def test_l1_loss_value(self):
with self.test_session():
predicted = tf.constant([1, 1], dtype=tf.float32, name='predicted')
labels = tf.constant([1, 0], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='L1Loss')
computed_l1_loss = test_loss_func(predicted, labels)
self.assertAlmostEqual(computed_l1_loss.eval(), 0.5)
def test_MAE_loss_value_weight(self):
with self.test_session():
weights = tf.constant([1, 2], dtype=tf.float32, name='weights')
predicted = tf.constant([1, 1], dtype=tf.float32, name='predicted')
labels = tf.constant([1, 0], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='MAE')
computed_MAE_loss = test_loss_func(predicted, labels, weights)
self.assertAlmostEqual(computed_MAE_loss.eval(), 2.0 / 3.0)
def test_MAE_loss_value(self):
with self.test_session():
predicted = tf.constant([[1, 2]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[1.2, 0]], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='MAE')
computed_MAE_loss = test_loss_func(predicted, labels)
self.assertAlmostEqual(computed_MAE_loss.eval(), 1.1)
def test_rmse_loss_value(self):
with self.test_session():
predicted = tf.constant([[1.2, 1]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[1, 0]], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='RMSE')
computed_rmse_loss = test_loss_func(predicted, labels)
self.assertAlmostEqual(computed_rmse_loss.eval(), 0.7211, places=4)
def test_rmse_loss_value_weight(self):
with self.cached_session():
weights = tf.constant([[1, 2.1]], dtype=tf.float32, name='weights')
predicted = tf.constant([[1, 1]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[1, 0]], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='RMSE')
computed_rmse_loss = test_loss_func(predicted, labels, weights)
self.assertAlmostEqual(computed_rmse_loss.eval(), 0.8231, places=4)
def test_huber_loss_value(self):
with self.test_session():
predicted = tf.constant([[1, 2, 0.5]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[1, 0, 1]], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='Huber')
computed_huber_loss = test_loss_func(predicted, labels)
self.assertAlmostEqual(computed_huber_loss.eval(),
0.5417,
places=4)
def test_smooth_loss_value(self):
with self.cached_session():
predicted = tf.constant([[1, 2.375, 0.5]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[1, 0, 1]], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='SmoothL1')
computed_smooth_loss = test_loss_func(predicted, labels)
self.assertAlmostEqual(computed_smooth_loss.eval(),
2.125 / 3,
places=4)
def test_l2_loss_value_weight(self):
with self.test_session():
weights = tf.constant([[1, 2]], dtype=tf.float32, name='weights')
predicted = tf.constant([[1, 2]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[1, 0]], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='L2Loss')
computed_l2_loss = test_loss_func(predicted, labels, weights)
self.assertAlmostEqual(computed_l2_loss.eval(),
8.0 / 9.0,
places=3)
def test_cosine_loss_value_equal2(self):
with self.cached_session():
predicted = tf.constant([[[1, 0], [0.5, 0.5]]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[[1, 0], [0.5, 0.5]]],
dtype=tf.float32,
name='labels')
test_loss_func = LossFunction(loss_type='Cosine')
computed_cosine_loss = test_loss_func(predicted, labels)
self.assertAlmostEqual(computed_cosine_loss.eval(), 0)
def test_huber_loss_value_weight(self):
with self.test_session():
weights = tf.constant([1, 2, 1], dtype=tf.float32, name='weights')
predicted = tf.constant([1, 2, 0.5],
dtype=tf.float32,
name='predicted')
labels = tf.constant([1, 0, 1], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='Huber')
computed_huber_loss = test_loss_func(predicted,
labels,
weight_map=weights)
self.assertAlmostEqual(computed_huber_loss.eval(), 3.125 / 4)
def test_cosine_loss_value_weight(self):
with self.cached_session():
weights = tf.constant([[[2], [1]]],
dtype=tf.float32,
name='weights')
predicted = tf.constant([[[1, 0], [0.5, 0.5]]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[[0, 1], [0.5, 0.5]]],
dtype=tf.float32,
name='labels')
test_loss_func = LossFunction(loss_type='Cosine')
computed_cosine_loss = test_loss_func(predicted, labels, weights)
self.assertAlmostEqual(computed_cosine_loss.eval(), 2.0 / 3.0)
def test_smooth_loss_value_weight(self):
with self.cached_session():
weights = tf.constant([[1, 2, 1]],
dtype=tf.float32,
name='weights')
predicted = tf.constant([[1, 2.375, 0.5]],
dtype=tf.float32,
name='predicted')
labels = tf.constant([[1, 0, 1]], dtype=tf.float32, name='labels')
test_loss_func = LossFunction(loss_type='SmoothL1')
computed_smooth_l1_loss = test_loss_func(predicted,
labels,
weight_map=weights)
self.assertAlmostEqual(computed_smooth_l1_loss.eval(), 4.125 / 4)
def connect_data_and_network(self,
outputs_collector=None,
gradients_collector=None):
def switch_sampler(for_training):
with tf.name_scope('train' if for_training else 'validation'):
sampler = self.get_sampler()[0][0 if for_training else -1]
return sampler.pop_batch_op()
if self.is_training:
if self.action_param.validation_every_n > 0:
data_dict = tf.cond(tf.logical_not(self.is_validation),
lambda: switch_sampler(True),
lambda: switch_sampler(False))
else:
data_dict = switch_sampler(for_training=True)
image = tf.cast(data_dict['image'], tf.float32)
net_args = {
'is_training': self.is_training,
'keep_prob': self.net_param.keep_prob
}
net_out = self.net(image, **net_args)
with tf.name_scope('Optimiser'):
optimiser_class = OptimiserFactory.create(
name=self.action_param.optimiser)
self.optimiser = optimiser_class.get_instance(
learning_rate=self.action_param.lr)
loss_func = LossFunction(loss_type=self.action_param.loss_type)
crop_layer = CropLayer(border=self.regression_param.loss_border,
name='crop-88')
prediction = crop_layer(net_out)
ground_truth = crop_layer(data_dict.get('output', None))
weight_map = None if data_dict.get('weight', None) is None \
else crop_layer(data_dict.get('weight', None))
data_loss = loss_func(prediction=prediction,
ground_truth=ground_truth,
weight_map=weight_map)
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
if self.net_param.decay > 0.0 and reg_losses:
reg_loss = tf.reduce_mean(
[tf.reduce_mean(reg_loss) for reg_loss in reg_losses])
loss = data_loss + reg_loss
else:
loss = data_loss
grads = self.optimiser.compute_gradients(loss)
# Gradient Clipping associated with VDSR3D
# Gradients are clipped by value, instead of clipping by global norm.
# The authors of VDSR do not specify a threshold for the clipping process.
# grads2, vars2 = zip(*grads)
# grads2, _ = tf.clip_by_global_norm(grads2, 5.0)
# grads = zip(grads2, vars2)
grads = [(tf.clip_by_value(grad, -0.00001 / self.action_param.lr,
+0.00001 / self.action_param.lr), val)
for grad, val in grads if grad is not None]
# collecting gradients variables
gradients_collector.add_to_collection([grads])
# collecting output variables
outputs_collector.add_to_collection(var=data_loss,
name='Loss',
average_over_devices=False,
collection=CONSOLE)
outputs_collector.add_to_collection(var=data_loss,
name='Loss',
average_over_devices=True,
summary_type='scalar',
collection=TF_SUMMARIES)
elif self.is_inference:
data_dict = switch_sampler(for_training=False)
image = tf.cast(data_dict['image'], tf.float32)
net_args = {
'is_training': self.is_training,
'keep_prob': self.net_param.keep_prob
}
net_out = self.net(image, **net_args)
crop_layer = CropLayer(border=0, name='crop-88')
post_process_layer = PostProcessingLayer('IDENTITY')
net_out = post_process_layer(crop_layer(net_out))
outputs_collector.add_to_collection(var=net_out,
name='window',
average_over_devices=False,
collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(
var=data_dict['image_location'],
name='location',
average_over_devices=False,
collection=NETWORK_OUTPUT)
self.initialise_aggregator()
def connect_data_and_network(self,
outputs_collector=None,
gradients_collector=None):
def switch_sampler(for_training):
with tf.name_scope('train' if for_training else 'validation'):
sampler = self.get_sampler()[0][0 if for_training else -1]
return sampler.pop_batch_op()
if self.is_training:
if self.action_param.validation_every_n > 0:
data_dict = tf.cond(tf.logical_not(self.is_validation),
lambda: switch_sampler(True),
lambda: switch_sampler(False))
else:
data_dict = switch_sampler(for_training=True)
image = tf.cast(data_dict['image'], tf.float32)
net_out = self.net(image, is_training=self.is_training)
with tf.name_scope('Optimiser'):
optimiser_class = OptimiserFactory.create(
name=self.action_param.optimiser)
self.optimiser = optimiser_class.get_instance(
learning_rate=self.action_param.lr)
loss_func = LossFunction(
loss_type=self.action_param.loss_type)
crop_layer = CropLayer(
border=self.regression_param.loss_border, name='crop-88')
prediction = crop_layer(net_out)
ground_truth = crop_layer(data_dict.get('output', None))
weight_map = None if data_dict.get('weight', None) is None \
else crop_layer(data_dict.get('weight', None))
data_loss = loss_func(prediction=prediction,
ground_truth=ground_truth,
weight_map=weight_map)
reg_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
if self.net_param.decay > 0.0 and reg_losses:
reg_loss = tf.reduce_mean(
[tf.reduce_mean(reg_loss) for reg_loss in reg_losses])
loss = data_loss + reg_loss
else:
loss = data_loss
grads = self.optimiser.compute_gradients(loss)
# collecting gradients variables
gradients_collector.add_to_collection([grads])
# collecting output variables
outputs_collector.add_to_collection(
var=data_loss, name='Loss',
average_over_devices=False, collection=CONSOLE)
outputs_collector.add_to_collection(
var=data_loss, name='Loss',
average_over_devices=True, summary_type='scalar',
collection=TF_SUMMARIES)
else:
data_dict = switch_sampler(for_training=False)
image = tf.cast(data_dict['image'], tf.float32)
net_out = self.net(image, is_training=self.is_training)
crop_layer = CropLayer(border=0, name='crop-88')
post_process_layer = PostProcessingLayer('IDENTITY')
net_out = post_process_layer(crop_layer(net_out))
outputs_collector.add_to_collection(
var=net_out, name='window',
average_over_devices=False, collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(
var=data_dict['image_location'], name='location',
average_over_devices=False, collection=NETWORK_OUTPUT)
init_aggregator = \
self.SUPPORTED_SAMPLING[self.net_param.window_sampling][2]
init_aggregator()
def connect_data_and_network(self,
outputs_collector=None,
gradients_collector=None):
def switch_sampler(for_training):
with tf.name_scope('train' if for_training else 'validation'):
sampler = self.get_sampler()[0][0 if for_training else -1]
return sampler.pop_batch_op()
if self.is_training:
if self.action_param.validation_every_n > 0:
data_dict = tf.cond(tf.logical_not(self.is_validation),
lambda: switch_sampler(for_training=True),
lambda: switch_sampler(for_training=False))
else:
data_dict = switch_sampler(for_training=True)
image = tf.cast(data_dict['image'], tf.float32)
net_args = {
'is_training': self.is_training,
'keep_prob': self.net_param.keep_prob
}
net_out = self.net(image, **net_args)
with tf.name_scope('Optimiser'):
optimiser_class = OptimiserFactory.create(
name=self.action_param.optimiser)
self.optimiser = optimiser_class.get_instance(
learning_rate=self.action_param.lr)
loss_func = LossFunction(loss_type=self.action_param.loss_type)
crop_layer = CropLayer(border=self.regression_param.loss_border)
weight_map = data_dict.get('weight', None)
weight_map = None if weight_map is None else crop_layer(weight_map)
data_loss = loss_func(prediction=crop_layer(net_out),
ground_truth=crop_layer(data_dict['output']),
weight_map=weight_map)
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
if self.net_param.decay > 0.0 and reg_losses:
reg_loss = tf.reduce_mean(
[tf.reduce_mean(reg_loss) for reg_loss in reg_losses])
loss = data_loss + reg_loss
else:
loss = data_loss
# Get all vars
to_optimise = tf.trainable_variables()
vars_to_freeze = \
self.action_param.vars_to_freeze or \
self.action_param.vars_to_restore
if vars_to_freeze:
import re
var_regex = re.compile(vars_to_freeze)
# Only optimise vars that are not frozen
to_optimise = \
[v for v in to_optimise if not var_regex.search(v.name)]
tf.logging.info(
"Optimizing %d out of %d trainable variables, "
"the other variables are fixed (--vars_to_freeze %s)",
len(to_optimise), len(tf.trainable_variables()),
vars_to_freeze)
grads = self.optimiser.compute_gradients(
loss, var_list=to_optimise, colocate_gradients_with_ops=True)
# collecting gradients variables
gradients_collector.add_to_collection([grads])
# collecting output variables
outputs_collector.add_to_collection(var=data_loss,
name='loss',
average_over_devices=False,
collection=CONSOLE)
outputs_collector.add_to_collection(var=data_loss,
name='loss',
average_over_devices=True,
summary_type='scalar',
collection=TF_SUMMARIES)
elif self.is_inference:
data_dict = switch_sampler(for_training=False)
image = tf.cast(data_dict['image'], tf.float32)
net_args = {
'is_training': self.is_training,
'keep_prob': self.net_param.keep_prob
}
net_out = self.net(image, **net_args)
net_out = PostProcessingLayer('IDENTITY')(net_out)
outputs_collector.add_to_collection(var=net_out,
name='window',
average_over_devices=False,
collection=NETWORK_OUTPUT)
outputs_collector.add_to_collection(
var=data_dict['image_location'],
name='location',
average_over_devices=False,
collection=NETWORK_OUTPUT)
self.initialise_aggregator()
