def __call__(self, true_counts, inds, y_true, boxes, classes, y_pred):
"""Call function to compute the loss and a set of metrics per FPN level.
Args:
true_counts: `Tensor` of shape [batchsize, height, width, num_anchors]
represeneting how many boxes are in a given pixel [j, i] in the output
map.
inds: `Tensor` of shape [batchsize, None, 3] indicating the location [j,
i] that a given box is associatied with in the FPN prediction map.
y_true: `Tensor` of shape [batchsize, None, 8] indicating the actual box
associated with each index in the inds tensor list.
boxes: `Tensor` of shape [batchsize, None, 4] indicating the original
ground truth boxes for each image as they came from the decoder used for
bounding box search.
classes: `Tensor` of shape [batchsize, None, 1] indicating the original
ground truth classes for each image as they came from the decoder used
for bounding box search.
y_pred: `Tensor` of shape [batchsize, height, width, output_depth] holding
the models output at a specific FPN level.
Returns:
loss: `float` for the actual loss.
box_loss: `float` loss on the boxes used for metrics.
conf_loss: `float` loss on the confidence used for metrics.
class_loss: `float` loss on the classes used for metrics.
avg_iou: `float` metric for the average iou between predictions and ground
truth.
avg_obj: `float` metric for the average confidence of the model for
predictions.
"""
(loss, box_loss, conf_loss, class_loss, mean_loss, iou, pred_conf,
ind_mask, grid_mask) = self._compute_loss(true_counts, inds, y_true,
boxes, classes, y_pred)
# Temporary metrics
box_loss = tf.stop_gradient(0.05 * box_loss / self._iou_normalizer)
# Metric compute using done here to save time and resources.
sigmoid_conf = tf.stop_gradient(tf.sigmoid(pred_conf))
iou = tf.stop_gradient(iou)
avg_iou = loss_utils.average_iou(
loss_utils.apply_mask(tf.squeeze(ind_mask, axis=-1), iou))
avg_obj = loss_utils.average_iou(
tf.squeeze(sigmoid_conf, axis=-1) * grid_mask)
return (loss, box_loss, conf_loss, class_loss, mean_loss,
tf.stop_gradient(avg_iou), tf.stop_gradient(avg_obj))
def _compute_loss(self, true_counts, inds, y_true, boxes, classes, y_pred):
"""Per FPN path loss logic for Yolov4-csp, Yolov4-Large, and Yolov5."""
# Generate shape constants.
shape = tf.shape(true_counts)
batch_size, width, height, num = shape[0], shape[1], shape[2], shape[3]
fwidth = tf.cast(width, tf.float32)
fheight = tf.cast(height, tf.float32)
# Cast all input compontnts to float32 and stop gradient to save memory.
y_true = tf.cast(y_true, tf.float32)
true_counts = tf.cast(true_counts, tf.float32)
true_conf = tf.clip_by_value(true_counts, 0.0, 1.0)
grid_points, anchor_grid = self._anchor_generator(width,
height,
batch_size,
dtype=tf.float32)
# Split the y_true list.
(true_box, ind_mask, true_class) = tf.split(y_true, [4, 1, 1], axis=-1)
grid_mask = true_conf = tf.squeeze(true_conf, axis=-1)
true_class = tf.squeeze(true_class, axis=-1)
num_objs = tf.cast(tf.reduce_sum(ind_mask), dtype=y_pred.dtype)
# Split up the predicitons.
y_pred = tf.cast(
tf.reshape(y_pred, [batch_size, width, height, num, -1]),
tf.float32)
pred_box, pred_conf, pred_class = tf.split(y_pred, [4, 1, -1], axis=-1)
# Decode the boxes for loss compute.
scale, pred_box, pbg = self._decode_boxes(fwidth,
fheight,
pred_box,
anchor_grid,
grid_points,
darknet=False)
# If the ignore threshold is enabled, search all boxes ignore all
# IOU valeus larger than the ignore threshold that are not in the
# noted ground truth list.
if self._ignore_thresh != 0.0:
(_, obj_mask) = self._tiled_global_box_search(
pbg,
tf.stop_gradient(tf.sigmoid(pred_class)),
boxes,
classes,
true_conf,
smoothed=False,
scale=None)
# Scale and shift and select the ground truth boxes
# and predictions to the prediciton domain.
if self._box_type == 'anchor_free':
true_box = loss_utils.apply_mask(
ind_mask, (scale * self._path_stride * true_box))
else:
offset = tf.cast(tf.gather_nd(grid_points, inds, batch_dims=1),
true_box.dtype)
offset = tf.concat([offset, tf.zeros_like(offset)], axis=-1)
true_box = loss_utils.apply_mask(ind_mask,
(scale * true_box) - offset)
pred_box = loss_utils.apply_mask(
ind_mask, tf.gather_nd(pred_box, inds, batch_dims=1))
# Select the correct/used prediction classes.
true_class = tf.one_hot(tf.cast(true_class, tf.int32),
depth=tf.shape(pred_class)[-1],
dtype=pred_class.dtype)
true_class = loss_utils.apply_mask(ind_mask, true_class)
pred_class = loss_utils.apply_mask(
ind_mask, tf.gather_nd(pred_class, inds, batch_dims=1))
# Compute the box loss.
_, iou, box_loss = self.box_loss(true_box, pred_box, darknet=False)
box_loss = loss_utils.apply_mask(tf.squeeze(ind_mask, axis=-1),
box_loss)
box_loss = math_ops.divide_no_nan(tf.reduce_sum(box_loss), num_objs)
# Use the box IOU to build the map for confidence loss computation.
iou = tf.maximum(tf.stop_gradient(iou), 0.0)
smoothed_iou = ((
(1 - self._objectness_smooth) * tf.cast(ind_mask, iou.dtype)) +
self._objectness_smooth * tf.expand_dims(iou, axis=-1))
smoothed_iou = loss_utils.apply_mask(ind_mask, smoothed_iou)
true_conf = loss_utils.build_grid(inds,
smoothed_iou,
pred_conf,
ind_mask,
update=self._update_on_repeat)
true_conf = tf.squeeze(true_conf, axis=-1)
# Compute the cross entropy loss for the confidence map.
bce = tf.keras.losses.binary_crossentropy(tf.expand_dims(true_conf,
axis=-1),
pred_conf,
from_logits=True)
if self._ignore_thresh != 0.0:
bce = loss_utils.apply_mask(obj_mask, bce)
conf_loss = tf.reduce_sum(bce) / tf.reduce_sum(obj_mask)
else:
conf_loss = tf.reduce_mean(bce)
# Compute the cross entropy loss for the class maps.
class_loss = tf.keras.losses.binary_crossentropy(
true_class,
pred_class,
label_smoothing=self._label_smoothing,
from_logits=True)
class_loss = loss_utils.apply_mask(tf.squeeze(ind_mask, axis=-1),
class_loss)
class_loss = math_ops.divide_no_nan(tf.reduce_sum(class_loss),
num_objs)
# Apply the weights to each loss.
box_loss *= self._iou_normalizer
class_loss *= self._cls_normalizer
conf_loss *= self._obj_normalizer
# Add all the losses together then take the sum over the batches.
mean_loss = box_loss + class_loss + conf_loss
loss = mean_loss * tf.cast(batch_size, mean_loss.dtype)
return (loss, box_loss, conf_loss, class_loss, mean_loss, iou,
pred_conf, ind_mask, grid_mask)
def _compute_loss(self, true_counts, inds, y_true, boxes, classes, y_pred):
"""Per FPN path loss logic used for Yolov3, Yolov4, and Yolo-Tiny."""
if self._box_type == 'scaled':
# Darknet Model Propagates a sigmoid once in back prop so we replicate
# that behaviour
y_pred = grad_sigmoid(y_pred)
# Generate and store constants and format output.
shape = tf.shape(true_counts)
batch_size, width, height, num = shape[0], shape[1], shape[2], shape[3]
fwidth = tf.cast(width, tf.float32)
fheight = tf.cast(height, tf.float32)
grid_points, anchor_grid = self._anchor_generator(width,
height,
batch_size,
dtype=tf.float32)
# Cast all input compontnts to float32 and stop gradient to save memory.
boxes = tf.stop_gradient(tf.cast(boxes, tf.float32))
classes = tf.stop_gradient(tf.cast(classes, tf.float32))
y_true = tf.stop_gradient(tf.cast(y_true, tf.float32))
true_counts = tf.stop_gradient(tf.cast(true_counts, tf.float32))
true_conf = tf.stop_gradient(tf.clip_by_value(true_counts, 0.0, 1.0))
grid_points = tf.stop_gradient(grid_points)
anchor_grid = tf.stop_gradient(anchor_grid)
# Split all the ground truths to use as seperate items in loss computation.
(true_box, ind_mask, true_class) = tf.split(y_true, [4, 1, 1], axis=-1)
true_conf = tf.squeeze(true_conf, axis=-1)
true_class = tf.squeeze(true_class, axis=-1)
grid_mask = true_conf
# Splits all predictions.
y_pred = tf.cast(
tf.reshape(y_pred, [batch_size, width, height, num, -1]),
tf.float32)
pred_box, pred_conf, pred_class = tf.split(y_pred, [4, 1, -1], axis=-1)
# Decode the boxes to be used for loss compute.
_, _, pred_box = self._decode_boxes(fwidth,
fheight,
pred_box,
anchor_grid,
grid_points,
darknet=True)
# If the ignore threshold is enabled, search all boxes ignore all
# IOU valeus larger than the ignore threshold that are not in the
# noted ground truth list.
if self._ignore_thresh != 0.0:
(true_conf, obj_mask) = self._tiled_global_box_search(
pred_box,
tf.stop_gradient(tf.sigmoid(pred_class)),
boxes,
classes,
true_conf,
smoothed=self._objectness_smooth > 0)
# Build the one hot class list that are used for class loss.
true_class = tf.one_hot(tf.cast(true_class, tf.int32),
depth=tf.shape(pred_class)[-1],
dtype=pred_class.dtype)
true_classes = tf.stop_gradient(
loss_utils.apply_mask(ind_mask, true_class))
# Reorganize the one hot class list as a grid.
true_class = loss_utils.build_grid(inds,
true_classes,
pred_class,
ind_mask,
update=False)
true_class = tf.stop_gradient(true_class)
# Use the class mask to find the number of objects located in
# each predicted grid cell/pixel.
counts = true_class
counts = tf.reduce_sum(counts, axis=-1, keepdims=True)
reps = tf.gather_nd(counts, inds, batch_dims=1)
reps = tf.squeeze(reps, axis=-1)
reps = tf.stop_gradient(tf.where(reps == 0.0, tf.ones_like(reps),
reps))
# Compute the loss for only the cells in which the boxes are located.
pred_box = loss_utils.apply_mask(
ind_mask, tf.gather_nd(pred_box, inds, batch_dims=1))
iou, _, box_loss = self.box_loss(true_box, pred_box, darknet=True)
box_loss = loss_utils.apply_mask(tf.squeeze(ind_mask, axis=-1),
box_loss)
box_loss = math_ops.divide_no_nan(box_loss, reps)
box_loss = tf.cast(tf.reduce_sum(box_loss, axis=1), dtype=y_pred.dtype)
# Compute the sigmoid binary cross entropy for the class maps.
class_loss = tf.reduce_mean(loss_utils.sigmoid_bce(
tf.expand_dims(true_class, axis=-1),
tf.expand_dims(pred_class, axis=-1), self._label_smoothing),
axis=-1)
# Apply normalization to the class losses.
if self._cls_normalizer < 1.0:
# Build a mask based on the true class locations.
cls_norm_mask = true_class
# Apply the classes weight to class indexes were one_hot is one.
class_loss *= ((1 - cls_norm_mask) +
cls_norm_mask * self._cls_normalizer)
# Mask to the class loss and compute the sum over all the objects.
class_loss = tf.reduce_sum(class_loss, axis=-1)
class_loss = loss_utils.apply_mask(grid_mask, class_loss)
class_loss = math_ops.rm_nan_inf(class_loss, val=0.0)
class_loss = tf.cast(tf.reduce_sum(class_loss, axis=(1, 2, 3)),
dtype=y_pred.dtype)
# Compute the sigmoid binary cross entropy for the confidence maps.
bce = tf.reduce_mean(loss_utils.sigmoid_bce(
tf.expand_dims(true_conf, axis=-1), pred_conf, 0.0),
axis=-1)
# Mask the confidence loss and take the sum across all the grid cells.
if self._ignore_thresh != 0.0:
bce = loss_utils.apply_mask(obj_mask, bce)
conf_loss = tf.cast(tf.reduce_sum(bce, axis=(1, 2, 3)),
dtype=y_pred.dtype)
# Apply the weights to each loss.
box_loss *= self._iou_normalizer
conf_loss *= self._obj_normalizer
# Add all the losses together then take the mean over the batches.
loss = box_loss + class_loss + conf_loss
loss = tf.reduce_mean(loss)
# Reduce the mean of the losses to use as a metric.
box_loss = tf.reduce_mean(box_loss)
conf_loss = tf.reduce_mean(conf_loss)
class_loss = tf.reduce_mean(class_loss)
return (loss, box_loss, conf_loss, class_loss, loss, iou, pred_conf,
ind_mask, grid_mask)
