def compute_stats(self, confm_list, val_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(
confm_list)
mean_accuracy = compute_accuracy(TP_list, TN_list, FP_list,
FN_list)
self.stats.val.acc = np.nanmean(mean_accuracy)
self.stats.val.loss = val_loss.avg
def compute_stats(self, confm_list, train_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(
confm_list)
mean_accuracy = compute_accuracy(TP_list, TN_list, FP_list,
FN_list)
self.stats.train.acc = np.nanmean(mean_accuracy)
self.stats.train.loss = float(train_loss.avg.cpu().data)
def compute_stats(self, confm_list, val_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(confm_list)
mean_IoU = compute_mIoU(TP_list, FP_list, FN_list)
mean_accuracy = compute_accuracy_segmentation(TP_list, FN_list)
self.stats.val.acc = np.nanmean(mean_accuracy)
self.stats.val.mIoU_perclass = mean_IoU
self.stats.val.mIoU = np.nanmean(mean_IoU)
if val_loss is not None:
self.stats.val.loss = val_loss.avg
def compute_stats(self, confm_list, train_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(
confm_list)
mean_accuracy = compute_accuracy(TP_list, TN_list, FP_list,
FN_list)
mean_precision = compute_precision(TP_list, FP_list)
mean_recall = compute_recall(TP_list, FN_list)
mean_f1score = compute_f1score(TP_list, FP_list, FN_list)
self.stats.train.acc = np.nanmean(mean_accuracy)
self.stats.train.recall = np.nanmean(mean_recall)
self.stats.train.precision = np.nanmean(mean_precision)
self.stats.train.f1score = np.nanmean(mean_f1score)
if train_loss is not None:
self.stats.train.loss = train_loss.avg
def compute_stats(self, confm_list, val_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(
confm_list)
mean_accuracy = compute_accuracy(TP_list, TN_list, FP_list,
FN_list)
mean_precision = compute_precision(TP_list, FP_list)
mean_recall = compute_recall(TP_list, FN_list)
# self.stats.val.acc = np.nanmean(mean_accuracy)
self.stats.val.acc = np.sum(TP_list) / (np.sum(FP_list) +
np.sum(TP_list))
self.stats.val.acc = np.nanmean(mean_accuracy)
self.stats.val.recall = np.nanmean(mean_recall)
self.stats.val.precision = np.nanmean(mean_precision)
self.stats.val.f1score = np.nanmean(mean_f1score)
if val_loss is not None:
self.stats.val.loss = val_loss.avg
def compute_stats(self, confm_list, val_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(confm_list)
tp = np.sum(TP_list)
tn = np.sum(TN_list)
fp = np.sum(FP_list)
fn = np.sum(FN_list)
r = tp / (tp + fn)
p = tp / (tp + fp)
self.stats.val.acc = (tp + tn) / (tp + tn + fp + fn)
self.stats.val.recall = r
self.stats.val.precision = p
self.stats.val.f1score = 2 * (r * p) / (r + p)
if val_loss is not None:
self.stats.val.loss = val_loss.avg
def compute_stats(self, confm_list, val_loss):
TP_list, TN_list, FP_list, FN_list = extract_stats_from_confm(
confm_list)
TP = np.sum(TP_list)
TN = np.sum(TN_list)
FP = np.sum(FP_list)
FN = np.sum(FN_list)
precision = TP / (TP + FP)
recall = TP / (TP + FN)
self.stats.val.acc = np.sum(TP_list) / (np.sum(FP_list) +
np.sum(TP_list))
self.stats.val.recall = recall
self.stats.val.precision = precision
print("VAL results: accuracy", self.stats.val.acc, "precision",
precision, "recall", recall)
self.stats.val.f1score = 2 * (recall * precision) / (recall +
precision)
if val_loss is not None:
self.stats.val.loss = val_loss.avg
