#!/usr/bin/python3
import numpy as np
import tensorflow as tf
import keras.backend as KB
import keras.layers as KL
import keras.regularizers as KR
def identity_block(kernel_size, filters, stage, block, weight_decay=0., batch_momentum=0.99):
'''The identity_block is the block that has no conv layer at shortcut
# Arguments
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
'''
def f(input_tensor):
nb_filter1, nb_filter2, nb_filter3 = filters
if KB.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a',
kernel_regularizer=KR.l2(weight_decay))(input_tensor)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size),
padding='same', name=conv_name_base + '2b', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +
'2c', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x)
x = KL.Add()([x, input_tensor])
x = KL.Activation('relu')(x)
return x
return f
def conv_block(kernel_size, filters, stage, block, weight_decay=0., strides=(2, 2), batch_momentum=0.99):
'''conv_block is the block that has a conv layer at shortcut
# Arguments
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
Note that from stage 3, the first conv layer at main path is with strides=(2,2)
And the shortcut should have strides=(2,2) as well
'''
def f(input_tensor):
nb_filter1, nb_filter2, nb_filter3 = filters
if KB.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = KL.Conv2D(nb_filter1, (1, 1), strides=strides,
name=conv_name_base + '2a', kernel_regularizer=KR.l2(weight_decay))(input_tensor)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same',
name=conv_name_base + '2b', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +
'2c', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x)
shortcut = KL.Conv2D(nb_filter3, (1, 1), strides=strides,
name=conv_name_base + '1', kernel_regularizer=KR.l2(weight_decay))(input_tensor)
shortcut = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '1', momentum=batch_momentum)(shortcut)
x = KL.Add()([x, shortcut])
x = KL.Activation('relu')(x)
return x
return f
# Atrous-Convolution version of residual blocks
def atrous_identity_block(kernel_size, filters, stage, block, weight_decay=0., atrous_rate=(2, 2), batch_momentum=0.99):
'''The identity_block is the block that has no conv layer at shortcut
# Arguments
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
'''
def f(input_tensor):
nb_filter1, nb_filter2, nb_filter3 = filters
if KB.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = KL.Conv2D(nb_filter1, (1, 1), name=conv_name_base + '2a',
kernel_regularizer=KR.l2(weight_decay))(input_tensor)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), dilation_rate=atrous_rate,
padding='same', name=conv_name_base + '2b', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +
'2c', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x)
x = KL.Add()([x, input_tensor])
x = KL.Activation('relu')(x)
return x
return f
def atrous_conv_block(kernel_size, filters, stage, block, weight_decay=0., strides=(1, 1), atrous_rate=(2, 2), batch_momentum=0.99):
'''conv_block is the block that has a conv layer at shortcut
# Arguments
kernel_size: defualt 3, the kernel size of middle conv layer at main path
filters: list of integers, the nb_filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
'''
def f(input_tensor):
nb_filter1, nb_filter2, nb_filter3 = filters
if KB.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = KL.Conv2D(nb_filter1, (1, 1), strides=strides,
name=conv_name_base + '2a', kernel_regularizer=KR.l2(weight_decay))(input_tensor)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2a', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter2, (kernel_size, kernel_size), padding='same', dilation_rate=atrous_rate,
name=conv_name_base + '2b', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2b', momentum=batch_momentum)(x)
x = KL.Activation('relu')(x)
x = KL.Conv2D(nb_filter3, (1, 1), name=conv_name_base +
'2c', kernel_regularizer=KR.l2(weight_decay))(x)
x = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '2c', momentum=batch_momentum)(x)
shortcut = KL.Conv2D(nb_filter3, (1, 1), strides=strides,
name=conv_name_base + '1', kernel_regularizer=KR.l2(weight_decay))(input_tensor)
shortcut = KL.BatchNormalization(
axis=bn_axis, name=bn_name_base + '1', momentum=batch_momentum)(shortcut)
x = KL.Add()([x, shortcut])
x = KL.Activation('relu')(x)
return x
return f
def resize_images_bilinear(X, height_factor=1, width_factor=1, target_height=None, target_width=None, data_format='default'):
'''Resizes the images contained in a 4D tensor of shape
- [batch, channels, height, width] (for 'channels_first' data_format)
- [batch, height, width, channels] (for 'channels_last' data_format)
by a factor of (height_factor, width_factor). Both factors should be
positive integers.
'''
if data_format == 'default':
data_format = KB.image_data_format()
if data_format == 'channels_first':
original_shape = KB.int_shape(X)
if target_height and target_width:
new_shape = tf.constant(
np.array((target_height, target_width)).astype('int32'))
else:
new_shape = tf.shape(X)[2:]
new_shape *= tf.constant(
np.array([height_factor, width_factor]).astype('int32'))
X = KB.permute_dimensions(X, [0, 2, 3, 1])
X = tf.image.resize_bilinear(X, new_shape)
X = KB.permute_dimensions(X, [0, 3, 1, 2])
if target_height and target_width:
X.set_shape((None, None, target_height, target_width))
else:
X.set_shape(
(None, None, original_shape[2] * height_factor, original_shape[3] * width_factor))
return X
elif data_format == 'channels_last':
original_shape = KB.int_shape(X)
if target_height and target_width:
new_shape = tf.constant(
np.array((target_height, target_width)).astype('int32'))
else:
new_shape = tf.shape(X)[1:3]
new_shape *= tf.constant(
np.array([height_factor, width_factor]).astype('int32'))
X = tf.image.resize_bilinear(X, new_shape)
if target_height and target_width:
X.set_shape((None, target_height, target_width, None))
else:
X.set_shape(
(None, original_shape[1] * height_factor, original_shape[2] * width_factor, None))
return X
else:
raise Exception('Invalid data_format: ' + data_format)
