def get_metric_function(metric, output_shape=None, loss_fn=None):
"""Returns the metric function corresponding to the given metric input.
Arguments:
metric: Metric function name or reference.
output_shape: The shape of the output that this metric
will be calculated for.
loss_fn: The loss function used.
Returns:
The metric function.
"""
if metric in ['accuracy', 'acc']:
if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy:
return metrics_module.binary_accuracy # case: binary accuracy
elif loss_fn == losses.sparse_categorical_crossentropy:
# case: categorical accuracy with sparse targets
return metrics_module.sparse_categorical_accuracy
return metrics_module.categorical_accuracy # case: categorical accuracy
elif metric in ['crossentropy', 'ce']:
if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy:
return metrics_module.binary_crossentropy # case: binary cross-entropy
elif loss_fn == losses.sparse_categorical_crossentropy:
# case: categorical cross-entropy with sparse targets
return metrics_module.sparse_categorical_crossentropy
# case: categorical cross-entropy
return metrics_module.categorical_crossentropy
return metrics_module.get(metric)
def get_metric_function(metric, output_shape=None, loss_fn=None):
"""Returns the metric function corresponding to the given metric input.
Arguments:
metric: Metric function name or reference.
output_shape: The shape of the output that this metric
will be calculated for.
loss_fn: The loss function used.
Returns:
The metric function.
"""
if metric in ['accuracy', 'acc']:
if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy:
return metrics_module.binary_accuracy # case: binary accuracy
elif loss_fn == losses.sparse_categorical_crossentropy:
# case: categorical accuracy with sparse targets
return metrics_module.sparse_categorical_accuracy
return metrics_module.categorical_accuracy # case: categorical accuracy
elif metric in ['crossentropy', 'ce']:
if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy:
return metrics_module.binary_crossentropy # case: binary cross-entropy
elif loss_fn == losses.sparse_categorical_crossentropy:
# case: categorical cross-entropy with sparse targets
return metrics_module.sparse_categorical_crossentropy
# case: categorical cross-entropy
return metrics_module.categorical_crossentropy
return metrics_module.get(metric)
def get_metric_name(metric, weighted=False):
"""Returns the name corresponding to the given metric input.
Arguments:
metric: Metric function name or reference.
weighted: Boolean indicating if the given metric is weighted.
Returns:
The metric name.
"""
metric_name_prefix = 'weighted_' if weighted else ''
if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
if metric in ('accuracy', 'acc'):
suffix = 'acc'
elif metric in ('crossentropy', 'ce'):
suffix = 'ce'
else:
metric_fn = metrics_module.get(metric)
# Get metric name as string
if hasattr(metric_fn, 'name'):
suffix = metric_fn.name
else:
suffix = metric_fn.__name__
metric_name = metric_name_prefix + suffix
return metric_name
def _get_metric_object(self, metric, y_t, y_p):
"""Converts user-supplied metric to a `Metric` object.
Args:
metric: A string, function, or `Metric` object.
y_t: Sample of label.
y_p: Sample of output.
Returns:
A `Metric` object.
"""
if metric is None:
return None # Ok to have no metric for an output.
# Convenience feature for selecting b/t binary, categorical,
# and sparse categorical.
if metric not in ['accuracy', 'acc', 'crossentropy', 'ce']:
metric_obj = metrics_mod.get(metric)
else:
y_t_rank = len(y_t.shape.as_list())
y_p_rank = len(y_p.shape.as_list())
y_t_last_dim = y_t.shape.as_list()[-1]
y_p_last_dim = y_p.shape.as_list()[-1]
is_binary = y_p_last_dim == 1
is_sparse_categorical = (y_t_rank < y_p_rank
or y_t_last_dim == 1 and y_p_last_dim > 1)
if metric in ['accuracy', 'acc']:
if is_binary:
metric_obj = metrics_mod.binary_accuracy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_accuracy
else:
metric_obj = metrics_mod.categorical_accuracy
else:
if is_binary:
metric_obj = metrics_mod.binary_crossentropy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_crossentropy
else:
metric_obj = metrics_mod.categorical_crossentropy
if isinstance(metric_obj, losses_mod.Loss):
metric_obj._allow_sum_over_batch_size = True # pylint: disable=protected-access
if not isinstance(metric_obj, metrics_mod.Metric):
if isinstance(metric, six.string_types):
metric_name = metric
else:
metric_name = get_custom_object_name(metric)
if metric_name is None:
raise ValueError(
'Metric should be a callable, found: {}'.format(
metric))
metric_obj = metrics_mod.MeanMetricWrapper(metric_obj,
name=metric_name)
return metric_obj
def get_metric_name(metric, weighted=False):
"""Returns the name corresponding to the given metric input.
Arguments:
metric: Metric function name or reference.
weighted: Boolean indicating if the given metric is weighted.
Returns:
The metric name.
"""
metric_name_prefix = 'weighted_' if weighted else ''
if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
if metric in ('accuracy', 'acc'):
suffix = 'acc'
elif metric in ('crossentropy', 'ce'):
suffix = 'ce'
else:
metric_fn = metrics_module.get(metric)
# Get metric name as string
if hasattr(metric_fn, 'name'):
suffix = metric_fn.name
else:
suffix = metric_fn.__name__
metric_name = metric_name_prefix + suffix
return metric_name
def get_base_metric_name(metric, weighted=False):
"""Returns the metric name given the metric function.
Arguments:
metric: Metric function name or reference.
weighted: Boolean indicating if the metric for which we are adding
names is weighted.
Returns:
a metric name.
"""
metric_name_prefix = 'weighted_' if weighted else ''
if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
if metric in ('accuracy', 'acc'):
suffix = 'acc'
elif metric in ('crossentropy', 'ce'):
suffix = 'ce'
metric_name = metric_name_prefix + suffix
else:
metric_fn = metrics_module.get(metric)
# Get metric name as string
if hasattr(metric_fn, 'name'):
metric_name = metric_fn.name
else:
metric_name = metric_fn.__name__
metric_name = metric_name_prefix + metric_name
return metric_name
def get_base_metric_name(metric, weighted=False):
"""Returns the metric name given the metric function.
Arguments:
metric: Metric function name or reference.
weighted: Boolean indicating if the metric for which we are adding
names is weighted.
Returns:
a metric name.
"""
metric_name_prefix = 'weighted_' if weighted else ''
if metric in ('accuracy', 'acc', 'crossentropy', 'ce'):
if metric in ('accuracy', 'acc'):
suffix = 'acc'
elif metric in ('crossentropy', 'ce'):
suffix = 'ce'
metric_name = metric_name_prefix + suffix
else:
metric_fn = metrics_module.get(metric)
# Get metric name as string
if hasattr(metric_fn, 'name'):
metric_name = metric_fn.name
else:
metric_name = metric_fn.__name__
metric_name = metric_name_prefix + metric_name
return metric_name
def get_model(depth, optim, mets):
"""
Build a U-Net model
Parameters:
depth (int): number of training features (i.e. bands)
optim (tf.keras.optimizer): keras optimizer
mets (list<tf.keras.metrics): list of keras metrics
Returns:
tf.keras.model: compiled U-Net model
"""
inputs = layers.Input(shape=[None, None, len(BANDS)]) # 256
encoder0_pool, encoder0 = encoder_block(inputs, 32) # 128
encoder1_pool, encoder1 = encoder_block(encoder0_pool, 64) # 64
encoder2_pool, encoder2 = encoder_block(encoder1_pool, 128) # 32
encoder3_pool, encoder3 = encoder_block(encoder2_pool, 256) # 16
encoder4_pool, encoder4 = encoder_block(encoder3_pool, 512) # 8
center = conv_block(encoder4_pool, 1024) # center
decoder4 = decoder_block(center, encoder4, 512) # 16
decoder3 = decoder_block(decoder4, encoder3, 256) # 32
decoder2 = decoder_block(decoder3, encoder2, 128) # 64
decoder1 = decoder_block(decoder2, encoder1, 64) # 128
decoder0 = decoder_block(decoder1, encoder0, 32) # 256
outputs = layers.Conv2D(1, (1, 1), activation='sigmoid')(decoder0)
model = models.Model(inputs=[inputs], outputs=[outputs])
model.compile(
optimizer=optim,
loss = weighted_bce,
#loss=losses.get(LOSS),
metrics=[metrics.get(metric) for metric in mets])
return model
def _get_metric_object(self, metric, y_t, y_p):
"""Converts user-supplied metric to a `Metric` object.
Arguments:
metric: A string, function, or `Metric` object.
y_t: Sample of label.
y_p: Sample of output.
Returns:
A `Metric` object.
"""
if metric is None:
return None # Ok to have no metric for an output.
# Convenience feature for selecting b/t binary, categorical,
# and sparse categorical.
if metric not in ['accuracy', 'acc', 'crossentropy', 'ce']:
metric_obj = metrics_mod.get(metric)
else:
y_t_rank = len(y_t.shape.as_list())
y_p_rank = len(y_p.shape.as_list())
y_t_last_dim = y_t.shape.as_list()[-1]
y_p_last_dim = y_p.shape.as_list()[-1]
is_binary = y_p_last_dim == 1
is_sparse_categorical = (y_t_rank < y_p_rank
or y_t_last_dim == 1 and y_p_last_dim > 1)
if metric in ['accuracy', 'acc']:
if is_binary:
metric_obj = metrics_mod.binary_accuracy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_accuracy
else:
metric_obj = metrics_mod.categorical_accuracy
else:
if is_binary:
metric_obj = metrics_mod.binary_crossentropy
elif is_sparse_categorical:
metric_obj = metrics_mod.sparse_categorical_crossentropy
else:
metric_obj = metrics_mod.categorical_crossentropy
if isinstance(metric_obj, losses_mod.Loss):
metric_obj._allow_sum_over_batch_size = True # pylint: disable=protected-access
if not isinstance(metric_obj, metrics_mod.Metric):
if isinstance(metric, six.string_types):
metric_name = metric
elif hasattr(metric, 'name'):
metric_name = metric.name # TODO(omalleyt): Is this needed?
else:
# function was passed.
metric_name = metric.__name__
metric_obj = metrics_mod.MeanMetricWrapper(metric_obj,
name=metric_name)
return metric_obj
def get_cost(*inputs):
ctx = get_current_tower_context()
input_tensors = list(inputs[:nr_inputs])
target_tensors = list(inputs[nr_inputs:])
# TODO mapping between target tensors & output tensors
outputs = model_caller(input_tensors)
if isinstance(outputs, tf.Tensor):
outputs = [outputs]
assert len(outputs) == len(target_tensors), \
"len({}) != len({})".format(str(outputs), str(target_tensors))
assert len(outputs) == len(loss), \
"len({}) != len({})".format(str(outputs), str(loss))
loss_tensors = []
for idx, loss_name in enumerate(loss):
with cached_name_scope('keras_loss', top_level=False):
loss_fn = keras.losses.get(loss_name)
curr_loss = loss_fn(target_tensors[idx], outputs[idx])
curr_loss = tf.reduce_mean(curr_loss, name=loss_name)
_check_name(curr_loss, loss_name)
loss_tensors.append(curr_loss)
loss_reg = regularize_cost_from_collection()
if loss_reg is not None:
total_loss = tf.add_n(loss_tensors + [loss_reg],
name=TOTAL_LOSS_NAME)
add_moving_summary(loss_reg, total_loss, *loss_tensors)
else:
total_loss = tf.add_n(loss_tensors, name=TOTAL_LOSS_NAME)
add_moving_summary(total_loss, *loss_tensors)
if metrics and (ctx.is_main_training_tower or not ctx.is_training):
# for list: one metric for each output
metric_tensors = []
for oid, metric_name in enumerate(metrics):
output_tensor = outputs[oid]
target_tensor = target_tensors[
oid] # TODO may not have the same mapping?
with cached_name_scope('keras_metric', top_level=False):
metric_fn = metrics_module.get(metric_name)
metric_tensor = metric_fn(target_tensor, output_tensor)
metric_tensor = tf.reduce_mean(metric_tensor, name=metric_name)
_check_name(metric_tensor, metric_name)
# check name conflict here
metric_tensors.append(metric_tensor)
add_moving_summary(*metric_tensors)
return total_loss
def get_cost(*inputs):
ctx = get_current_tower_context()
input_tensors = list(inputs[:nr_inputs])
target_tensors = list(inputs[nr_inputs:])
# TODO mapping between target tensors & output tensors
outputs = model_caller(input_tensors)
if isinstance(outputs, tf.Tensor):
outputs = [outputs]
assert len(outputs) == len(target_tensors), \
"len({}) != len({})".format(str(outputs), str(target_tensors))
assert len(outputs) == len(loss), \
"len({}) != len({})".format(str(outputs), str(loss))
loss_tensors = []
for idx, loss_name in enumerate(loss):
with cached_name_scope('keras_loss', top_level=False):
loss_fn = keras.losses.get(loss_name)
curr_loss = loss_fn(target_tensors[idx], outputs[idx])
curr_loss = tf.reduce_mean(curr_loss, name=loss_name)
_check_name(curr_loss, loss_name)
loss_tensors.append(curr_loss)
loss_reg = regularize_cost_from_collection()
if loss_reg is not None:
total_loss = tf.add_n(loss_tensors + [loss_reg], name=TOTAL_LOSS_NAME)
add_moving_summary(loss_reg, total_loss, *loss_tensors)
else:
add_moving_summary(*loss_tensors)
total_loss = tf.add_n(loss_tensors, name=TOTAL_LOSS_NAME)
if metrics and (ctx.is_main_training_tower or not ctx.is_training):
# for list: one metric for each output
metric_tensors = []
for oid, metric_name in enumerate(metrics):
output_tensor = outputs[oid]
target_tensor = target_tensors[oid] # TODO may not have the same mapping?
with cached_name_scope('keras_metric', top_level=False):
metric_fn = metrics_module.get(metric_name)
metric_tensor = metric_fn(target_tensor, output_tensor)
metric_tensor = tf.reduce_mean(metric_tensor, name=metric_name)
_check_name(metric_tensor, metric_name)
# check name conflict here
metric_tensors.append(metric_tensor)
add_moving_summary(*metric_tensors)
return total_loss
def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None):
if metric == 'accuracy' or metric == 'acc':
# custom handling of accuracy
# (because of class mode duality)
output_shape = internal_output_shapes
if output_shape[-1] == 1 or loss_func == losses.binary_crossentropy:
# case: binary accuracy
acc_fn = metrics_module.binary_accuracy
elif loss_func == losses.sparse_categorical_crossentropy:
# case: categorical accuracy with sparse targets
acc_fn = metrics_module.sparse_categorical_accuracy
else:
acc_fn = metrics_module.categorical_accuracy
metric_name = 'acc'
return metric_name, acc_fn
else:
metric_fn = metrics_module.get(metric)
metric_name = metric_fn.__name__
return metric_name, metric_fn
def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None):
if metric == 'accuracy' or metric == 'acc':
# custom handling of accuracy
# (because of class mode duality)
output_shape = internal_output_shapes
if output_shape[-1] == 1 or loss_func == losses.binary_crossentropy:
# case: binary accuracy
acc_fn = metrics_module.binary_accuracy
elif loss_func == losses.sparse_categorical_crossentropy:
# case: categorical accuracy with sparse targets
acc_fn = metrics_module.sparse_categorical_accuracy
else:
acc_fn = metrics_module.categorical_accuracy
metric_name = 'acc'
return metric_name, acc_fn
else:
metric_fn = metrics_module.get(metric)
metric_name = metric_fn.__name__
return metric_name, metric_fn
