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.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

Convolutional Neural Networks/Car detection for Autonomous Driving/yad2k/models/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)