def make(self, cf, valid_gen):
cb = []
# Jaccard callback
if cf.dataset.class_mode == 'segmentation':
print(' Jaccard metric')
cb += [Jacc_new(cf.dataset.n_classes)]
# Save image results
if cf.save_results_enabled:
print(' Save image result')
cb += [
Save_results(n_classes=cf.dataset.n_classes,
void_label=cf.dataset.void_class,
save_path=cf.savepath,
generator=valid_gen,
epoch_length=int(
math.ceil(cf.save_results_nsamples /
float(cf.save_results_batch_size))),
color_map=cf.dataset.color_map,
classes=cf.dataset.classes,
tag='valid')
]
# Early stopping
if cf.earlyStopping_enabled:
print(' Early stopping')
cb += [
EarlyStopping(monitor=cf.earlyStopping_monitor,
mode=cf.earlyStopping_mode,
patience=cf.earlyStopping_patience,
verbose=cf.earlyStopping_verbose)
]
# Define model saving callbacks
if cf.checkpoint_enabled:
print(' Model Checkpoint')
cb += [
ModelCheckpoint(
filepath=os.path.join(cf.savepath, "weights.hdf5"),
verbose=cf.checkpoint_verbose,
monitor=cf.checkpoint_monitor,
mode=cf.checkpoint_mode,
save_best_only=cf.checkpoint_save_best_only,
save_weights_only=cf.checkpoint_save_weights_only)
]
# Plot the loss after every epoch.
if cf.plotHist_enabled:
print(' Plot per epoch')
cb += [
History_plot(cf.dataset.n_classes, cf.savepath,
cf.train_metrics, cf.valid_metrics,
cf.best_metric, cf.best_type, cf.plotHist_verbose)
]
# Decay learning rate at specific epochs
if cf.lrDecayScheduler_enabled:
print(' Learning rate decay scheduler (Deprecated)')
cb += [
LRDecayScheduler(cf.lrDecayScheduler_epochs,
cf.lrDecayScheduler_rate)
]
# Save the log
cb += [
CSVLogger(os.path.join(cf.savepath, 'logFile.csv'),
separator=',',
append=False)
]
# Learning rate scheduler
if cf.LRScheduler_enabled:
print(' Learning rate cheduler by batch')
scheduler = Scheduler(cf.LRScheduler_type, cf.learning_rate,
cf.LRScheduler_M, cf.LRScheduler_decay,
cf.LRScheduler_S, cf.LRScheduler_power)
if cf.LRScheduler_batch_epoch == 'batch':
cb += [
LearningRateSchedulerBatch(scheduler.scheduler_function)
]
elif cf.LRScheduler_batch_epoch == 'epoch':
cb += [LearningRateScheduler(scheduler.scheduler_function)]
else:
raise ValueError('Unknown scheduler mode: ' +
cf.LRScheduler_batch_epoch)
# TensorBoard callback
if cf.TensorBoard_enabled:
print(' Tensorboard')
if cf.TensorBoard_logs_folder is None:
log_dir = os.path.join(cf.usr_path, 'TensorBoardLogs')
if not os.path.exists(log_dir):
os.makedirs(log_dir)
cb += [
TensorBoard(log_dir=log_dir,
histogram_freq=cf.TensorBoard_histogram_freq,
write_graph=cf.TensorBoard_write_graph,
write_images=cf.TensorBoard_write_images)
]
# Output the list of callbacks
return cb
def make(self, cf, valid_gen):
cb = []
# Jaccard callback
if cf.dataset.class_mode == 'segmentation':
print(' Jaccard metric')
cb += [Jacc_new(cf.dataset.n_classes)]
# Save image results
if cf.save_results_enabled:
print(' Save image result')
cb += [
Save_results(n_classes=cf.dataset.n_classes,
void_label=cf.dataset.void_class,
save_path=cf.savepath,
generator=valid_gen,
epoch_length=int(
math.ceil(cf.save_results_nsamples /
float(cf.save_results_batch_size))),
color_map=cf.dataset.color_map,
classes=cf.dataset.classes,
tag='valid')
]
# Early stopping
if cf.earlyStopping_enabled:
print(' Early stopping')
cb += [
EarlyStopping(monitor=cf.earlyStopping_monitor,
mode=cf.earlyStopping_mode,
patience=cf.earlyStopping_patience,
verbose=cf.earlyStopping_verbose)
]
# Define model saving callbacks
if cf.checkpoint_enabled:
print(' Model Checkpoint')
cb += [
ModelCheckpoint(
filepath=os.path.join(cf.savepath, "weights.hdf5"),
verbose=cf.checkpoint_verbose,
monitor=cf.checkpoint_monitor,
mode=cf.checkpoint_mode,
save_best_only=cf.checkpoint_save_best_only,
save_weights_only=cf.checkpoint_save_weights_only)
]
# Plot the loss after every epoch.
if cf.plotHist_enabled:
print(' Plot per epoch')
cb += [
History_plot(cf.dataset.n_classes, cf.savepath,
cf.train_metrics, cf.valid_metrics,
cf.best_metric, cf.best_type, cf.plotHist_verbose)
]
# Save the log
cb += [
CSVLogger(os.path.join(cf.savepath, 'logFile.csv'),
separator=',',
append=False)
]
# Output the list of callbacks
return cb