def darknet53_residual_block(inputs, filters, data_format, strides=1):
"""
Creates a residual block for Darknet.
"""
shortcut = inputs
inputs = Conv2D(inputs,
filters=filters,
kernel_size=1,
strides=strides,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
filters *= 2
inputs = Conv2D(inputs,
filters=filters,
kernel_size=3,
strides=strides,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
inputs += shortcut
return inputs
def UpsampleConv(name, input_dim, output_dim, filter_size, inputs, he_init=True, biases=True):
output = inputs
output = tf.concat([output, output, output, output], axis=1)
output = tf.transpose(output, [0, 2, 3, 1])
output = tf.depth_to_space(output, 2)
output = tf.transpose(output, [0, 3, 1, 2])
output = Conv2D(name, input_dim, output_dim, filter_size, output, he_init=he_init, biases=biases)
return output
def ConvMeanPool(name,
input_dim,
output_dim,
filter_size,
inputs,
he_init=True,
biases=True):
output = Conv2D(name,
input_dim,
output_dim,
filter_size,
inputs,
he_init=he_init,
biases=biases)
output = tf.add_n([
output[:, :, ::2, ::2], output[:, :, 1::2, ::2],
output[:, :, ::2, 1::2], output[:, :, 1::2, 1::2]
]) / 4.
return output
def feature_pyramid_network(inputs, filters, data_format):
"""
Creates convolution operations layer used after Darknet
"""
inputs = Conv2D(inputs,
filters=filters,
kernel_size=1,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
inputs = Conv2D(inputs,
filters=(filters * 2),
kernel_size=3,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
inputs = Conv2D(inputs,
filters=filters,
kernel_size=1,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
inputs = Conv2D(inputs,
filters=(filters * 2),
kernel_size=3,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
inputs = Conv2D(inputs,
filters=filters,
kernel_size=1,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
route = inputs
inputs = Conv2D(inputs,
filters=(filters * 2),
kernel_size=3,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
return inputs, route
def darknet53(inputs, data_format):
"""
Creates Darknet53 model
"""
inputs = Conv2D(inputs, filters=32, kernel_size=3, data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
inputs = Conv2D(inputs,
filters=64,
kernel_size=3,
strides=2,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
inputs = darknet53_residual_block(inputs,
filters=32,
data_format=data_format)
inputs = Conv2D(inputs,
filters=128,
kernel_size=3,
strides=2,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
for _ in range(2):
inputs = darknet53_residual_block(inputs,
filters=64,
data_format=data_format)
inputs = Conv2D(inputs,
filters=256,
kernel_size=3,
strides=2,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
for _ in range(8):
inputs = darknet53_residual_block(inputs,
filters=128,
data_format=data_format)
route4 = inputs # layers 36
inputs = Conv2D(inputs,
filters=512,
kernel_size=3,
strides=2,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
for _ in range(8):
inputs = darknet53_residual_block(inputs,
filters=256,
data_format=data_format)
route2 = inputs # layers 61
inputs = Conv2D(inputs,
filters=1024,
kernel_size=3,
strides=2,
data_format=data_format)
inputs = BatchNormalization(inputs, data_format=data_format)
inputs = LeakyReLU(inputs)
for _ in range(4):
inputs = darknet53_residual_block(inputs,
filters=512,
data_format=data_format)
return inputs, route2, route4
def __call__(self, inputs):
"""
Generate Computation Graph
"""
with tf.variable_scope(self.scope, reuse=tf.AUTO_REUSE):
if self.data_format == 'channels_first':
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = inputs / 255
inputs, route2, route4 = darknet53(inputs,
data_format=self.data_format)
inputs, route1 = feature_pyramid_network(
inputs, filters=512, data_format=self.data_format)
detect1 = yolo_layer(inputs,
n_classes=self.n_classes,
anchors=_ANCHORS[6:],
img_size=self.input_size,
data_format=self.data_format)
inputs = Conv2D(route1,
filters=256,
kernel_size=1,
data_format=self.data_format)
inputs = BatchNormalization(inputs, data_format=self.data_format)
inputs = LeakyReLU(inputs)
upsample_size = route2.get_shape().as_list()
inputs = upsample(inputs,
out_shape=upsample_size,
data_format=self.data_format)
axis = 1 if self.data_format == 'channels_first' else 3
inputs = tf.concat([inputs, route2], axis=axis)
inputs, route3 = feature_pyramid_network(
inputs, filters=256, data_format=self.data_format)
detect2 = yolo_layer(inputs,
n_classes=self.n_classes,
anchors=_ANCHORS[3:6],
img_size=self.input_size,
data_format=self.data_format)
inputs = Conv2D(route3,
filters=128,
kernel_size=1,
data_format=self.data_format)
inputs = BatchNormalization(inputs, data_format=self.data_format)
inputs = LeakyReLU(inputs)
upsample_size = route4.get_shape().as_list()
inputs = upsample(inputs,
out_shape=upsample_size,
data_format=self.data_format)
axis = 1 if self.data_format == 'channels_first' else 3
inputs = tf.concat([inputs, route4], axis=axis)
inputs, _ = feature_pyramid_network(inputs,
filters=128,
data_format=self.data_format)
detect3 = yolo_layer(inputs,
n_classes=self.n_classes,
anchors=_ANCHORS[:3],
img_size=self.input_size,
data_format=self.data_format)
inputs = tf.concat([detect1, detect2, detect3], axis=1)
inputs = build_boxes(inputs)
boxes_dicts = non_max_suppression(
inputs,
n_classes=self.n_classes,
max_output_size=self.max_output_size,
iou_threshold=self.iou_threshold,
confidence_threshold=self.confidence_threshold)
return boxes_dicts