def simple(
img_path='/media/dengpingfan/leone/dpfan/gepeng/Dataset/3Dataset/img',
gt_path='/media/dengpingfan/leone/dpfan/gepeng/Dataset/3Dataset/gt',
batchSize=7,
target_size=(256, 256),
epoch=24,
lr=0.03,
steps_per_epoch=663,
model_save_path='./models/FPN/fpn_camouflage_baseline.h5'):
seed = 1
data_gen_args = dict(horizontal_flip=True, fill_mode='nearest')
img_datagen = ImageDataGenerator(**data_gen_args)
data_gen_args['rescale'] = 1. / 255
mask_datagen = ImageDataGenerator(**data_gen_args)
img_gen = img_datagen.flow_from_directory(img_path,
batch_size=batchSize,
target_size=target_size,
shuffle=True,
class_mode=None,
seed=seed)
mask_gen = mask_datagen.flow_from_directory(gt_path,
color_mode='grayscale',
batch_size=batchSize,
target_size=target_size,
shuffle=True,
class_mode=None,
seed=seed)
train_gen = zip(img_gen, mask_gen)
# model = Xnet(backbone_name='vgg16', encoder_weights='imagenet', decoder_block_type='transpose')
model = FPN(backbone_name='resnet50', classes=1, activation='sigmoid')
print(model.summary())
opt = optimizers.SGD(lr=lr, momentum=0.9, decay=0.0001)
model.compile(loss='binary_crossentropy',
optimizer=opt,
metrics=['binary_accuracy'])
save_best = callbacks.ModelCheckpoint(filepath=model_save_path,
monitor='loss',
save_best_only=True,
verbose=1)
early_stopping = callbacks.EarlyStopping(monitor='val_loss',
patience=30,
verbose=2,
mode='min')
callbacks_list = [save_best, early_stopping]
model.fit_generator(train_gen,
steps_per_epoch=steps_per_epoch,
epochs=epoch,
verbose=1,
callbacks=callbacks_list)
# define model
#model = Unet(BACKBONE, classes=number_of_classes, encoder_weights='imagenet',activation='softmax')
# model = Linknet(BACKBONE, classes=number_of_classes, encoder_weights='imagenet')
model = FPN(BACKBONE, classes=number_of_classes, encoder_weights='imagenet',activation='softmax')
# model = PSPNet(BACKBONE, classes=number_of_classes, encoder_weights='imagenet')
# for multiclass segmentation choose another loss and metric
model.compile('Adam', loss='categorical_crossentropy', metrics=['categorical_accuracy'])
#model.compile('Adam', loss=cce_jaccard_loss, metrics=[jaccard_score])
model.summary()
#from keras.utils import plot_model
#plot_model(model, to_file='model.png')
modelCheckpoint = keras.callbacks.ModelCheckpoint(filepath=model_checkpoint_prefix+'_weights.{epoch:02d}-{val_loss:.4f}.hdf5',
monitor='val_loss',
verbose=0, save_best_only=False, save_weights_only=False,
mode='auto', period=1)
reduceLROnPlateau = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=7, verbose=1,
mode='auto', min_delta=0.001, cooldown=0, min_lr=10e-7)
model.fit_generator(generator=train_generator, steps_per_epoch=None, epochs=20, verbose=1,
callbacks=[reduceLROnPlateau, modelCheckpoint],
validation_data=val_generator, validation_steps=None, class_weight=None, max_queue_size=10,