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resnet.py
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resnet.py
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import sys
import numpy as np
from keras.models import Model
from keras.layers import Input, Activation, merge, Dense, Flatten
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.layers.convolutional import AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
from keras.layers.merge import add
from keras import backend as K
sys.setrecursionlimit(10000)
BN_AXIS = 3
def ResNet(depth, n_class=10, input_tensor=None):
"""
total number of layers: 2 + 6 * depth
:param depth:
:param n_class:
:param input_tensor:
:return: sequence of layers until the logits
"""
num_conv = 3
decay = 2e-3
# 1 conv + BN + relu
filters = 16
b = Conv2D(filters=filters, kernel_size=(num_conv, num_conv),
kernel_initializer="he_normal", padding="same",
kernel_regularizer=l2(decay), bias_regularizer=l2(0))(input_tensor)
b = Activation("relu")(b)
filters *= 2
# 1 res, no striding
b = residual(num_conv, filters, decay, first=True)(b) # 2 layers inside
for _ in np.arange(1, depth): # start from 1 => 2 * depth in total
b = residual(num_conv, filters, decay)(b)
filters *= 2
# 2 res, with striding
b = residual(num_conv, filters, decay, more_filters=True)(b)
for _ in np.arange(1, depth):
b = residual(num_conv, filters, decay)(b)
filters *= 2
# 3 res, with striding
b = residual(num_conv, filters, decay, more_filters=True)(b)
for _ in np.arange(1, depth):
b = residual(num_conv, filters, decay)(b)
b = BatchNormalization(axis=BN_AXIS)(b)
b = Activation("relu")(b)
b = AveragePooling2D(pool_size=(8, 8), strides=(1, 1),
padding="valid")(b)
b = Flatten(name='features')(b)
dense = Dense(units=n_class, kernel_initializer="he_normal",
kernel_regularizer=l2(decay), bias_regularizer=l2(0), name='logits')(b)
return dense
def WideResNet(depth, n_class=10, input_tensor=None):
"""
10 times wider than ResNet.
total number of layers: 2 + 6 * depth
:param depth:
:param n_class:
:param input_tensor:
:return: sequence of layers until the logits
"""
num_conv = 3
decay = 2e-3
# 1 conv + BN + relu
filters = 16
b = Conv2D(filters=filters, kernel_size=(num_conv, num_conv),
kernel_initializer="he_normal", padding="same",
kernel_regularizer=l2(decay), bias_regularizer=l2(0))(input_tensor)
b = Activation("relu")(b)
filters *= 10 # wide
# 1 res, no striding
b = residual(num_conv, filters, decay, first=True)(b) # 2 layers inside
for _ in np.arange(1, depth): # start from 1 => 2 * depth in total
b = residual(num_conv, filters, decay)(b)
filters *= 2
# 2 res, with striding
b = residual(num_conv, filters, decay, more_filters=True)(b)
for _ in np.arange(1, depth):
b = residual(num_conv, filters, decay)(b)
filters *= 2
# 3 res, with striding
b = residual(num_conv, filters, decay, more_filters=True)(b)
for _ in np.arange(1, depth):
b = residual(num_conv, filters, decay)(b)
b = BatchNormalization(axis=BN_AXIS)(b)
b = Activation("relu")(b)
b = AveragePooling2D(pool_size=(8, 8), strides=(1, 1),
padding="valid")(b)
b = Flatten(name='features')(b)
dense = Dense(units=n_class, kernel_initializer="he_normal",
kernel_regularizer=l2(decay), bias_regularizer=l2(0), name='logits')(b)
return dense
def residual(num_conv, filters, decay, more_filters=False, first=False):
def f(input):
# in_channel = input._keras_shape[1]
out_channel = filters
if more_filters and not first:
# out_channel = in_channel * 2
stride = 2
else:
# out_channel = in_channel
stride = 1
if not first:
b = BatchNormalization(axis=BN_AXIS)(input)
b = Activation("relu")(b)
b = Activation("relu")(input)
else:
b = input
b = Conv2D(filters=out_channel,
kernel_size=(num_conv, num_conv),
strides=(stride, stride),
kernel_initializer="he_normal", padding="same",
kernel_regularizer=l2(decay), bias_regularizer=l2(0))(b)
b = BatchNormalization(axis=BN_AXIS)(b)
b = Activation("relu")(b)
res = Conv2D(filters=out_channel,
kernel_size=(num_conv, num_conv),
kernel_initializer="he_normal", padding="same",
kernel_regularizer=l2(decay), bias_regularizer=l2(0))(b)
# check and match number of filter for the shortcut
input_shape = K.int_shape(input)
residual_shape = K.int_shape(res)
if not input_shape[3] == residual_shape[3]:
stride_width = int(round(input_shape[1] / residual_shape[1]))
stride_height = int(round(input_shape[2] / residual_shape[2]))
input = Conv2D(filters=residual_shape[3], kernel_size=(1, 1),
strides=(stride_width, stride_height),
kernel_initializer="he_normal",
padding="valid", kernel_regularizer=l2(decay))(input)
return add([input, res])
return f