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model.py
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# -*- coding: utf-8 -*-
from tensorflow import keras
from tensorflow.keras.layers import *
from tensorflow.keras import backend as K
# from keras.models import *
# from keras.layers import *
# from keras.optimizers import *
# from keras import backend as K
# From https://github.com/zhixuhao/unet
def dice_coef(y_true, y_pred, smooth=1):
"""
Dice = (2*|X & Y|)/ (|X|+ |Y|)
= 2*sum(|A*B|)/(sum(A^2)+sum(B^2))
ref: https://arxiv.org/pdf/1606.04797v1.pdf
"""
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
return (2. * intersection + smooth) / (K.sum(K.square(y_true),-1) + K.sum(K.square(y_pred),-1) + smooth)
def dice_coef_loss(y_true, y_pred):
return 1-dice_coef(y_true, y_pred)
def jaccard_distance_loss(y_true, y_pred, smooth=100):
"""
Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
= sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
The jaccard distance loss is usefull for unbalanced datasets. This has been
shifted so it converges on 0 and is smoothed to avoid exploding or disapearing
gradient.
Ref: https://en.wikipedia.org/wiki/Jaccard_index
@url: https://gist.github.com/wassname/f1452b748efcbeb4cb9b1d059dce6f96
@author: wassname
"""
intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac) * smooth
def dice_coef_binary(y_true, y_pred, smooth=1e-7):
'''
Dice coefficient for 2 categories. Ignores background pixel label 0
Pass to model as metric during compile statement
'''
y_true_f = K.flatten(K.one_hot(K.cast(y_true, 'int32'), num_classes=2)[...,1:])
y_pred_f = K.flatten(y_pred[...,1:])
intersect = K.sum(y_true_f * y_pred_f, axis=-1)
denom = K.sum(y_true_f + y_pred_f, axis=-1)
return K.mean((2. * intersect / (denom + smooth)))
def dice_coef_binary_loss(y_true, y_pred):
'''
Dice loss to minimize. Pass to model as loss during compile statement
'''
return 1 - dice_coef_binary(y_true, y_pred)
# This one is used
def generalized_dice_coeff(y_true, y_pred):
Ncl = y_pred.shape[-1]
w = K.zeros(shape=(Ncl,))
w = K.sum(y_true, axis=(0, 1, 2))
# Original:
w = 1/(w**2+0.000001)
# 2020-2-8:
# Changing to 0.00001 seems would get the loss to be 1.0
# and the result will be all dark
# w = 1 / (w ** 2 + 0.00001)
# Compute gen dice coef:
numerator = y_true*y_pred
numerator = w*K.sum(numerator, (0, 1, 2, 3))
numerator = K.sum(numerator)
denominator = y_true+y_pred
denominator = w*K.sum(denominator, (0, 1, 2, 3))
denominator = K.sum(denominator)
gen_dice_coef = 2*numerator/denominator
return gen_dice_coef
def generalized_dice_loss(y_true, y_pred):
return 1 - generalized_dice_coeff(y_true, y_pred)
def unet(pretrained_weights=None, input_size=(256, 256, 1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv1 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv2 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv3 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge6)
conv6 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
up7 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv6))
merge7 = concatenate([conv3, up7], axis=3)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge7)
conv7 = Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
up8 = Conv2D(128, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv7))
merge8 = concatenate([conv2, up8], axis=3)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge8)
conv8 = Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(
UpSampling2D(size=(2, 2))(conv8))
merge9 = concatenate([conv1, up9], axis=3)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge9)
conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
# conv9 = Conv2D(3, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
# conv10 = Conv2D(3, 1, activation='sigmoid')(conv9)
model = keras.Model(inputs=inputs, outputs=conv10)
# model.compile(optimizer=Adam(lr=1e-7), loss='binary_crossentropy', metrics=['accuracy'])
# 2020-2-8:
# lr=1e-6 is good for the meteor data at present
# model.compile(optimizer=Adam(lr=1e-6), loss=generalized_dice_loss, metrics=['accuracy'])
# lr=1e-5 seems also works
# lr=1e-4 seems also works
#
# 2020-2-8:
# Tried several times, seems lr=le-6 would be more safe
# Other bigger values would have chance to trigger the loss be 1.0 and cannot be back
# model.compile(optimizer=Adam(lr=1e-6), loss=generalized_dice_loss, metrics=['accuracy'])
# model.summary()
if (pretrained_weights):
model.load_weights(pretrained_weights)
return model
def unet_plus_plus(input_size=(256, 256, 3), base_filter_num=64):
inputs = Input(input_size)
conv0_0 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(inputs)
conv0_0 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv0_0)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv0_0)
conv1_0 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool1)
conv1_0 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1_0)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv1_0)
up1_0 = Conv2DTranspose(base_filter_num, (2, 2), strides=(2, 2), padding='same')(conv1_0)
merge00_10 = concatenate([conv0_0,up1_0], axis=-1)
conv0_1 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge00_10)
conv0_1 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv0_1)
conv2_0 = Conv2D(base_filter_num*4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool2)
conv2_0 = Conv2D(base_filter_num*4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2_0)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv2_0)
up2_0 = Conv2DTranspose(base_filter_num*2, (2, 2), strides=(2, 2), padding='same')(conv2_0)
merge10_20 = concatenate([conv1_0, up2_0], axis=-1)
conv1_1 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge10_20)
conv1_1 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1_1)
up1_1 = Conv2DTranspose(base_filter_num, (2, 2), strides=(2, 2), padding='same')(conv1_1)
merge01_11 = concatenate([conv0_0,conv0_1, up1_1], axis=-1)
conv0_2 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge01_11)
conv0_2 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv0_2)
conv3_0 = Conv2D(base_filter_num*8, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool3)
conv3_0 = Conv2D(base_filter_num*8, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3_0)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv3_0)
up3_0 = Conv2DTranspose(base_filter_num*4, (2, 2), strides=(2, 2), padding='same')(conv3_0)
merge20_30 = concatenate([conv2_0, up3_0], axis=-1)
conv2_1 = Conv2D(base_filter_num*4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge20_30)
conv2_1 = Conv2D(base_filter_num*4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2_1)
up2_1 = Conv2DTranspose(base_filter_num*2, (2, 2), strides=(2, 2), padding='same')(conv2_1)
merge11_21 = concatenate([conv1_0, conv1_1, up2_1], axis=-1)
conv1_2 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge11_21)
conv1_2 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1_2)
up1_2 = Conv2DTranspose(base_filter_num, (2, 2), strides=(2, 2), padding='same')(conv1_2)
merge02_12 = concatenate([conv0_0, conv0_1, conv0_2, up1_2], axis=-1)
conv0_3 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge02_12)
conv0_3 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv0_3)
conv4_0 = Conv2D(base_filter_num*16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(pool4)
conv4_0 = Conv2D(base_filter_num*16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4_0)
up4_0 = Conv2DTranspose(base_filter_num*8, (2, 2), strides=(2, 2), padding='same')(conv4_0)
merge30_40 = concatenate([conv3_0, up4_0], axis=-1)
conv3_1 = Conv2D(base_filter_num*8, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge30_40)
conv3_1 = Conv2D(base_filter_num*8, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3_1)
up3_1 = Conv2DTranspose(base_filter_num*4, (2, 2), strides=(2, 2), padding='same')(conv3_1)
merge21_31 = concatenate([conv2_0, conv2_1, up3_1], axis=-1)
conv2_2 = Conv2D(base_filter_num*4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge21_31)
conv2_2 = Conv2D(base_filter_num*4, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2_2)
up2_2 = Conv2DTranspose(base_filter_num*2, (2, 2), strides=(2, 2), padding='same')(conv2_2)
merge12_22 = concatenate([conv1_0, conv1_1, conv1_2, up2_2], axis=-1)
conv1_3 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge12_22)
conv1_3 = Conv2D(base_filter_num*2, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1_3)
up1_3 = Conv2DTranspose(base_filter_num, (2, 2), strides=(2, 2), padding='same')(conv1_3)
merge03_13 = concatenate([conv0_0, conv0_1, conv0_2, conv0_3, up1_3], axis=-1)
conv0_4 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(merge03_13)
conv0_4 = Conv2D(base_filter_num, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv0_4)
# 二分类任务
conv0_4 = Conv2D(1, 1, activation='sigmoid')(conv0_4)
# 2021-7-22:
# Updated the parameter to TF2
model = keras.Model(inputs=inputs, outputs=conv0_4)
# model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['acc'])
# model.summary()
return model
def cnn_11(input_size=(256, 256, 3)):
model = keras.Sequential()
# model.add(Conv2D(64, (3, 3), strides=(2, 2), activation='relu', padding='same', input_shape=input_size))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same', input_shape=input_size))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(512, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Conv2D(1024, (3, 3), activation='relu', padding='same'))
# model.add(Conv2D(1024, (3, 3), activation='relu', padding='same'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
'''
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(Conv2D(512, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
'''
model.add(Flatten())
# model.add(Dropout(0.5))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(2, activation='sigmoid'))
return model
# if __name__ == "__main__":
# model = unet(input_size=(640, 640, 3))
# model.summary()