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cneud 2024-04-10 22:20:23 +02:00
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metrics.py
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@ -2,8 +2,8 @@ from tensorflow.keras import backend as K
import tensorflow as tf
import numpy as np
def focal_loss(gamma=2., alpha=4.):
def focal_loss(gamma=2., alpha=4.):
gamma = float(gamma)
alpha = float(alpha)
@ -37,8 +37,10 @@ def focal_loss(gamma=2., alpha=4.):
fl = tf.multiply(alpha, tf.multiply(weight, ce))
reduced_fl = tf.reduce_max(fl, axis=1)
return tf.reduce_mean(reduced_fl)
return focal_loss_fixed
def weighted_categorical_crossentropy(weights=None):
""" weighted_categorical_crossentropy
@ -50,117 +52,131 @@ def weighted_categorical_crossentropy(weights=None):
def loss(y_true, y_pred):
labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
if weights is not None:
weight_mask = tf.maximum(tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
* labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss)
return loss
def image_categorical_cross_entropy(y_true, y_pred, weights=None):
"""
:param y_true: tensor of shape (batch_size, height, width) representing the ground truth.
:param y_pred: tensor of shape (batch_size, height, width) representing the prediction.
:return: The mean cross-entropy on softmaxed tensors.
"""
labels_floats = tf.cast(y_true, tf.float32)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats,logits=y_pred)
per_pixel_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=labels_floats, logits=y_pred)
if weights is not None:
weight_mask = tf.maximum(
tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
tf.reduce_max(tf.constant(
np.array(weights, dtype=np.float32)[None, None, None])
* labels_floats, axis=-1), 1.0)
per_pixel_loss = per_pixel_loss * weight_mask[:, :, :, None]
return tf.reduce_mean(per_pixel_loss)
def class_tversky(y_true, y_pred):
smooth = 1.0#1.00
y_true = K.permute_dimensions(y_true, (3,1,2,0))
y_pred = K.permute_dimensions(y_pred, (3,1,2,0))
return tf.reduce_mean(per_pixel_loss)
def class_tversky(y_true, y_pred):
smooth = 1.0 # 1.00
y_true = K.permute_dimensions(y_true, (3, 1, 2, 0))
y_pred = K.permute_dimensions(y_pred, (3, 1, 2, 0))
y_true_pos = K.batch_flatten(y_true)
y_pred_pos = K.batch_flatten(y_pred)
true_pos = K.sum(y_true_pos * y_pred_pos, 1)
false_neg = K.sum(y_true_pos * (1-y_pred_pos), 1)
false_pos = K.sum((1-y_true_pos)*y_pred_pos, 1)
alpha = 0.2#0.5
beta=0.8
return (true_pos + smooth)/(true_pos + alpha*false_neg + (beta)*false_pos + smooth)
false_neg = K.sum(y_true_pos * (1 - y_pred_pos), 1)
false_pos = K.sum((1 - y_true_pos) * y_pred_pos, 1)
alpha = 0.2 # 0.5
beta = 0.8
return (true_pos + smooth) / (true_pos + alpha * false_neg + beta * false_pos + smooth)
def focal_tversky_loss(y_true,y_pred):
def focal_tversky_loss(y_true, y_pred):
pt_1 = class_tversky(y_true, y_pred)
gamma =1.3#4./3.0#1.3#4.0/3.00# 0.75
return K.sum(K.pow((1-pt_1), gamma))
gamma = 1.3 # 4./3.0#1.3#4.0/3.00# 0.75
return K.sum(K.pow((1 - pt_1), gamma))
def generalized_dice_coeff2(y_true, y_pred):
n_el = 1
for dim in y_true.shape:
for dim in y_true.shape:
n_el *= int(dim)
n_cl = y_true.shape[-1]
w = K.zeros(shape=(n_cl,))
w = (K.sum(y_true, axis=(0,1,2)))/(n_el)
w = 1/(w**2+0.000001)
numerator = y_true*y_pred
numerator = w*K.sum(numerator,(0,1,2))
w = (K.sum(y_true, axis=(0, 1, 2))) / n_el
w = 1 / (w ** 2 + 0.000001)
numerator = y_true * y_pred
numerator = w * K.sum(numerator, (0, 1, 2))
numerator = K.sum(numerator)
denominator = y_true+y_pred
denominator = w*K.sum(denominator,(0,1,2))
denominator = y_true + y_pred
denominator = w * K.sum(denominator, (0, 1, 2))
denominator = K.sum(denominator)
return 2*numerator/denominator
return 2 * numerator / denominator
def generalized_dice_coeff(y_true, y_pred):
axes = tuple(range(1, len(y_pred.shape)-1))
axes = tuple(range(1, len(y_pred.shape) - 1))
Ncl = y_pred.shape[-1]
w = K.zeros(shape=(Ncl,))
w = K.sum(y_true, axis=axes)
w = 1/(w**2+0.000001)
w = 1 / (w ** 2 + 0.000001)
# Compute gen dice coef:
numerator = y_true*y_pred
numerator = w*K.sum(numerator,axes)
numerator = y_true * y_pred
numerator = w * K.sum(numerator, axes)
numerator = K.sum(numerator)
denominator = y_true+y_pred
denominator = w*K.sum(denominator,axes)
denominator = y_true + y_pred
denominator = w * K.sum(denominator, axes)
denominator = K.sum(denominator)
gen_dice_coef = 2*numerator/denominator
gen_dice_coef = 2 * numerator / denominator
return gen_dice_coef
def generalized_dice_loss(y_true, y_pred):
return 1 - generalized_dice_coeff2(y_true, y_pred)
def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
'''
def soft_dice_loss(y_true, y_pred, epsilon=1e-6):
"""
Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.
Assumes the `channels_last` format.
# Arguments
y_true: b x X x Y( x Z...) x c One hot encoding of ground truth
y_pred: b x X x Y( x Z...) x c Network output, must sum to 1 over c channel (such as after softmax)
y_pred: b x X x Y( x Z...) x c Network output, must sum to 1 over c channel (such as after softmax)
epsilon: Used for numerical stability to avoid divide by zero errors
# References
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
https://arxiv.org/abs/1606.04797
More details on Dice loss formulation
More details on Dice loss formulation
https://mediatum.ub.tum.de/doc/1395260/1395260.pdf (page 72)
Adapted from https://github.com/Lasagne/Recipes/issues/99#issuecomment-347775022
'''
"""
# skip the batch and class axis for calculating Dice score
axes = tuple(range(1, len(y_pred.shape)-1))
axes = tuple(range(1, len(y_pred.shape) - 1))
numerator = 2. * K.sum(y_pred * y_true, axes)
denominator = K.sum(K.square(y_pred) + K.square(y_true), axes)
return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
return 1.00 - K.mean(numerator / (denominator + epsilon)) # average over classes and batch
def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last = True, mean_per_class=False, verbose=False):
def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last=True, mean_per_class=False,
verbose=False):
"""
Compute mean metrics of two segmentation masks, via Keras.
@ -193,13 +209,13 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
H = height,
N = number of classes
"""
flag_soft = (metric_type == 'soft')
flag_naive_mean = (metric_type == 'naive')
# always assume one or more classes
num_classes = K.shape(y_true)[-1]
if not flag_soft:
# get one-hot encoded masks from y_pred (true masks should already be one-hot)
y_pred = K.one_hot(K.argmax(y_pred), num_classes)
@ -211,29 +227,29 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
y_pred = K.cast(y_pred, 'float32')
# intersection and union shapes are batch_size * n_classes (values = area in pixels)
axes = (1,2) # W,H axes of each image
axes = (1, 2) # W,H axes of each image
intersection = K.sum(K.abs(y_true * y_pred), axis=axes)
mask_sum = K.sum(K.abs(y_true), axis=axes) + K.sum(K.abs(y_pred), axis=axes)
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
union = mask_sum - intersection # or, np.logical_or(y_pred, y_true) for one-hot
smooth = .001
iou = (intersection + smooth) / (union + smooth)
dice = 2 * (intersection + smooth)/(mask_sum + smooth)
dice = 2 * (intersection + smooth) / (mask_sum + smooth)
metric = {'iou': iou, 'dice': dice}[metric_name]
# define mask to be 0 when no pixels are present in either y_true or y_pred, 1 otherwise
mask = K.cast(K.not_equal(union, 0), 'float32')
mask = K.cast(K.not_equal(union, 0), 'float32')
if drop_last:
metric = metric[:,:-1]
mask = mask[:,:-1]
metric = metric[:, :-1]
mask = mask[:, :-1]
if verbose:
print('intersection, union')
print(K.eval(intersection), K.eval(union))
print(K.eval(intersection/union))
print(K.eval(intersection / union))
# return mean metrics: remaining axes are (batch, classes)
if flag_naive_mean:
return K.mean(metric)
@ -243,13 +259,14 @@ def seg_metrics(y_true, y_pred, metric_name, metric_type='standard', drop_last =
non_zero = tf.greater(class_count, 0)
non_zero_sum = tf.boolean_mask(K.sum(metric * mask, axis=0), non_zero)
non_zero_count = tf.boolean_mask(class_count, non_zero)
if verbose:
print('Counts of inputs with class present, metrics for non-absent classes')
print(K.eval(class_count), K.eval(non_zero_sum / non_zero_count))
return K.mean(non_zero_sum / non_zero_count)
def mean_iou(y_true, y_pred, **kwargs):
"""
Compute mean Intersection over Union of two segmentation masks, via Keras.
@ -257,65 +274,69 @@ def mean_iou(y_true, y_pred, **kwargs):
Calls metrics_k(y_true, y_pred, metric_name='iou'), see there for allowed kwargs.
"""
return seg_metrics(y_true, y_pred, metric_name='iou', **kwargs)
def Mean_IOU(y_true, y_pred):
nb_classes = K.int_shape(y_pred)[-1]
iou = []
true_pixels = K.argmax(y_true, axis=-1)
pred_pixels = K.argmax(y_pred, axis=-1)
void_labels = K.equal(K.sum(y_true, axis=-1), 0)
for i in range(0, nb_classes): # exclude first label (background) and last label (void)
true_labels = K.equal(true_pixels, i)# & ~void_labels
pred_labels = K.equal(pred_pixels, i)# & ~void_labels
for i in range(0, nb_classes): # exclude first label (background) and last label (void)
true_labels = K.equal(true_pixels, i) # & ~void_labels
pred_labels = K.equal(pred_pixels, i) # & ~void_labels
inter = tf.to_int32(true_labels & pred_labels)
union = tf.to_int32(true_labels | pred_labels)
legal_batches = K.sum(tf.to_int32(true_labels), axis=1)>0
ious = K.sum(inter, axis=1)/K.sum(union, axis=1)
iou.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects
legal_batches = K.sum(tf.to_int32(true_labels), axis=1) > 0
ious = K.sum(inter, axis=1) / K.sum(union, axis=1)
iou.append(K.mean(tf.gather(ious, indices=tf.where(legal_batches)))) # returns average IoU of the same objects
iou = tf.stack(iou)
legal_labels = ~tf.debugging.is_nan(iou)
iou = tf.gather(iou, indices=tf.where(legal_labels))
return K.mean(iou)
def iou_vahid(y_true, y_pred):
nb_classes = tf.shape(y_true)[-1]+tf.to_int32(1)
nb_classes = tf.shape(y_true)[-1] + tf.to_int32(1)
true_pixels = K.argmax(y_true, axis=-1)
pred_pixels = K.argmax(y_pred, axis=-1)
iou = []
for i in tf.range(nb_classes):
tp=K.sum( tf.to_int32( K.equal(true_pixels, i) & K.equal(pred_pixels, i) ) )
fp=K.sum( tf.to_int32( K.not_equal(true_pixels, i) & K.equal(pred_pixels, i) ) )
fn=K.sum( tf.to_int32( K.equal(true_pixels, i) & K.not_equal(pred_pixels, i) ) )
iouh=tp/(tp+fp+fn)
tp = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.equal(pred_pixels, i)))
fp = K.sum(tf.to_int32(K.not_equal(true_pixels, i) & K.equal(pred_pixels, i)))
fn = K.sum(tf.to_int32(K.equal(true_pixels, i) & K.not_equal(pred_pixels, i)))
iouh = tp / (tp + fp + fn)
iou.append(iouh)
return K.mean(iou)
def IoU_metric(Yi,y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
def IoU_metric(Yi, y_predi):
# mean Intersection over Union
# Mean IoU = TP/(FN + TP + FP)
y_predi = np.argmax(y_predi, axis=3)
y_testi = np.argmax(Yi, axis=3)
IoUs = []
Nclass = int(np.max(Yi)) + 1
for c in range(Nclass):
TP = np.sum( (Yi == c)&(y_predi==c) )
FP = np.sum( (Yi != c)&(y_predi==c) )
FN = np.sum( (Yi == c)&(y_predi != c))
IoU = TP/float(TP + FP + FN)
TP = np.sum((Yi == c) & (y_predi == c))
FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum((Yi == c) & (y_predi != c))
IoU = TP / float(TP + FP + FN)
IoUs.append(IoU)
return K.cast( np.mean(IoUs) ,dtype='float32' )
return K.cast(np.mean(IoUs), dtype='float32')
def IoU_metric_keras(y_true, y_pred):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
# mean Intersection over Union
# Mean IoU = TP/(FN + TP + FP)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
return IoU_metric(y_true.eval(session=sess), y_pred.eval(session=sess))
def jaccard_distance_loss(y_true, y_pred, smooth=100):
"""
Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
@ -334,5 +355,3 @@ def jaccard_distance_loss(y_true, y_pred, smooth=100):
sum_ = K.sum(K.abs(y_true) + K.abs(y_pred), axis=-1)
jac = (intersection + smooth) / (sum_ - intersection + smooth)
return (1 - jac) * smooth