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import os
import time
import json
import numpy as np
from scipy.io import savemat
import torch
from torchvision import transforms
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
class Eval_thread():
def __init__(self, loader, method='', dataset='', output_dir='', epoch='', cuda=True):
self.loader = loader
self.method = method
self.dataset = dataset
self.cuda = cuda
self.output_dir = output_dir
self.epoch = epoch.split('ep')[-1]
self.logfile = os.path.join(output_dir, 'result.txt')
self.dataset2smeasure_bottom_bound = {'CoCA': 0.673, 'CoSOD3k': 0.802, 'CoSal2015': 0.845} # S_measures of GCoNet
def run(self, AP=False, AUC=False, save_metrics=False, continue_eval=True):
Res = {}
start_time = time.time()
if continue_eval:
s = self.Eval_Smeasure()
if s > self.dataset2smeasure_bottom_bound[self.dataset]:
mae = self.Eval_mae()
Em = self.Eval_Emeasure()
max_e = Em.max().item()
mean_e = Em.mean().item()
Em = Em.cpu().numpy()
Fm, prec, recall = self.Eval_fmeasure()
max_f = Fm.max().item()
mean_f = Fm.mean().item()
Fm = Fm.cpu().numpy()
else:
mae = 1
Em = torch.zeros(255).cpu().numpy()
max_e = 0
mean_e = 0
Fm, prec, recall = 0, 0, 0
max_f = 0
mean_f = 0
continue_eval = False
else:
s = 0
mae = 1
Em = torch.zeros(255).cpu().numpy()
max_e = 0
mean_e = 0
Fm, prec, recall = 0, 0, 0
max_f = 0
mean_f = 0
continue_eval = False
if AP:
prec = prec.cpu().numpy()
recall = recall.cpu().numpy()
avg_p = self.Eval_AP(prec, recall)
if AUC:
auc, TPR, FPR = self.Eval_auc()
TPR = TPR.cpu().numpy()
FPR = FPR.cpu().numpy()
if save_metrics:
os.makedirs(os.path.join(self.output_dir, self.method, self.epoch), exist_ok=True)
Res['Sm'] = s
if s > self.dataset2smeasure_bottom_bound[self.dataset]:
Res['MAE'] = mae
Res['MaxEm'] = max_e
Res['MeanEm'] = mean_e
Res['Em'] = Em
Res['Fm'] = Fm
else:
Res['MAE'] = 1
Res['MaxEm'] = 0
Res['MeanEm'] = 0
Res['Em'] = torch.zeros(255).cpu().numpy()
Res['Fm'] = 0
if AP:
Res['MaxFm'] = max_f
Res['MeanFm'] = mean_f
Res['AP'] = avg_p
Res['Prec'] = prec
Res['Recall'] = recall
if AUC:
Res['AUC'] = auc
Res['TPR'] = TPR
Res['FPR'] = FPR
os.makedirs(os.path.join(self.output_dir, self.method, self.epoch), exist_ok=True)
savemat(os.path.join(self.output_dir, self.method, self.epoch, self.dataset + '.mat'), Res)
info = '{} ({}): {:.4f} max-Emeasure || {:.4f} S-measure || {:.4f} max-fm || {:.4f} mae || {:.4f} mean-Emeasure || {:.4f} mean-fm'.format(
self.dataset, self.method+'-ep{}'.format(self.epoch), max_e, s, max_f, mae, mean_e, mean_f
)
if AP:
info += ' || {:.4f} AP'.format(avg_p)
if AUC:
info += ' || {:.4f} AUC'.format(auc)
info += '.'
self.LOG(info + '\n')
return '[cost:{:.4f}s] '.format(time.time() - start_time) + info, continue_eval
def Eval_mae(self):
if self.epoch:
print('Evaluating MAE...')
avg_mae, img_num = 0.0, 0.0
with torch.no_grad():
trans = transforms.Compose([transforms.ToTensor()])
for pred, gt in self.loader:
if self.cuda:
pred = trans(pred).cuda()
gt = trans(gt).cuda()
else:
pred = trans(pred)
gt = trans(gt)
mea = torch.abs(pred - gt).mean()
if mea == mea: # for Nan
avg_mae += mea
img_num += 1.0
avg_mae /= img_num
return avg_mae.item()
def Eval_fmeasure(self):
print('Evaluating FMeasure...')
beta2 = 0.3
avg_f, avg_p, avg_r, img_num = 0.0, 0.0, 0.0, 0.0
with torch.no_grad():
trans = transforms.Compose([transforms.ToTensor()])
for pred, gt in self.loader:
if self.cuda:
pred = trans(pred).cuda()
gt = trans(gt).cuda()
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
else:
pred = trans(pred)
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt)
prec, recall = self._eval_pr(pred, gt, 255)
f_score = (1 + beta2) * prec * recall / (beta2 * prec + recall)
f_score[f_score != f_score] = 0 # for Nan
avg_f += f_score
avg_p += prec
avg_r += recall
img_num += 1.0
Fm = avg_f / img_num
avg_p = avg_p / img_num
avg_r = avg_r / img_num
return Fm, avg_p, avg_r
def Eval_auc(self):
print('Evaluating AUC...')
avg_tpr, avg_fpr, avg_auc, img_num = 0.0, 0.0, 0.0, 0.0
with torch.no_grad():
trans = transforms.Compose([transforms.ToTensor()])
for pred, gt in self.loader:
if self.cuda:
pred = trans(pred).cuda()
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt).cuda()
else:
pred = trans(pred)
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt)
TPR, FPR = self._eval_roc(pred, gt, 255)
avg_tpr += TPR
avg_fpr += FPR
img_num += 1.0
avg_tpr = avg_tpr / img_num
avg_fpr = avg_fpr / img_num
sorted_idxes = torch.argsort(avg_fpr)
avg_tpr = avg_tpr[sorted_idxes]
avg_fpr = avg_fpr[sorted_idxes]
avg_auc = torch.trapz(avg_tpr, avg_fpr)
return avg_auc.item(), avg_tpr, avg_fpr
def Eval_Emeasure(self):
print('Evaluating EMeasure...')
avg_e, img_num = 0.0, 0.0
with torch.no_grad():
trans = transforms.Compose([transforms.ToTensor()])
Em = torch.zeros(255)
if self.cuda:
Em = Em.cuda()
for pred, gt in self.loader:
if self.cuda:
pred = trans(pred).cuda()
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt).cuda()
else:
pred = trans(pred)
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt)
Em += self._eval_e(pred, gt, 255)
img_num += 1.0
Em /= img_num
return Em
def select_by_Smeasure(self, bar=0.9, loader_comp=None, bar_comp=0.1):
print('Evaluating SMeasure...')
good_ones = []
good_ones_comp = []
good_ones_gt = []
alpha, avg_q, img_num = 0.5, 0.0, 0.0
with torch.no_grad():
trans = transforms.Compose([transforms.ToTensor()])
for (pred, gt, predpath, gtpath), (pred_comp, gt_comp, predpath_comp) in zip(self.loader, loader_comp):
# pred X gt
if self.cuda:
pred = trans(pred).cuda()
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt).cuda()
else:
pred = trans(pred)
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt)
y = gt.mean()
if y == 0:
x = pred.mean()
Q = 1.0 - x
elif y == 1:
x = pred.mean()
Q = x
else:
gt[gt >= 0.5] = 1
gt[gt < 0.5] = 0
Q = alpha * self._S_object(
pred, gt) + (1 - alpha) * self._S_region(pred, gt)
if Q.item() < 0:
Q = torch.FloatTensor([0.0])
img_num += 1.0
avg_q += Q.item()
# pred_comp X gt
if self.cuda:
pred_comp = trans(pred_comp).cuda()
pred_comp = (pred_comp - torch.min(pred_comp)) / (torch.max(pred_comp) -
torch.min(pred_comp) + 1e-20)
gt_comp = trans(gt_comp).cuda()
else:
pred_comp = trans(pred_comp)
pred_comp = (pred_comp - torch.min(pred_comp)) / (torch.max(pred_comp) -
torch.min(pred_comp) + 1e-20)
gt_comp = trans(gt_comp)
y = gt_comp.mean()
if y == 0:
x = pred_comp.mean()
Q_comp = 1.0 - x
elif y == 1:
x = pred_comp.mean()
Q_comp = x
else:
gt_comp[gt_comp >= 0.5] = 1
gt_comp[gt_comp < 0.5] = 0
Q_comp = alpha * self._S_object(
pred_comp, gt_comp) + (1 - alpha) * self._S_region(pred_comp, gt_comp)
if Q_comp.item() < 0:
Q_comp = torch.FloatTensor([0.0])
if Q.item() > bar and (Q.item() - Q_comp.item()) > bar_comp:
good_ones.append(predpath)
good_ones_comp.append(predpath_comp)
good_ones_gt.append(gtpath)
avg_q /= img_num
return avg_q, good_ones, good_ones_comp, good_ones_gt
def Eval_Smeasure(self):
print('Evaluating SMeasure...')
alpha, avg_q, img_num = 0.5, 0.0, 0.0
with torch.no_grad():
trans = transforms.Compose([transforms.ToTensor()])
for pred, gt in self.loader:
if self.cuda:
pred = trans(pred).cuda()
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt).cuda()
else:
pred = trans(pred)
pred = (pred - torch.min(pred)) / (torch.max(pred) -
torch.min(pred) + 1e-20)
gt = trans(gt)
y = gt.mean()
if y == 0:
x = pred.mean()
Q = 1.0 - x
elif y == 1:
x = pred.mean()
Q = x
else:
gt[gt >= 0.5] = 1
gt[gt < 0.5] = 0
Q = alpha * self._S_object(
pred, gt) + (1 - alpha) * self._S_region(pred, gt)
if Q.item() < 0:
Q = torch.FloatTensor([0.0])
img_num += 1.0
avg_q += Q.item()
avg_q /= img_num
return avg_q
def LOG(self, output):
os.makedirs(self.output_dir, exist_ok=True)
with open(self.logfile, 'a') as f:
f.write(output)
def _eval_e(self, y_pred, y, num):
if self.cuda:
score = torch.zeros(num).cuda()
thlist = torch.linspace(0, 1 - 1e-10, num).cuda()
else:
score = torch.zeros(num)
thlist = torch.linspace(0, 1 - 1e-10, num)
for i in range(num):
y_pred_th = (y_pred >= thlist[i]).float()
fm = y_pred_th - y_pred_th.mean()
gt = y - y.mean()
align_matrix = 2 * gt * fm / (gt * gt + fm * fm + 1e-20)
enhanced = ((align_matrix + 1) * (align_matrix + 1)) / 4
score[i] = torch.sum(enhanced) / (y.numel() - 1 + 1e-20)
return score
def _eval_pr(self, y_pred, y, num):
if self.cuda:
prec, recall = torch.zeros(num).cuda(), torch.zeros(num).cuda()
thlist = torch.linspace(0, 1 - 1e-10, num).cuda()
else:
prec, recall = torch.zeros(num), torch.zeros(num)
thlist = torch.linspace(0, 1 - 1e-10, num)
for i in range(num):
y_temp = (y_pred >= thlist[i]).float()
tp = (y_temp * y).sum()
prec[i], recall[i] = tp / (y_temp.sum() + 1e-20), tp / (y.sum() + 1e-20)
return prec, recall
def _eval_roc(self, y_pred, y, num):
if self.cuda:
TPR, FPR = torch.zeros(num).cuda(), torch.zeros(num).cuda()
thlist = torch.linspace(0, 1 - 1e-10, num).cuda()
else:
TPR, FPR = torch.zeros(num), torch.zeros(num)
thlist = torch.linspace(0, 1 - 1e-10, num)
for i in range(num):
y_temp = (y_pred >= thlist[i]).float()
tp = (y_temp * y).sum()
fp = (y_temp * (1 - y)).sum()
tn = ((1 - y_temp) * (1 - y)).sum()
fn = ((1 - y_temp) * y).sum()
TPR[i] = tp / (tp + fn + 1e-20)
FPR[i] = fp / (fp + tn + 1e-20)
return TPR, FPR
def _S_object(self, pred, gt):
fg = torch.where(gt == 0, torch.zeros_like(pred), pred)
bg = torch.where(gt == 1, torch.zeros_like(pred), 1 - pred)
o_fg = self._object(fg, gt)
o_bg = self._object(bg, 1 - gt)
u = gt.mean()
Q = u * o_fg + (1 - u) * o_bg
return Q
def _object(self, pred, gt):
temp = pred[gt == 1]
x = temp.mean()
sigma_x = temp.std()
score = 2.0 * x / (x * x + 1.0 + sigma_x + 1e-20)
return score
def _S_region(self, pred, gt):
X, Y = self._centroid(gt)
gt1, gt2, gt3, gt4, w1, w2, w3, w4 = self._divideGT(gt, X, Y)
p1, p2, p3, p4 = self._dividePrediction(pred, X, Y)
Q1 = self._ssim(p1, gt1)
Q2 = self._ssim(p2, gt2)
Q3 = self._ssim(p3, gt3)
Q4 = self._ssim(p4, gt4)
Q = w1 * Q1 + w2 * Q2 + w3 * Q3 + w4 * Q4
return Q
def _centroid(self, gt):
rows, cols = gt.size()[-2:]
gt = gt.view(rows, cols)
if gt.sum() == 0:
if self.cuda:
X = torch.eye(1).cuda() * round(cols / 2)
Y = torch.eye(1).cuda() * round(rows / 2)
else:
X = torch.eye(1) * round(cols / 2)
Y = torch.eye(1) * round(rows / 2)
else:
total = gt.sum()
if self.cuda:
i = torch.from_numpy(np.arange(0, cols)).cuda().float()
j = torch.from_numpy(np.arange(0, rows)).cuda().float()
else:
i = torch.from_numpy(np.arange(0, cols)).float()
j = torch.from_numpy(np.arange(0, rows)).float()
X = torch.round((gt.sum(dim=0) * i).sum() / total + 1e-20)
Y = torch.round((gt.sum(dim=1) * j).sum() / total + 1e-20)
return X.long(), Y.long()
def _divideGT(self, gt, X, Y):
h, w = gt.size()[-2:]
area = h * w
gt = gt.view(h, w)
LT = gt[:Y, :X]
RT = gt[:Y, X:w]
LB = gt[Y:h, :X]
RB = gt[Y:h, X:w]
X = X.float()
Y = Y.float()
w1 = X * Y / area
w2 = (w - X) * Y / area
w3 = X * (h - Y) / area
w4 = 1 - w1 - w2 - w3
return LT, RT, LB, RB, w1, w2, w3, w4
def _dividePrediction(self, pred, X, Y):
h, w = pred.size()[-2:]
pred = pred.view(h, w)
LT = pred[:Y, :X]
RT = pred[:Y, X:w]
LB = pred[Y:h, :X]
RB = pred[Y:h, X:w]
return LT, RT, LB, RB
def _ssim(self, pred, gt):
gt = gt.float()
h, w = pred.size()[-2:]
N = h * w
x = pred.mean()
y = gt.mean()
sigma_x2 = ((pred - x) * (pred - x)).sum() / (N - 1 + 1e-20)
sigma_y2 = ((gt - y) * (gt - y)).sum() / (N - 1 + 1e-20)
sigma_xy = ((pred - x) * (gt - y)).sum() / (N - 1 + 1e-20)
aplha = 4 * x * y * sigma_xy
beta = (x * x + y * y) * (sigma_x2 + sigma_y2)
if aplha != 0:
Q = aplha / (beta + 1e-20)
elif aplha == 0 and beta == 0:
Q = 1.0
else:
Q = 0
return Q
def Eval_AP(self, prec, recall):
# Ref:
# https://github.com/facebookresearch/Detectron/blob/05d04d3a024f0991339de45872d02f2f50669b3d/lib/datasets/voc_eval.py#L54
print('Evaluating AP...')
ap_r = np.concatenate(([0.], recall, [1.]))
ap_p = np.concatenate(([0.], prec, [0.]))
sorted_idxes = np.argsort(ap_r)
ap_r = ap_r[sorted_idxes]
ap_p = ap_p[sorted_idxes]
count = ap_r.shape[0]
for i in range(count - 1, 0, -1):
ap_p[i - 1] = max(ap_p[i], ap_p[i - 1])
i = np.where(ap_r[1:] != ap_r[:-1])[0]
ap = np.sum((ap_r[i + 1] - ap_r[i]) * ap_p[i + 1])
return ap
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