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keras_ssd3Dbv_loss.py
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keras_ssd3Dbv_loss.py
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'''The Keras-compatible loss function for the SSD3DBV model. Currently supports TensorFlow only.'''
import tensorflow as tf
class SSD3DBV_Loss:
'''
The SSD loss, see https://arxiv.org/abs/1512.02325.
'''
def __init__(self,
neg_pos_ratio=3,
n_neg_min=0,
alpha=1.0):
'''
Arguments:
neg_pos_ratio (int, optional): The maximum number of negative (i.e. background)
ground truth boxes to include in the loss computation. There are no
actual background ground truth boxes of course, but `y_true`
contains default boxes labeled with the background class. Since
the number of background boxes in `y_true` will ususally exceed
the number of positive boxes by far, it is necessary to balance
their influence on the loss. Defaults to 3 following the paper.
n_neg_min (int, optional): The minimum number of negative ground truth boxes to
enter the loss computation *per batch*. This argument can be used to make
sure that the model learns from a minimum number of negatives in batches
in which there are very few, or even none at all, positive ground truth
boxes. It defaults to 0 and if used, it should be set to a value that
stands in reasonable proportion to the batch size used for training.
alpha (float, optional): A factor to weight the localization loss in the
computation of the total loss. Defaults to 1.0 following the paper.
'''
self.neg_pos_ratio = tf.constant(neg_pos_ratio)
self.n_neg_min = tf.constant(n_neg_min)
self.alpha = tf.constant(alpha)
def smooth_L1_loss(self, y_true, y_pred):
'''
Compute smooth L1 loss, see references.
Arguments:
y_true (nD tensor): A TensorFlow tensor of any shape containing the ground truth data.
In this context, the expected tensor has shape `(batch_size, #boxes, k)` and
contains the ground truth bounding box coordinates, where the last dimension
contains `k` box coordinates.
y_pred (nD tensor): A TensorFlow tensor of identical structure to `y_true` containing
the predicted data, in this context the predicted bounding box coordinates.
Returns:
The smooth L1 loss, a nD-1 Tensorflow tensor. In this context a 2D tensor
of shape (batch, n_boxes_total).
References:
https://arxiv.org/abs/1504.08083
'''
absolute_loss = tf.abs(y_true - y_pred)
square_loss = 0.5 * (y_true - y_pred)**2
l1_loss = tf.where(tf.less(absolute_loss, 1.0), square_loss, absolute_loss - 0.5)
return tf.reduce_sum(l1_loss, axis=-1)
def log_loss(self, y_true, y_pred):
'''
Compute the softmax log loss.
Arguments:
y_true (nD tensor): A TensorFlow tensor of any shape containing the ground truth data.
In this context, the expected tensor has shape (batch_size, #boxes, #classes)
and contains the ground truth bounding box categories.
y_pred (nD tensor): A TensorFlow tensor of identical structure to `y_true` containing
the predicted data, in this context the predicted bounding box categories.
Returns:
The softmax log loss, a nD-1 Tensorflow tensor. In this context a 2D tensor
of shape (batch, n_boxes_total).
'''
# Make sure that `y_pred` doesn't contain any zeros (which would break the log function)
y_pred = tf.maximum(y_pred, 1e-15)
# Compute the log loss
log_loss = -tf.reduce_sum(y_true * tf.log(y_pred), axis=-1)
return log_loss
def compute_loss(self, y_true, y_pred):
'''
Compute the loss of the SSD model prediction against the ground truth.
Arguments:
y_true (array): A Numpy array of shape `(batch_size, #boxes, #classes + 18)`,
where `#boxes` is the total number of boxes that the model predicts
per image. Be careful to make sure that the index of each given
box in `y_true` is the same as the index for the corresponding
box in `y_pred`. The last axis must have length `#classes + 18` and contain
`[classes one-hot encoded, 9 ground truth box coordinates, 9 arbitrary entries]`
in this order, including the background class. The last 9 entries of the
last axis are not used by this function and therefore their contents are
irrelevant, they only exist so that `y_true` has the same shape as `y_pred`,
where the last 9 entries of the last axis contain the anchor box
coordinates, which are needed during inference. Important: Boxes that
you want the cost function to ignore need to have a one-hot
class vector of all zeros.
y_pred (Keras tensor): The model prediction. The shape is identical
to that of `y_true`.
Returns:
A scalar, the total multitask loss for classification and localization.
'''
batch_size = tf.shape(y_pred)[0] # Output dtype: tf.int32
n_boxes = tf.shape(y_pred)[1] # Output dtype: tf.int32, note that `n_boxes` in this context denotes the total number of boxes per image, not the number of boxes per cell
# 1: Compute the losses for class and box predictions for every box
classification_loss = tf.to_float(self.log_loss(y_true[:,:,:-18], y_pred[:,:,:-18])) # Output shape: (batch_size, n_boxes)
localization_loss = tf.to_float(self.smooth_L1_loss(y_true[:,:,-18:-9], y_pred[:,:,-18:-9])) # Output shape: (batch_size, n_boxes)
# 2: Compute the classification losses for the positive and negative targets
# Create masks for the positive and negative ground truth classes
negatives = y_true[:,:,0] # Tensor of shape (batch_size, n_boxes)
positives = tf.to_float(tf.reduce_max(y_true[:,:,1:-18], axis=-1)) # Tensor of shape (batch_size, n_boxes)
# Count the number of positive boxes (classes 1 to n) in y_true across the whole batch
n_positive = tf.reduce_sum(positives)
# Now mask all negative boxes and sum up the losses for the positive boxes PER batch item
# (Keras loss functions must output one scalar loss value PER batch item, rather than just
# one scalar for the entire batch, that's why we're not summing across all axes)
pos_class_loss = tf.reduce_sum(classification_loss * positives, axis=-1) # Tensor of shape (batch_size,)
# Compute the classification loss for the negative default boxes (if there are any)
# First, compute the classification loss for all negative boxes
neg_class_loss_all = classification_loss * negatives # Tensor of shape (batch_size, n_boxes)
n_neg_losses = tf.count_nonzero(neg_class_loss_all, dtype=tf.int32) # The number of non-zero loss entries in `neg_class_loss_all`
# What's the point of `n_neg_losses`? For the next step, which will be to compute which negative boxes enter the classification
# loss, we don't just want to know how many negative ground truth boxes there are, but for how many of those there actually is
# a positive (i.e. non-zero) loss. This is necessary because `tf.nn.top-k()` in the function below will pick the top k boxes with
# the highest losses no matter what, even if it receives a vector where all losses are zero. In the unlikely event that all negative
# classification losses ARE actually zero though, this behavior might lead to `tf.nn.top-k()` returning the indices of positive
# boxes, leading to an incorrect negative classification loss computation, and hence an incorrect overall loss computation.
# We therefore need to make sure that `n_negative_keep`, which assumes the role of the `k` argument in `tf.nn.top-k()`,
# is at most the number of negative boxes for which there is a positive classification loss.
# Compute the number of negative examples we want to account for in the loss
# We'll keep at most `self.neg_pos_ratio` times the number of positives in `y_true`, but at least `self.n_neg_min` (unless `n_neg_loses` is smaller)
n_negative_keep = tf.minimum(tf.maximum(self.neg_pos_ratio * tf.to_int32(n_positive), self.n_neg_min), n_neg_losses)
# In the unlikely case when either (1) there are no negative ground truth boxes at all
# or (2) the classification loss for all negative boxes is zero, return zero as the `neg_class_loss`
def f1():
return tf.zeros([batch_size])
# Otherwise compute the negative loss
def f2():
# Now we'll identify the top-k (where k == `n_negative_keep`) boxes with the highest confidence loss that
# belong to the background class in the ground truth data. Note that this doesn't necessarily mean that the model
# predicted the wrong class for those boxes, it just means that the loss for those boxes is the highest.
# To do this, we reshape `neg_class_loss_all` to 1D...
neg_class_loss_all_1D = tf.reshape(neg_class_loss_all, [-1]) # Tensor of shape (batch_size * n_boxes,)
# ...and then we get the indices for the `n_negative_keep` boxes with the highest loss out of those...
values, indices = tf.nn.top_k(neg_class_loss_all_1D, n_negative_keep, False) # We don't need sorting
# ...and with these indices we'll create a mask...
negatives_keep = tf.scatter_nd(tf.expand_dims(indices, axis=1), updates=tf.ones_like(indices, dtype=tf.int32), shape=tf.shape(neg_class_loss_all_1D)) # Tensor of shape (batch_size * n_boxes,)
negatives_keep = tf.to_float(tf.reshape(negatives_keep, [batch_size, n_boxes])) # Tensor of shape (batch_size, n_boxes)
# ...and use it to keep only those boxes and mask all other classification losses
neg_class_loss = tf.reduce_sum(classification_loss * negatives_keep, axis=-1) # Tensor of shape (batch_size,)
return neg_class_loss
neg_class_loss = tf.cond(tf.equal(n_neg_losses, tf.constant(0)), f1, f2)
class_loss = pos_class_loss + neg_class_loss # Tensor of shape (batch_size,)
# 3: Compute the localization loss for the positive targets
# We don't penalize localization loss for negative predicted boxes (obviously: there are no ground truth boxes they would correspond to)
loc_loss = tf.reduce_sum(localization_loss * positives, axis=-1) # Tensor of shape (batch_size,)
# 4: Compute the total loss
total_loss = (class_loss + self.alpha * loc_loss) / tf.maximum(1.0, n_positive) # In case `n_positive == 0`
return total_loss