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util.py
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util.py
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Utilities for manipulating the loss collections."""
from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import confusion_matrix
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.tf_export import tf_export
def squeeze_or_expand_dimensions(y_pred, y_true=None, sample_weight=None):
"""Squeeze or expand last dimension if needed.
1. Squeezes last dim of `y_pred` or `y_true` if their rank differs by 1
(using `confusion_matrix.remove_squeezable_dimensions`).
2. Squeezes or expands last dim of `sample_weight` if its rank differs by 1
from the new rank of `y_pred`.
If `sample_weight` is scalar, it is kept scalar.
This will use static shape if available. Otherwise, it will add graph
operations, which could result in a performance hit.
Args:
y_pred: Predicted values, a `Tensor` of arbitrary dimensions.
y_true: Optional label `Tensor` whose dimensions match `y_pred`.
sample_weight: Optional weight scalar or `Tensor` whose dimensions match
`y_pred`.
Returns:
Tuple of `y_pred`, `y_true` and `sample_weight`. Each of them possibly has
the last dimension squeezed,
`sample_weight` could be extended by one dimension.
If `sample_weight` is None, (y_pred, y_true) is returned.
"""
y_pred_shape = y_pred.shape
y_pred_rank = y_pred_shape.ndims
if y_true is not None:
# If sparse matrix is provided as `y_true`, the last dimension in `y_pred`
# may be > 1. Eg: y_true = [0, 1, 2] (shape=(3,)),
# y_pred = [[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]] (shape=(3, 3))
# In this case, we should not try to remove squeezable dimension.
y_true_shape = y_true.shape
y_true_rank = y_true_shape.ndims
if (y_true_rank is not None) and (y_pred_rank is not None):
# Use static rank for `y_true` and `y_pred`.
if (y_pred_rank - y_true_rank != 1) or y_pred_shape[-1] == 1:
y_true, y_pred = confusion_matrix.remove_squeezable_dimensions(
y_true, y_pred)
else:
# Use dynamic rank.
rank_diff = array_ops.rank(y_pred) - array_ops.rank(y_true)
squeeze_dims = lambda: confusion_matrix.remove_squeezable_dimensions( # pylint: disable=g-long-lambda
y_true, y_pred)
is_last_dim_1 = math_ops.equal(1, array_ops.shape(y_pred)[-1])
maybe_squeeze_dims = lambda: control_flow_ops.cond( # pylint: disable=g-long-lambda
is_last_dim_1, squeeze_dims, lambda: (y_true, y_pred))
y_true, y_pred = control_flow_ops.cond(
math_ops.equal(1, rank_diff), maybe_squeeze_dims, squeeze_dims)
if sample_weight is None:
return y_pred, y_true
weights_shape = sample_weight.shape
weights_rank = weights_shape.ndims
if weights_rank == 0: # If weights is scalar, do nothing.
return y_pred, y_true, sample_weight
if (y_pred_rank is not None) and (weights_rank is not None):
# Use static rank.
if weights_rank - y_pred_rank == 1:
sample_weight = array_ops.squeeze(sample_weight, [-1])
elif y_pred_rank - weights_rank == 1:
sample_weight = array_ops.expand_dims(sample_weight, [-1])
return y_pred, y_true, sample_weight
# Use dynamic rank.
weights_rank_tensor = array_ops.rank(sample_weight)
rank_diff = weights_rank_tensor - array_ops.rank(y_pred)
maybe_squeeze_weights = lambda: array_ops.squeeze(sample_weight, [-1])
def _maybe_expand_weights():
expand_weights = lambda: array_ops.expand_dims(sample_weight, [-1])
return control_flow_ops.cond(
math_ops.equal(rank_diff, -1), expand_weights, lambda: sample_weight)
def _maybe_adjust_weights():
return control_flow_ops.cond(
math_ops.equal(rank_diff, 1), maybe_squeeze_weights,
_maybe_expand_weights)
# squeeze or expand last dim of `sample_weight` if its rank differs by 1
# from the new rank of `y_pred`.
sample_weight = control_flow_ops.cond(
math_ops.equal(weights_rank_tensor, 0), lambda: sample_weight,
_maybe_adjust_weights)
return y_pred, y_true, sample_weight
def scale_losses_by_sample_weight(losses, sample_weight):
"""Scales loss values by the given sample weights.
`sample_weight` dimensions are updated to match with the dimension of `losses`
if possible by using squeeze/expand/broadcast.
Args:
losses: Loss tensor.
sample_weight: Sample weights tensor.
Returns:
`losses` scaled by `sample_weight` with dtype float32.
"""
# TODO(psv): Handle the casting here in a better way, eg. if losses is float64
# we do not want to lose precision.
losses = math_ops.cast(losses, dtypes.float32)
sample_weight = math_ops.cast(sample_weight, dtypes.float32)
# Update dimensions of `sample_weight` to match with `losses` if possible.
losses, _, sample_weight = squeeze_or_expand_dimensions(
losses, None, sample_weight)
return math_ops.multiply(losses, sample_weight)
@tf_contextlib.contextmanager
def check_per_example_loss_rank(per_example_loss):
"""Context manager that checks that the rank of per_example_loss is at least 1.
Args:
per_example_loss: Per example loss tensor.
Yields:
A context manager.
"""
loss_rank = per_example_loss.shape.rank
if loss_rank is not None:
# Handle static rank.
if loss_rank == 0:
raise ValueError(
"Invalid value passed for `per_example_loss`. Expected a tensor with "
f"at least rank 1. Received per_example_loss={per_example_loss} with "
f"rank {loss_rank}")
yield
else:
# Handle dynamic rank.
with ops.control_dependencies([
check_ops.assert_greater_equal(
array_ops.rank(per_example_loss),
math_ops.cast(1, dtype=dtypes.int32),
message="Invalid value passed for `per_example_loss`. Expected a "
"tensor with at least rank 1.")
]):
yield
@tf_export(v1=["losses.add_loss"])
def add_loss(loss, loss_collection=ops.GraphKeys.LOSSES):
"""Adds a externally defined loss to the collection of losses.
Args:
loss: A loss `Tensor`.
loss_collection: Optional collection to add the loss to.
"""
# Since we have no way of figuring out when a training iteration starts or
# ends, holding on to a loss when executing eagerly is indistinguishable from
# leaking memory. We instead leave the collection empty.
if loss_collection and not context.executing_eagerly():
ops.add_to_collection(loss_collection, loss)
@tf_export(v1=["losses.get_losses"])
def get_losses(scope=None, loss_collection=ops.GraphKeys.LOSSES):
"""Gets the list of losses from the loss_collection.
Args:
scope: An optional scope name for filtering the losses to return.
loss_collection: Optional losses collection.
Returns:
a list of loss tensors.
"""
return ops.get_collection(loss_collection, scope)
@tf_export(v1=["losses.get_regularization_losses"])
def get_regularization_losses(scope=None):
"""Gets the list of regularization losses.
Args:
scope: An optional scope name for filtering the losses to return.
Returns:
A list of regularization losses as Tensors.
"""
return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope)
@tf_export(v1=["losses.get_regularization_loss"])
def get_regularization_loss(scope=None, name="total_regularization_loss"):
"""Gets the total regularization loss.
Args:
scope: An optional scope name for filtering the losses to return.
name: The name of the returned tensor.
Returns:
A scalar regularization loss.
"""
losses = get_regularization_losses(scope)
if losses:
return math_ops.add_n(losses, name=name)
else:
return constant_op.constant(0.0)
@tf_export(v1=["losses.get_total_loss"])
def get_total_loss(add_regularization_losses=True,
name="total_loss",
scope=None):
"""Returns a tensor whose value represents the total loss.
In particular, this adds any losses you have added with `tf.add_loss()` to
any regularization losses that have been added by regularization parameters
on layers constructors e.g. `tf.layers`. Be very sure to use this if you
are constructing a loss_op manually. Otherwise regularization arguments
on `tf.layers` methods will not function.
Args:
add_regularization_losses: A boolean indicating whether or not to use the
regularization losses in the sum.
name: The name of the returned tensor.
scope: An optional scope name for filtering the losses to return. Note that
this filters the losses added with `tf.add_loss()` as well as the
regularization losses to that scope.
Returns:
A `Tensor` whose value represents the total loss.
Raises:
ValueError: if `losses` is not iterable.
"""
losses = get_losses(scope=scope)
if add_regularization_losses:
losses += get_regularization_losses(scope=scope)
return math_ops.add_n(losses, name=name)