def average_precision(precision, recall, name=None):
"""Compute a average precision from precision and recall Tensors.
Implementation following Pascal 2012 and ILSVRC guidelines.
"""
with tf.name_scope(name, 'average_precision', [precision, recall]):
# Convert to float64 to decrease error on Riemann sums.
precision = tf.cast(precision, dtype=tf.float64)
recall = tf.cast(recall, dtype=tf.float64)
# Add bounds values to precision and recall.
precision = tf.concat([[0.], precision, [0.]], axis=0)
recall = tf.concat([[0.], recall, [1.]], axis=0)
# Ensures precision is increasing in reverse order.
precision = tfe_math.cummax(precision, reverse=True)
# Riemann sums for estimating the integral.
# mean_pre = (precision[1:] + precision[:-1]) / 2.
mean_pre = precision[1:]
diff_rec = recall[1:] - recall[:-1]
ap = tf.reduce_sum(mean_pre * diff_rec)
return ap
def precision_recall_values(xvals, precision, recall, name=None):
"""Compute values on the precision/recall curve.
Args:
x: Python list of floats;
precision: 1D Tensor decreasing.
recall: 1D Tensor increasing.
Return:
list of precision values.
"""
with ops.name_scope(name, "precision_recall_values",
[precision, recall]) as name:
# Add bounds values to precision and recall.
precision = tf.concat([[0.], precision, [0.]], axis=0)
recall = tf.concat([[0.], recall, [1.]], axis=0)
precision = tfe_math.cummax(precision, reverse=True)
prec_values = []
for x in xvals:
mask = tf.less_equal(recall, x)
val = tf.reduce_min(tf.boolean_mask(precision, mask))
prec_values.append(val)
return tf.tuple(prec_values)
def precision_recall_values(xvals, precision, recall, name=None):
"""Compute values on the precision/recall curve.
Args:
x: Python list of floats;
precision: 1D Tensor decreasing.
recall: 1D Tensor increasing.
Return:
list of precision values.
"""
with ops.name_scope(name, "precision_recall_values",
[precision, recall]) as name:
# Add bounds values to precision and recall.
precision = tf.concat([[0.], precision, [0.]], axis=0)
recall = tf.concat([[0.], recall, [1.]], axis=0)
precision = tfe_math.cummax(precision, reverse=True)
prec_values = []
for x in xvals:
mask = tf.less_equal(recall, x)
val = tf.reduce_min(tf.boolean_mask(precision, mask))
prec_values.append(val)
return tf.tuple(prec_values)
def average_precision_voc12(precision, recall, name=None):
"""Compute (interpolated) average precision from precision and recall Tensors.
The implementation follows Pascal 2012 and ILSVRC guidelines.
See also: https://sanchom.wordpress.com/tag/average-precision/
"""
with tf.name_scope(name, 'average_precision_voc12', [precision, recall]):
# Convert to float64 to decrease error on Riemann sums.
precision = tf.cast(precision, dtype=tf.float64)
recall = tf.cast(recall, dtype=tf.float64)
# Add bounds values to precision and recall.
precision = tf.concat([[0.], precision, [0.]], axis=0)
recall = tf.concat([[0.], recall, [1.]], axis=0)
# Ensures precision is increasing in reverse order.
precision = tfe_math.cummax(precision, reverse=True)
# Riemann sums for estimating the integral.
# mean_pre = (precision[1:] + precision[:-1]) / 2.
mean_pre = precision[1:]
diff_rec = recall[1:] - recall[:-1]
ap = tf.reduce_sum(mean_pre * diff_rec)
return ap
def average_precision_voc12(precision, recall, name=None):
"""Compute (interpolated) average precision from precision and recall Tensors.
The implementation follows Pascal 2012 and ILSVRC guidelines.
See also: https://sanchom.wordpress.com/tag/average-precision/
"""
with tf.compat.v1.name_scope(name, 'average_precision_voc12',
[precision, recall]):
# Convert to float64 to decrease error on Riemann sums.
precision = tf.cast(precision, dtype=tf.float64)
recall = tf.cast(recall, dtype=tf.float64)
# Add bounds values to precision and recall.
precision = tf.concat([[0.], precision, [0.]], axis=0)
recall = tf.concat([[0.], recall, [1.]], axis=0)
# Ensures precision is increasing in reverse order.
precision = tfe_math.cummax(precision, reverse=True)
# Riemann sums for estimating the integral.
# mean_pre = (precision[1:] + precision[:-1]) / 2.
mean_pre = precision[1:]
diff_rec = recall[1:] - recall[:-1]
ap = tf.reduce_sum(input_tensor=mean_pre * diff_rec)
return ap
