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model.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Jan 27 19:13:36 2017
@author: Weidi Xie
@description:
This is the file to create the model, similar as the paper,
but with batch normalization,
make it more easier to train.
U-net version is also provided.
"""
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
from keras import backend as K
from keras.models import Sequential, Model
from keras.layers import (
Input,
Activation,
# Merge,
merge,
Dropout,
Reshape,
Permute,
Dense,
UpSampling2D,
Flatten
)
from keras.optimizers import SGD, RMSprop
from keras.layers.convolutional import (
Convolution2D)
from keras.layers.pooling import (
MaxPooling2D,
AveragePooling2D
)
from keras.layers.normalization import BatchNormalization
from keras.regularizers import l2
weight_decay = 1e-5
K.set_image_dim_ordering('tf')
def _conv_bn_relu(nb_filter, row, col, subsample = (1,1)):
def f(input):
conv_a = Convolution2D(nb_filter, row, col, subsample = subsample,
init = 'orthogonal',
border_mode='same', bias = False)(input)
norm_a = BatchNormalization()(conv_a)
act_a = Activation(activation = 'relu')(norm_a)
return act_a
return f
def _conv_bn_relu_x2(nb_filter, row, col, subsample = (1,1)):
def f(input):
conv_a = Convolution2D(nb_filter, row, col, subsample = subsample,
init = 'orthogonal', border_mode = 'same',bias = False,
W_regularizer = l2(weight_decay),
b_regularizer = l2(weight_decay))(input)
norm_a = BatchNormalization()(conv_a)
act_a = Activation(activation = 'relu')(norm_a)
conv_b = Convolution2D(nb_filter, row, col, subsample = subsample,
init = 'orthogonal', border_mode = 'same',bias = False,
W_regularizer = l2(weight_decay),
b_regularizer = l2(weight_decay))(act_a)
norm_b = BatchNormalization()(conv_b)
act_b = Activation(activation = 'relu')(norm_b)
return act_b
return f
def FCRN_A_base(input):
block1 = _conv_bn_relu(32,3,3)(input)
pool1 = MaxPooling2D(pool_size=(2,2))(block1)
# =========================================================================
block2 = _conv_bn_relu(64,3,3)(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(block2)
# =========================================================================
block3 = _conv_bn_relu(128,3,3)(pool2)
pool3 = MaxPooling2D(pool_size=(2, 2))(block3)
# =========================================================================
block4 = _conv_bn_relu(512,3,3)(pool3)
# =========================================================================
up5 = UpSampling2D(size=(2, 2))(block4)
block5 = _conv_bn_relu(128,3,3)(up5)
# =========================================================================
up6 = UpSampling2D(size=(2, 2))(block5)
block6 = _conv_bn_relu(64,3,3)(up6)
# =========================================================================
up7 = UpSampling2D(size=(2, 2))(block6)
block7 = _conv_bn_relu(32,3,3)(up7)
return block7
def FCRN_A_base_v2(input):
block1 = _conv_bn_relu_x2(32,3,3)(input)
pool1 = MaxPooling2D(pool_size=(2,2))(block1)
# =========================================================================
block2 = _conv_bn_relu_x2(64,3,3)(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(block2)
# =========================================================================
block3 = _conv_bn_relu_x2(128,3,3)(pool2)
pool3 = MaxPooling2D(pool_size=(2, 2))(block3)
# =========================================================================
block4 = _conv_bn_relu(512,3,3)(pool3)
# =========================================================================
up5 = UpSampling2D(size=(2, 2))(block4)
block5 = _conv_bn_relu_x2(128,3,3)(up5)
# =========================================================================
up6 = UpSampling2D(size=(2, 2))(block5)
block6 = _conv_bn_relu_x2(64,3,3)(up6)
# =========================================================================
up7 = UpSampling2D(size=(2, 2))(block6)
block7 = _conv_bn_relu_x2(32,3,3)(up7)
return block7
def U_net_base(input, nb_filter = 64):
block1 = _conv_bn_relu_x2(nb_filter,3,3)(input)
pool1 = MaxPooling2D(pool_size=(2,2))(block1)
# =========================================================================
block2 = _conv_bn_relu_x2(nb_filter,3,3)(pool1)
pool2 = MaxPooling2D(pool_size=(2, 2))(block2)
# =========================================================================
block3 = _conv_bn_relu_x2(nb_filter,3,3)(pool2)
pool3 = MaxPooling2D(pool_size=(2, 2))(block3)
# =========================================================================
block4 = _conv_bn_relu_x2(nb_filter,3,3)(pool3)
up4 = merge.concatenate([UpSampling2D(size=(2, 2))(block4), block3], axis=-1)
# =========================================================================
block5 = _conv_bn_relu_x2(nb_filter,3,3)(up4)
up5 = merge.concatenate([UpSampling2D(size=(2, 2))(block5), block2], axis=-1)
# =========================================================================
block6 = _conv_bn_relu_x2(nb_filter,3,3)(up5)
up6 = merge.concatenate([UpSampling2D(size=(2, 2))(block6), block1], axis=-1)
# =========================================================================
block7 = _conv_bn_relu(nb_filter,3,3)(up6)
return block7
def buildModel_FCRN_A (input_dim):
input_ = Input (shape = (input_dim))
# =========================================================================
act_ = FCRN_A_base (input_)
# =========================================================================
density_pred = Convolution2D(1, 1, 1, bias = False, activation='linear',\
init='orthogonal',name='pred',border_mode='same')(act_)
# =========================================================================
model = Model (input = input_, output = density_pred)
opt = SGD(lr = 1e-2, momentum = 0.9, nesterov = True)
model.compile(optimizer = opt, loss = 'mse')
return model
def buildModel_FCRN_A_v2 (input_dim):
input_ = Input (shape = (input_dim))
# =========================================================================
act_ = FCRN_A_base_v2 (input_)
# =========================================================================
density_pred = Convolution2D(1, 1, 1, bias = False, activation='linear',\
init='orthogonal',name='pred',border_mode='same')(act_)
# =========================================================================
model = Model (input = input_, output = density_pred)
opt = SGD(lr = 1e-2, momentum = 0.9, nesterov = True)
model.compile(optimizer = opt, loss = 'mse')
return model
def buildModel_U_net (input_dim):
input_ = Input (shape = (input_dim))
# =========================================================================
act_ = U_net_base (input_, nb_filter = 64 )
# =========================================================================
density_pred = Convolution2D(1, 1, 1, bias = False, activation='linear',\
init='orthogonal',name='pred',border_mode='same')(act_)
# =========================================================================
model = Model (input = input_, output = density_pred)
opt = RMSprop(1e-3)
model.compile(optimizer = opt, loss = 'mse')
return model