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metrics.py
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metrics.py
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import tensorflow as tf
import numpy as np
from keras import backend as K
# src: https://www.kaggle.com/aglotero/another-iou-metric
def get_iou_vector(A, B):
batch_size = A.shape[0]
metric = []
for batch in range(batch_size):
t, p = A[batch]>0, B[batch]>0
intersection = np.logical_and(t, p)
union = np.logical_or(t, p)
iou = (np.sum(intersection > 0) + 1e-10 )/ (np.sum(union > 0) + 1e-10)
thresholds = np.arange(0.5, 1, 0.05)
s = []
for thresh in thresholds:
s.append(iou > thresh)
metric.append(np.mean(s))
return np.mean(metric)
# src: https://www.kaggle.com/aglotero/another-iou-metric
def tf_iou_metric(label, pred):
return tf.py_func(get_iou_vector, [label, pred>0.5], tf.float64)
# src: https://www.kaggle.com/aglotero/another-iou-metric
def tf_iou_metric_2(label, pred):
return tf.py_func(get_iou_vector, [label, pred>0.5], tf.float64)
# code download from: https://github.com/bermanmaxim/LovaszSoftmax
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
# --------------------------- BINARY LOSSES ---------------------------
# code download from: https://github.com/bermanmaxim/LovaszSoftmax
def lovasz_hinge(logits, labels, per_image=True, ignore=None):
"""
Binary Lovasz hinge loss
logits: [B, H, W] Variable, logits at each pixel (between -\infty and +\infty)
labels: [B, H, W] Tensor, binary ground truth masks (0 or 1)
per_image: compute the loss per image instead of per batch
ignore: void class id
"""
if per_image:
def treat_image(log_lab):
log, lab = log_lab
log, lab = tf.expand_dims(log, 0), tf.expand_dims(lab, 0)
log, lab = flatten_binary_scores(log, lab, ignore)
return lovasz_hinge_flat(log, lab)
losses = tf.map_fn(treat_image, (logits, labels), dtype=tf.float32)
loss = tf.reduce_mean(losses)
else:
loss = lovasz_hinge_flat(*flatten_binary_scores(logits, labels, ignore))
return loss
# code download from: https://github.com/bermanmaxim/LovaszSoftmax
def lovasz_hinge_flat(logits, labels):
"""
Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore
"""
def compute_loss():
labelsf = tf.cast(labels, logits.dtype)
signs = 2. * labelsf - 1.
errors = 1. - logits * tf.stop_gradient(signs)
errors_sorted, perm = tf.nn.top_k(errors, k=tf.shape(errors)[0], name="descending_sort")
gt_sorted = tf.gather(labelsf, perm)
grad = lovasz_grad(gt_sorted)
loss = tf.tensordot(tf.nn.relu(errors_sorted), tf.stop_gradient(grad), 1, name="loss_non_void")
return loss
# deal with the void prediction case (only void pixels)
loss = tf.cond(tf.equal(tf.shape(logits)[0], 0),
lambda: tf.reduce_sum(logits) * 0.,
compute_loss,
strict=True,
name="loss"
)
return loss
# code download from: https://github.com/bermanmaxim/LovaszSoftmax
def flatten_binary_scores(scores, labels, ignore=None):
"""
Flattens predictions in the batch (binary case)
Remove labels equal to 'ignore'
"""
scores = tf.reshape(scores, (-1,))
labels = tf.reshape(labels, (-1,))
if ignore is None:
return scores, labels
valid = tf.not_equal(labels, ignore)
vscores = tf.boolean_mask(scores, valid, name='valid_scores')
vlabels = tf.boolean_mask(labels, valid, name='valid_labels')
return vscores, vlabels
# code download from: https://github.com/bermanmaxim/LovaszSoftmax
def lovasz_loss(y_true, y_pred):
y_true, y_pred = K.cast(K.squeeze(y_true, -1), 'int32'), K.cast(K.squeeze(y_pred, -1), 'float32')
logits = y_pred #Jiaxin
loss = lovasz_hinge(logits, y_true, per_image = True, ignore = None)
return loss
# src: https://www.kaggle.com/aglotero/another-iou-metric
def iou_metric(y_true_in, y_pred_in, print_table=False):
labels = y_true_in
y_pred = y_pred_in
true_objects = 2
pred_objects = 2
# if all zeros, original code generate wrong bins [-0.5 0 0.5],
temp1 = np.histogram2d(labels.flatten(), y_pred.flatten(), bins=([0,0.5,1], [0,0.5, 1]))
intersection = temp1[0]
# Compute areas (needed for finding the union between all objects)
area_true = np.histogram(labels,bins=[0,0.5,1])[0]
area_pred = np.histogram(y_pred, bins=[0,0.5,1])[0]
area_true = np.expand_dims(area_true, -1)
area_pred = np.expand_dims(area_pred, 0)
# Compute union
union = area_true + area_pred - intersection
# Exclude background from the analysis
intersection = intersection[1:,1:]
intersection[intersection == 0] = 1e-9
union = union[1:,1:]
union[union == 0] = 1e-9
# Compute the intersection over union
iou = intersection / union
# Precision helper function
def precision_at(threshold, iou):
matches = iou > threshold
true_positives = np.sum(matches, axis=1) == 1 # Correct objects
false_positives = np.sum(matches, axis=0) == 0 # Missed objects
false_negatives = np.sum(matches, axis=1) == 0 # Extra objects
tp, fp, fn = np.sum(true_positives), np.sum(false_positives), np.sum(false_negatives)
return tp, fp, fn
# Loop over IoU thresholds
prec = []
if print_table:
print("Thresh\tTP\tFP\tFN\tPrec.")
for t in np.arange(0.5, 1.0, 0.05):
tp, fp, fn = precision_at(t, iou)
if (tp + fp + fn) > 0:
p = tp / (tp + fp + fn)
else:
p = 0
if print_table:
print("{:1.3f}\t{}\t{}\t{}\t{:1.3f}".format(t, tp, fp, fn, p))
prec.append(p)
if print_table:
print("AP\t-\t-\t-\t{:1.3f}".format(np.mean(prec)))
return np.mean(prec)
# src: https://www.kaggle.com/aglotero/another-iou-metric
def iou_metric_batch(y_true_in, y_pred_in):
batch_size = y_true_in.shape[0]
metric = []
for batch in range(batch_size):
value = iou_metric(y_true_in[batch], y_pred_in[batch])
metric.append(value)
return np.mean(metric)