def call(self, y_true, y_pred):
from tensorflow_addons.losses import metric_learning
self.sd.update_state(y_true, y_pred)
labels = tf.cast(
tf.convert_to_tensor(y_true, name="labels"),
dtype=tf.dtypes.float32
)
if len(labels.shape) == 1:
labels = tf.reshape(labels, (1, -1))
embeddings = tf.convert_to_tensor(y_pred, name="embeddings")
convert_to_float32 = (
(embeddings.dtype == tf.dtypes.float16) or
(embeddings.dtype == tf.dtypes.bfloat16)
)
precise_embeddings = (
tf.cast(embeddings, tf.dtypes.float32)
if convert_to_float32
else embeddings
)
# Reshape label tensor to [batch_size, 1].
# lshape = tf.shape(labels)
# labels = tf.reshape(labels, [lshape[0], 1])
# Build pairwise squared distance matrix
distance_metric = self.distance_metric
if distance_metric == "L2":
pdist_matrix = metric_learning.pairwise_distance(
precise_embeddings, squared=False
)
elif distance_metric == "squared-L2":
pdist_matrix = metric_learning.pairwise_distance(
precise_embeddings, squared=True
)
elif distance_metric == "angular":
pdist_matrix = metric_learning.angular_distance(precise_embeddings)
else:
pdist_matrix = distance_metric(precise_embeddings)
# Fetch pairwise labels as adjacency matrix.
adjacency = self.response_diffs(labels)
# Invert so we can select negatives only.
adjacency_not = tf.math.logical_not(adjacency)
batch_size = tf.size(labels)
# Compute the mask.
pdist_matrix_tile = tf.tile(pdist_matrix, [batch_size, 1])
mask = tf.math.logical_and(
tf.tile(adjacency_not, [batch_size, 1]),
tf.math.greater(
pdist_matrix_tile,
tf.reshape(tf.transpose(pdist_matrix), [-1, 1])
),
)
mask_final = tf.reshape(
tf.math.greater(
tf.math.reduce_sum(
tf.cast(mask, dtype=tf.dtypes.float32),
1,
keepdims=True
),
0.0,
),
[batch_size, batch_size],
)
mask_final = tf.transpose(mask_final)
adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32)
mask = tf.cast(mask, dtype=tf.dtypes.float32)
# negatives_outside: smallest D_an where D_an > D_ap.
negatives_outside = tf.reshape(
_masked_minimum(pdist_matrix_tile, mask), [batch_size, batch_size]
)
negatives_outside = tf.transpose(negatives_outside)
# negatives_inside: largest D_an.
negatives_inside = tf.tile(
_masked_maximum(pdist_matrix, adjacency_not), [1, batch_size]
)
semi_hard_negatives = tf.where(
mask_final,
negatives_outside,
negatives_inside
)
loss_mat = tf.math.add(self.margin, pdist_matrix - semi_hard_negatives)
mask_positives = (
tf.cast(adjacency, dtype=tf.dtypes.float32) -
tf.linalg.diag(tf.ones([batch_size]))
)
# In lifted-struct, the authors multiply 0.5 for upper triangular
# in semihard, they take all positive pairs except the diagonal.
# Max(n, 1) necessary to stop nan loss, which just stops the whole
# model from running.
# Setting to 1 will just mean zero loss, since everything
# else will be 0.
num_positives = tf.math.maximum(
tf.math.reduce_sum(mask_positives),
1.0
)
triplet_loss = tf.math.truediv(
tf.math.reduce_sum(
tf.math.maximum(
tf.math.multiply(loss_mat, mask_positives),
0.0
)
),
num_positives,
)
if convert_to_float32:
return tf.cast(triplet_loss, embeddings.dtype)
else:
return triplet_loss
def call(self, y_true, y_pred):
from tensorflow_addons.losses import metric_learning
self.sd.update_state(y_true, y_pred)
labels = tf.cast(
tf.convert_to_tensor(y_true, name="labels"),
dtype=tf.dtypes.float32
)
if len(labels.shape) == 1:
labels = tf.reshape(labels, (1, -1))
embeddings = tf.convert_to_tensor(y_pred, name="embeddings")
convert_to_float32 = (
(embeddings.dtype == tf.dtypes.float16) or
(embeddings.dtype == tf.dtypes.bfloat16)
)
precise_embeddings = (
tf.cast(embeddings, tf.dtypes.float32)
if convert_to_float32
else embeddings
)
# Reshape label tensor to [batch_size, 1].
# lshape = tf.shape(labels)
# labels = tf.reshape(labels, [lshape[0], 1])
# Build pairwise squared distance matrix
distance_metric = self.distance_metric
if distance_metric == "L2":
pdist_matrix = metric_learning.pairwise_distance(
precise_embeddings, squared=False
)
elif distance_metric == "squared-L2":
pdist_matrix = metric_learning.pairwise_distance(
precise_embeddings, squared=True
)
elif distance_metric == "angular":
pdist_matrix = metric_learning.angular_distance(precise_embeddings)
else:
pdist_matrix = distance_metric(precise_embeddings)
# Fetch pairwise labels as adjacency matrix.
adjacency = self.response_diffs(labels)
# Invert so we can select negatives only.
adjacency_not = tf.math.logical_not(adjacency)
adjacency = tf.cast(adjacency, dtype=tf.dtypes.float32)
adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32)
hard_negatives = _masked_minimum(pdist_matrix, adjacency_not)
batch_size = tf.size(labels)
mask_positives = (
tf.cast(adjacency, dtype=tf.dtypes.float32) -
tf.linalg.diag(tf.ones([batch_size]))
)
# hard positives: largest D_ap.
hard_positives = _masked_maximum(pdist_matrix, mask_positives)
if self.soft:
triplet_loss = tf.math.log1p(
tf.math.exp(hard_positives - hard_negatives))
else:
triplet_loss = tf.maximum(
hard_positives - hard_negatives + self.margin,
0.0
)
# Get final mean triplet loss
triplet_loss = tf.reduce_mean(triplet_loss)
if convert_to_float32:
return tf.cast(triplet_loss, embeddings.dtype)
else:
return triplet_loss
def call(self, y_true, y_pred):
from tensorflow_addons.losses import metric_learning
self.sd.update_state(y_true, y_pred)
labels = tf.cast(
tf.convert_to_tensor(y_true, name="labels"),
dtype=tf.dtypes.float32
)
if len(labels.shape) == 1:
labels = tf.reshape(labels, (1, -1))
batch_size = tf.shape(labels)[0]
embeddings = tf.convert_to_tensor(y_pred, name="embeddings")
convert_to_float32 = (
(embeddings.dtype == tf.dtypes.float16) or
(embeddings.dtype == tf.dtypes.bfloat16)
)
precise_embeddings = (
tf.cast(embeddings, tf.dtypes.float32)
if convert_to_float32
else embeddings
)
# Reshape label tensor to [batch_size, 1].
# lshape = tf.shape(labels)
# labels = tf.reshape(labels, [lshape[0], 1])
# Build pairwise squared distance matrix
distance_metric = self.distance_metric
if distance_metric == "L2":
pdist_matrix = metric_learning.pairwise_distance(
precise_embeddings, squared=False
)
elif distance_metric == "squared-L2":
pdist_matrix = metric_learning.pairwise_distance(
precise_embeddings, squared=True
)
elif distance_metric == "angular":
pdist_matrix = metric_learning.angular_distance(precise_embeddings)
else:
pdist_matrix = distance_metric(precise_embeddings)
# Fetch pairwise labels as adjacency matrix.
adjacency = self.response_diffs(labels)
# Invert so we can select negatives only.
adjacency_not = tf.math.logical_not(adjacency)
radii = (
tf.reduce_mean(pdist_matrix, axis=1) -
(tf.math.reduce_std(pdist_matrix, axis=1) / 2.)
)
neighbors = tf.math.less(pdist_matrix, tf.reshape(radii, (-1, 1)))
hits = (
tf.cast(
tf.math.logical_and(neighbors, adjacency),
tf.dtypes.float32
) - tf.linalg.diag(tf.ones([batch_size]))
)
misses = tf.cast(
tf.math.logical_and(neighbors, adjacency_not),
tf.dtypes.float32
)
nhits = tf.reduce_sum(hits)
nmisses = tf.reduce_sum(misses)
n = tf.cast(batch_size, tf.dtypes.float32)
hits_dists = tf.multiply(pdist_matrix, hits)
hits_dists = tf.math.divide_no_nan(
hits_dists,
tf.math.multiply(n, nhits)
)
misses_dists = tf.multiply(pdist_matrix, misses)
misses_dists = tf.math.divide_no_nan(
misses_dists,
tf.math.multiply(n, nmisses)
)
loss = tf.subtract(misses_dists, hits_dists)
loss = tf.reduce_sum(loss, axis=1)
if convert_to_float32:
return tf.cast(loss, embeddings.dtype)
else:
return loss
def triplet_semihard_loss(
y_true: TensorLike,
y_pred: TensorLike,
margin: FloatTensorLike = 1.0,
distance_metric: Union[str, Callable] = "L2",
) -> tf.Tensor:
"""Computes the triplet loss with semi-hard negative mining.
Args:
y_true: 1-D integer `Tensor` with shape [batch_size] of
multiclass integer labels.
y_pred: 2-D float `Tensor` of embedding vectors. Embeddings should
be l2 normalized.
margin: Float, margin term in the loss definition.
distance_metric: str or function, determines distance metric:
"L2" for l2-norm distance
"squared-L2" for squared l2-norm distance
"angular" for cosine similarity
A custom function returning a 2d adjacency
matrix of a chosen distance metric can
also be passed here. e.g.
def custom_distance(batch):
batch = 1 - batch @ batch.T
return batch
triplet_semihard_loss(batch, labels,
distance_metric=custom_distance
)
Returns:
triplet_loss: float scalar with dtype of y_pred.
"""
labels, embeddings = y_true, y_pred
convert_to_float32 = (embeddings.dtype == tf.dtypes.float16
or embeddings.dtype == tf.dtypes.bfloat16)
precise_embeddings = (tf.cast(embeddings, tf.dtypes.float32)
if convert_to_float32 else embeddings)
# Reshape label tensor to [batch_size, 1].
lshape = tf.shape(labels)
labels = tf.reshape(labels, [lshape[0], 1])
# Build pairwise squared distance matrix
if distance_metric == "L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=False)
elif distance_metric == "squared-L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=True)
elif distance_metric == "angular":
pdist_matrix = metric_learning.angular_distance(precise_embeddings)
else:
pdist_matrix = distance_metric(precise_embeddings)
# Build pairwise binary adjacency matrix.
adjacency = tf.math.equal(labels, tf.transpose(labels))
# Invert so we can select negatives only.
adjacency_not = tf.math.logical_not(adjacency)
batch_size = tf.size(labels)
# Compute the mask.
pdist_matrix_tile = tf.tile(pdist_matrix, [batch_size, 1])
mask = tf.math.logical_and(
tf.tile(adjacency_not, [batch_size, 1]),
tf.math.greater(pdist_matrix_tile,
tf.reshape(tf.transpose(pdist_matrix), [-1, 1])),
)
mask_final = tf.reshape(
tf.math.greater(
tf.math.reduce_sum(tf.cast(mask, dtype=tf.dtypes.float32),
1,
keepdims=True),
0.0,
),
[batch_size, batch_size],
)
mask_final = tf.transpose(mask_final)
adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32)
mask = tf.cast(mask, dtype=tf.dtypes.float32)
# negatives_outside: smallest D_an where D_an > D_ap.
negatives_outside = tf.reshape(_masked_minimum(pdist_matrix_tile, mask),
[batch_size, batch_size])
negatives_outside = tf.transpose(negatives_outside)
# negatives_inside: largest D_an.
negatives_inside = tf.tile(_masked_maximum(pdist_matrix, adjacency_not),
[1, batch_size])
semi_hard_negatives = tf.where(mask_final, negatives_outside,
negatives_inside)
loss_mat = tf.math.add(margin, pdist_matrix - semi_hard_negatives)
mask_positives = tf.cast(adjacency,
dtype=tf.dtypes.float32) - tf.linalg.diag(
tf.ones([batch_size]))
# In lifted-struct, the authors multiply 0.5 for upper triangular
# in semihard, they take all positive pairs except the diagonal.
num_positives = tf.math.reduce_sum(mask_positives)
triplet_loss = tf.math.truediv(
tf.math.reduce_sum(
tf.math.maximum(tf.math.multiply(loss_mat, mask_positives), 0.0)),
num_positives,
)
if convert_to_float32:
return tf.cast(triplet_loss, embeddings.dtype)
else:
return triplet_loss
def triplet_hard_loss(
y_true: TensorLike,
y_pred: TensorLike,
margin: FloatTensorLike = 1.0,
soft: bool = False,
distance_metric: Union[str, Callable] = "L2",
) -> tf.Tensor:
"""Computes the triplet loss with hard negative and hard positive mining.
Args:
y_true: 1-D integer `Tensor` with shape [batch_size] of
multiclass integer labels.
y_pred: 2-D float `Tensor` of embedding vectors. Embeddings should
be l2 normalized.
margin: Float, margin term in the loss definition.
soft: Boolean, if set, use the soft margin version.
distance_metric: str or function, determines distance metric:
"L2" for l2-norm distance
"squared-L2" for squared l2-norm distance
"angular" for cosine similarity
A custom function returning a 2d adjacency
matrix of a chosen distance metric can
also be passed here. e.g.
def custom_distance(batch):
batch = 1 - batch @ batch.T
return batch
triplet_semihard_loss(batch, labels,
distance_metric=custom_distance
)
Returns:
triplet_loss: float scalar with dtype of y_pred.
"""
labels, embeddings = y_true, y_pred
convert_to_float32 = (embeddings.dtype == tf.dtypes.float16
or embeddings.dtype == tf.dtypes.bfloat16)
precise_embeddings = (tf.cast(embeddings, tf.dtypes.float32)
if convert_to_float32 else embeddings)
# Reshape label tensor to [batch_size, 1].
lshape = tf.shape(labels)
labels = tf.reshape(labels, [lshape[0], 1])
# Build pairwise squared distance matrix.
if distance_metric == "L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=False)
elif distance_metric == "squared-L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=True)
elif distance_metric == "angular":
pdist_matrix = metric_learning.angular_distance(precise_embeddings)
else:
pdist_matrix = distance_metric(precise_embeddings)
# Build pairwise binary adjacency matrix.
adjacency = tf.math.equal(labels, tf.transpose(labels))
# Invert so we can select negatives only.
adjacency_not = tf.math.logical_not(adjacency)
adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32)
# hard negatives: smallest D_an.
hard_negatives = _masked_minimum(pdist_matrix, adjacency_not)
batch_size = tf.size(labels)
adjacency = tf.cast(adjacency, dtype=tf.dtypes.float32)
mask_positives = tf.cast(adjacency,
dtype=tf.dtypes.float32) - tf.linalg.diag(
tf.ones([batch_size]))
# hard positives: largest D_ap.
hard_positives = _masked_maximum(pdist_matrix, mask_positives)
if soft:
triplet_loss = tf.math.log1p(
tf.math.exp(hard_positives - hard_negatives))
else:
triplet_loss = tf.maximum(hard_positives - hard_negatives + margin,
0.0)
# Get final mean triplet loss
triplet_loss = tf.reduce_mean(triplet_loss)
if convert_to_float32:
return tf.cast(triplet_loss, embeddings.dtype)
else:
return triplet_loss
def triplet_semihard_loss(
y_true: TensorLike,
y_pred: TensorLike,
margin: FloatTensorLike = 1.0,
distance_metric: Union[str, Callable] = "L2",
) -> tf.Tensor:
r"""Computes the triplet loss with semi-hard negative mining.
Usage:
>>> y_true = tf.convert_to_tensor([0, 0])
>>> y_pred = tf.convert_to_tensor([[0.0, 1.0], [1.0, 0.0]])
>>> tfa.losses.triplet_semihard_loss(y_true, y_pred, distance_metric="L2")
<tf.Tensor: shape=(), dtype=float32, numpy=2.4142137>
>>> # Calling with callable `distance_metric`
>>> distance_metric = lambda x: tf.linalg.matmul(x, x, transpose_b=True)
>>> tfa.losses.triplet_semihard_loss(y_true, y_pred, distance_metric=distance_metric)
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
Args:
y_true: 1-D integer `Tensor` with shape `[batch_size]` of
multiclass integer labels.
y_pred: 2-D float `Tensor` of embedding vectors. Embeddings should
be l2 normalized.
margin: Float, margin term in the loss definition.
distance_metric: `str` or a `Callable` that determines distance metric.
Valid strings are "L2" for l2-norm distance,
"squared-L2" for squared l2-norm distance,
and "angular" for cosine similarity.
A `Callable` should take a batch of embeddings as input and
return the pairwise distance matrix.
Returns:
triplet_loss: float scalar with dtype of `y_pred`.
"""
labels, embeddings = y_true, y_pred
convert_to_float32 = (embeddings.dtype == tf.dtypes.float16
or embeddings.dtype == tf.dtypes.bfloat16)
precise_embeddings = (tf.cast(embeddings, tf.dtypes.float32)
if convert_to_float32 else embeddings)
# Reshape label tensor to [batch_size, 1].
lshape = tf.shape(labels)
labels = tf.reshape(labels, [lshape[0], 1])
# Build pairwise squared distance matrix
if distance_metric == "L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=False)
elif distance_metric == "squared-L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=True)
elif distance_metric == "angular":
pdist_matrix = metric_learning.angular_distance(precise_embeddings)
else:
pdist_matrix = distance_metric(precise_embeddings)
# Build pairwise binary adjacency matrix.
adjacency = tf.math.equal(labels, tf.transpose(labels))
# Invert so we can select negatives only.
adjacency_not = tf.math.logical_not(adjacency)
batch_size = tf.size(labels)
# Compute the mask.
pdist_matrix_tile = tf.tile(pdist_matrix, [batch_size, 1])
mask = tf.math.logical_and(
tf.tile(adjacency_not, [batch_size, 1]),
tf.math.greater(pdist_matrix_tile,
tf.reshape(tf.transpose(pdist_matrix), [-1, 1])),
)
mask_final = tf.reshape(
tf.math.greater(
tf.math.reduce_sum(tf.cast(mask, dtype=tf.dtypes.float32),
1,
keepdims=True),
0.0,
),
[batch_size, batch_size],
)
mask_final = tf.transpose(mask_final)
adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32)
mask = tf.cast(mask, dtype=tf.dtypes.float32)
# negatives_outside: smallest D_an where D_an > D_ap.
negatives_outside = tf.reshape(_masked_minimum(pdist_matrix_tile, mask),
[batch_size, batch_size])
negatives_outside = tf.transpose(negatives_outside)
# negatives_inside: largest D_an.
negatives_inside = tf.tile(_masked_maximum(pdist_matrix, adjacency_not),
[1, batch_size])
semi_hard_negatives = tf.where(mask_final, negatives_outside,
negatives_inside)
loss_mat = tf.math.add(margin, pdist_matrix - semi_hard_negatives)
mask_positives = tf.cast(adjacency,
dtype=tf.dtypes.float32) - tf.linalg.diag(
tf.ones([batch_size]))
# In lifted-struct, the authors multiply 0.5 for upper triangular
# in semihard, they take all positive pairs except the diagonal.
num_positives = tf.math.reduce_sum(mask_positives)
triplet_loss = tf.math.truediv(
tf.math.reduce_sum(
tf.math.maximum(tf.math.multiply(loss_mat, mask_positives), 0.0)),
num_positives,
)
if convert_to_float32:
return tf.cast(triplet_loss, embeddings.dtype)
else:
return triplet_loss
def triplet_hard_loss(
y_true: TensorLike,
y_pred: TensorLike,
margin: FloatTensorLike = 1.0,
soft: bool = False,
distance_metric: Union[str, Callable] = "L2",
) -> tf.Tensor:
r"""Computes the triplet loss with hard negative and hard positive mining.
Usage:
>>> y_true = tf.convert_to_tensor([0, 0])
>>> y_pred = tf.convert_to_tensor([[0.0, 1.0], [1.0, 0.0]])
>>> tfa.losses.triplet_hard_loss(y_true, y_pred, distance_metric="L2")
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
>>> # Calling with callable `distance_metric`
>>> distance_metric = lambda x: tf.linalg.matmul(x, x, transpose_b=True)
>>> tfa.losses.triplet_hard_loss(y_true, y_pred, distance_metric=distance_metric)
<tf.Tensor: shape=(), dtype=float32, numpy=0.0>
Args:
y_true: 1-D integer `Tensor` with shape `[batch_size]` of
multiclass integer labels.
y_pred: 2-D float `Tensor` of embedding vectors. Embeddings should
be l2 normalized.
margin: Float, margin term in the loss definition.
soft: Boolean, if set, use the soft margin version.
distance_metric: `str` or a `Callable` that determines distance metric.
Valid strings are "L2" for l2-norm distance,
"squared-L2" for squared l2-norm distance,
and "angular" for cosine similarity.
A `Callable` should take a batch of embeddings as input and
return the pairwise distance matrix.
Returns:
triplet_loss: float scalar with dtype of `y_pred`.
"""
labels, embeddings = y_true, y_pred
convert_to_float32 = (embeddings.dtype == tf.dtypes.float16
or embeddings.dtype == tf.dtypes.bfloat16)
precise_embeddings = (tf.cast(embeddings, tf.dtypes.float32)
if convert_to_float32 else embeddings)
# Reshape label tensor to [batch_size, 1].
lshape = tf.shape(labels)
labels = tf.reshape(labels, [lshape[0], 1])
# Build pairwise squared distance matrix.
if distance_metric == "L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=False)
elif distance_metric == "squared-L2":
pdist_matrix = metric_learning.pairwise_distance(precise_embeddings,
squared=True)
elif distance_metric == "angular":
pdist_matrix = metric_learning.angular_distance(precise_embeddings)
else:
pdist_matrix = distance_metric(precise_embeddings)
# Build pairwise binary adjacency matrix.
adjacency = tf.math.equal(labels, tf.transpose(labels))
# Invert so we can select negatives only.
adjacency_not = tf.math.logical_not(adjacency)
adjacency_not = tf.cast(adjacency_not, dtype=tf.dtypes.float32)
# hard negatives: smallest D_an.
hard_negatives = _masked_minimum(pdist_matrix, adjacency_not)
batch_size = tf.size(labels)
adjacency = tf.cast(adjacency, dtype=tf.dtypes.float32)
mask_positives = tf.cast(adjacency,
dtype=tf.dtypes.float32) - tf.linalg.diag(
tf.ones([batch_size]))
# hard positives: largest D_ap.
hard_positives = _masked_maximum(pdist_matrix, mask_positives)
if soft:
triplet_loss = tf.math.log1p(
tf.math.exp(hard_positives - hard_negatives))
else:
triplet_loss = tf.maximum(hard_positives - hard_negatives + margin,
0.0)
# Get final mean triplet loss
triplet_loss = tf.reduce_mean(triplet_loss)
if convert_to_float32:
return tf.cast(triplet_loss, embeddings.dtype)
else:
return triplet_loss
