def add_softmax_cross_entropy_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
scope=None):
"""Adds softmax cross entropy loss for logits of each scale.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits have shape [batch, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
num_classes: Integer, number of target classes.
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
scope: String, the scope for the loss.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
total_loss = 0
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(logits,
preprocess_utils.resolve_shape(
labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
not_ignore_mask = tf.to_float(tf.not_equal(scaled_labels,
ignore_label)) * loss_weight
one_hot_labels = slim.one_hot_encoding(scaled_labels,
num_classes,
on_value=1.0,
off_value=0.0)
total_loss += tf.losses.softmax_cross_entropy(
one_hot_labels,
tf.reshape(logits, shape=[-1, num_classes]),
weights=not_ignore_mask,
scope=loss_scope)
return total_loss
def add_focal_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
alpha=1,
gamma=2,
loss_weight=1.0,
upsample_logits=True,
scope=None):
"""
Focal loss implementation reference: https://github.com/kornia/kornia/blob/master/kornia/losses/focal.py
"""
if labels is None:
raise ValueError('No label for focal loss.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(
logits,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
not_ignore_mask = tf.to_float(tf.not_equal(scaled_labels,
ignore_label)) * loss_weight
one_hot_labels = tf.one_hot(
scaled_labels, num_classes, on_value=1.0, off_value=0.0)
logits = tf.reshape(logits, shape=[-1, num_classes])
weights = not_ignore_mask
epsilon = 1.e-9
with tf.name_scope(loss_scope, 'softmax_focal_loss',
[logits, one_hot_labels, weights]):
one_hot_labels = tf.stop_gradient(
one_hot_labels, name='labels_stop_gradient')
# Compute softmax over class axis
logits_soft = tf.nn.softmax(logits, axis=-1, name="softmax_before_focal_loss") + epsilon
# compute the actual focal loss
weight = tf.pow(tf.subtract(1., logits_soft), gamma)
fl = -alpha * tf.multiply(one_hot_labels, tf.multiply(weight, tf.log(logits_soft)))
fl = tf.reduce_sum(fl, axis=-1)
total_loss = tf.reduce_mean(fl)
tf.losses.add_loss(total_loss)
def add_softmax_cross_entropy_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
scope=None):
"""Adds softmax cross entropy loss for logits of each scale.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits have shape [batch, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
num_classes: Integer, number of target classes.
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
scope: String, the scope for the loss.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(
logits,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
not_ignore_mask = tf.to_float(tf.not_equal(scaled_labels,
ignore_label)) * loss_weight
one_hot_labels = slim.one_hot_encoding(
scaled_labels, num_classes, on_value=1.0, off_value=0.0)
tf.losses.softmax_cross_entropy(
one_hot_labels,
tf.reshape(logits, shape=[-1, num_classes]),
weights=not_ignore_mask,
scope=loss_scope)
def add_l1_loss_to_each_dimension(scales_to_logits,
labels,
upsample_logits=True,
top_k_percent_pixels=1.0,
scope=None):
"""Adds L1 loss to each channel logits of each scale.
"""
if labels is None:
raise ValueError('No label for offset regression loss.')
label1, label2, label3 = tf.split(labels, num_or_size_splits=3, axis=3)
labels = tf.concat([label1, label2], 3)
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(logits,
preprocess_utils.resolve_shape(
labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
offset_loss = 'instance_offset_loss'
default_loss_scope = 'instance_offset_l1_loss'
with tf.name_scope(loss_scope, default_loss_scope,
[logits, scaled_labels, offset_loss]):
center_loss = tf.abs(
tf.subtract(logits, tf.stop_gradient(scaled_labels)))
center_loss = tf.reduce_mean(center_loss, 0)
num_present = tf.reduce_sum(
tf.cast(tf.not_equal(center_loss, 0), tf.float32))
loss = tf.reduce_sum(center_loss)
loss = _div_maybe_zero(loss, num_present)
'''scaled_labels = tf.reshape(scaled_labels, shape=[-1])
logits = tf.reshape(logits, shape=[-1])
center_loss = tf.subtract(logits,
tf.stop_gradient(scaled_labels,
name='train_labels_stop_gradient'))
num_pixels = tf.not_equal(center_loss, 0)
center_loss = tf.abs(center_loss, name='l1_loss')
total_loss = tf.reduce_sum(center_loss)
num_present = tf.reduce_sum(tf.cast(num_pixels, tf.float32))
loss = _div_maybe_zero(total_loss, num_present)'''
tf.losses.add_loss(loss)
def scale_logits_to_labels(logits, labels, upsample_logits):
""" Scaled logits and labels to the same scale."""
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
scaled_logits = tf.image.resize_bilinear(
logits,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_logits = logits
assert scaled_labels.get_shape()[1:3] == scaled_logits.get_shape(
)[1:
3], 'The potentially reshaped logits and labels should match in shapes!'
# assert scaled_labels.dtype == scaled_logits.dtype, 'The potentially reshaped logits and labels should match in types!'
return scaled_logits, scaled_labels
def add_heatmap_regression(scales_to_logits,
labels,
upsample_logits=True,
top_k_percent_pixels=1.0,
scope=None):
"""Adds heatmap regression loss for logits of each scale.
"""
if labels is None:
raise ValueError('No label for heatmap regression loss.')
# If input groundtruth is a matting map of confidence, check if the input
# labels are floating point values.
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(logits,
preprocess_utils.resolve_shape(
labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
center_loss = 'instance_center_loss'
default_loss_scope = 'instance_center_l2_loss'
with tf.name_scope(loss_scope, default_loss_scope,
[logits, scaled_labels, center_loss]):
center_loss = tf.squared_difference(
logits, tf.stop_gradient(scaled_labels))
center_loss = tf.reduce_mean(center_loss, 0)
# num_present = tf.reduce_sum(tf.cast(tf.not_equal(center_loss, 0), tf.float64))
num_present = tf.reduce_sum(
tf.cast(tf.not_equal(center_loss, 0), tf.float32))
loss = tf.reduce_sum(center_loss)
loss = _div_maybe_zero(loss, num_present)
'''scaled_labels = tf.reshape(scaled_labels, shape=[-1])
logits = tf.reshape(logits, shape=[-1])
center_loss = tf.subtract(tf.stop_gradient(
scaled_labels, name='train_labels_stop_gradient'), logits)
center_loss = tf.square(center_loss, name='l2_loss_squaring')
total_loss = tf.reduce_sum(center_loss)
num_present = tf.reduce_sum(tf.cast(tf.not_equal(center_loss, 0), tf.float32))
loss = _div_maybe_zero(total_loss, num_present)'''
tf.losses.add_loss(loss)
def _log_summaries(input_image, label, num_of_classes, output, ignore_label):
"""Logs the summaries for the model.
Args:
input_image: Input image of the model. Its shape is [batch_size, height,
width, channel].
label: Label of the image. Its shape is [batch_size, height, width].
num_of_classes: The number of classes of the dataset.
output: Output of the model. Its shape is [batch_size, height, width].
"""
# Add summaries for model variables.
for model_var in tf.model_variables():
tf.summary.histogram(model_var.op.name, model_var)
logits = tf.image.resize_bilinear(
output,
preprocess_utils.resolve_shape(label, 4)[1:3])
pred = tf.argmax(logits, 3)
label = tf.squeeze(label, [3])
weights = tf.to_float(tf.not_equal(label, ignore_label))
label_valid = tf.where(tf.equal(label, ignore_label), tf.zeros_like(label),
label)
acc = tf.metrics.accuracy(label_valid, pred, weights=weights)
miou, update_op = tf.metrics.mean_iou(label_valid,
pred,
num_of_classes,
weights=weights)
tf.add_to_collections(tf.GraphKeys.UPDATE_OPS, update_op)
miou = tf.Print(miou, [miou], 'mIoU :')
acc = tf.Print(acc[1], [acc[1]], 'ACC is :')
tf.summary.scalar('px_accuracy/train_px_accuracy', acc)
tf.summary.scalar('mean_iou/train_mean_iou', miou)
# Add summaries for images, labels, semantic predictions.
if FLAGS.save_summaries_images:
img = tf.cast(input_image, tf.uint8)
summary_label = get_dataset_colormap.label_to_color2(
label, FLAGS.dataset)
summary_predictions = get_dataset_colormap.label_to_color2(
pred, FLAGS.dataset)
img = tf.concat(axis=2,
values=[img, summary_label, summary_predictions])
tf.summary.image('samples', img, max_outputs=6)
def _log_summaries(input_image, label, num_of_classes, output):
"""Logs the summaries for the model.
Args:
input_image: Input image of the model. Its shape is [batch_size, height,
width, channel].
label: Label of the image. Its shape is [batch_size, height, width].
num_of_classes: The number of classes of the dataset.
output: Output of the model. Its shape is [batch_size, height, width].
"""
# Add summaries for model variables.
for model_var in tf.model_variables():
tf.summary.histogram(model_var.op.name, model_var)
# Add summaries for images, labels, semantic predictions.
if FLAGS.save_summaries_images:
print("adding summaries")
tf.summary.image('samples/%s' % common.IMAGE, input_image)
# Scale up summary image pixel values for better visualization.
pixel_scaling = max(1, 255 // num_of_classes)
summary_label = tf.cast(label * pixel_scaling, tf.uint8)
tf.summary.image('samples/%s' % common.LABEL, summary_label)
predictions = tf.expand_dims(tf.argmax(output, 3), -1)
summary_predictions = tf.cast(predictions * pixel_scaling, tf.uint8)
tf.summary.image('samples/%s' % common.OUTPUT_TYPE, summary_predictions)
# Image summary.
images_summary = tf.py_func(train_utils.inv_preprocess, [input_image, 1], tf.uint8)
labels_summary = tf.py_func(train_utils.decode_labels, [tf.squeeze(label, [3])[0], 1], tf.uint8)
out = tf.image.resize_bilinear(output, preprocess_utils.resolve_shape(label, 4)[1:3], align_corners=True)
preds = tf.expand_dims(tf.argmax(out, 3), -1)
# sess = tf.Session()
# print(sess.run(out))
# print()
# print(sess.run(preds))
# spreds = tf.cast(preds * pixel_scaling, tf.uint8)
# spreds = tf.cast(preds, tf.uint8)
preds_summary = tf.py_func(train_utils.decode_labels, [tf.squeeze(preds, [3])[0], 1], tf.uint8)
tf.summary.image('images', tf.concat([images_summary, tf.expand_dims (labels_summary, 0), tf.expand_dims(preds_summary, 0)], axis=2), max_outputs=2) # Concatenate row-wise.
def add_softmax_cross_entropy_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
hard_example_mining_step=0,
top_k_percent_pixels=1.0,
gt_is_matting_map=False,
scope=None):
"""Adds softmax cross entropy loss for logits of each scale.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits have shape [batch, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
num_classes: Integer, number of target classes.
ignore_label: Integer, label to ignore.
loss_weight: A float or a list of loss weights. If it is a float, it means
all the labels have the same weight. If it is a list of weights, then each
element in the list represents the weight for the label of its index, for
example, loss_weight = [0.1, 0.5] means the weight for label 0 is 0.1 and
the weight for label 1 is 0.5.
upsample_logits: Boolean, upsample logits or not.
hard_example_mining_step: An integer, the training step in which the hard
exampling mining kicks off. Note that we gradually reduce the mining
percent to the top_k_percent_pixels. For example, if
hard_example_mining_step = 100K and top_k_percent_pixels = 0.25, then
mining percent will gradually reduce from 100% to 25% until 100K steps
after which we only mine top 25% pixels.
top_k_percent_pixels: A float, the value lies in [0.0, 1.0]. When its value
< 1.0, only compute the loss for the top k percent pixels (e.g., the top
20% pixels). This is useful for hard pixel mining.
gt_is_matting_map: If true, the groundtruth is a matting map of confidence
score. If false, the groundtruth is an integer valued class mask.
scope: String, the scope for the loss.
Raises:
ValueError: Label or logits is None, or groundtruth is matting map while
label is not floating value.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
# If input groundtruth is a matting map of confidence, check if the input
# labels are floating point values.
if gt_is_matting_map and not labels.dtype.is_floating:
raise ValueError('Labels must be floats if groundtruth is a matting map.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(
logits,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
# When gt_is_matting_map = true, label downsampling with nearest neighbor
# method may introduce artifacts. However, to avoid ignore_label from
# being interpolated with other labels, we still perform nearest neighbor
# interpolation.
# TODO(huizhongc): Change to bilinear interpolation by processing padded
# and non-padded label separately.
if gt_is_matting_map:
tf.logging.warning(
'Label downsampling with nearest neighbor may introduce artifacts.')
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
ignore_weight = 0
label0_weight = 1 # background clean
label1_weight = 5 # class 1 opaque
label2_weight = 25 # class 2 transparent
not_ignore_mask = tf.to_float(tf.equal(scaled_labels, 0)) * label0_weight + tf.to_float(tf.equal(scaled_labels, 1)) * label1_weight +tf.to_float(tf.equal(scaled_labels, 2)) * label2_weight + tf.to_float(tf.equal(scaled_labels, ignore_label)) * ignore_weight
weights = utils.get_label_weight_mask(
scaled_labels, ignore_label, num_classes, label_weights=loss_weight)
# Dimension of keep_mask is equal to the total number of pixels.
keep_mask = tf.cast(
tf.not_equal(scaled_labels, ignore_label), dtype=tf.float32)
train_labels = None
logits = tf.reshape(logits, shape=[-1, num_classes])
if gt_is_matting_map:
# When the groundtruth is integer label mask, we can assign class
# dependent label weights to the loss. When the groundtruth is image
# matting confidence, we do not apply class-dependent label weight (i.e.,
# label_weight = 1.0).
if loss_weight != 1.0:
raise ValueError(
'loss_weight must equal to 1 if groundtruth is matting map.')
# Assign label value 0 to ignore pixels. The exact label value of ignore
# pixel does not matter, because those ignore_value pixel losses will be
# multiplied to 0 weight.
train_labels = scaled_labels * keep_mask
train_labels = tf.expand_dims(train_labels, 1)
train_labels = tf.concat([1 - train_labels, train_labels], axis=1)
else:
train_labels = tf.one_hot(
scaled_labels, num_classes, on_value=1.0, off_value=0.0)
default_loss_scope = ('softmax_all_pixel_loss'
if top_k_percent_pixels == 1.0 else
'softmax_hard_example_mining')
with tf.name_scope(loss_scope, default_loss_scope,
[logits, train_labels, weights]):
# Compute the loss for all pixels.
pixel_losses = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=tf.stop_gradient(
train_labels, name='train_labels_stop_gradient'),
logits=logits,
name='pixel_losses')
weighted_pixel_losses = tf.multiply(pixel_losses, weights)
if top_k_percent_pixels == 1.0:
total_loss = tf.reduce_sum(weighted_pixel_losses)
num_present = tf.reduce_sum(keep_mask)
loss = _div_maybe_zero(total_loss, num_present)
tf.losses.add_loss(loss)
else:
num_pixels = tf.to_float(tf.shape(logits)[0])
# Compute the top_k_percent pixels based on current training step.
if hard_example_mining_step == 0:
# Directly focus on the top_k pixels.
top_k_pixels = tf.to_int32(top_k_percent_pixels * num_pixels)
else:
# Gradually reduce the mining percent to top_k_percent_pixels.
global_step = tf.to_float(tf.train.get_or_create_global_step())
ratio = tf.minimum(1.0, global_step / hard_example_mining_step)
top_k_pixels = tf.to_int32(
(ratio * top_k_percent_pixels + (1.0 - ratio)) * num_pixels)
top_k_losses, _ = tf.nn.top_k(weighted_pixel_losses,
k=top_k_pixels,
sorted=True,
name='top_k_percent_pixels')
total_loss = tf.reduce_sum(top_k_losses)
num_present = tf.reduce_sum(
tf.to_float(tf.not_equal(top_k_losses, 0.0)))
loss = _div_maybe_zero(total_loss, num_present)
tf.losses.add_loss(loss)
def add_softmax_cross_entropy_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
hard_example_mining_step=0,
top_k_percent_pixels=1.0,
scope=None):
"""Adds softmax cross entropy loss for logits of each scale.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits have shape [batch, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
num_classes: Integer, number of target classes.
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
hard_example_mining_step: An integer, the training step in which the hard
exampling mining kicks off. Note that we gradually reduce the mining
percent to the top_k_percent_pixels. For example, if
hard_example_mining_step = 100K and top_k_percent_pixels = 0.25, then
mining percent will gradually reduce from 100% to 25% until 100K steps
after which we only mine top 25% pixels.
top_k_percent_pixels: A float, the value lies in [0.0, 1.0]. When its value
< 1.0, only compute the loss for the top k percent pixels (e.g., the top
20% pixels). This is useful for hard pixel mining.
scope: String, the scope for the loss.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(
logits,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
irgore_weight = 0
label0_weight = 1
label1_weight = 5
not_ignore_mask = tf.to_float(tf.equal(scaled_labels, 0)) * label0_weight + tf.to_float(tf.equal(scaled_labels, 1)) * label1_weight + tf.to_float(tf.equal(scaled_labels, ignore_label)) * irgore_weight
one_hot_labels = tf.one_hot(
scaled_labels, num_classes, on_value=1.0, off_value=0.0)
if top_k_percent_pixels == 1.0:
# Compute the loss for all pixels.
tf.losses.softmax_cross_entropy(
one_hot_labels,
tf.reshape(logits, shape=[-1, num_classes]),
weights=not_ignore_mask,
scope=loss_scope)
else:
logits = tf.reshape(logits, shape=[-1, num_classes])
weights = not_ignore_mask
with tf.name_scope(loss_scope, 'softmax_hard_example_mining',
[logits, one_hot_labels, weights]):
one_hot_labels = tf.stop_gradient(
one_hot_labels, name='labels_stop_gradient')
pixel_losses = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=one_hot_labels,
logits=logits,
name='pixel_losses')
weighted_pixel_losses = tf.multiply(pixel_losses, weights)
num_pixels = tf.to_float(tf.shape(logits)[0])
# Compute the top_k_percent pixels based on current training step.
if hard_example_mining_step == 0:
# Directly focus on the top_k pixels.
top_k_pixels = tf.to_int32(top_k_percent_pixels * num_pixels)
else:
# Gradually reduce the mining percent to top_k_percent_pixels.
global_step = tf.to_float(tf.train.get_or_create_global_step())
ratio = tf.minimum(1.0, global_step / hard_example_mining_step)
top_k_pixels = tf.to_int32(
(ratio * top_k_percent_pixels + (1.0 - ratio)) * num_pixels)
top_k_losses, _ = tf.nn.top_k(weighted_pixel_losses,
k=top_k_pixels,
sorted=True,
name='top_k_percent_pixels')
total_loss = tf.reduce_sum(top_k_losses)
num_present = tf.reduce_sum(
tf.to_float(tf.not_equal(top_k_losses, 0.0)))
loss = _div_maybe_zero(total_loss, num_present)
tf.losses.add_loss(loss)
def add_softmax_cross_entropy_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
hard_example_mining_step=0,
top_k_percent_pixels=1.0,
scope=None):
"""Adds softmax cross entropy loss for logits of each scale.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits have shape [batch, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
num_classes: Integer, number of target classes.
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
hard_example_mining_step: An integer, the training step in which the hard
exampling mining kicks off. Note that we gradually reduce the mining
percent to the top_k_percent_pixels. For example, if
hard_example_mining_step = 100K and top_k_percent_pixels = 0.25, then
mining percent will gradually reduce from 100% to 25% until 100K steps
after which we only mine top 25% pixels.
top_k_percent_pixels: A float, the value lies in [0.0, 1.0]. When its value
< 1.0, only compute the loss for the top k percent pixels (e.g., the top
20% pixels). This is useful for hard pixel mining.
scope: String, the scope for the loss.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(
logits,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
not_ignore_mask = tf.to_float(tf.not_equal(scaled_labels,
ignore_label)) * loss_weight
one_hot_labels = tf.one_hot(
scaled_labels, num_classes, on_value=1.0, off_value=0.0)
if top_k_percent_pixels == 1.0:
# Compute the loss for all pixels.
tf.losses.softmax_cross_entropy(
one_hot_labels,
tf.reshape(logits, shape=[-1, num_classes]),
weights=not_ignore_mask,
scope=loss_scope)
else:
logits = tf.reshape(logits, shape=[-1, num_classes])
weights = not_ignore_mask
with tf.name_scope(loss_scope, 'softmax_hard_example_mining',
[logits, one_hot_labels, weights]):
one_hot_labels = tf.stop_gradient(
one_hot_labels, name='labels_stop_gradient')
pixel_losses = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=one_hot_labels,
logits=logits,
name='pixel_losses')
weighted_pixel_losses = tf.multiply(pixel_losses, weights)
num_pixels = tf.to_float(tf.shape(logits)[0])
# Compute the top_k_percent pixels based on current training step.
if hard_example_mining_step == 0:
# Directly focus on the top_k pixels.
top_k_pixels = tf.to_int32(top_k_percent_pixels * num_pixels)
else:
# Gradually reduce the mining percent to top_k_percent_pixels.
global_step = tf.to_float(tf.train.get_or_create_global_step())
ratio = tf.minimum(1.0, global_step / hard_example_mining_step)
top_k_pixels = tf.to_int32(
(ratio * top_k_percent_pixels + (1.0 - ratio)) * num_pixels)
top_k_losses, _ = tf.nn.top_k(weighted_pixel_losses,
k=top_k_pixels,
sorted=True,
name='top_k_percent_pixels')
total_loss = tf.reduce_sum(top_k_losses)
num_present = tf.reduce_sum(
tf.to_float(tf.not_equal(top_k_losses, 0.0)))
loss = _div_maybe_zero(total_loss, num_present)
tf.losses.add_loss(loss)
def add_edge_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
scope=None,
edge_filters=[],
norm='l2',
smoothing=False):
"""Adds edge loss for logits of each scale.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits have shape [batch, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
num_classes: Integer, number of target classes.
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
scope: String, the scope for the loss.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
print("edge_filters", edge_filters)
print("scales_to_logits")
print("scope", scope)
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = 'edge_loss_%s_%s' % (scope, scale)
print("loss_scope", loss_scope)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(logits,
preprocess_utils.resolve_shape(
labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
y_pred = tf.sigmoid(logits)
not_ignore_mask = tf.to_float(tf.not_equal(scaled_labels,
ignore_label)) * loss_weight
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
one_hot_labels = slim.one_hot_encoding(scaled_labels,
num_classes,
on_value=1.0,
off_value=0.0)
y_true = tf.reshape(one_hot_labels, y_pred.shape)
relevant_mask = tf.to_float(tf.not_equal(one_hot_labels, 0))
relevant_mask = tf.reshape(relevant_mask, y_pred.shape)
weights = relevant_mask * not_ignore_mask
sobel_x_kernel = tf.reshape(tf.constant(
[[1, 2, 1], [0, 0, 0], [-1, -2, -1]], dtype=tf.float32),
shape=[3, 3, 1, 1],
name='sobel_x_kernel')
sobel_y_kernel = tf.reshape(tf.constant(
[[1, 0, -1], [2, 0, -2], [1, 0, -1]], dtype=tf.float32),
shape=[3, 3, 1, 1],
name='sobel_y_kernel')
# laplace kernel
laplacian_kernel = tf.reshape(tf.constant(
[[1, 1, 1], [1, -8, 1], [1, 1, 1]], dtype=tf.float32),
shape=[3, 3, 1, 1],
name='laplacian_kernel')
gaussian_kernel = tf.reshape(tf.constant(
[[0.077847, 0.123317, 0.077847], [0.123317, 0.195346, 0.1233179],
[0.077847, 0.123317, 0.077847]],
dtype=tf.float32),
shape=[3, 3, 1, 1],
name='gaussian_kernel')
filter_map = {
"sobel-x": sobel_x_kernel,
"sobel-y": sobel_y_kernel,
"laplace": laplacian_kernel
}
lp_norm_map = {"l1": 1, "l2": 2, "l3": 3, "l4": 4, "l5": 5}
if norm not in lp_norm_map:
raise ValueError(
"The `norm` '{0}' is not supported. Supported values are: [l1...l5]"
.format(norm))
edge_filters = tf.concat([filter_map[x] for x in edge_filters],
axis=-1)
def conv_single_channel(x):
x = tf.expand_dims(x, -1)
conv = tf.nn.conv2d(input=x,
filter=edge_filters,
strides=[1, 1, 1, 1],
padding='SAME')
conv = tf.squeeze(conv, -1)
return conv
y_pred_edges = tf.transpose(
tf.map_fn(conv_single_channel, tf.transpose(y_pred, (3, 0, 1, 2))),
(1, 2, 3, 0))
if smoothing:
# First filter with gaussian to smooth edges of groundtruth
y_true = tf.nn.conv2d(input=y_true,
filter=gaussian_kernel,
strides=[1, 1, 1, 1],
padding='SAME')
y_true_edges = tf.transpose(
tf.map_fn(conv_single_channel, tf.transpose(y_true, (3, 0, 1, 2))),
(1, 2, 3, 0))
def append_magnitude(edges, name=None):
magnitude = tf.expand_dims(tf.sqrt(edges[:, :, :, 0]**2 +
edges[:, :, :, 1]**2),
axis=-1)
return tf.concat([edges, magnitude], axis=-1, name=name)
def lp_loss(y_true, y_pred, p):
return tf.pow(tf.abs(y_pred - y_true), p)
def smoothness_loss(y_true, y_pred, p):
weight_smoothness = tf.exp(tf.negative(tf.abs(y_true)))
smoothness = y_pred * weight_smoothness
smoothness = smoothness[:, :, :, 0] + smoothness[:, :, :, 1]
return tf.reduce_mean(tf.pow(tf.abs(smoothness), p))
weights = not_ignore_mask * relevant_mask
print("weights", weights.shape)
with tf.name_scope(loss_scope, "mean_squared_error",
(y_pred_edges, y_true_edges, weights)) as scope:
# calculate the edge agreement loss per pixel
pixel_wise_edge_loss = lp_loss(y_true=y_true_edges,
y_pred=y_pred_edges,
p=lp_norm_map[norm])
print("pixel_wise_edge_loss", pixel_wise_edge_loss.shape)
error = tf.losses.compute_weighted_loss(
pixel_wise_edge_loss,
weights=weights,
scope=scope,
loss_collection=tf.GraphKeys.LOSSES,
reduction=tf.losses.Reduction.SUM_BY_NONZERO_WEIGHTS)
return error
def add_dynamic_softmax_cross_entropy_loss_for_each_scale(
scales_to_logits,
labels,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
scope=None,
top_k_percent_pixels=1.0,
hard_example_mining_step=100000):
"""Adds softmax cross entropy loss per scale for logits with varying classes.
Also adds summaries for mIoU.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits are a list of length batch_size of tensors of shape
[time, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch_size * time, image_height,
image_width, 1].
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
scope: String, the scope for the loss.
top_k_percent_pixels: A float, the value lies in [0.0, 1.0]. When its
value < 1.0, only compute the loss for the top k percent pixels (e.g.,
the top 20% pixels). This is useful for hard pixel mining.
hard_example_mining_step: An integer, the training step in which the
hard exampling mining kicks off. Note that we gradually reduce the
mining percent to the top_k_percent_pixels. For example, if
hard_example_mining_step=100K and top_k_percent_pixels=0.25, then
mining percent will gradually reduce from 100% to 25% until 100K steps
after which we only mine top 25% pixels.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
if top_k_percent_pixels < 0 or top_k_percent_pixels > 1:
raise ValueError('Unexpected value of top_k_percent_pixels.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
assert isinstance(logits, collections.Sequence)
logits = [
tf.image.resize_bilinear(x,
preprocess_utils.resolve_shape(
labels, 4)[1:3],
align_corners=True) for x in logits
]
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
assert isinstance(logits, collections.Sequence)
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits[0], 4)[1:3],
align_corners=True)
batch_size = len(logits)
num_time = preprocess_utils.resolve_shape(logits[0])[0]
reshaped_labels = tf.reshape(
scaled_labels, ([batch_size, num_time] +
preprocess_utils.resolve_shape(scaled_labels)[1:]))
for n, logits_n in enumerate(logits):
labels_n = reshaped_labels[n]
labels_n = tf.reshape(labels_n, shape=[-1])
not_ignore_mask = tf.to_float(tf.not_equal(
labels_n, ignore_label)) * loss_weight
num_classes_n = tf.shape(logits_n)[-1]
one_hot_labels = slim.one_hot_encoding(labels_n,
num_classes_n,
on_value=1.0,
off_value=0.0)
logits_n_flat = tf.reshape(logits_n, shape=[-1, num_classes_n])
if top_k_percent_pixels == 1.0:
tf.losses.softmax_cross_entropy(one_hot_labels,
logits_n_flat,
weights=not_ignore_mask,
scope=loss_scope)
else:
# Only compute the loss for top k percent pixels.
# First, compute the loss for all pixels. Note we do not put the loss
# to loss_collection and set reduction = None to keep the shape.
num_pixels = tf.to_float(tf.shape(logits_n_flat)[0])
pixel_losses = tf.losses.softmax_cross_entropy(
one_hot_labels,
logits_n_flat,
weights=not_ignore_mask,
scope='pixel_losses',
loss_collection=None,
reduction=tf.losses.Reduction.NONE)
# Compute the top_k_percent pixels based on current training step.
if hard_example_mining_step == 0:
# Directly focus on the top_k pixels.
top_k_pixels = tf.to_int32(top_k_percent_pixels *
num_pixels)
else:
# Gradually reduce the mining percent to top_k_percent_pixels.
global_step = tf.to_float(
tf.train.get_or_create_global_step())
ratio = tf.minimum(1.0,
global_step / hard_example_mining_step)
top_k_pixels = tf.to_int32((ratio * top_k_percent_pixels +
(1.0 - ratio)) * num_pixels)
_, top_k_indices = tf.nn.top_k(pixel_losses,
k=top_k_pixels,
sorted=True,
name='top_k_percent_pixels')
# Compute the loss for the top k percent pixels.
tf.losses.softmax_cross_entropy(
tf.gather(one_hot_labels, top_k_indices),
tf.gather(logits_n_flat, top_k_indices),
weights=tf.gather(not_ignore_mask, top_k_indices),
scope=loss_scope)
pred_n = tf.argmax(logits_n, axis=-1,
output_type=tf.int32)[..., tf.newaxis]
labels_n = labels[n * num_time:(n + 1) * num_time]
miou = eval_utils.calculate_multi_object_miou_tf(pred_n, labels_n)
tf.summary.scalar('miou', miou)
def add_softmax_cross_entropy_loss_for_each_scale(scales_to_logits,
labels,
num_classes,
ignore_label,
loss_weight=1.0,
upsample_logits=True,
scope=None):
"""Adds softmax cross entropy loss for logits of each scale.
Args:
scales_to_logits: A map from logits names for different scales to logits.
The logits have shape [batch, logits_height, logits_width, num_classes].
labels: Groundtruth labels with shape [batch, image_height, image_width, 1].
num_classes: Integer, number of target classes.
ignore_label: Integer, label to ignore.
loss_weight: Float, loss weight.
upsample_logits: Boolean, upsample logits or not.
scope: String, the scope for the loss.
Raises:
ValueError: Label or logits is None.
"""
if labels is None:
raise ValueError('No label for softmax cross entropy loss.')
for scale, logits in six.iteritems(scales_to_logits):
loss_scope = None
if scope:
loss_scope = '%s_%s' % (scope, scale)
if upsample_logits:
# Label is not downsampled, and instead we upsample logits.
logits = tf.image.resize_bilinear(
logits,
preprocess_utils.resolve_shape(labels, 4)[1:3],
align_corners=True)
scaled_labels = labels
else:
# Label is downsampled to the same size as logits.
scaled_labels = tf.image.resize_nearest_neighbor(
labels,
preprocess_utils.resolve_shape(logits, 4)[1:3],
align_corners=True)
scaled_labels = tf.reshape(scaled_labels, shape=[-1])
# not_ignore_mask = tf.to_float(tf.not_equal(scaled_labels,ignore_label)) * loss_weight
loss_weight0 = 1.5
loss_weight1 = 2.3
loss_weight2 = 2.5
loss_weight3 = 2
loss_weight4 = 2
loss_weight5 = 2
loss_weight6 = 2
loss_weight7 = 2
loss_weight8 = 4.5
loss_weight9 = 0
loss_weight10 = 2
loss_weight11 = 0
loss_weight12 = 1.5
loss_weight13 = 0
loss_weight14 = 2
loss_weight15 = 2
loss_weight16 = 2
loss_weight17 = 0
loss_weight18 = 2.5
loss_weight19 = 5
loss_weight20 = 5
loss_weight21 = 10
loss_weight22 = 5
loss_weight23 = 0
loss_weight24 = 3
loss_weight25 = 10
loss_weight26 = 10
loss_weight27 = 10
loss_weight28 = 0
loss_weight29 = 0
loss_weight30 = 0
loss_weight31 = 20
loss_weight32 = 4
loss_weight33 = 12
loss_weight34 = 4
loss_weight35 = 4
loss_weight_ignore = 0
not_ignore_mask = tf.to_float(tf.equal(scaled_labels, 0)) * loss_weight0 + \
tf.to_float(tf.equal(scaled_labels, 1)) * loss_weight1 + \
tf.to_float(tf.equal(scaled_labels, 2)) * loss_weight2 + \
tf.to_float(tf.equal(scaled_labels, 3)) * loss_weight3 + \
tf.to_float(tf.equal(scaled_labels, 4)) * loss_weight4 + \
tf.to_float(tf.equal(scaled_labels, 5)) * loss_weight5 + \
tf.to_float(tf.equal(scaled_labels, 6)) * loss_weight6 + \
tf.to_float(tf.equal(scaled_labels, 7)) * loss_weight7 + \
tf.to_float(tf.equal(scaled_labels, 8)) * loss_weight8 + \
tf.to_float(tf.equal(scaled_labels, 9)) * loss_weight9 + \
tf.to_float(tf.equal(scaled_labels, 10)) * loss_weight10 + \
tf.to_float(tf.equal(scaled_labels, 11)) * loss_weight11 + \
tf.to_float(tf.equal(scaled_labels, 12)) * loss_weight12 + \
tf.to_float(tf.equal(scaled_labels, 13)) * loss_weight13 + \
tf.to_float(tf.equal(scaled_labels, 14)) * loss_weight14 + \
tf.to_float(tf.equal(scaled_labels, 15)) * loss_weight15 + \
tf.to_float(tf.equal(scaled_labels, 16)) * loss_weight16 + \
tf.to_float(tf.equal(scaled_labels, 17)) * loss_weight17 + \
tf.to_float(tf.equal(scaled_labels, 18)) * loss_weight18 + \
tf.to_float(tf.equal(scaled_labels, 19)) * loss_weight19 + \
tf.to_float(tf.equal(scaled_labels, 20)) * loss_weight20 + \
tf.to_float(tf.equal(scaled_labels, 21)) * loss_weight21 + \
tf.to_float(tf.equal(scaled_labels, 22)) * loss_weight22 + \
tf.to_float(tf.equal(scaled_labels, 23)) * loss_weight23 + \
tf.to_float(tf.equal(scaled_labels, 24)) * loss_weight24 + \
tf.to_float(tf.equal(scaled_labels, 25)) * loss_weight25 + \
tf.to_float(tf.equal(scaled_labels, 26)) * loss_weight26 + \
tf.to_float(tf.equal(scaled_labels, 27)) * loss_weight27 + \
tf.to_float(tf.equal(scaled_labels, 28)) * loss_weight28 + \
tf.to_float(tf.equal(scaled_labels, 29)) * loss_weight29 + \
tf.to_float(tf.equal(scaled_labels, 30)) * loss_weight30 + \
tf.to_float(tf.equal(scaled_labels, 31)) * loss_weight31 + \
tf.to_float(tf.equal(scaled_labels, 32)) * loss_weight32 + \
tf.to_float(tf.equal(scaled_labels, 33)) * loss_weight33 + \
tf.to_float(tf.equal(scaled_labels, 34)) * loss_weight34 + \
tf.to_float(tf.equal(scaled_labels, 35)) * loss_weight35 + \
tf.to_float(tf.equal(scaled_labels, ignore_label)) * loss_weight_ignore
one_hot_labels = slim.one_hot_encoding(
scaled_labels, num_classes, on_value=1.0, off_value=0.0)
tf.losses.softmax_cross_entropy(
one_hot_labels,
tf.reshape(logits, shape=[-1, num_classes]),
weights=not_ignore_mask,
scope=loss_scope)
