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"""
Metrics for computing evalutation results
Modified from vanilla PANet code by Wang et al.
"""
import numpy as np
class Metric(object):
"""
Compute evaluation result
Args:
max_label:
max label index in the data (0 denoting background)
n_scans:
number of test scans
"""
def __init__(self, max_label=20, n_scans=None):
self.labels = list(range(max_label + 1)) # all class labels
self.n_scans = 1 if n_scans is None else n_scans
# list of list of array, each array save the TP/FP/FN statistic of a testing sample
self.tp_lst = [[] for _ in range(self.n_scans)]
self.fp_lst = [[] for _ in range(self.n_scans)]
self.fn_lst = [[] for _ in range(self.n_scans)]
self.slice_counter = [0 for _ in range(self.n_scans)]
def reset(self):
"""
Reset accumulated evaluation.
"""
# assert self.n_scans == 1, 'Should not reset accumulated result when we are not doing one-time batch-wise validation'
del self.tp_lst, self.fp_lst, self.fn_lst
self.tp_lst = [[] for _ in range(self.n_scans)]
self.fp_lst = [[] for _ in range(self.n_scans)]
self.fn_lst = [[] for _ in range(self.n_scans)]
def reset_scan(self, n_scan, labels:list=None):
"""
Reset accumulated evaluation for a specific scan.
"""
if labels is None:
labels = self.labels
for slice_idx in range(len(self.tp_lst[n_scan])):
for label in labels:
self.tp_lst[n_scan][slice_idx][label] = np.nan
self.fp_lst[n_scan][slice_idx][label] = np.nan
self.fn_lst[n_scan][slice_idx][label] = np.nan
def record(self, pred, target, labels=None, n_scan=None):
"""
Record the evaluation result for each sample and each class label, including:
True Positive, False Positive, False Negative
Args:
pred:
predicted mask array, expected shape is H x W
target:
target mask array, expected shape is H x W
labels:
only count specific label, used when knowing all possible labels in advance
"""
assert pred.shape == target.shape
if self.n_scans == 1:
n_scan = 0
# array to save the TP/FP/FN statistic for each class (plus BG)
tp_arr = np.full(len(self.labels), np.nan)
fp_arr = np.full(len(self.labels), np.nan)
fn_arr = np.full(len(self.labels), np.nan)
if labels is None:
labels = self.labels
else:
labels = [0,] + labels
for j, label in enumerate(labels):
# Get the location of the pixels that are predicted as class j
# idx = np.where(np.logical_and(pred == j, target != 255))
# pred_idx_j = set(zip(idx[0].tolist(), idx[1].tolist()))
# # Get the location of the pixels that are class j in ground truth
# idx = np.where(target == j)
# target_idx_j = set(zip(idx[0].tolist(), idx[1].tolist()))
# # this should not work: if target_idx_j: # if ground-truth contains this class
# # the author is adding posion to the code
# tp_arr[label] = len(set.intersection(pred_idx_j, target_idx_j))
# fp_arr[label] = len(pred_idx_j - target_idx_j)
# fn_arr[label] = len(target_idx_j - pred_idx_j)
# calc the tp, fp and fn normally and compare the 2 values
tp = ((pred == j).astype(int) * (target == j).astype(int)).sum()
fp = ((pred == j).astype(int) * (target != j).astype(int)).sum()
fn = ((pred != j).astype(int) * (target == j).astype(int)).sum()
tp_arr[label] = tp
fp_arr[label] = fp
fn_arr[label] = fn
# assert tp == tp_arr[label]
# assert fp == fp_arr[label]
# assert fn == fn_arr[label]
self.tp_lst[n_scan].append(tp_arr)
self.fp_lst[n_scan].append(fp_arr)
self.fn_lst[n_scan].append(fn_arr)
self.slice_counter[n_scan] += 1
def get_mIoU(self, labels=None, n_scan=None):
"""
Compute mean IoU
Args:
labels:
specify a subset of labels to compute mean IoU, default is using all classes
"""
if labels is None:
labels = self.labels
# Sum TP, FP, FN statistic of all samples
if n_scan is None:
tp_sum = [np.nansum(np.vstack(self.tp_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
fp_sum = [np.nansum(np.vstack(self.fp_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
fn_sum = [np.nansum(np.vstack(self.fn_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
# Compute mean IoU classwisely
# Average across n_scans, then average over classes
mIoU_class = np.vstack([tp_sum[_scan] / (tp_sum[_scan] + fp_sum[_scan] + fn_sum[_scan])
for _scan in range(self.n_scans)])
mIoU = mIoU_class.mean(axis=1)
return (mIoU_class.mean(axis=0), mIoU_class.std(axis=0),
mIoU.mean(axis=0), mIoU.std(axis=0))
else:
tp_sum = np.nansum(np.vstack(self.tp_lst[n_scan]), axis=0).take(labels)
fp_sum = np.nansum(np.vstack(self.fp_lst[n_scan]), axis=0).take(labels)
fn_sum = np.nansum(np.vstack(self.fn_lst[n_scan]), axis=0).take(labels)
# Compute mean IoU classwisely and average over classes
mIoU_class = tp_sum / (tp_sum + fp_sum + fn_sum)
mIoU = mIoU_class.mean()
return mIoU_class, mIoU
def get_mDice(self, labels=None, n_scan=None, give_raw = False):
"""
Compute mean Dice score (in 3D scan level)
Args:
labels:
specify a subset of labels to compute mean IoU, default is using all classes
"""
# NOTE: unverified
if labels is None:
labels = self.labels
# Sum TP, FP, FN statistic of all samples
if n_scan is None:
tp_sum = [np.nansum(np.vstack(self.tp_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
fp_sum = [np.nansum(np.vstack(self.fp_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
fn_sum = [np.nansum(np.vstack(self.fn_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
# Average across n_scans, then average over classes
mDice_class = np.vstack([ 2 * tp_sum[_scan] / ( 2 * tp_sum[_scan] + fp_sum[_scan] + fn_sum[_scan])
for _scan in range(self.n_scans)])
mDice = mDice_class.mean(axis=1)
print(f"mDice_class:\n {mDice_class}")
if not give_raw:
return (mDice_class.mean(axis=0), mDice_class.std(axis=0),
mDice.mean(axis=0), mDice.std(axis=0))
else:
return (mDice_class.mean(axis=0), mDice_class.std(axis=0),
mDice.mean(axis=0), mDice.std(axis=0), mDice_class)
else:
tp_sum = np.nansum(np.vstack(self.tp_lst[n_scan]), axis=0).take(labels)
fp_sum = np.nansum(np.vstack(self.fp_lst[n_scan]), axis=0).take(labels)
fn_sum = np.nansum(np.vstack(self.fn_lst[n_scan]), axis=0).take(labels)
# Compute mean IoU classwisely and average over classes
mDice_class = 2 * tp_sum / ( 2 * tp_sum + fp_sum + fn_sum)
mDice = mDice_class.mean()
if not give_raw:
return (mDice_class, mDice, mDice_class)
return (mDice_class, mDice, mDice_class)
def get_mPrecRecall(self, labels=None, n_scan=None, give_raw = False):
"""
Compute precision and recall
Args:
labels:
specify a subset of labels to compute mean IoU, default is using all classes
"""
# NOTE: unverified
if labels is None:
labels = self.labels
# Sum TP, FP, FN statistic of all samples
if n_scan is None:
tp_sum = [np.nansum(np.vstack(self.tp_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
fp_sum = [np.nansum(np.vstack(self.fp_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
fn_sum = [np.nansum(np.vstack(self.fn_lst[_scan]), axis=0).take(labels)
for _scan in range(self.n_scans)]
# Compute mean IoU classwisely
# Average across n_scans, then average over classes
mPrec_class = np.vstack([ tp_sum[_scan] / ( tp_sum[_scan] + fp_sum[_scan] )
for _scan in range(self.n_scans)])
mRec_class = np.vstack([ tp_sum[_scan] / ( tp_sum[_scan] + fn_sum[_scan] )
for _scan in range(self.n_scans)])
mPrec = mPrec_class.mean(axis=1)
mRec = mRec_class.mean(axis=1)
if not give_raw:
return (mPrec_class.mean(axis=0), mPrec_class.std(axis=0), mPrec.mean(axis=0), mPrec.std(axis=0), mRec_class.mean(axis=0), mRec_class.std(axis=0), mRec.mean(axis=0), mRec.std(axis=0))
else:
return (mPrec_class.mean(axis=0), mPrec_class.std(axis=0), mPrec.mean(axis=0), mPrec.std(axis=0), mRec_class.mean(axis=0), mRec_class.std(axis=0), mRec.mean(axis=0), mRec.std(axis=0), mPrec_class, mRec_class)
else:
tp_sum = np.nansum(np.vstack(self.tp_lst[n_scan]), axis=0).take(labels)
fp_sum = np.nansum(np.vstack(self.fp_lst[n_scan]), axis=0).take(labels)
fn_sum = np.nansum(np.vstack(self.fn_lst[n_scan]), axis=0).take(labels)
# Compute mean IoU classwisely and average over classes
mPrec_class = tp_sum / (tp_sum + fp_sum)
mPrec = mPrec_class.mean()
mRec_class = tp_sum / (tp_sum + fn_sum)
mRec = mRec_class.mean()
return mPrec_class, None, mPrec, None, mRec_class, None, mRec, None, mPrec_class, mRec_class
def get_mIoU_binary(self, n_scan=None):
"""
Compute mean IoU for binary scenario
(sum all foreground classes as one class)
"""
# Sum TP, FP, FN statistic of all samples
if n_scan is None:
tp_sum = [np.nansum(np.vstack(self.tp_lst[_scan]), axis=0)
for _scan in range(self.n_scans)]
fp_sum = [np.nansum(np.vstack(self.fp_lst[_scan]), axis=0)
for _scan in range(self.n_scans)]
fn_sum = [np.nansum(np.vstack(self.fn_lst[_scan]), axis=0)
for _scan in range(self.n_scans)]
# Sum over all foreground classes
tp_sum = [np.c_[tp_sum[_scan][0], np.nansum(tp_sum[_scan][1:])]
for _scan in range(self.n_scans)]
fp_sum = [np.c_[fp_sum[_scan][0], np.nansum(fp_sum[_scan][1:])]
for _scan in range(self.n_scans)]
fn_sum = [np.c_[fn_sum[_scan][0], np.nansum(fn_sum[_scan][1:])]
for _scan in range(self.n_scans)]
# Compute mean IoU classwisely and average across classes
mIoU_class = np.vstack([tp_sum[_scan] / (tp_sum[_scan] + fp_sum[_scan] + fn_sum[_scan])
for _scan in range(self.n_scans)])
mIoU = mIoU_class.mean(axis=1)
return (mIoU_class.mean(axis=0), mIoU_class.std(axis=0),
mIoU.mean(axis=0), mIoU.std(axis=0))
else:
tp_sum = np.nansum(np.vstack(self.tp_lst[n_scan]), axis=0)
fp_sum = np.nansum(np.vstack(self.fp_lst[n_scan]), axis=0)
fn_sum = np.nansum(np.vstack(self.fn_lst[n_scan]), axis=0)
# Sum over all foreground classes
tp_sum = np.c_[tp_sum[0], np.nansum(tp_sum[1:])]
fp_sum = np.c_[fp_sum[0], np.nansum(fp_sum[1:])]
fn_sum = np.c_[fn_sum[0], np.nansum(fn_sum[1:])]
mIoU_class = tp_sum / (tp_sum + fp_sum + fn_sum)
mIoU = mIoU_class.mean()
return mIoU_class, mIoU
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