def __init__(self, num_feature_dimensions, vessel_metadata, metrics):
super(self.__class__, self).__init__(num_feature_dimensions,
vessel_metadata)
def length_or_none(mmsi):
length = vessel_metadata.vessel_label('length', mmsi)
if length == '':
return None
return np.float32(length)
self.classification_objective = MultiClassificationObjective(
"Multiclass", "Vessel-class", vessel_metadata, metrics=metrics)
self.length_objective = RegressionObjective('length',
'Vessel-length',
length_or_none,
loss_weight=0.1,
metrics=metrics)
self.fishing_localisation_objective = FishingLocalizationObjectiveCrossEntropy(
'fishing_localisation',
'Fishing-localisation',
vessel_metadata,
loss_weight=100,
metrics=metrics)
self.summary_objective = SummaryObjective('histograms',
'Histograms',
metrics=metrics)
self.objectives = [
self.classification_objective, self.length_objective,
self.fishing_localisation_objective, self.summary_objective
]
def __init__(self, num_feature_dimensions, vessel_metadata, metrics):
super(self.__class__, self).__init__(num_feature_dimensions,
vessel_metadata)
def length_or_none(mmsi):
length = vessel_metadata.vessel_label('length', mmsi)
if length == '':
return None
return np.float32(length)
self.classification_objective = MultiClassificationObjective(
"Multiclass", "Vessel-class", vessel_metadata, metrics=metrics)
self.summary_objective = SummaryObjective('histograms',
'Histograms',
metrics=metrics)
self.objectives = [
self.classification_objective, self.summary_objective
]
class Model(ModelBase):
initial_learning_rate = 0.01
learning_decay_rate = 0.99
decay_examples = 10000
momentum = 0.9
fishing_dense_layer = 128
tower_params = [
TowerParams(*x) for x in [(32, [3], 2, 2, 1.0, True)] * 10 +
[(32, [2], 2, 2, 0.8, True)]
]
def __init__(self, num_feature_dimensions, vessel_metadata, metrics):
super(self.__class__, self).__init__(num_feature_dimensions,
vessel_metadata)
def length_or_none(mmsi):
length = vessel_metadata.vessel_label('length', mmsi)
if length == '':
return None
return np.float32(length)
self.classification_objective = MultiClassificationObjective(
"Multiclass", "Vessel-class", vessel_metadata, metrics=metrics)
self.length_objective = RegressionObjective('length',
'Vessel-length',
length_or_none,
loss_weight=0.1,
metrics=metrics)
self.fishing_localisation_objective = FishingLocalizationObjectiveCrossEntropy(
'fishing_localisation',
'Fishing-localisation',
vessel_metadata,
loss_weight=100,
metrics=metrics)
self.summary_objective = SummaryObjective('histograms',
'Histograms',
metrics=metrics)
self.objectives = [
self.classification_objective, self.length_objective,
self.fishing_localisation_objective, self.summary_objective
]
@property
def max_window_duration_seconds(self):
return 90 * 24 * 3600
@property
def window_max_points(self):
length = 1
for tp in reversed(self.tower_params):
length = length * tp.pool_stride + (tp.pool_size - tp.pool_stride)
return length
def build_stack(self, current, is_training, tower_params):
stack = [current]
for i, tp in enumerate(tower_params):
with tf.variable_scope('tower-segment-{}'.format(i + 1)):
# Misconception stack
mc = current
for j, w in enumerate(tp.filter_widths):
H, W, C = [int(x) for x in mc.get_shape().dims[1:]]
mc = misconception_layer(mc,
tp.filter_count,
is_training,
filter_size=w,
padding="SAME",
name='misconception-{}'.format(j))
if tp.shunt:
# Build a shunt layer (resnet) to help convergence
with tf.variable_scope('shunt'):
# Trim current before making the skip layer so that it matches the dimensons of
# the mc stack
shunt = tf.nn.elu(
batch_norm(
conv1d_layer(current, 1, tp.filter_count),
is_training))
current = shunt + mc
else:
current = mc
stack.append(current)
current = tf.nn.max_pool(current, [1, 1, tp.pool_size, 1],
[1, 1, tp.pool_stride, 1],
padding="VALID")
if tp.keep_prob < 1:
current = dropout_layer(current, is_training, tp.keep_prob)
# Remove extra dimensions
H, W, C = [int(x) for x in current.get_shape().dims[1:]]
output = tf.reshape(current, (-1, C))
return output, stack
def build_model(self, is_training, current):
self.summary_objective.build(current)
# Build a tower consisting of stacks of misconception layers in parallel
# with size 1 convolutional shortcuts to help train.
with tf.variable_scope('classification-tower'):
classification_output, _ = self.build_stack(
current, is_training, self.tower_params)
self.classification_objective.build(classification_output)
with tf.variable_scope('length-tower'):
length_output, _ = self.build_stack(current, is_training,
self.tower_params)
self.length_objective.build(length_output)
with tf.variable_scope('localization-tower'):
_, localization_layers = self.build_stack(current, is_training,
self.tower_params)
# Assemble the fishing score logits
fishing_sublayers = []
for l in reversed(localization_layers):
H, W, C = [int(x) for x in l.get_shape().dims[1:]]
assert self.window_max_points % W == 0
# Use repeat + tile + reshape to achieve same effect a np.repeat
l = tf.reshape(l, (-1, 1, W, 1, C))
l = tf.tile(l, [1, 1, 1, self.window_max_points // W, 1])
l = tf.reshape(l, [-1, 1, self.window_max_points, C])
fishing_sublayers.append(l)
current = tf.concat(3, fishing_sublayers)
current = tf.nn.elu(
batch_norm(
conv1d_layer(current,
1,
self.fishing_dense_layer,
name="fishing1"), is_training))
current = conv1d_layer(current, 1, 1, name="fishing_logits")
fishing_outputs = tf.reshape(current, (-1, self.window_max_points))
self.fishing_localisation_objective.build(fishing_outputs)
def build_inference_net(self, features, timestamps, mmsis):
self.build_model(tf.constant(False), features)
evaluations = []
for obj in self.objectives:
evaluations.append(obj.build_evaluation(timestamps, mmsis))
return evaluations
def build_training_net(self, features, timestamps, mmsis):
self.build_model(tf.constant(True), features)
trainers = []
for obj in self.objectives:
trainers.append(obj.build_trainer(timestamps, mmsis))
example = slim.get_or_create_global_step() * self.batch_size
learning_rate = tf.train.exponential_decay(self.initial_learning_rate,
example,
self.decay_examples,
self.learning_decay_rate)
optimizer = tf.train.MomentumOptimizer(learning_rate, self.momentum)
return TrainNetInfo(optimizer, trainers)
class Model(ModelBase):
final_size = 25
filter_count = 80
tower_depth = 9
def __init__(self, num_feature_dimensions, vessel_metadata, metrics):
super(self.__class__, self).__init__(num_feature_dimensions,
vessel_metadata)
def length_or_none(mmsi):
length = vessel_metadata.vessel_label('length', mmsi)
if length == '':
return None
return np.float32(length)
self.classification_objective = MultiClassificationObjective(
"Multiclass", "Vessel-class", vessel_metadata, metrics=metrics)
self.summary_objective = SummaryObjective('histograms',
'Histograms',
metrics=metrics)
self.objectives = [
self.classification_objective, self.summary_objective
]
@property
def max_window_duration_seconds(self):
return 90 * 24 * 3600
@property
def window_max_points(self):
length = self.final_size
for _ in range(self.tower_depth):
length = 2 * length + 2
return length
def build_stack(self, current, is_training):
for i in range(self.tower_depth):
with tf.variable_scope('tower-segment-{}'.format(i + 1)):
# Misconception stack
mc = misconception_layer(current,
self.filter_count,
is_training,
filter_size=3,
stride=2,
padding="VALID",
name='misconception-{}'.format(1))
if i > 0:
shunt = tf.nn.avg_pool(current, [1, 1, 3, 1], [1, 1, 2, 1],
padding="VALID")
current = mc + shunt
else:
current = mc
current = tf.nn.elu(
batch_norm(current, is_training=is_training))
#
current = slim.flatten(current)
current = dropout_layer(current, is_training, 0.1)
return current
def build_model(self, is_training, current):
self.summary_objective.build(current)
with tf.variable_scope('classification-tower'):
output = self.build_stack(current, is_training)
self.classification_objective.build(output)
def build_inference_net(self, features, timestamps, mmsis):
self.build_model(tf.constant(False), features)
evaluations = []
for obj in self.objectives:
evaluations.append(obj.build_evaluation(timestamps, mmsis))
return evaluations
def build_training_net(self, features, timestamps, mmsis):
self.build_model(tf.constant(True), features)
trainers = []
for obj in self.objectives:
trainers.append(obj.build_trainer(timestamps, mmsis))
optimizer = tf.train.AdamOptimizer()
return TrainNetInfo(optimizer, trainers)