def main(argv):
print(argv)
print(FLAGS)
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
out_dir = os.path.join(FLAGS.output_dir, 'EnsembleMCdropout', current_time)
##########################
# Hyperparmeters & Model #
##########################
input_shape = dict(medium=(256, 256, 3),
realworld=(512, 512, 3))[FLAGS.level]
hparams = dict(dropout_rate=FLAGS.dropout_rate,
num_base_filters=FLAGS.num_base_filters,
learning_rate=FLAGS.learning_rate,
l2_reg=FLAGS.l2_reg,
input_shape=input_shape)
classifiers = list()
for checkpoint in FLAGS.model_checkpoints:
classifier = VGGDrop(**hparams)
classifier.load_weights(checkpoint)
classifier.summary()
classifiers.append(classifier)
#############
# Load Task #
#############
dtask = bdlb.load(
benchmark="diabetic_retinopathy_diagnosis",
level=FLAGS.level,
batch_size=FLAGS.batch_size,
download_and_prepare=False, # do not download data from this script
)
_, _, ds_test = dtask.datasets
##############
# Evaluation #
##############
dtask.evaluate(functools.partial(predict,
models=classifiers,
num_samples=FLAGS.num_mc_samples,
type=FLAGS.uncertainty),
dataset=ds_test,
output_dir=os.path.join(out_dir, 'evaluation'))
def main(argv):
# print(argv)
# print(FLAGS)
current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
out_dir = os.path.join(FLAGS.output_dir, 'Deterministic', current_time)
##########################
# Hyperparmeters & Model #
##########################
input_shape = dict(medium=(256, 256, 3), realworld=(512, 512, 3))[FLAGS.level]
hparams = dict(dropout_rate=FLAGS.dropout_rate,
num_base_filters=FLAGS.num_base_filters,
learning_rate=FLAGS.learning_rate,
l2_reg=FLAGS.l2_reg,
input_shape=input_shape)
classifier = VGGDrop(**hparams)
# classifier.summary()
#############
# Load Task #
#############
dtask = bdlb.load(
benchmark="diabetic_retinopathy_diagnosis",
level=FLAGS.level,
batch_size=FLAGS.batch_size,
download_and_prepare=False, # do not download data from this script
)
ds_train, ds_validation, ds_test = dtask.datasets
#################
# Training Loop #
#################
history = classifier.fit(
ds_train,
epochs=FLAGS.num_epochs,
validation_data=ds_validation,
class_weight=dtask.class_weight(),
callbacks=[
tfk.callbacks.TensorBoard(
log_dir=os.path.join(out_dir, "tensorboard"),
update_freq="epoch",
write_graph=True,
histogram_freq=1,
),
tfk.callbacks.ModelCheckpoint(
filepath=os.path.join(
out_dir,
"checkpoints",
"weights-{epoch}.ckpt",
),
verbose=1,
save_weights_only=True,
)
],
)
plotting.tfk_history(history,
output_dir=os.path.join(out_dir, "history"))
##############
# Evaluation #
##############
additional_metrics = []
try:
import sail.metrics
additional_metrics.append(('ECE', sail.metrics.GPleissCalibrationError()))
except ImportError:
import warnings
warnings.warn('Could not import SAIL metrics.')
dtask.evaluate(functools.partial(predict, model=classifier, type=FLAGS.uncertainty),
dataset=ds_test,
output_dir=os.path.join(out_dir, 'evaluation'),
additional_metrics=additional_metrics)
def main(argv):
print(argv)
print(FLAGS)
##########################
# Hyperparmeters & Model #
##########################
input_shape = dict(medium=(256, 256, 3),
realworld=(512, 512, 3))[FLAGS.level]
hparams = dict(dropout_rate=FLAGS.dropout_rate,
num_base_filters=FLAGS.num_base_filters,
learning_rate=FLAGS.learning_rate,
l2_reg=FLAGS.l2_reg,
input_shape=input_shape)
classifier = VGGDrop(**hparams)
classifier.summary()
#############
# Load Task #
#############
dtask = bdlb.load(
benchmark="diabetic_retinopathy_diagnosis",
level=FLAGS.level,
batch_size=FLAGS.batch_size,
download_and_prepare=False, # do not download data from this script
)
ds_train, ds_validation, ds_test = dtask.datasets
#################
# Training Loop #
#################
history = classifier.fit(
ds_train,
epochs=FLAGS.num_epochs,
validation_data=ds_validation,
class_weight=dtask.class_weight(),
callbacks=[
tfk.callbacks.TensorBoard(
log_dir=os.path.join(FLAGS.output_dir, "tensorboard"),
update_freq="epoch",
write_graph=True,
histogram_freq=1,
),
tfk.callbacks.ModelCheckpoint(
filepath=os.path.join(
FLAGS.output_dir,
"checkpoints",
"weights-{epoch}.ckpt",
),
verbose=1,
save_weights_only=True,
)
],
)
plotting.tfk_history(history,
output_dir=os.path.join(FLAGS.output_dir, "history"))
##############
# Evaluation #
##############
dtask.evaluate(functools.partial(predict,
model=classifier,
type=FLAGS.uncertainty),
dataset=ds_test,
output_dir=FLAGS.output_dir)
