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inception_resnet_v2.py
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import tensorflow as tf
from inception_modules import Stem, ReductionA, BasicConv2D, Conv2DLinear
NUM_CLASSES = 10
class InceptionResNetA(tf.keras.layers.Layer):
def __init__(self):
super(InceptionResNetA, self).__init__()
self.b1_conv = BasicConv2D(filters=32,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv1 = BasicConv2D(filters=32,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv2 = BasicConv2D(filters=32,
kernel_size=(3, 3),
strides=1,
padding="same")
self.b3_conv1 = BasicConv2D(filters=32,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b3_conv2 = BasicConv2D(filters=48,
kernel_size=(3, 3),
strides=1,
padding="same")
self.b3_conv3 = BasicConv2D(filters=64,
kernel_size=(3, 3),
strides=1,
padding="same")
self.conv = Conv2DLinear(filters=384,
kernel_size=(1, 1),
strides=1,
padding="same")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_conv(inputs, training=training)
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)
x = tf.concat(values=[b1, b2, b3], axis=-1)
x = self.conv(x, training=training)
output = tf.keras.layers.add([x, inputs])
return tf.nn.relu(output)
class InceptionResNetB(tf.keras.layers.Layer):
def __init__(self):
super(InceptionResNetB, self).__init__()
self.b1_conv = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv1 = BasicConv2D(filters=128,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv2 = BasicConv2D(filters=160,
kernel_size=(1, 7),
strides=1,
padding="same")
self.b2_conv3 = BasicConv2D(filters=192,
kernel_size=(7, 1),
strides=1,
padding="same")
self.conv = Conv2DLinear(filters=1152,
kernel_size=(1, 1),
strides=1,
padding="same")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_conv(inputs, training=training)
b2 = self.b2_conv1(inputs, training=training)
b2 = self.b2_conv2(b2, training=training)
b2 = self.b2_conv3(b2, training=training)
x = tf.concat(values=[b1, b2], axis=-1)
x = self.conv(x, training=training)
output = tf.keras.layers.add([x, inputs])
return tf.nn.relu(output)
class InceptionResNetC(tf.keras.layers.Layer):
def __init__(self):
super(InceptionResNetC, self).__init__()
self.b1_conv = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv1 = BasicConv2D(filters=192,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv2 = BasicConv2D(filters=224,
kernel_size=(1, 3),
strides=1,
padding="same")
self.b2_conv3 = BasicConv2D(filters=256,
kernel_size=(3, 1),
strides=1,
padding="same")
self.conv = Conv2DLinear(filters=2144,
kernel_size=(1, 1),
strides=1,
padding="same")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_conv(inputs, training=training)
b2 = self.b2_conv1(inputs, training=training)
b2 = self.b2_conv2(b2, training=training)
b2 = self.b2_conv3(b2, training=training)
x = tf.concat(values=[b1, b2], axis=-1)
x = self.conv(x, training=training)
output = tf.keras.layers.add([x, inputs])
return tf.nn.relu(output)
class ReductionB(tf.keras.layers.Layer):
def __init__(self):
super(ReductionB, self).__init__()
self.b1_maxpool = tf.keras.layers.MaxPool2D(pool_size=(3, 3),
strides=2,
padding="valid")
self.b2_conv1 = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b2_conv2 = BasicConv2D(filters=384,
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=288,
kernel_size=(3, 3),
strides=2,
padding="valid")
self.b4_conv1 = BasicConv2D(filters=256,
kernel_size=(1, 1),
strides=1,
padding="same")
self.b4_conv2 = BasicConv2D(filters=288,
kernel_size=(3, 3),
strides=1,
padding="same")
self.b4_conv3 = BasicConv2D(filters=320,
kernel_size=(3, 3),
strides=2,
padding="valid")
def call(self, inputs, training=None, **kwargs):
b1 = self.b1_maxpool(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)
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)
def build_inception_resnet_a(n):
block = tf.keras.Sequential()
for _ in range(n):
block.add(InceptionResNetA())
return block
def build_inception_resnet_b(n):
block = tf.keras.Sequential()
for _ in range(n):
block.add(InceptionResNetB())
return block
def build_inception_resnet_c(n):
block = tf.keras.Sequential()
for _ in range(n):
block.add(InceptionResNetC())
return block
class InceptionResNetV2(tf.keras.Model):
def __init__(self):
super(InceptionResNetV2, self).__init__()
self.stem = Stem()
self.inception_resnet_a = build_inception_resnet_a(5)
self.reduction_a = ReductionA(k=256, l=256, m=384, n=384)
self.inception_resnet_b = build_inception_resnet_b(10)
self.reduction_b = ReductionB()
self.inception_resnet_c = build_inception_resnet_c(5)
self.avgpool = tf.keras.layers.AveragePooling2D(pool_size=(8, 8))
self.dropout = tf.keras.layers.Dropout(rate=0.2)
self.flat = tf.keras.layers.Flatten()
self.fc = tf.keras.layers.Dense(units=NUM_CLASSES,
activation=tf.keras.activations.softmax)
def call(self, inputs, training=None, mask=None):
x = self.stem(inputs, training=training)
x = self.inception_resnet_a(x, training=training)
x = self.reduction_a(x, training=training)
x = self.inception_resnet_b(x, training=training)
x = self.reduction_b(x, training=training)
x = self.inception_resnet_c(x, training=training)
x = self.avgpool(x)
x = self.dropout(x, training=training)
x = self.flat(x)
x = self.fc(x)
return x