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inception_modules.py
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inception_modules.py
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import tensorflow as tf
class BasicConv2D(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, strides, padding):
super(BasicConv2D, self).__init__()
self.conv = tf.keras.layers.Conv2D(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding)
self.bn = tf.keras.layers.BatchNormalization()
def call(self, inputs, training=None, **kwargs):
x = self.conv(inputs)
x = self.bn(x, training=training)
x = tf.nn.relu(x)
return x
class Conv2DLinear(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, strides, padding):
super(Conv2DLinear, self).__init__()
self.conv = tf.keras.layers.Conv2D(filters=filters,
kernel_size=kernel_size,
strides=strides,
padding=padding)
self.bn = tf.keras.layers.BatchNormalization()
def call(self, inputs, training=None, **kwargs):
x = self.conv(inputs)
x = self.bn(x, training=training)
return x
class Stem(tf.keras.layers.Layer):
def __init__(self):
super(Stem, self).__init__()
self.conv1 = BasicConv2D(filters=32,
kernel_size=(3, 3),
strides=2,
padding="valid")
self.conv2 = BasicConv2D(filters=32,
kernel_size=(3, 3),
strides=1,
padding="valid")
self.conv3 = BasicConv2D(filters=64,
kernel_size=(3, 3),
strides=1,
padding="same")
self.b1_maxpool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="valid")
self.b2_conv = BasicConv2D(filters=96,
kernel_size=(3, 3),
strides=2,
padding="valid")
self.b3_conv1 = BasicConv2D(filters=64,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv2 = BasicConv2D(filters=96,
kernel_size=(3, 3),
strides=1,
padding="valid")
self.b4_conv1 = BasicConv2D(filters=64,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b4_conv2 = BasicConv2D(filters=64,
kernel_size=(7, 1),
strides=1,
padding="same")
self.b4_conv3 = BasicConv2D(filters=64,
kernel_size=(1, 7),
strides=1,
padding="same")
self.b4_conv4 = BasicConv2D(filters=96,
kernel_size=(3, 3),
strides=1,
padding="valid")
self.b5_conv = BasicConv2D(filters=192,
kernel_size=(3, 3),
strides=2,
padding="valid")
self.b6_maxpool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="valid")
def call(self, inputs, training=None, **kwargs):
x = self.conv1(inputs, training=training)
x = self.conv2(x, training=training)
x = self.conv3(x, training=training)
branch_1 = self.b1_maxpool(x)
branch_2 = self.b2_conv(x, training=training)
x = tf.concat(values=[branch_1, branch_2], axis=-1)
branch_3 = self.b3_conv1(x, training=training)
branch_3 = self.b3_conv2(branch_3, training=training)
branch_4 = self.b4_conv1(x, training=training)
branch_4 = self.b4_conv2(branch_4, training=training)
branch_4 = self.b4_conv3(branch_4, training=training)
branch_4 = self.b4_conv4(branch_4, training=training)
x = tf.concat(values=[branch_3, branch_4], axis=-1)
branch_5 = self.b5_conv(x, training=training)
branch_6 = self.b6_maxpool(x, training=training)
x = tf.concat(values=[branch_5, branch_6], axis=-1)
return x
class InceptionBlockA(tf.keras.layers.Layer):
def __init__(self):
super(InceptionBlockA, self).__init__()
self.b1_pool = tf.keras.layers.AveragePooling2D(pool_size=(3, 3),
strides=1,
padding="same")
self.b1_conv = BasicConv2D(filters=96,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv = BasicConv2D(filters=96,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv1 = BasicConv2D(filters=64,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv2 = BasicConv2D(filters=96,
kernel_size=(3, 3),
strides=1,
padding="same")
self.b4_conv1 = BasicConv2D(filters=64,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b4_conv2 = BasicConv2D(filters=96,
kernel_size=(3, 3),
strides=1,
padding="same")
self.b4_conv3 = BasicConv2D(filters=96,
kernel_size=(3, 3),
strides=1,
padding="same")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_pool(inputs)
b1 = self.b1_conv(b1, training=training)
b2 = self.b2_conv(inputs, training=training)
b3 = self.b3_conv1(inputs, training=training)
b3 = self.b3_conv2(b3, training=training)
b4 = self.b4_conv1(inputs, training=training)
b4 = self.b4_conv2(b4, training=training)
b4 = self.b4_conv3(b4, training=training)
return tf.concat(values=[b1, b2, b3, b4], axis=-1)
class ReductionA(tf.keras.layers.Layer):
def __init__(self, k, l, m, n):
super(ReductionA, self).__init__()
self.b1_pool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="valid")
self.b2_conv = BasicConv2D(filters=n,
kernel_size=(3, 3),
strides=2,
padding="valid")
self.b3_conv1 = BasicConv2D(filters=k,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv2 = BasicConv2D(filters=l,
kernel_size=(3, 3),
strides=1,
padding="same")
self.b3_conv3 = BasicConv2D(filters=m,
kernel_size=(3, 3),
strides=2,
padding="valid")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_pool(inputs)
b2 = self.b2_conv(inputs, training=training)
b3 = self.b3_conv1(inputs, training=training)
b3 = self.b3_conv2(b3, training=training)
b3 = self.b3_conv3(b3, training=training)
return tf.concat(values=[b1, b2, b3], axis=-1)
class InceptionBlockB(tf.keras.layers.Layer):
def __init__(self):
super(InceptionBlockB, self).__init__()
self.b1_pool = tf.keras.layers.AveragePooling2D(pool_size=(3, 3),
strides=1,
padding="same")
self.b1_conv = BasicConv2D(filters=128,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv = BasicConv2D(filters=384,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv1 = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv2 = BasicConv2D(filters=224,
kernel_size=(1, 7),
strides=1,
padding="same")
self.b3_conv3 = BasicConv2D(filters=256,
kernel_size=(1, 7),
strides=1,
padding="same")
self.b4_conv1 = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b4_conv2 = BasicConv2D(filters=192,
kernel_size=(1, 7),
strides=1,
padding="same")
self.b4_conv3 = BasicConv2D(filters=224,
kernel_size=(7, 1),
strides=1,
padding="same")
self.b4_conv4 = BasicConv2D(filters=224,
kernel_size=(1, 7),
strides=1,
padding="same")
self.b4_conv5 = BasicConv2D(filters=256,
kernel_size=(7, 1),
strides=1,
padding="same")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_pool(inputs)
b1 = self.b1_conv(b1, training=training)
b2 = self.b2_conv(inputs, training=training)
b3 = self.b3_conv1(inputs, training=training)
b3 = self.b3_conv2(b3, training=training)
b3 = self.b3_conv3(b3, training=training)
b4 = self.b4_conv1(inputs, training=training)
b4 = self.b4_conv2(b4, training=training)
b4 = self.b4_conv3(b4, training=training)
b4 = self.b4_conv4(b4, training=training)
b4 = self.b4_conv5(b4, training=training)
return tf.concat(values=[b1, b2, b3, b4], axis=-1)
class ReductionB(tf.keras.layers.Layer):
def __init__(self):
super(ReductionB, self).__init__()
self.b1_pool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="valid")
self.b2_conv1 = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv2 = BasicConv2D(filters=192,
kernel_size=(3, 3),
strides=2,
padding="valid")
self.b3_conv1 = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv2 = BasicConv2D(filters=256,
kernel_size=(1, 7),
strides=1,
padding="same")
self.b3_conv3 = BasicConv2D(filters=320,
kernel_size=(7, 1),
strides=1,
padding="same")
self.b3_conv4 = BasicConv2D(filters=320,
kernel_size=(3, 3),
strides=2,
padding="valid")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_pool(inputs)
b2 = self.b2_conv1(inputs, training=training)
b2 = self.b2_conv2(b2, training=training)
b3 = self.b3_conv1(inputs, training=training)
b3 = self.b3_conv2(b3, training=training)
b3 = self.b3_conv3(b3, training=training)
b3 = self.b3_conv4(b3, training=training)
return tf.concat(values=[b1, b2, b3], axis=-1)
class InceptionBlockC(tf.keras.layers.Layer):
def __init__(self):
super(InceptionBlockC, self).__init__()
self.b1_pool = tf.keras.layers.AveragePooling2D(pool_size=(3, 3),
strides=1,
padding="same")
self.b1_conv = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv1 = BasicConv2D(filters=384,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv2 = BasicConv2D(filters=256,
kernel_size=(1, 3),
strides=1,
padding="same")
self.b3_conv3 = BasicConv2D(filters=256,
kernel_size=(3, 1),
strides=1,
padding="same")
self.b4_conv1 = BasicConv2D(filters=384,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b4_conv2 = BasicConv2D(filters=448,
kernel_size=(1, 3),
strides=1,
padding="same")
self.b4_conv3 = BasicConv2D(filters=512,
kernel_size=(3, 1),
strides=1,
padding="same")
self.b4_conv4 = BasicConv2D(filters=256,
kernel_size=(3, 1),
strides=1,
padding="same")
self.b4_conv5 = BasicConv2D(filters=256,
kernel_size=(1, 3),
strides=1,
padding="same")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_pool(inputs)
b1 = self.b1_conv(b1, training=training)
b2 = self.b2_conv(inputs, training=training)
b3 = self.b3_conv1(inputs, training=training)
b3_1 = self.b3_conv2(b3, training=training)
b3_2 = self.b3_conv3(b3, training=training)
b4 = self.b4_conv1(inputs, training=training)
b4 = self.b4_conv2(b4, training=training)
b4 = self.b4_conv3(b4, training=training)
b4_1 = self.b4_conv4(b4, training=training)
b4_2 = self.b4_conv5(b4, training=training)
return tf.concat(values=[b1, b2, b3_1, b3_2, b4_1, b4_2], axis=-1)