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GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C4 - Convolutional Neural Networks/Week 3/Car detection for Autonomous Driving/yad2k/models/keras_darknet19.py
Views: 4822
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"""Darknet19 Model Defined in Keras."""

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import functools

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from functools import partial

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from tensorflow.keras.layers import Conv2D, MaxPooling2D

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from tensorflow.keras.layers import LeakyReLU

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from tensorflow.keras.layers import BatchNormalization

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from tensorflow.keras.models import Model

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from tensorflow.keras.regularizers import l2

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from ..utils import compose

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# Partial wrapper for Convolution2D with static default argument.

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_DarknetConv2D = partial(Conv2D, padding='same')

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@functools.wraps(Conv2D)

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def DarknetConv2D(*args, **kwargs):

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    """Wrapper to set Darknet weight regularizer for Convolution2D."""

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    darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}

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    darknet_conv_kwargs.update(kwargs)

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    return _DarknetConv2D(*args, **darknet_conv_kwargs)

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def DarknetConv2D_BN_Leaky(*args, **kwargs):

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    """Darknet Convolution2D followed by BatchNormalization and LeakyReLU."""

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    no_bias_kwargs = {'use_bias': False}

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    no_bias_kwargs.update(kwargs)

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    return compose(

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        DarknetConv2D(*args, **no_bias_kwargs),

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        BatchNormalization(),

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        LeakyReLU(alpha=0.1))

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def bottleneck_block(outer_filters, bottleneck_filters):

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    """Bottleneck block of 3x3, 1x1, 3x3 convolutions."""

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    return compose(

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        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)),

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        DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),

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        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))

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def bottleneck_x2_block(outer_filters, bottleneck_filters):

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    """Bottleneck block of 3x3, 1x1, 3x3, 1x1, 3x3 convolutions."""

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    return compose(

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        bottleneck_block(outer_filters, bottleneck_filters),

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        DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),

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        DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))

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def darknet_body():

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    """Generate first 18 conv layers of Darknet-19."""

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    return compose(

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        DarknetConv2D_BN_Leaky(32, (3, 3)),

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        MaxPooling2D(),

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        DarknetConv2D_BN_Leaky(64, (3, 3)),

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        MaxPooling2D(),

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        bottleneck_block(128, 64),

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        MaxPooling2D(),

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        bottleneck_block(256, 128),

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        MaxPooling2D(),

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        bottleneck_x2_block(512, 256),

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        MaxPooling2D(),

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        bottleneck_x2_block(1024, 512))

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def darknet19(inputs):

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    """Generate Darknet-19 model for Imagenet classification."""

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    body = darknet_body()(inputs)

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    logits = DarknetConv2D(1000, (1, 1), activation='softmax')(body)

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    return Model(inputs, logits)

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