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losses.py
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import keras.backend as K
import tensorflow as tf
import numpy as np
def focal_loss(y_true, y_pred):
gamma = 0.75
alpha = 0.25
pt_1 = tf.where(tf.equal(y_true, 1), y_pred, tf.ones_like(y_pred))
pt_0 = tf.where(tf.equal(y_true, 0), y_pred, tf.zeros_like(y_pred))
pt_1 = K.clip(pt_1, 1e-3, .999)
pt_0 = K.clip(pt_0, 1e-3, .999)
return -K.sum(alpha * K.pow(1. - pt_1, gamma) * K.log(pt_1)) - K.sum(
(1 - alpha) * K.pow(pt_0, gamma) * K.log(1. - pt_0))
def dice_coef(y_true, y_pred):
smooth = 1.
dice_all = 0
for layer in range(y_pred.shape[3]):
true_layer = y_true[:, :, :, layer]
pred_layer = y_pred[:, :, :, layer]
y_true_f = K.flatten(true_layer)
y_pred_f = K.flatten(pred_layer)
intersection = K.sum(y_true_f * y_pred_f)
dice = (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
dice_all += dice
return dice_all / int(y_pred.shape[3])
def jaccard_coef(y_true, y_pred):
smooth = 1e-12
jaccard = 0
for layer in range(y_pred.shape[3]):
true_layer = y_true[:, :, :, layer]
pred_layer = y_pred[:, :, :, layer]
intersection = K.sum(true_layer * pred_layer, axis=[0, -1, -2])
sum_ = K.sum(true_layer + pred_layer, axis=[0, -1, -2])
jac = (intersection + smooth) / (sum_ - intersection + smooth)
jaccard += jac
return jaccard / int(y_pred.shape[3])
def dice_loss(y_true, y_pred):
return 1. - dice_coef(y_true, y_pred)
def jaccard_loss(y_true, y_pred):
return 1. - jaccard_coef(y_true, y_pred)
def mean_iou(y_true, y_pred):
prec = []
for t in np.arange(0.5, 1.0, 0.05):
y_pred_ = tf.to_int32(y_pred > t)
score, up_opt = tf.metrics.mean_iou(y_true, y_pred_, 20)
K.get_session().run(tf.local_variables_initializer())
with tf.control_dependencies([up_opt]):
score = tf.identity(score)
prec.append(score)
return K.mean(K.stack(prec), axis=0)
def custom_loss(y_true, y_pred):
return 0.25 * dice_loss(y_true, y_pred) + 0.25 * mean_iou(y_true, y_pred) + 0.5 * jaccard_loss(y_true, y_pred)
def flatten_probas(probas, labels, ignore=None, order='BHWC'):
"""
Flattens predictions in the batch
"""
if order == 'BCHW':
probas = tf.transpose(probas, (0, 2, 3, 1), name="BCHW_to_BHWC")
order = 'BHWC'
if order != 'BHWC':
raise NotImplementedError('Order {} unknown'.format(order))
C = 1
probas = tf.reshape(probas, (-1, C))
labels = tf.reshape(labels, (-1,))
if ignore is None:
return probas, labels
valid = tf.not_equal(labels, ignore)
vprobas = tf.boolean_mask(probas, valid, name='valid_probas')
vlabels = tf.boolean_mask(labels, valid, name='valid_labels')
return vprobas, vlabels
def lovasz_softmax(probas, labels, classes='all', per_image=False, ignore=None, order='BHWC'):
"""
Multi-class Lovasz-Softmax loss
probas: [B, H, W, C] or [B, C, H, W] Variable, class probabilities at each prediction (between 0 and 1)
labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1)
classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
per_image: compute the loss per image instead of per batch
ignore: void class labels
order: use BHWC or BCHW
"""
if per_image:
def treat_image(prob_lab):
prob, lab = prob_lab
prob, lab = tf.expand_dims(prob, 0), tf.expand_dims(lab, 0)
prob, lab = flatten_probas(prob, lab, ignore, order)
return lovasz_softmax_flat(prob, lab, classes=classes)
losses = tf.map_fn(treat_image, (probas, labels), dtype=tf.float32)
loss = tf.reduce_mean(losses)
else:
loss = lovasz_softmax_flat(*flatten_probas(probas, labels, ignore, order), classes=classes)
return loss
def lovasz_grad(gt_sorted):
"""
Computes gradient of the Lovasz extension w.r.t sorted errors
See Alg. 1 in paper
"""
gts = tf.reduce_sum(gt_sorted)
intersection = gts - tf.cumsum(gt_sorted)
union = gts + tf.cumsum(1. - gt_sorted)
jaccard = 1. - intersection / union
jaccard = tf.concat((jaccard[0:1], jaccard[1:] - jaccard[:-1]), 0)
return jaccard
def lovasz_softmax_flat(probas, labels, classes='all'):
"""
Multi-class Lovasz-Softmax loss
probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
labels: [P] Tensor, ground truth labels (between 0 and C - 1)
classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average.
"""
C = 1
losses = []
present = []
class_to_sum = list(range(C)) if classes in ['all', 'present'] else classes
for c in class_to_sum:
fg = tf.cast(tf.equal(labels, c), probas.dtype) # foreground for class c
if classes == 'present':
present.append(tf.reduce_sum(fg) > 0)
errors = tf.abs(fg - probas[:, c])
errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], name="descending_sort_{}".format(c))
fg_sorted = tf.gather(fg, perm)
grad = lovasz_grad(fg_sorted)
losses.append(
tf.tensordot(errors_sorted, tf.stop_gradient(grad), 1, name="loss_class_{}".format(c))
)
if len(class_to_sum) == 1: # short-circuit mean when only one class
return losses[0]
losses_tensor = tf.stack(losses)
if classes == 'present':
present = tf.stack(present)
losses_tensor = tf.boolean_mask(losses_tensor, present)
loss = tf.reduce_mean(losses_tensor)
return loss
def keras_lovasz_softmax(labels,probas):
#return lovasz_softmax(probas, labels)+binary_crossentropy(labels, probas)
return lovasz_softmax(probas, labels)