def testOptimizerWithCallbacks(self):
np.random.seed(1331)
input_np = np.random.random((10, 3))
output_np = np.random.random((10, 4))
a = input_layer.Input(shape=(3, ), name='input_a')
model = sequential.Sequential()
model.add(core.Dense(4, name='dense'))
model.add(core.Dropout(0.5, name='dropout'))
model(a)
optimizer = gradient_descent.SGD(learning_rate=0.1)
model.compile(optimizer, loss='mse', metrics=['mae'])
# This does not reduce the LR after the first epoch (due to low delta).
cbks = [
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
min_delta=0,
patience=1,
cooldown=5)
]
model.fit(input_np,
output_np,
batch_size=10,
validation_data=(input_np, output_np),
callbacks=cbks,
epochs=2,
verbose=0)
self.assertAllClose(float(backend.get_value(model.optimizer.lr)),
0.1,
atol=1e-4)
# This should reduce the LR after the first epoch (due to high delta).
cbks = [
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
min_delta=10,
patience=1,
cooldown=5)
]
model.fit(input_np,
output_np,
batch_size=10,
validation_data=(input_np, output_np),
callbacks=cbks,
epochs=2,
verbose=2)
self.assertAllClose(float(backend.get_value(model.optimizer.lr)),
0.01,
atol=1e-4)
def test_validate_callbacks_predefined_callbacks(self):
supported_predefined_callbacks = [
callbacks.TensorBoard(),
callbacks.CSVLogger(filename='./log.csv'),
callbacks.EarlyStopping(),
callbacks.ModelCheckpoint(filepath='./checkpoint'),
callbacks.TerminateOnNaN(),
callbacks.ProgbarLogger(),
callbacks.History(),
callbacks.RemoteMonitor()
]
distributed_training_utils.validate_callbacks(
supported_predefined_callbacks, adam.Adam())
unsupported_predefined_callbacks = [
callbacks.ReduceLROnPlateau(),
callbacks.LearningRateScheduler(schedule=lambda epoch: 0.001)
]
for callback in unsupported_predefined_callbacks:
with self.assertRaisesRegex(ValueError,
'You must specify a Keras Optimizer V2'):
distributed_training_utils.validate_callbacks([callback],
v1_adam.AdamOptimizer())
def create_callbacks(early_stopping, model_checkpoint, reduce_lr_on_plateau,
tensor_board):
'''
Создание списка callbacks
:param early_stopping: остановка обучения, если параметр 'monitor' не меняется в течении 'patience' эпох
:param model_checkpoint: сохранение весов сети с лучшим показателем параметра 'monitor'
:param reduce_lr_on_plateau: уменьшение learning rate в процессе обучения
:param tensor_board:
:return:
'''
callbacks_list = []
# if early_stopping == True:
# callbacks_list.append(callbacks.EarlyStopping(monitor='val_acc', patience=7))
if model_checkpoint == True:
callbacks_list.append(
callbacks.ModelCheckpoint(
filepath=
'weight_checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5',
monitor='val_loss',
save_best_only=True))
if reduce_lr_on_plateau == True:
callbacks_list.append(
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10))
# if tensor_board == True:
# callbacks_list.append(callbacks.TensorBoard(log_dir='log_dir', histogram_freq=1))
return callbacks_list
def get_callbacks(use_early_stopping=True, use_reduce_lr=True):
callback_list = []
if (use_early_stopping):
callback_list.append(
callbacks.EarlyStopping(monitor='val_loss',
min_delta=0,
patience=10,
verbose=keras_verbosity,
mode='auto'))
if (use_reduce_lr):
callback_list.append(
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
verbose=keras_verbosity,
mode='auto',
epsilon=0.0001,
cooldown=0,
min_lr=0))
return callback_list
def dense_train(space):
''' train lightgbm booster based on training / validaton set -> give predictions of Y '''
params = space.copy()
input_shape = (X_train.shape[-1], ) # input shape depends on x_fields used
input_img = Input(shape=input_shape)
init_nodes = params['init_nodes'] # fisrt dense layer - number of nodes
nodes_mult = params['nodes_mult'] # nodes growth rate
mult_freq = params['mult_freq'] # grow every X layer
mult_start = params['mult_start'] # grow from X layer
end_nodes = params['end_nodes'] # maximum number of nodes
if params['num_Dense_layer'] < 4:
params['init_nodes'] = init_nodes = 16
d_1 = Dense(init_nodes, activation=params['activation'])(
input_img) # remove kernel_regularizer=regularizers.l1(params['l1'])
d_1 = Dropout(params['dropout'])(d_1)
for i in range(1, params['num_Dense_layer']):
temp_nodes = int(
min(
init_nodes * (2**(nodes_mult * max(
(i - mult_start + 3) // mult_freq, 0))), end_nodes))
d_1 = Dense(temp_nodes, activation=params['activation'])(d_1)
if i != params[
'num_Dense_layer'] - 1: # last dense layer has no dropout
d_1 = Dropout(params['dropout'])(d_1)
f_x = Dense(1)(d_1)
callbacks_list = [
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10),
callbacks.EarlyStopping(monitor='val_loss', patience=10, mode='auto')
] # add callbacks
lr_val = 10**-int(params['learning_rate'])
adam = optimizers.Adam(lr=lr_val)
model = Model(input_img, f_x)
model.compile(adam, loss='mae')
model.summary()
history = model.fit(X_train,
Y_train,
epochs=50,
batch_size=params['batch_size'],
validation_data=(X_valid, Y_valid),
callbacks=callbacks_list,
verbose=1)
Y_test_pred = model.predict(X_test)
Y_train_pred = model.predict(X_train)
Y_valid_pred = model.predict(X_valid)
return Y_test_pred, Y_train_pred, Y_valid_pred, history
def callableForTestReduceLROnPlateau(model, test_obj, train_ds, num_epoch,
steps, strategy, saving_filepath,
**kwargs):
cbks = [
callbacks.ReduceLROnPlateau(monitor='loss',
factor=0.1,
min_delta=1,
patience=1,
cooldown=5,
verbose=1)
]
# It is expected that the learning rate would drop by `factor` within
# 3 epochs with `min_delta=1`.
model.fit(x=train_ds, epochs=3, steps_per_epoch=steps, callbacks=cbks)
test_obj.assertAllClose(float(K.get_value(model.optimizer.lr)),
0.0001,
atol=1e-8)
# It is expected that the learning rate would drop by another `factor`
# within 3 epochs with `min_delta=1`.
model.fit(x=train_ds, epochs=3, steps_per_epoch=steps, callbacks=cbks)
test_obj.assertAllClose(float(K.get_value(model.optimizer.lr)),
0.00001,
atol=1e-8)
def create_learning_rate_reducer(cfg_solver: dict) -> callbacks.ReduceLROnPlateau:
"""Create a ReduceLROnPlateau callback.
Args:
cfg_solver: dict, solver subsection of config.
Returns:
ReduceLROnPlateau, ReduceLROnPlateau callback.
"""
params = cfg_solver["learning_rate_reducer"]
params["verbose"] = 1
return callbacks.ReduceLROnPlateau(**params)
def get_callbacks(args):
"""Define callbacks for distributed training."""
callbacks = [
# This is necessary to ensure consistent initialization of all workers
hvd.callbacks.BroadcastGlobalVariablesCallback(0),
# Note: must be in the list before the ReduceLROnPlateau or other metrics-based callbacks.
hvd.callbacks.MetricAverageCallback(),
# Adjust Learning Rate
hvd.callbacks.LearningRateWarmupCallback(
warmup_epochs=args.warmup_epochs)
]
if args.train_only:
# Reduce learning rate on a schedule
onethirds_point = int(math.floor(args.epochs / 3))
twothirds_point = int(math.floor(args.epochs / 3 * 2))
callbacks.append(
hvd.callbacks.LearningRateScheduleCallback(
start_epoch=args.warmup_epochs,
end_epoch=onethirds_point,
multiplier=1.))
callbacks.append(
hvd.callbacks.LearningRateScheduleCallback(
start_epoch=onethirds_point,
end_epoch=twothirds_point,
multiplier=1e-1))
callbacks.append(
hvd.callbacks.LearningRateScheduleCallback(
start_epoch=twothirds_point,
end_epoch=args.epochs + 1,
multiplier=1e-2))
else:
# Reduce learning rate on validation loss plateau
callbacks.append(
cb.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=5,
min_lr=0.001,
verbose=1 if hvd.rank() == 0 else 0))
if args.early_stopping:
callbacks.append(
cb.EarlyStopping(monitor='loss',
patience=7,
restore_best_weights=True))
print('Callbacks created on rank ' + str(hvd.rank()))
return callbacks
def callb(path_checkpoint):
callback_checkpoint = tf_cb.ModelCheckpoint(
filepath=path_checkpoint, monitor = 'loss', verbose=1,
save_weights_only=True, save_best_only=True)
callback_earlystopping = tf_cb.EarlyStopping(monitor='loss',
patience=20, verbose=1)
callback_reduce_lr = tf_cb.ReduceLROnPlateau(monitor='loss',
factor=0.98,
min_lr=0.3e-4,
patience=0,
verbose=1)
callBacks = [
callback_checkpoint,
callback_earlystopping,
callback_reduce_lr
]
return callBacks
def test_TensorBoard_with_ReduceLROnPlateau(self):
with self.cached_session():
temp_dir = self.get_temp_dir()
self.addCleanup(shutil.rmtree, temp_dir, ignore_errors=True)
(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
train_samples=TRAIN_SAMPLES,
test_samples=TEST_SAMPLES,
input_shape=(INPUT_DIM, ),
num_classes=NUM_CLASSES)
y_test = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
model = testing_utils.get_small_sequential_mlp(
num_hidden=NUM_HIDDEN,
num_classes=NUM_CLASSES,
input_dim=INPUT_DIM)
model.compile(loss='binary_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
cbks = [
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.5,
patience=4,
verbose=1),
callbacks_v1.TensorBoard(log_dir=temp_dir)
]
model.fit(x_train,
y_train,
batch_size=BATCH_SIZE,
validation_data=(x_test, y_test),
callbacks=cbks,
epochs=2,
verbose=0)
assert os.path.exists(temp_dir)
def create_learning_rate_reducer(
cfg_solver: dict,
metrics_names: List[str]) -> callbacks.ReduceLROnPlateau:
"""Create a ReduceLROnPlateau callback.
Args:
cfg_solver: dict, solver subsection of config.
metrics_names: list[str], 'metrics' names.
Returns:
ReduceLROnPlateau, ReduceLROnPlateau callback.
Raises:
ValueError, monitor not in 'metrics' names.
"""
monitor = cfg_solver["learning_rate_reducer"]["monitor"]
val_metrics_names = [f"val_{mm}" for mm in metrics_names]
if (monitor not in metrics_names) and (monitor not in val_metrics_names):
raise ValueError(
f"monitor: {monitor} not found in model metrics names: "
f"{metrics_names + val_metrics_names}")
params = cfg_solver["learning_rate_reducer"]
params["verbose"] = 1
return callbacks.ReduceLROnPlateau(**params)
def train_model_retinanet(model,
dataset,
backbone,
expt='',
test_size=.1,
n_epoch=10,
batch_size=1,
num_gpus=None,
include_masks=False,
panoptic=False,
panoptic_weight=1,
anchor_params=None,
pyramid_levels=['P3', 'P4', 'P5', 'P6', 'P7'],
mask_size=(28, 28),
optimizer=SGD(lr=0.01,
decay=1e-6,
momentum=0.9,
nesterov=True),
log_dir='/data/tensorboard_logs',
model_dir='/data/models',
model_name=None,
sigma=3.0,
alpha=0.25,
gamma=2.0,
score_threshold=0.01,
iou_threshold=0.5,
max_detections=100,
weighted_average=True,
lr_sched=rate_scheduler(lr=0.01, decay=0.95),
rotation_range=0,
flip=True,
shear=0,
zoom_range=0,
seed=None,
**kwargs):
"""Train a RetinaNet model from the given backbone
Adapted from:
https://github.com/fizyr/keras-retinanet &
https://github.com/fizyr/keras-maskrcnn
"""
is_channels_first = K.image_data_format() == 'channels_first'
if model_name is None:
todays_date = datetime.datetime.now().strftime('%Y-%m-%d')
data_name = os.path.splitext(os.path.basename(dataset))[0]
model_name = '{}_{}_{}'.format(todays_date, data_name, expt)
model_path = os.path.join(model_dir, '{}.h5'.format(model_name))
loss_path = os.path.join(model_dir, '{}.npz'.format(model_name))
train_dict, test_dict = get_data(dataset, seed=seed, test_size=test_size)
channel_axis = 1 if is_channels_first else -1
n_classes = model.layers[-1].output_shape[channel_axis]
if panoptic:
n_semantic_classes = model.get_layer(
name='semantic').output_shape[channel_axis]
# the data, shuffled and split between train and test sets
print('X_train shape:', train_dict['X'].shape)
print('y_train shape:', train_dict['y'].shape)
print('X_test shape:', test_dict['X'].shape)
print('y_test shape:', test_dict['y'].shape)
print('Output Shape:', model.layers[-1].output_shape)
print('Number of Classes:', n_classes)
if num_gpus is None:
num_gpus = train_utils.count_gpus()
if num_gpus >= 1e6:
batch_size = batch_size * num_gpus
model = train_utils.MultiGpuModel(model, num_gpus)
print('Training on {} GPUs'.format(num_gpus))
# evaluation of model is done on `retinanet_bbox`
if include_masks:
prediction_model = model
else:
prediction_model = retinanet_bbox(model,
nms=True,
anchor_params=anchor_params,
panoptic=panoptic,
class_specific_filter=False)
retinanet_losses = losses.RetinaNetLosses(sigma=sigma,
alpha=alpha,
gamma=gamma,
iou_threshold=iou_threshold,
mask_size=mask_size)
def semantic_loss(y_pred, y_true):
return panoptic_weight * losses.weighted_categorical_crossentropy(
y_pred, y_true, n_classes=n_semantic_classes)
loss = {
'regression': retinanet_losses.regress_loss,
'classification': retinanet_losses.classification_loss
}
if include_masks:
loss['masks'] = retinanet_losses.mask_loss
if panoptic:
loss['semantic'] = semantic_loss
model.compile(loss=loss, optimizer=optimizer)
if num_gpus >= 2:
# Each GPU must have at least one validation example
if test_dict['y'].shape[0] < num_gpus:
raise ValueError('Not enough validation data for {} GPUs. '
'Received {} validation sample.'.format(
test_dict['y'].shape[0], num_gpus))
# When using multiple GPUs and skip_connections,
# the training data must be evenly distributed across all GPUs
num_train = train_dict['y'].shape[0]
nb_samples = num_train - num_train % batch_size
if nb_samples:
train_dict['y'] = train_dict['y'][:nb_samples]
train_dict['X'] = train_dict['X'][:nb_samples]
# this will do preprocessing and realtime data augmentation
datagen = image_generators.RetinaNetGenerator(
# fill_mode='constant', # for rotations
rotation_range=rotation_range,
shear_range=shear,
zoom_range=zoom_range,
horizontal_flip=flip,
vertical_flip=flip)
datagen_val = image_generators.RetinaNetGenerator(
# fill_mode='constant', # for rotations
rotation_range=0,
shear_range=0,
zoom_range=0,
horizontal_flip=0,
vertical_flip=0)
if 'vgg' in backbone or 'densenet' in backbone:
compute_shapes = make_shapes_callback(model)
else:
compute_shapes = guess_shapes
train_data = datagen.flow(train_dict,
seed=seed,
include_masks=include_masks,
panoptic=panoptic,
pyramid_levels=pyramid_levels,
anchor_params=anchor_params,
compute_shapes=compute_shapes,
batch_size=batch_size)
val_data = datagen_val.flow(test_dict,
seed=seed,
include_masks=include_masks,
panoptic=panoptic,
pyramid_levels=pyramid_levels,
anchor_params=anchor_params,
compute_shapes=compute_shapes,
batch_size=batch_size)
tensorboard_callback = callbacks.TensorBoard(
log_dir=os.path.join(log_dir, model_name))
# fit the model on the batches generated by datagen.flow()
loss_history = model.fit_generator(
train_data,
steps_per_epoch=train_data.y.shape[0] // batch_size,
epochs=n_epoch,
validation_data=val_data,
validation_steps=val_data.y.shape[0] // batch_size,
callbacks=[
callbacks.LearningRateScheduler(lr_sched),
callbacks.ModelCheckpoint(model_path,
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=num_gpus >= 2),
tensorboard_callback,
callbacks.ReduceLROnPlateau(monitor='loss',
factor=0.1,
patience=10,
verbose=1,
mode='auto',
min_delta=0.0001,
cooldown=0,
min_lr=0),
RedirectModel(
Evaluate(val_data,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
max_detections=max_detections,
tensorboard=tensorboard_callback,
weighted_average=weighted_average), prediction_model),
])
model.save_weights(model_path)
np.savez(loss_path, loss_history=loss_history.history)
average_precisions = evaluate(
val_data,
prediction_model,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
max_detections=max_detections,
)
# print evaluation
total_instances = []
precisions = []
for label, (average_precision,
num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations), label,
'with average precision: {:.4f}'.format(average_precision))
total_instances.append(num_annotations)
precisions.append(average_precision)
if sum(total_instances) == 0:
print('No test instances found.')
else:
print(
'mAP using the weighted average of precisions among classes: {:.4f}'
.format(
sum([a * b for a, b in zip(total_instances, precisions)]) /
sum(total_instances)))
print('mAP: {:.4f}'.format(
sum(precisions) / sum(x > 0 for x in total_instances)))
return model
print(model.summary())
# '''
file_path = os.path.join(save_dir, model_name)
checkpoint = callbacks.ModelCheckpoint(
file_path,
monitor='val_predictions_categorical_accuracy',
verbose=1,
save_best_only=True,
mode='auto',
save_weights_only=True,
period=1)
reduce_lr = callbacks.ReduceLROnPlateau(monitor='val_predictions_loss',
factor=0.25,
patience=10,
verbose=1,
mode='auto',
min_delta=1e-6,
cooldown=0,
min_lr=0)
csv_logger = callbacks.CSVLogger(os.path.join(save_dir, 'Log_V1.log'),
separator=',',
append=False)
train_data_generator = Train_data_generator(batch_size)
valid_data_generator = Valid_data_generator(batch_size)
model.fit_generator(generator=train_data_generator,
steps_per_epoch=int(210030 / batch_size),
epochs=epochs,
verbose=1,
callbacks=[checkpoint, reduce_lr, csv_logger],
validation_data=valid_data_generator,
def rnn_train(space): #functional
''' train lightgbm booster based on training / validaton set -> give predictions of Y '''
params = space.copy()
lookback = 20 # lookback = 5Y * 4Q = 20Q
x_fields = 10 # lgbm top15 features -> 10 features in rnn
inputs_loss_weight = 0.1 # loss weights for individual outputs from each rnn model
dense_loss_weight = 2 # loss weights for final output
loss_weights = [inputs_loss_weight] * x_fields + [
dense_loss_weight
] # loss weights for training
loss = [args.objective] * (
x_fields + 1) # use MAE loss function for all inputs and final
metrics = [args.objective] * (x_fields + 1)
input_img = Input(shape=(lookback, x_fields))
outputs = []
states = []
for col in range(10): # build model for each feature
g_1 = K.expand_dims(
input_img[:, :, col], axis=2
) # add dimension to certain feature: shape = (samples, 20, 1)
for i in range(params['num_gru_layer']):
temp_nodes = int(
min(params['gru_nodes'] * (2**(params['gru_nodes_mult'] * i)),
8))
extra = dict(return_sequences=True)
if args.bi == False:
if i == params['num_gru_layer'] - 1:
extra = dict(return_sequences=False)
g_state = GRU(temp_nodes, **extra)(g_1) # forecast state
elif i == 0:
g_1 = GRU(temp_nodes, **extra)(g_1)
else:
g_1 = GRU(temp_nodes,
dropout=params['gru_dropout'],
**extra)(g_1)
else: # try bidirectional one
if i == params['num_gru_layer'] - 1:
extra = dict(return_sequences=False)
g_state = GRU(temp_nodes, **extra)(g_1) # forecast state
elif i == 0:
g_1 = Bidirectional(GRU(temp_nodes, **extra))(g_1)
else:
g_1 = Bidirectional(
GRU(temp_nodes, dropout=params['gru_dropout'],
**extra))(g_1)
g_output = Dense(1)(g_state)
states.append(g_state)
outputs.append(g_output)
f_x = Concatenate(axis=1)(states)
for i in range(
params['num_dense_layer']): # for second or third dense layers
f_x = Dense(10)(f_x)
f_x = Dense(1, name='final_dense')(f_x)
outputs.append(f_x)
model = Model(
inputs=input_img,
outputs=outputs) # outputs = 10 forecast states + final forecast
callbacks_list = [
callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.1,
patience=10),
callbacks.EarlyStopping(monitor='val_loss', patience=10, mode='auto')
] # add callbacks
lr_val = 10**-int(params['learning_rate'])
adam = optimizers.Adam(lr=lr_val)
model.compile(adam, loss=loss, metrics=metrics, loss_weights=loss_weights)
model.summary()
history = model.fit(X_train, [Y_train] * (x_fields + 1),
epochs=50,
batch_size=params['batch_size'],
validation_data=(X_valid, [Y_valid] * (x_fields + 1)),
verbose=1,
callbacks=callbacks_list)
Y_test_pred = model.predict(X_test)[-1] # final dense predictions
Y_train_pred = model.predict(X_train)[-1]
Y_valid_pred = model.predict(X_valid)[-1]
return Y_test_pred, Y_train_pred, Y_valid_pred, history
def train_model_retinanet(model,
dataset,
backbone,
expt='',
test_size=.1,
n_epoch=10,
batch_size=1,
num_gpus=None,
include_masks=False,
mask_size=(28, 28),
optimizer=SGD(lr=0.01,
decay=1e-6,
momentum=0.9,
nesterov=True),
log_dir='/data/tensorboard_logs',
model_dir='/data/models',
model_name=None,
sigma=3.0,
alpha=0.25,
gamma=2.0,
score_threshold=0.01,
iou_threshold=0.5,
max_detections=100,
weighted_average=True,
lr_sched=rate_scheduler(lr=0.01, decay=0.95),
rotation_range=0,
flip=True,
shear=0,
zoom_range=0,
**kwargs):
"""Train a RetinaNet model from the given backbone
Adapted from:
https://github.com/fizyr/keras-retinanet &
https://github.com/fizyr/keras-maskrcnn
"""
is_channels_first = K.image_data_format() == 'channels_first'
if model_name is None:
todays_date = datetime.datetime.now().strftime('%Y-%m-%d')
data_name = os.path.splitext(os.path.basename(dataset))[0]
model_name = '{}_{}_{}'.format(todays_date, data_name, expt)
model_path = os.path.join(model_dir, '{}.h5'.format(model_name))
loss_path = os.path.join(model_dir, '{}.npz'.format(model_name))
train_dict, test_dict = get_data(dataset, mode='conv', test_size=test_size)
n_classes = model.layers[-1].output_shape[1 if is_channels_first else -1]
# the data, shuffled and split between train and test sets
print('X_train shape:', train_dict['X'].shape)
print('y_train shape:', train_dict['y'].shape)
print('X_test shape:', test_dict['X'].shape)
print('y_test shape:', test_dict['y'].shape)
print('Output Shape:', model.layers[-1].output_shape)
print('Number of Classes:', n_classes)
if num_gpus is None:
num_gpus = train_utils.count_gpus()
if num_gpus >= 1e6:
batch_size = batch_size * num_gpus
model = train_utils.MultiGpuModel(model, num_gpus)
print('Training on {} GPUs'.format(num_gpus))
def regress_loss(y_true, y_pred):
# separate target and state
regression = y_pred
regression_target = y_true[..., :-1]
anchor_state = y_true[..., -1]
# filter out "ignore" anchors
indices = tf.where(K.equal(anchor_state, 1))
regression = tf.gather_nd(regression, indices)
regression_target = tf.gather_nd(regression_target, indices)
# compute the loss
loss = losses.smooth_l1(regression_target, regression, sigma=sigma)
# compute the normalizer: the number of positive anchors
normalizer = K.maximum(1, K.shape(indices)[0])
normalizer = K.cast(normalizer, dtype=K.floatx())
return K.sum(loss) / normalizer
def classification_loss(y_true, y_pred):
# TODO: try weighted_categorical_crossentropy
labels = y_true[..., :-1]
# -1 for ignore, 0 for background, 1 for object
anchor_state = y_true[..., -1]
classification = y_pred
# filter out "ignore" anchors
indices = tf.where(K.not_equal(anchor_state, -1))
labels = tf.gather_nd(labels, indices)
classification = tf.gather_nd(classification, indices)
# compute the loss
loss = losses.focal(labels, classification, alpha=alpha, gamma=gamma)
# compute the normalizer: the number of positive anchors
normalizer = tf.where(K.equal(anchor_state, 1))
normalizer = K.cast(K.shape(normalizer)[0], K.floatx())
normalizer = K.maximum(K.cast_to_floatx(1.0), normalizer)
return K.sum(loss) / normalizer
def mask_loss(y_true, y_pred):
def _mask(y_true, y_pred, iou_threshold=0.5, mask_size=(28, 28)):
# split up the different predicted blobs
boxes = y_pred[:, :, :4]
masks = y_pred[:, :, 4:]
# split up the different blobs
annotations = y_true[:, :, :5]
width = K.cast(y_true[0, 0, 5], dtype='int32')
height = K.cast(y_true[0, 0, 6], dtype='int32')
masks_target = y_true[:, :, 7:]
# reshape the masks back to their original size
masks_target = K.reshape(masks_target,
(K.shape(masks_target)[0] *
K.shape(masks_target)[1], height, width))
masks = K.reshape(masks, (K.shape(masks)[0] * K.shape(masks)[1],
mask_size[0], mask_size[1], -1))
# batch size > 1 fix
boxes = K.reshape(boxes, (-1, K.shape(boxes)[2]))
annotations = K.reshape(annotations, (-1, K.shape(annotations)[2]))
# compute overlap of boxes with annotations
iou = overlap(boxes, annotations)
argmax_overlaps_inds = K.argmax(iou, axis=1)
max_iou = K.max(iou, axis=1)
# filter those with IoU > 0.5
indices = tf.where(K.greater_equal(max_iou, iou_threshold))
boxes = tf.gather_nd(boxes, indices)
masks = tf.gather_nd(masks, indices)
argmax_overlaps_inds = tf.gather_nd(argmax_overlaps_inds, indices)
argmax_overlaps_inds = K.cast(argmax_overlaps_inds, 'int32')
labels = K.gather(annotations[:, 4], argmax_overlaps_inds)
labels = K.cast(labels, 'int32')
# make normalized boxes
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
boxes = K.stack([
y1 / (K.cast(height, dtype=K.floatx()) - 1),
x1 / (K.cast(width, dtype=K.floatx()) - 1),
(y2 - 1) / (K.cast(height, dtype=K.floatx()) - 1),
(x2 - 1) / (K.cast(width, dtype=K.floatx()) - 1),
],
axis=1)
# crop and resize masks_target
# append a fake channel dimension
masks_target = K.expand_dims(masks_target, axis=3)
masks_target = tf.image.crop_and_resize(masks_target, boxes,
argmax_overlaps_inds,
mask_size)
# remove fake channel dimension
masks_target = masks_target[:, :, :, 0]
# gather the predicted masks using the annotation label
masks = tf.transpose(masks, (0, 3, 1, 2))
label_indices = K.stack([tf.range(K.shape(labels)[0]), labels],
axis=1)
masks = tf.gather_nd(masks, label_indices)
# compute mask loss
mask_loss = K.binary_crossentropy(masks_target, masks)
normalizer = K.shape(masks)[0] * K.shape(masks)[1] * K.shape(
masks)[2]
normalizer = K.maximum(K.cast(normalizer, K.floatx()), 1)
mask_loss = K.sum(mask_loss) / normalizer
return mask_loss
# if there are no masks annotations, return 0; else, compute the masks loss
return tf.cond(
K.any(K.equal(K.shape(y_true), 0)), lambda: K.cast_to_floatx(0.0),
lambda: _mask(y_true,
y_pred,
iou_threshold=iou_threshold,
mask_size=mask_size))
# evaluation of model is done on `retinanet_bbox`
if include_masks:
prediction_model = model
else:
prediction_model = retinanet_bbox(model,
nms=True,
class_specific_filter=False)
loss = {'regression': regress_loss, 'classification': classification_loss}
if include_masks:
loss['masks'] = mask_loss
model.compile(loss=loss, optimizer=optimizer)
if num_gpus >= 2:
# Each GPU must have at least one validation example
if test_dict['y'].shape[0] < num_gpus:
raise ValueError('Not enough validation data for {} GPUs. '
'Received {} validation sample.'.format(
test_dict['y'].shape[0], num_gpus))
# When using multiple GPUs and skip_connections,
# the training data must be evenly distributed across all GPUs
num_train = train_dict['y'].shape[0]
nb_samples = num_train - num_train % batch_size
if nb_samples:
train_dict['y'] = train_dict['y'][:nb_samples]
train_dict['X'] = train_dict['X'][:nb_samples]
# this will do preprocessing and realtime data augmentation
datagen = image_generators.RetinaNetGenerator(
# fill_mode='constant', # for rotations
rotation_range=rotation_range,
shear_range=shear,
zoom_range=zoom_range,
horizontal_flip=flip,
vertical_flip=flip)
datagen_val = image_generators.RetinaNetGenerator(
# fill_mode='constant', # for rotations
rotation_range=0,
shear_range=0,
zoom_range=0,
horizontal_flip=0,
vertical_flip=0)
if 'vgg' in backbone or 'densenet' in backbone:
compute_shapes = make_shapes_callback(model)
else:
compute_shapes = guess_shapes
train_data = datagen.flow(train_dict,
include_masks=include_masks,
compute_shapes=compute_shapes,
batch_size=batch_size)
val_data = datagen_val.flow(test_dict,
include_masks=include_masks,
compute_shapes=compute_shapes,
batch_size=batch_size)
tensorboard_callback = callbacks.TensorBoard(
log_dir=os.path.join(log_dir, model_name))
# fit the model on the batches generated by datagen.flow()
loss_history = model.fit_generator(
train_data,
steps_per_epoch=train_data.y.shape[0] // batch_size,
epochs=n_epoch,
validation_data=val_data,
validation_steps=val_data.y.shape[0] // batch_size,
callbacks=[
callbacks.LearningRateScheduler(lr_sched),
callbacks.ModelCheckpoint(model_path,
monitor='val_loss',
verbose=1,
save_best_only=True,
save_weights_only=num_gpus >= 2),
tensorboard_callback,
callbacks.ReduceLROnPlateau(monitor='loss',
factor=0.1,
patience=10,
verbose=1,
mode='auto',
min_delta=0.0001,
cooldown=0,
min_lr=0),
RedirectModel(
Evaluate(val_data,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
max_detections=max_detections,
tensorboard=tensorboard_callback,
weighted_average=weighted_average), prediction_model),
])
model.save_weights(model_path)
np.savez(loss_path, loss_history=loss_history.history)
average_precisions = evaluate(
val_data,
prediction_model,
iou_threshold=iou_threshold,
score_threshold=score_threshold,
max_detections=max_detections,
)
# print evaluation
total_instances = []
precisions = []
for label, (average_precision,
num_annotations) in average_precisions.items():
print('{:.0f} instances of class'.format(num_annotations), label,
'with average precision: {:.4f}'.format(average_precision))
total_instances.append(num_annotations)
precisions.append(average_precision)
if sum(total_instances) == 0:
print('No test instances found.')
else:
print(
'mAP using the weighted average of precisions among classes: {:.4f}'
.format(
sum([a * b for a, b in zip(total_instances, precisions)]) /
sum(total_instances)))
print('mAP: {:.4f}'.format(
sum(precisions) / sum(x > 0 for x in total_instances)))
return model
image_width,
image_depth),
pooling=None,
size_final_dense=256,
num_classes=num_classes,
trainable=True,
weights=None)
#model = multi_gpu_model(model, gpus=2)
# Now train it
opt_RMSprop = RMSprop(lr=0.0002)
model.compile(optimizer=opt_RMSprop,
loss='categorical_crossentropy',
metrics=['accuracy'])
callback_lr_plateau = callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=5)
train_start = time.time()
os.makedirs(os.path.dirname(dir_tensorboard_logs),
exist_ok=True) # Make tensorboard log directory
model.fit(dataset_train,
epochs=num_epochs,
steps_per_epoch=num_steps_per_epoch,
validation_data=dataset_valid,
validation_steps=num_steps_per_epoch_valid,
callbacks=[callback_tensorboard, callback_lr_plateau])
print("Training time: %s seconds" % (time.time() - train_start))
print(model.summary())
# Save the model
def main(params):
'''create save dir if does not exist'''
try:
os.stat(params["save_path"])
except:
os.makedirs(params["save_path"])
'''load data files'''
img_dir = os.path.join(params["data_dir"], "train_images")
label_dir = os.path.join(params["data_dir"], "train_labels")
img_val_dir = os.path.join(params["data_dir"], "test_images")
label_val_dir = os.path.join(params["data_dir"], "test_labels")
ids_train = [i for i in os.listdir(img_dir)]
ids_val = [i for i in os.listdir(img_val_dir)]
num_training_examples = len(ids_train)
# im_batch, labels_batch, im_displayed \
# = lnf.get_clinic_train_data(im_dir=img_dir, seg_dir=label_dir, img_shape=params["img_shape"],
# batch_size=params["batch_size"])
#
# # Running next element in our graph will produce a batch of images
# plt.figure(figsize=(10, 10))
#
# plt.subplot(2, 2, 1)
# plt.imshow(im_batch[0,:,:,:])
#
# plt.subplot(2, 2, 2)
# plt.imshow(labels_batch[0, :, :, 0])
#
# plt.subplot(2, 2, 3)
# plt.imshow(im_batch[1,:,:,:])
#
# plt.subplot(2, 2, 4)
# plt.imshow(labels_batch[1, :, :, 0])
#
# plt.show()
'''get model'''
inputs, outputs = model_fn(params["img_shape"])
model = models.Model(inputs=[inputs], outputs=[outputs])
'''Compile model'''
adam = optimizers.Adam(lr=params["learning_rate"],
beta_1=0.9,
beta_2=0.999,
epsilon=None,
decay=0.0,
amsgrad=False)
if params["loss_function"] == "dice_loss":
model.compile(optimizer=adam, loss=dice_loss, metrics=[dice_loss])
if params["loss_function"] == "bce_dice_loss":
model.compile(optimizer=adam, loss=bce_dice_loss, metrics=[dice_loss])
model.summary()
'''train and save model'''
save_model_path = os.path.join(params["save_path"], "weights.hdf5")
cp = tf.keras.callbacks.ModelCheckpoint(filepath=save_model_path,
monitor='val_dice_loss',
save_best_only=True,
verbose=1,
save_weights_only=True)
learning_rate_reduction = callbacks.ReduceLROnPlateau(
monitor='val_dice_loss',
patience=5,
verbose=0,
factor=0.5,
min_lr=0.0001)
if params["continuing_training"] == True:
'''Load models trained weights'''
model = models.load_model(save_model_path,
custom_objects={
'bce_dice_loss': bce_dice_loss,
'dice_loss': dice_loss
})
for i in range(0, params["epochs"] * len(ids_train)):
im_batch_val, labels_batch_val, im_displayed_val \
= lnf.get_clinic_train_data(im_dir=img_val_dir, seg_dir=label_val_dir, img_shape=params["img_shape"],
batch_size=params["batch_size"])
im_batch, labels_batch, im_displayed \
= lnf.get_clinic_train_data(im_dir=img_dir, seg_dir=label_dir, img_shape=params["img_shape"],
batch_size=params["batch_size"])
history = model.fit(x=im_batch,
y=labels_batch,
steps_per_epoch=2,
validation_data=(im_batch_val, labels_batch_val),
validation_steps=1,
callbacks=[cp, learning_rate_reduction])
'''Visualize the training process'''
dice = history.history['dice_loss']
val_dice = history.history['val_dice_loss']
loss = history.history['loss']
val_loss = history.history['val_loss']
np.save(os.path.join(params["save_path"], "train_loss"), np.array(loss))
np.save(os.path.join(params["save_path"], "validation_loss"),
np.array(val_loss))
np.save(os.path.join(params["save_path"], "train_dice"), np.array(dice))
np.save(os.path.join(params["save_path"], "validation_dice"),
np.array(val_dice))
