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model.py
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import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.losses import binary_crossentropy
from keras.utils import multi_gpu_model
from utils import *
from keras import losses
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.utils import plot_model
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as K
import segmentation_models as sm
from segmodel import *
loss_dict = {
"bceja": sm.losses.bce_jaccard_loss,
"ja": sm.losses.jaccard_loss,
"focal": sm.losses.binary_focal_loss,
"bce":sm.losses.binary_crossentropy,
"focalja": sm.losses.binary_focal_jaccard_loss,
"focaldice":sm.losses.binary_focal_dice_loss,
"dice": sm.losses.dice_loss,
"bcedice": sm.losses.bce_dice_loss,
"l1": losses.mean_absolute_percentage_error,
"l2": losses.mean_squared_error
}
def unet(pretrained_weights=None, input_size=(256, 256, 3), lr=1E-3, multi_gpu=False, loss="l1"):
# 所有等号左侧其实不是层而是张量吗...
# 是的！ 因为这里使用了keras的函数式API。每一个层都是可以调用的...而在左边返回输出的张量
def resblock(layer_input, filters, f_size=3):
r = Conv2D(filters, kernel_size=f_size, strides=1, padding='same')(layer_input)
r = LeakyReLU(alpha=0.2)(r)
r = BatchNormalization()(r)
r = Conv2D(filters, kernel_size=f_size, strides=1, padding='same')(r)
r = LeakyReLU(alpha=0.2)(r)
r = Add()([r, layer_input])
return BatchNormalization()(r)
def resblockn(n, layer_input, filters, f_size=3):
x = layer_input
for k in range(n):
x = resblock(x, filters, f_size)
return x
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)
conv1 = BatchNormalization()(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)
conv2 = BatchNormalization()(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)
conv3 = BatchNormalization()(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)
conv4 = BatchNormalization()(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)
conv5 = BatchNormalization()(conv5)
drop5 = Dropout(0.5)(conv5)
#res5 = resblockn(9, drop5, 1024)
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)
conv6 = BatchNormalization()(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)
conv7 = BatchNormalization()(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)
conv8 = BatchNormalization()(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 = BatchNormalization()(conv9)
conv10 = Conv2D(1, 1, activation='sigmoid')(conv9)
model = Model(inputs=inputs, outputs=conv10)
if multi_gpu:
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = multi_gpu_model(model, gpus=2)
model.compile(optimizer=Adam(lr=lr),
loss=sm.losses.binary_focal_jaccard_loss, metrics=[sm.metrics.iou_score, 'accuracy'])
else:
model.compile(optimizer=Adam(lr=lr),
loss=loss_dict[loss], metrics=[sm.metrics.iou_score, 'accuracy'])
# model.summary()
if pretrained_weights:
model.load_weights(pretrained_weights)
return model
def smunet(loss="focal", pretrained_weights=None):
model = sm.Unet(backbone_name = 'densenet121',
input_shape=(None, None, 3),
classes=1,
activation='sigmoid',
weights=None,
encoder_weights='imagenet',
encoder_freeze=False,
encoder_features='default',
decoder_block_type='upsampling',
decoder_filters=(256, 128, 64, 32, 16),
decoder_use_batchnorm=True)
model.compile(optimizer='adam',
loss=loss_dict[loss], metrics=[sm.metrics.iou_score, 'accuracy'])
if pretrained_weights:
model.load_weights(pretrained_weights)
return model
model_dict = {
"unet": unet,
"unetxx": unetxx,
"unet++": unetxx,
"denseunet": denseunet
}