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| from tensorflow.keras.optimizers import Adam | |
| from keras.callbacks import EarlyStopping | |
| from keras.models import load_model | |
| from keras.utils.generic_utils import CustomObjectScope | |
| from models.unets import Unet2D | |
| from models.deeplab import Deeplabv3, relu6, DepthwiseConv2D, BilinearUpsampling | |
| from models.FCN import FCN_Vgg16_16s | |
| from models.SegNet import SegNet | |
| from utils.learning.metrics import dice_coef, precision, recall | |
| from utils.learning.losses import dice_coef_loss | |
| from utils.io.data import DataGen, save_results, save_history, load_data | |
| # manually set cuda 10.0 path | |
| #os.system('export LD_LIBRARY_PATH=/usr/local/cuda-10.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}') | |
| #os.system('export PATH=/usr/local/cuda-10.0/bin:/usr/local/cuda-10.0/NsightCompute-1.0${PATH:+:${PATH}}') | |
| # Varibales and data generator | |
| input_dim_x=224 | |
| input_dim_y=224 | |
| n_filters = 32 | |
| dataset = 'Medetec_foot_ulcer_224' | |
| data_gen = DataGen('./data/' + dataset + '/', split_ratio=0.2, x=input_dim_x, y=input_dim_y) | |
| ######### Get the deep learning models ######### | |
| ######### Unet ########## | |
| # unet2d = Unet2D(n_filters=n_filters, input_dim_x=None, input_dim_y=None, num_channels=3) | |
| # model, model_name = unet2d.get_unet_model_yuanqing() | |
| ######### MobilenetV2 ########## | |
| model = Deeplabv3(input_shape=(input_dim_x, input_dim_y, 3), classes=1) | |
| model_name = 'MobilenetV2' | |
| with CustomObjectScope({'relu6': relu6,'DepthwiseConv2D': DepthwiseConv2D, 'BilinearUpsampling': BilinearUpsampling}): | |
| model = load_model('training_history/2019-12-19 01%3A53%3A15.480800.hdf5' | |
| , custom_objects={'dice_coef': dice_coef, 'precision':precision, 'recall':recall}) | |
| ######### Vgg16 ########## | |
| # model, model_name = FCN_Vgg16_16s(input_shape=(input_dim_x, input_dim_y, 3)) | |
| ######### SegNet ########## | |
| # segnet = SegNet(n_filters, input_dim_x, input_dim_y, num_channels=3) | |
| # model, model_name = segnet.get_SegNet() | |
| # plot_model(model, to_file=model_name+'.png') | |
| # training | |
| batch_size = 2 | |
| epochs = 2000 | |
| learning_rate = 1e-4 | |
| loss = 'binary_crossentropy' | |
| es = EarlyStopping(monitor='val_dice_coef', patience=200, mode='max', restore_best_weights=True) | |
| #training_history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs | |
| # , validation_split=0.2, verbose=1, callbacks=[]) | |
| model.summary() | |
| model.compile(optimizer=Adam(lr=learning_rate), loss=loss, metrics=[dice_coef, precision, recall]) | |
| training_history = model.fit_generator(data_gen.generate_data(batch_size=batch_size, train=True), | |
| steps_per_epoch=int(data_gen.get_num_data_points(train=True) / batch_size), | |
| callbacks=[es], | |
| validation_data=data_gen.generate_data(batch_size=batch_size, val=True), | |
| validation_steps=int(data_gen.get_num_data_points(val=True) / batch_size), | |
| epochs=epochs) | |
| ### save the model weight file and its training history | |
| save_history(model, model_name, training_history, dataset, n_filters, epochs, learning_rate, loss, color_space='RGB', | |
| path='./training_history/') |