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keras_darknet19.py
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keras_darknet19.py
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"""Darknet19 Model Defined in Keras."""
import functools
from functools import partial
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.models import Model
from keras.regularizers import l2
from ..utils import compose
# Partial wrapper for Convolution2D with static default argument.
_DarknetConv2D = partial(Conv2D, padding='same')
@functools.wraps(Conv2D)
def DarknetConv2D(*args, **kwargs):
"""Wrapper to set Darknet weight regularizer for Convolution2D."""
darknet_conv_kwargs = {'kernel_regularizer': l2(5e-4)}
darknet_conv_kwargs.update(kwargs)
return _DarknetConv2D(*args, **darknet_conv_kwargs)
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))
def bottleneck_block(outer_filters, bottleneck_filters):
"""Bottleneck block of 3x3, 1x1, 3x3 convolutions."""
return compose(
DarknetConv2D_BN_Leaky(outer_filters, (3, 3)),
DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))
def bottleneck_x2_block(outer_filters, bottleneck_filters):
"""Bottleneck block of 3x3, 1x1, 3x3, 1x1, 3x3 convolutions."""
return compose(
bottleneck_block(outer_filters, bottleneck_filters),
DarknetConv2D_BN_Leaky(bottleneck_filters, (1, 1)),
DarknetConv2D_BN_Leaky(outer_filters, (3, 3)))
def darknet_body():
"""Generate first 18 conv layers of Darknet-19."""
return compose(
DarknetConv2D_BN_Leaky(32, (3, 3)),
MaxPooling2D(),
DarknetConv2D_BN_Leaky(64, (3, 3)),
MaxPooling2D(),
bottleneck_block(128, 64),
MaxPooling2D(),
bottleneck_block(256, 128),
MaxPooling2D(),
bottleneck_x2_block(512, 256),
MaxPooling2D(),
bottleneck_x2_block(1024, 512))
def darknet19(inputs):
"""Generate Darknet-19 model for Imagenet classification."""
body = darknet_body()(inputs)
logits = DarknetConv2D(1000, (1, 1), activation='softmax')(body)
return Model(inputs, logits)