File size: 7,442 Bytes
c4b2b37 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import numpy as np
import torch
from sklearn.metrics import (average_precision_score, f1_score,
precision_recall_curve, roc_curve)
SMOOTH = 1e-6
__all__ = [
"get_f1_scores",
"get_ap_scores",
"batch_pix_accuracy",
"batch_intersection_union",
"get_iou",
"get_pr",
"get_roc",
"get_ap_multiclass",
]
def get_iou(outputs: torch.Tensor, labels: torch.Tensor):
# You can comment out this line if you are passing tensors of equal shape
# But if you are passing output from UNet or something it will most probably
# be with the BATCH x 1 x H x W shape
outputs = outputs.squeeze(1) # BATCH x 1 x H x W => BATCH x H x W
labels = labels.squeeze(1) # BATCH x 1 x H x W => BATCH x H x W
intersection = (
(outputs & labels).float().sum((1, 2))
) # Will be zero if Truth=0 or Prediction=0
union = (outputs | labels).float().sum((1, 2)) # Will be zzero if both are 0
iou = (intersection + SMOOTH) / (
union + SMOOTH
) # We smooth our devision to avoid 0/0
return iou.cpu().numpy()
def get_f1_scores(predict, target, ignore_index=-1):
# Tensor process
batch_size = predict.shape[0]
predict = predict.data.cpu().numpy().reshape(-1)
target = target.data.cpu().numpy().reshape(-1)
pb = predict[target != ignore_index].reshape(batch_size, -1)
tb = target[target != ignore_index].reshape(batch_size, -1)
total = []
for p, t in zip(pb, tb):
total.append(np.nan_to_num(f1_score(t, p)))
return total
def get_roc(predict, target, ignore_index=-1):
target_expand = target.unsqueeze(1).expand_as(predict)
target_expand_numpy = target_expand.data.cpu().numpy().reshape(-1)
# Tensor process
x = torch.zeros_like(target_expand)
t = target.unsqueeze(1).clamp(min=0)
target_1hot = x.scatter_(1, t, 1)
batch_size = predict.shape[0]
predict = predict.data.cpu().numpy().reshape(-1)
target = target_1hot.data.cpu().numpy().reshape(-1)
pb = predict[target_expand_numpy != ignore_index].reshape(batch_size, -1)
tb = target[target_expand_numpy != ignore_index].reshape(batch_size, -1)
total = []
for p, t in zip(pb, tb):
total.append(roc_curve(t, p))
return total
def get_pr(predict, target, ignore_index=-1):
target_expand = target.unsqueeze(1).expand_as(predict)
target_expand_numpy = target_expand.data.cpu().numpy().reshape(-1)
# Tensor process
x = torch.zeros_like(target_expand)
t = target.unsqueeze(1).clamp(min=0)
target_1hot = x.scatter_(1, t, 1)
batch_size = predict.shape[0]
predict = predict.data.cpu().numpy().reshape(-1)
target = target_1hot.data.cpu().numpy().reshape(-1)
pb = predict[target_expand_numpy != ignore_index].reshape(batch_size, -1)
tb = target[target_expand_numpy != ignore_index].reshape(batch_size, -1)
total = []
for p, t in zip(pb, tb):
total.append(precision_recall_curve(t, p))
return total
def get_ap_scores(predict, target, ignore_index=-1):
total = []
for pred, tgt in zip(predict, target):
target_expand = tgt.unsqueeze(0).expand_as(pred)
target_expand_numpy = target_expand.data.cpu().numpy().reshape(-1)
# Tensor process
x = torch.zeros_like(target_expand)
t = tgt.unsqueeze(0).clamp(min=0).long()
target_1hot = x.scatter_(0, t, 1)
predict_flat = pred.data.cpu().numpy().reshape(-1)
target_flat = target_1hot.data.cpu().numpy().reshape(-1)
p = predict_flat[target_expand_numpy != ignore_index]
t = target_flat[target_expand_numpy != ignore_index]
total.append(np.nan_to_num(average_precision_score(t, p)))
return total
def get_ap_multiclass(predict, target):
total = []
for pred, tgt in zip(predict, target):
predict_flat = pred.data.cpu().numpy().reshape(-1)
target_flat = tgt.data.cpu().numpy().reshape(-1)
total.append(np.nan_to_num(average_precision_score(target_flat, predict_flat)))
return total
def batch_precision_recall(predict, target, thr=0.5):
"""Batch Precision Recall
Args:
predict: input 4D tensor
target: label 4D tensor
"""
# _, predict = torch.max(predict, 1)
predict = predict > thr
predict = predict.data.cpu().numpy() + 1
target = target.data.cpu().numpy() + 1
tp = np.sum(((predict == 2) * (target == 2)) * (target > 0))
fp = np.sum(((predict == 2) * (target == 1)) * (target > 0))
fn = np.sum(((predict == 1) * (target == 2)) * (target > 0))
precision = float(np.nan_to_num(tp / (tp + fp)))
recall = float(np.nan_to_num(tp / (tp + fn)))
return precision, recall
def batch_pix_accuracy(predict, target):
"""Batch Pixel Accuracy
Args:
predict: input 3D tensor
target: label 3D tensor
"""
# for thr in np.linspace(0, 1, slices):
_, predict = torch.max(predict, 0)
predict = predict.cpu().numpy() + 1
target = target.cpu().numpy() + 1
pixel_labeled = np.sum(target > 0)
pixel_correct = np.sum((predict == target) * (target > 0))
assert pixel_correct <= pixel_labeled, "Correct area should be smaller than Labeled"
return pixel_correct, pixel_labeled
def batch_intersection_union(predict, target, nclass):
"""Batch Intersection of Union
Args:
predict: input 3D tensor
target: label 3D tensor
nclass: number of categories (int)
"""
_, predict = torch.max(predict, 0)
mini = 1
maxi = nclass
nbins = nclass
predict = predict.cpu().numpy() + 1
target = target.cpu().numpy() + 1
predict = predict * (target > 0).astype(predict.dtype)
intersection = predict * (predict == target)
# areas of intersection and union
area_inter, _ = np.histogram(intersection, bins=nbins, range=(mini, maxi))
area_pred, _ = np.histogram(predict, bins=nbins, range=(mini, maxi))
area_lab, _ = np.histogram(target, bins=nbins, range=(mini, maxi))
area_union = area_pred + area_lab - area_inter
assert (
area_inter <= area_union
).all(), "Intersection area should be smaller than Union area"
return area_inter, area_union
# ref https://github.com/CSAILVision/sceneparsing/blob/master/evaluationCode/utils_eval.py
def pixel_accuracy(im_pred, im_lab):
im_pred = np.asarray(im_pred)
im_lab = np.asarray(im_lab)
# Remove classes from unlabeled pixels in gt image.
# We should not penalize detections in unlabeled portions of the image.
pixel_labeled = np.sum(im_lab > 0)
pixel_correct = np.sum((im_pred == im_lab) * (im_lab > 0))
# pixel_accuracy = 1.0 * pixel_correct / pixel_labeled
return pixel_correct, pixel_labeled
def intersection_and_union(im_pred, im_lab, num_class):
im_pred = np.asarray(im_pred)
im_lab = np.asarray(im_lab)
# Remove classes from unlabeled pixels in gt image.
im_pred = im_pred * (im_lab > 0)
# Compute area intersection:
intersection = im_pred * (im_pred == im_lab)
area_inter, _ = np.histogram(
intersection, bins=num_class - 1, range=(1, num_class - 1)
)
# Compute area union:
area_pred, _ = np.histogram(im_pred, bins=num_class - 1, range=(1, num_class - 1))
area_lab, _ = np.histogram(im_lab, bins=num_class - 1, range=(1, num_class - 1))
area_union = area_pred + area_lab - area_inter
return area_inter, area_union
|