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train.py
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train.py
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'''
Capsules for Object Segmentation (SegCaps)
Original Paper by Rodney LaLonde and Ulas Bagci (https://arxiv.org/abs/1804.04241)
Code written by: Rodney LaLonde
If you use significant portions of this code or the ideas from our paper, please cite it :)
If you have any questions, please email me at lalonde@knights.ucf.edu.
This file is used for training models. Please see the README for details about training.
'''
from __future__ import print_function
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.ioff()
from os.path import join
import numpy as np
from keras.optimizers import Adam
from keras import backend as K
K.set_image_data_format('channels_last')
from keras.utils.training_utils import multi_gpu_model
from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping, ReduceLROnPlateau, TensorBoard
import tensorflow as tf
from custom_losses import dice_hard, weighted_binary_crossentropy_loss, dice_loss, margin_loss
from load_3D_data import load_class_weights, generate_train_batches, generate_val_batches
def get_loss(root, split, net, recon_wei, choice):
if choice == 'w_bce':
pos_class_weight = load_class_weights(root=root, split=split)
loss = weighted_binary_crossentropy_loss(pos_class_weight)
elif choice == 'bce':
loss = 'binary_crossentropy'
elif choice == 'dice':
loss = dice_loss
elif choice == 'w_mar':
pos_class_weight = load_class_weights(root=root, split=split)
loss = margin_loss(margin=0.4, downweight=0.5, pos_weight=pos_class_weight)
elif choice == 'mar':
loss = margin_loss(margin=0.4, downweight=0.5, pos_weight=1.0)
else:
raise Exception("Unknow loss_type")
if net.find('caps') != -1:
return {'out_seg': loss, 'out_recon': 'mse'}, {'out_seg': 1., 'out_recon': recon_wei}
else:
return loss, None
def get_callbacks(arguments):
if arguments.net.find('caps') != -1:
monitor_name = 'val_out_seg_dice_hard'
else:
monitor_name = 'val_dice_hard'
csv_logger = CSVLogger(join(arguments.log_dir, arguments.output_name + '_log_' + arguments.time + '.csv'), separator=',')
tb = TensorBoard(arguments.tf_log_dir, batch_size=arguments.batch_size, histogram_freq=0)
model_checkpoint = ModelCheckpoint(join(arguments.check_dir, arguments.output_name + '_model_' + arguments.time + '.hdf5'),
monitor=monitor_name, save_best_only=True, save_weights_only=True,
verbose=1, mode='max')
lr_reducer = ReduceLROnPlateau(monitor=monitor_name, factor=0.05, cooldown=0, patience=5,verbose=1, mode='max')
early_stopper = EarlyStopping(monitor=monitor_name, min_delta=0, patience=25, verbose=0, mode='max')
return [model_checkpoint, csv_logger, lr_reducer, early_stopper, tb]
def compile_model(args, net_input_shape, uncomp_model):
# Set optimizer loss and metrics
opt = Adam(lr=args.initial_lr, beta_1=0.99, beta_2=0.999, decay=1e-6)
if args.net.find('caps') != -1:
metrics = {'out_seg': dice_hard}
else:
metrics = [dice_hard]
loss, loss_weighting = get_loss(root=args.data_root_dir, split=args.split_num, net=args.net,
recon_wei=args.recon_wei, choice=args.loss)
# If using CPU or single GPU
if args.gpus <= 1:
uncomp_model.compile(optimizer=opt, loss=loss, metrics=metrics)
return uncomp_model
# If using multiple GPUs
else:
with tf.device("/cpu:0"):
uncomp_model.compile(optimizer=opt, loss=loss, loss_weights=loss_weighting, metrics=metrics)
model = multi_gpu_model(uncomp_model, gpus=args.gpus)
model.__setattr__('callback_model', uncomp_model)
model.compile(optimizer=opt, loss=loss, loss_weights=loss_weighting, metrics=metrics)
return model
def plot_training(training_history, arguments):
f, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(10, 10))
f.suptitle(arguments.net, fontsize=18)
if arguments.net.find('caps') != -1:
ax1.plot(training_history.history['out_seg_dice_hard'])
ax1.plot(training_history.history['val_out_seg_dice_hard'])
else:
ax1.plot(training_history.history['dice_hard'])
ax1.plot(training_history.history['val_dice_hard'])
ax1.set_title('Dice Coefficient')
ax1.set_ylabel('Dice', fontsize=12)
ax1.legend(['Train', 'Val'], loc='upper left')
ax1.set_yticks(np.arange(0, 1.05, 0.05))
if arguments.net.find('caps') != -1:
ax1.set_xticks(np.arange(0, len(training_history.history['out_seg_dice_hard'])))
else:
ax1.set_xticks(np.arange(0, len(training_history.history['dice_hard'])))
ax1.grid(True)
gridlines1 = ax1.get_xgridlines() + ax1.get_ygridlines()
for line in gridlines1:
line.set_linestyle('-.')
ax2.plot(training_history.history['loss'])
ax2.plot(training_history.history['val_loss'])
ax2.set_title('Model Loss')
ax2.set_ylabel('Loss', fontsize=12)
ax2.set_xlabel('Epoch', fontsize=12)
ax2.legend(['Train', 'Val'], loc='upper right')
ax1.set_xticks(np.arange(0, len(training_history.history['loss'])))
ax2.grid(True)
gridlines2 = ax2.get_xgridlines() + ax2.get_ygridlines()
for line in gridlines2:
line.set_linestyle('-.')
f.savefig(join(arguments.output_dir, arguments.output_name + '_plots_' + arguments.time + '.png'))
plt.close()
def train(args, train_list, val_list, u_model, net_input_shape):
# Compile the loaded model
model = compile_model(args=args, net_input_shape=net_input_shape, uncomp_model=u_model)
# Set the callbacks
callbacks = get_callbacks(args)
# Training the network
history = model.fit_generator(
generate_train_batches(args.data_root_dir, train_list, net_input_shape, net=args.net,
batchSize=args.batch_size, numSlices=args.slices, subSampAmt=args.subsamp,
stride=args.stride, shuff=args.shuffle_data, aug_data=args.aug_data),
max_queue_size=40, workers=4, use_multiprocessing=False,
steps_per_epoch=10000,
validation_data=generate_val_batches(args.data_root_dir, val_list, net_input_shape, net=args.net,
batchSize=args.batch_size, numSlices=args.slices, subSampAmt=0,
stride=20, shuff=args.shuffle_data),
validation_steps=500, # Set validation stride larger to see more of the data.
epochs=200,
callbacks=callbacks,
verbose=1)
# Plot the training data collected
plot_training(history, args)