conv2D(num_channels, kernel_size=kernel_size, strides=strides, padding='same',
                       use_bias=False, name='{}_conv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)), (f1, f2, f3))


def build_BiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P7_in)

    # upsample
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = layers.Add()([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = layers.Add()([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = layers.Add()([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = layers.Add()([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    # downsample
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = layers.Add()([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = layers.Add()([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = layers.Add()([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = layers.Add()([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)

    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_wBiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P7_in)

    # upsample
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = wBiFPNAdd(name='w_bi_fpn_add' if id == 0 else f'w_bi_fpn_add_{8 * id}')([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 1}')([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 2}')([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 3}')([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    # downsample
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 4}')([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 5}')([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 6}')([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 7}')([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)

    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_regress_head(width, depth, num_anchors=9):
    options = {
        'kernel_size': 3,
        'strides': 1,
        'padding': 'same',
        # 'kernel_initializer': initializers.normal(mean=0.0, stddev=0.01, seed=None),
        'kernel_initializer': initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
        'bias_initializer': 'zeros'
    }

    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(
            filters=width,
            activation='relu',
            **options
        )(outputs)

    outputs = layers.Conv2D(num_anchors * 4, **options)(outputs)
    # (b, num_anchors_this_feature_map, 4)
    outputs = layers.Reshape((-1, 4))(outputs)

    return models.Model(inputs=inputs, outputs=outputs, name='box_head')


def build_class_head(width, depth, num_classes=20, num_anchors=9):
    options = {
        'kernel_size': 3,
        'strides': 1,
        'padding': 'same',
        # 'kernel_initializer': initializers.normal(mean=0.0, stddev=0.01, seed=None),
    }

    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(
            filters=width,
            activation='relu',
            kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
            bias_initializer='zeros',
            **options
        )(outputs)

    # outputs = layers.Conv2D(num_anchors * num_classes, **options)(outputs)
    outputs = layers.Conv2D(
        filters=num_classes * num_anchors,
        kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
        bias_initializer=PriorProbability(probability=0.01),
        name='pyramid_classification',
        **options
    )(outputs)
    # (b, num_anchors_this_feature_map, 4)
    outputs = layers.Reshape((-1, num_classes))(outputs)
    outputs = layers.Activation('sigmoid')(outputs)

    return models.Model(inputs=inputs, outputs=outputs, name='class_head')


def efficientdet(phi, num_classes=20, weighted_bifpn=False, freeze_bn=False, score_threshold=0.01):
    assert phi in range(7)
    input_size = image_sizes[phi]
    input_shape = (input_size, input_size, 3)
    # input_shape = (None, None, 3)
    image_input = layers.Input(input_shape)
    w_bifpn = w_bifpns[phi]
    d_bifpn = 2 + phi
    w_head = w_bifpn
    d_head = 3 + int(phi / 3)
    backbone_cls = backbones[phi]
    # features = backbone_cls(include_top=False, input_shape=input_shape, weights=weights)(image_input)
    features = backbone_cls(input_tensor=image_input, freeze_bn=freeze_bn)
    if weighted_bifpn:
        for i in range(d_bifpn):
            features = build_wBiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    else:
        for i in range(d_bifpn):
            features = build_BiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    regress_head = build_regress_head(w_head, d_head)
    class_head = build_class_head(w_head, d_head, num_classes=num_classes)
    regression = [regress_head(feature) for feature in features]
    regression = layers.Concatenate(axis=1, name='regression')(regression)
    classification = [class_head(feature) for feature in features]
    classification = layers.Concatenate(axis=1, name='classification')(classification)

    model = models.Model(inputs=[image_input], outputs=[regression, classification], name='efficientdet')

    # apply predicted regression to anchors
    # anchors = tf.tile(tf.expand_dims(tf.constant(anchors), axis=0), (tf.shape(regression)[0], 1, 1))
    anchors_input = layers.Input((None, 4))
    boxes = RegressBoxes(name='boxes')([anchors_input, regression])
    boxes = ClipBoxes(name='clipped_boxes')([image_input, boxes])

    # filter detections (apply NMS / score threshold / select top-k)
    detections = FilterDetections(
        name='filtered_detections',
        score_threshold=score_threshold
    )([boxes, classification])
    prediction_model = models.Model(inputs=[image_input, anchors_input], outputs=detections, name='efficientdet_p')
    return model, prediction_model




840be355a38a2b5a549a371f25c23f795f8e277b
<mask token>


def DepthwiseConvBlock(kernel_size, strides, name, freeze_bn=False):
    f1 = layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides,
        padding='same', use_bias=False, name='{}_dconv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)
        ), (f1, f2, f3))


<mask token>


def build_wBiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P7_in)
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = wBiFPNAdd(name='w_bi_fpn_add' if id == 0 else
        f'w_bi_fpn_add_{8 * id}')([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 1}')([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 2}')([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 3}')([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 4}')([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 5}')([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 6}')([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 7}')([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_regress_head(width, depth, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same',
        'kernel_initializer': initializers.RandomNormal(mean=0.0, stddev=
        0.01, seed=None), 'bias_initializer': 'zeros'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu', **options)(
            outputs)
    outputs = layers.Conv2D(num_anchors * 4, **options)(outputs)
    outputs = layers.Reshape((-1, 4))(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='box_head')


def build_class_head(width, depth, num_classes=20, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu',
            kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=
            0.01, seed=None), bias_initializer='zeros', **options)(outputs)
    outputs = layers.Conv2D(filters=num_classes * num_anchors,
        kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01,
        seed=None), bias_initializer=PriorProbability(probability=0.01),
        name='pyramid_classification', **options)(outputs)
    outputs = layers.Reshape((-1, num_classes))(outputs)
    outputs = layers.Activation('sigmoid')(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='class_head')


def efficientdet(phi, num_classes=20, weighted_bifpn=False, freeze_bn=False,
    score_threshold=0.01):
    assert phi in range(7)
    input_size = image_sizes[phi]
    input_shape = input_size, input_size, 3
    image_input = layers.Input(input_shape)
    w_bifpn = w_bifpns[phi]
    d_bifpn = 2 + phi
    w_head = w_bifpn
    d_head = 3 + int(phi / 3)
    backbone_cls = backbones[phi]
    features = backbone_cls(input_tensor=image_input, freeze_bn=freeze_bn)
    if weighted_bifpn:
        for i in range(d_bifpn):
            features = build_wBiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    else:
        for i in range(d_bifpn):
            features = build_BiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    regress_head = build_regress_head(w_head, d_head)
    class_head = build_class_head(w_head, d_head, num_classes=num_classes)
    regression = [regress_head(feature) for feature in features]
    regression = layers.Concatenate(axis=1, name='regression')(regression)
    classification = [class_head(feature) for feature in features]
    classification = layers.Concatenate(axis=1, name='classification')(
        classification)
    model = models.Model(inputs=[image_input], outputs=[regression,
        classification], name='efficientdet')
    anchors_input = layers.Input((None, 4))
    boxes = RegressBoxes(name='boxes')([anchors_input, regression])
    boxes = ClipBoxes(name='clipped_boxes')([image_input, boxes])
    detections = FilterDetections(name='filtered_detections',
        score_threshold=score_threshold)([boxes, classification])
    prediction_model = models.Model(inputs=[image_input, anchors_input],
        outputs=detections, name='efficientdet_p')
    return model, prediction_model

<mask token>


def DepthwiseConvBlock(kernel_size, strides, name, freeze_bn=False):
    f1 = layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides,
        padding='same', use_bias=False, name='{}_dconv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)
        ), (f1, f2, f3))


def ConvBlock(num_channels, kernel_size, strides, name, freeze_bn=False):
    f1 = layers.Conv2D(num_channels, kernel_size=kernel_size, strides=
        strides, padding='same', use_bias=False, name='{}_conv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)
        ), (f1, f2, f3))


def build_BiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P7_in)
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = layers.Add()([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = layers.Add()([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = layers.Add()([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = layers.Add()([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = layers.Add()([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = layers.Add()([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = layers.Add()([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = layers.Add()([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_wBiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P7_in)
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = wBiFPNAdd(name='w_bi_fpn_add' if id == 0 else
        f'w_bi_fpn_add_{8 * id}')([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 1}')([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 2}')([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 3}')([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 4}')([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 5}')([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 6}')([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 7}')([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_regress_head(width, depth, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same',
        'kernel_initializer': initializers.RandomNormal(mean=0.0, stddev=
        0.01, seed=None), 'bias_initializer': 'zeros'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu', **options)(
            outputs)
    outputs = layers.Conv2D(num_anchors * 4, **options)(outputs)
    outputs = layers.Reshape((-1, 4))(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='box_head')


def build_class_head(width, depth, num_classes=20, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu',
            kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=
            0.01, seed=None), bias_initializer='zeros', **options)(outputs)
    outputs = layers.Conv2D(filters=num_classes * num_anchors,
        kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01,
        seed=None), bias_initializer=PriorProbability(probability=0.01),
        name='pyramid_classification', **options)(outputs)
    outputs = layers.Reshape((-1, num_classes))(outputs)
    outputs = layers.Activation('sigmoid')(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='class_head')


def efficientdet(phi, num_classes=20, weighted_bifpn=False, freeze_bn=False,
    score_threshold=0.01):
    assert phi in range(7)
    input_size = image_sizes[phi]
    input_shape = input_size, input_size, 3
    image_input = layers.Input(input_shape)
    w_bifpn = w_bifpns[phi]
    d_bifpn = 2 + phi
    w_head = w_bifpn
    d_head = 3 + int(phi / 3)
    backbone_cls = backbones[phi]
    features = backbone_cls(input_tensor=image_input, freeze_bn=freeze_bn)
    if weighted_bifpn:
        for i in range(d_bifpn):
            features = build_wBiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    else:
        for i in range(d_bifpn):
            features = build_BiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    regress_head = build_regress_head(w_head, d_head)
    class_head = build_class_head(w_head, d_head, num_classes=num_classes)
    regression = [regress_head(feature) for feature in features]
    regression = layers.Concatenate(axis=1, name='regression')(regression)
    classification = [class_head(feature) for feature in features]
    classification = layers.Concatenate(axis=1, name='classification')(
        classification)
    model = models.Model(inputs=[image_input], outputs=[regression,
        classification], name='efficientdet')
    anchors_input = layers.Input((None, 4))
    boxes = RegressBoxes(name='boxes')([anchors_input, regression])
    boxes = ClipBoxes(name='clipped_boxes')([image_input, boxes])
    detections = FilterDetections(name='filtered_detections',
        score_threshold=score_threshold)([boxes, classification])
    prediction_model = models.Model(inputs=[image_input, anchors_input],
        outputs=detections, name='efficientdet_p')
    return model, prediction_model

<mask token>
w_bifpns = [64, 88, 112, 160, 224, 288, 384]
image_sizes = [512, 640, 768, 896, 1024, 1280, 1408]
backbones = [EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3,
    EfficientNetB4, EfficientNetB5, EfficientNetB6]


def DepthwiseConvBlock(kernel_size, strides, name, freeze_bn=False):
    f1 = layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides,
        padding='same', use_bias=False, name='{}_dconv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)
        ), (f1, f2, f3))


def ConvBlock(num_channels, kernel_size, strides, name, freeze_bn=False):
    f1 = layers.Conv2D(num_channels, kernel_size=kernel_size, strides=
        strides, padding='same', use_bias=False, name='{}_conv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)
        ), (f1, f2, f3))


def build_BiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P7_in)
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = layers.Add()([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = layers.Add()([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = layers.Add()([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = layers.Add()([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = layers.Add()([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = layers.Add()([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = layers.Add()([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = layers.Add()([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_wBiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P7_in)
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = wBiFPNAdd(name='w_bi_fpn_add' if id == 0 else
        f'w_bi_fpn_add_{8 * id}')([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 1}')([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 2}')([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 3}')([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 4}')([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 5}')([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 6}')([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 7}')([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_regress_head(width, depth, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same',
        'kernel_initializer': initializers.RandomNormal(mean=0.0, stddev=
        0.01, seed=None), 'bias_initializer': 'zeros'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu', **options)(
            outputs)
    outputs = layers.Conv2D(num_anchors * 4, **options)(outputs)
    outputs = layers.Reshape((-1, 4))(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='box_head')


def build_class_head(width, depth, num_classes=20, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu',
            kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=
            0.01, seed=None), bias_initializer='zeros', **options)(outputs)
    outputs = layers.Conv2D(filters=num_classes * num_anchors,
        kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01,
        seed=None), bias_initializer=PriorProbability(probability=0.01),
        name='pyramid_classification', **options)(outputs)
    outputs = layers.Reshape((-1, num_classes))(outputs)
    outputs = layers.Activation('sigmoid')(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='class_head')


def efficientdet(phi, num_classes=20, weighted_bifpn=False, freeze_bn=False,
    score_threshold=0.01):
    assert phi in range(7)
    input_size = image_sizes[phi]
    input_shape = input_size, input_size, 3
    image_input = layers.Input(input_shape)
    w_bifpn = w_bifpns[phi]
    d_bifpn = 2 + phi
    w_head = w_bifpn
    d_head = 3 + int(phi / 3)
    backbone_cls = backbones[phi]
    features = backbone_cls(input_tensor=image_input, freeze_bn=freeze_bn)
    if weighted_bifpn:
        for i in range(d_bifpn):
            features = build_wBiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    else:
        for i in range(d_bifpn):
            features = build_BiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    regress_head = build_regress_head(w_head, d_head)
    class_head = build_class_head(w_head, d_head, num_classes=num_classes)
    regression = [regress_head(feature) for feature in features]
    regression = layers.Concatenate(axis=1, name='regression')(regression)
    classification = [class_head(feature) for feature in features]
    classification = layers.Concatenate(axis=1, name='classification')(
        classification)
    model = models.Model(inputs=[image_input], outputs=[regression,
        classification], name='efficientdet')
    anchors_input = layers.Input((None, 4))
    boxes = RegressBoxes(name='boxes')([anchors_input, regression])
    boxes = ClipBoxes(name='clipped_boxes')([image_input, boxes])
    detections = FilterDetections(name='filtered_detections',
        score_threshold=score_threshold)([boxes, classification])
    prediction_model = models.Model(inputs=[image_input, anchors_input],
        outputs=detections, name='efficientdet_p')
    return model, prediction_model

from functools import reduce
from tensorflow.keras import layers
from tensorflow.keras import initializers
from tensorflow.keras import models
from .tfkeras import EfficientNetB0, EfficientNetB1, EfficientNetB2
from .tfkeras import EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6
from .layers import ClipBoxes, RegressBoxes, FilterDetections, wBiFPNAdd, BatchNormalization
from .initializers import PriorProbability
w_bifpns = [64, 88, 112, 160, 224, 288, 384]
image_sizes = [512, 640, 768, 896, 1024, 1280, 1408]
backbones = [EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3,
    EfficientNetB4, EfficientNetB5, EfficientNetB6]


def DepthwiseConvBlock(kernel_size, strides, name, freeze_bn=False):
    f1 = layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides,
        padding='same', use_bias=False, name='{}_dconv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)
        ), (f1, f2, f3))


def ConvBlock(num_channels, kernel_size, strides, name, freeze_bn=False):
    f1 = layers.Conv2D(num_channels, kernel_size=kernel_size, strides=
        strides, padding='same', use_bias=False, name='{}_conv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)
        ), (f1, f2, f3))


def build_BiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P7_in)
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = layers.Add()([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = layers.Add()([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = layers.Add()([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = layers.Add()([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = layers.Add()([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = layers.Add()([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = layers.Add()([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = layers.Add()([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_wBiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P3'.format(id))(P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P4'.format(id))(P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P5'.format(id))(P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P6'.format(id))(P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn
            =freeze_bn, name='BiFPN_{}_P7'.format(id))(P7_in)
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = wBiFPNAdd(name='w_bi_fpn_add' if id == 0 else
        f'w_bi_fpn_add_{8 * id}')([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 1}')([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 2}')([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 3}')([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 4}')([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 5}')([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 6}')([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 7}')([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=
        freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)
    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_regress_head(width, depth, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same',
        'kernel_initializer': initializers.RandomNormal(mean=0.0, stddev=
        0.01, seed=None), 'bias_initializer': 'zeros'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu', **options)(
            outputs)
    outputs = layers.Conv2D(num_anchors * 4, **options)(outputs)
    outputs = layers.Reshape((-1, 4))(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='box_head')


def build_class_head(width, depth, num_classes=20, num_anchors=9):
    options = {'kernel_size': 3, 'strides': 1, 'padding': 'same'}
    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(filters=width, activation='relu',
            kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=
            0.01, seed=None), bias_initializer='zeros', **options)(outputs)
    outputs = layers.Conv2D(filters=num_classes * num_anchors,
        kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01,
        seed=None), bias_initializer=PriorProbability(probability=0.01),
        name='pyramid_classification', **options)(outputs)
    outputs = layers.Reshape((-1, num_classes))(outputs)
    outputs = layers.Activation('sigmoid')(outputs)
    return models.Model(inputs=inputs, outputs=outputs, name='class_head')


def efficientdet(phi, num_classes=20, weighted_bifpn=False, freeze_bn=False,
    score_threshold=0.01):
    assert phi in range(7)
    input_size = image_sizes[phi]
    input_shape = input_size, input_size, 3
    image_input = layers.Input(input_shape)
    w_bifpn = w_bifpns[phi]
    d_bifpn = 2 + phi
    w_head = w_bifpn
    d_head = 3 + int(phi / 3)
    backbone_cls = backbones[phi]
    features = backbone_cls(input_tensor=image_input, freeze_bn=freeze_bn)
    if weighted_bifpn:
        for i in range(d_bifpn):
            features = build_wBiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    else:
        for i in range(d_bifpn):
            features = build_BiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    regress_head = build_regress_head(w_head, d_head)
    class_head = build_class_head(w_head, d_head, num_classes=num_classes)
    regression = [regress_head(feature) for feature in features]
    regression = layers.Concatenate(axis=1, name='regression')(regression)
    classification = [class_head(feature) for feature in features]
    classification = layers.Concatenate(axis=1, name='classification')(
        classification)
    model = models.Model(inputs=[image_input], outputs=[regression,
        classification], name='efficientdet')
    anchors_input = layers.Input((None, 4))
    boxes = RegressBoxes(name='boxes')([anchors_input, regression])
    boxes = ClipBoxes(name='clipped_boxes')([image_input, boxes])
    detections = FilterDetections(name='filtered_detections',
        score_threshold=score_threshold)([boxes, classification])
    prediction_model = models.Model(inputs=[image_input, anchors_input],
        outputs=detections, name='efficientdet_p')
    return model, prediction_model

from functools import reduce

# from keras import layers
# from keras import initializers
# from keras import models
# from keras_ import EfficientNetB0, EfficientNetB1, EfficientNetB2
# from keras_ import EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6

from tensorflow.keras import layers
from tensorflow.keras import initializers
from tensorflow.keras import models
from .tfkeras import EfficientNetB0, EfficientNetB1, EfficientNetB2
from .tfkeras import EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6

from .layers import ClipBoxes, RegressBoxes, FilterDetections, wBiFPNAdd, BatchNormalization
from .initializers import PriorProbability

w_bifpns = [64, 88, 112, 160, 224, 288, 384]
image_sizes = [512, 640, 768, 896, 1024, 1280, 1408]
backbones = [EfficientNetB0, EfficientNetB1, EfficientNetB2,
             EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6]


def DepthwiseConvBlock(kernel_size, strides, name, freeze_bn=False):
    f1 = layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides, padding='same',
                                use_bias=False, name='{}_dconv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)), (f1, f2, f3))


def ConvBlock(num_channels, kernel_size, strides, name, freeze_bn=False):
    f1 = layers.Conv2D(num_channels, kernel_size=kernel_size, strides=strides, padding='same',
                       use_bias=False, name='{}_conv'.format(name))
    f2 = BatchNormalization(freeze=freeze_bn, name='{}_bn'.format(name))
    f3 = layers.ReLU(name='{}_relu'.format(name))
    return reduce(lambda f, g: lambda *args, **kwargs: g(f(*args, **kwargs)), (f1, f2, f3))


def build_BiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P7_in)

    # upsample
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = layers.Add()([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = layers.Add()([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = layers.Add()([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = layers.Add()([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    # downsample
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = layers.Add()([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = layers.Add()([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = layers.Add()([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = layers.Add()([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)

    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_wBiFPN(features, num_channels, id, freeze_bn=False):
    if id == 0:
        _, _, C3, C4, C5 = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            C3)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            C4)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            C5)
        P6_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            C5)
        P7_in = ConvBlock(num_channels, kernel_size=3, strides=2, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P6_in)
    else:
        P3_in, P4_in, P5_in, P6_in, P7_in = features
        P3_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P3'.format(id))(
            P3_in)
        P4_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P4'.format(id))(
            P4_in)
        P5_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P5'.format(id))(
            P5_in)
        P6_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P6'.format(id))(
            P6_in)
        P7_in = ConvBlock(num_channels, kernel_size=1, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_P7'.format(id))(
            P7_in)

    # upsample
    P7_U = layers.UpSampling2D()(P7_in)
    P6_td = wBiFPNAdd(name='w_bi_fpn_add' if id == 0 else f'w_bi_fpn_add_{8 * id}')([P7_U, P6_in])
    P6_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P6'.format(id))(P6_td)
    P6_U = layers.UpSampling2D()(P6_td)
    P5_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 1}')([P6_U, P5_in])
    P5_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P5'.format(id))(P5_td)
    P5_U = layers.UpSampling2D()(P5_td)
    P4_td = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 2}')([P5_U, P4_in])
    P4_td = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P4'.format(id))(P4_td)
    P4_U = layers.UpSampling2D()(P4_td)
    P3_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 3}')([P4_U, P3_in])
    P3_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_U_P3'.format(id))(P3_out)
    # downsample
    P3_D = layers.MaxPooling2D(strides=(2, 2))(P3_out)
    P4_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 4}')([P3_D, P4_td, P4_in])
    P4_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P4'.format(id))(P4_out)
    P4_D = layers.MaxPooling2D(strides=(2, 2))(P4_out)
    P5_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 5}')([P4_D, P5_td, P5_in])
    P5_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P5'.format(id))(P5_out)
    P5_D = layers.MaxPooling2D(strides=(2, 2))(P5_out)
    P6_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 6}')([P5_D, P6_td, P6_in])
    P6_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P6'.format(id))(P6_out)
    P6_D = layers.MaxPooling2D(strides=(2, 2))(P6_out)
    P7_out = wBiFPNAdd(name=f'w_bi_fpn_add_{8 * id + 7}')([P6_D, P7_in])
    P7_out = DepthwiseConvBlock(kernel_size=3, strides=1, freeze_bn=freeze_bn, name='BiFPN_{}_D_P7'.format(id))(P7_out)

    return P3_out, P4_out, P5_out, P6_out, P7_out


def build_regress_head(width, depth, num_anchors=9):
    options = {
        'kernel_size': 3,
        'strides': 1,
        'padding': 'same',
        # 'kernel_initializer': initializers.normal(mean=0.0, stddev=0.01, seed=None),
        'kernel_initializer': initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
        'bias_initializer': 'zeros'
    }

    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(
            filters=width,
            activation='relu',
            **options
        )(outputs)

    outputs = layers.Conv2D(num_anchors * 4, **options)(outputs)
    # (b, num_anchors_this_feature_map, 4)
    outputs = layers.Reshape((-1, 4))(outputs)

    return models.Model(inputs=inputs, outputs=outputs, name='box_head')


def build_class_head(width, depth, num_classes=20, num_anchors=9):
    options = {
        'kernel_size': 3,
        'strides': 1,
        'padding': 'same',
        # 'kernel_initializer': initializers.normal(mean=0.0, stddev=0.01, seed=None),
    }

    inputs = layers.Input(shape=(None, None, width))
    outputs = inputs
    for i in range(depth):
        outputs = layers.Conv2D(
            filters=width,
            activation='relu',
            kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
            bias_initializer='zeros',
            **options
        )(outputs)

    # outputs = layers.Conv2D(num_anchors * num_classes, **options)(outputs)
    outputs = layers.Conv2D(
        filters=num_classes * num_anchors,
        kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.01, seed=None),
        bias_initializer=PriorProbability(probability=0.01),
        name='pyramid_classification',
        **options
    )(outputs)
    # (b, num_anchors_this_feature_map, 4)
    outputs = layers.Reshape((-1, num_classes))(outputs)
    outputs = layers.Activation('sigmoid')(outputs)

    return models.Model(inputs=inputs, outputs=outputs, name='class_head')


def efficientdet(phi, num_classes=20, weighted_bifpn=False, freeze_bn=False, score_threshold=0.01):
    assert phi in range(7)
    input_size = image_sizes[phi]
    input_shape = (input_size, input_size, 3)
    # input_shape = (None, None, 3)
    image_input = layers.Input(input_shape)
    w_bifpn = w_bifpns[phi]
    d_bifpn = 2 + phi
    w_head = w_bifpn
    d_head = 3 + int(phi / 3)
    backbone_cls = backbones[phi]
    # features = backbone_cls(include_top=False, input_shape=input_shape, weights=weights)(image_input)
    features = backbone_cls(input_tensor=image_input, freeze_bn=freeze_bn)
    if weighted_bifpn:
        for i in range(d_bifpn):
            features = build_wBiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    else:
        for i in range(d_bifpn):
            features = build_BiFPN(features, w_bifpn, i, freeze_bn=freeze_bn)
    regress_head = build_regress_head(w_head, d_head)
    class_head = build_class_head(w_head, d_head, num_classes=num_classes)
    regression = [regress_head(feature) for feature in features]
    regression = layers.Concatenate(axis=1, name='regression')(regression)
    classification = [class_head(feature) for feature in features]
    classification = layers.Concatenate(axis=1, name='classification')(classification)

    model = models.Model(inputs=[image_input], outputs=[regression, classification], name='efficientdet')

    # apply predicted regression to anchors
    # anchors = tf.tile(tf.expand_dims(tf.constant(anchors), axis=0), (tf.shape(regression)[0], 1, 1))
    anchors_input = layers.Input((None, 4))
    boxes = RegressBoxes(name='boxes')([anchors_input, regression])
    boxes = ClipBoxes(name='clipped_boxes')([image_input, boxes])

    # filter detections (apply NMS / score threshold / select top-k)
    detections = FilterDetections(
        name='filtered_detections',
        score_threshold=score_threshold
    )([boxes, classification])
    prediction_model = models.Model(inputs=[image_input, anchors_input], outputs=detections, name='efficientdet_p')
    return model, prediction_model



