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eval.py
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eval.py
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import numpy as np
def total_accuracy(self, y_pred, y_true):
return np.sum(np.argmax(y_pred, axis=1).flatten() == y_true) / len(y_true)
def classwise_accuracy(self, y_pred, y_true):
cls_acc = np.zeros(self.num_class)
y_pred = np.argmax(y_pred, axis=1).flatten()
for i in range(self.num_class):
class_num = np.sum(y_true==i)
if class_num==0:
cls_acc[i] = 0
else:
cls_acc[i] = np.sum(y_pred[y_true==i]==i)/class_num
return cls_acc
def get_labels_start_end_time(frame_wise_labels, bg_class=[]):
labels = []
starts = []
ends = []
last_label = frame_wise_labels[0]
if frame_wise_labels[0] not in bg_class:
labels.append(frame_wise_labels[0])
starts.append(0)
for i in range(len(frame_wise_labels)):
if frame_wise_labels[i] != last_label:
if frame_wise_labels[i] not in bg_class:
labels.append(frame_wise_labels[i])
starts.append(i)
if last_label not in bg_class:
ends.append(i)
last_label = frame_wise_labels[i]
if last_label not in bg_class:
ends.append(i + 1)
return labels, starts, ends
def levenstein(p, y, norm=False):
m_row = len(p)
n_col = len(y)
D = np.zeros([m_row+1, n_col+1], np.float)
for i in range(m_row+1):
D[i, 0] = i
for i in range(n_col+1):
D[0, i] = i
for j in range(1, n_col+1):
for i in range(1, m_row+1):
if y[j-1] == p[i-1]:
D[i, j] = D[i-1, j-1]
else:
D[i, j] = min(D[i-1, j] + 1,
D[i, j-1] + 1,
D[i-1, j-1] + 1)
if norm:
score = (1 - D[-1, -1]/max(m_row, n_col)) * 100
else:
score = D[-1, -1]
return score
def edit_score(recognized, ground_truth, norm=True, bg_class=[]):
P, _, _ = get_labels_start_end_time(recognized, bg_class)
Y, _, _ = get_labels_start_end_time(ground_truth, bg_class)
return levenstein(P, Y, norm)
def f_score(recognized, ground_truth, overlap, bg_class=[]):
p_label, p_start, p_end = get_labels_start_end_time(recognized, bg_class)
y_label, y_start, y_end = get_labels_start_end_time(ground_truth, bg_class)
tp = 0
fp = 0
IoU_list = []
hits = np.zeros(len(y_label))
for j in range(len(p_label)):
intersection = np.minimum(p_end[j], y_end) - np.maximum(p_start[j], y_start)
union = np.maximum(p_end[j], y_end) - np.minimum(p_start[j], y_start)
IoU = (1.0*intersection / union)*([p_label[j] == y_label[x] for x in range(len(y_label))])
# Get the best scoring segment
idx = np.array(IoU).argmax()
IoU_list.append(IoU[idx])
if IoU[idx] >= overlap and not hits[idx]:
tp += 1
hits[idx] = 1
else:
fp += 1
fn = len(y_label) - sum(hits)
return float(tp), float(fp), float(fn), np.mean(IoU_list)
def get_all_metrics(y_pred,y_true,bg_class=[]):
metrics = []
overlap = [.1, .25, .5]
tp, fp, fn = np.zeros(3), np.zeros(3), np.zeros(3)
correct = np.sum(y_pred==y_true)
for s in range(len(overlap)):
tp1, fp1, fn1, mean_IoU = f_score(y_pred, y_true, overlap[s], bg_class)
tp[s] += tp1
fp[s] += fp1
fn[s] += fn1
Acc = 100*float(correct)/len(y_pred)
metrics.append(Acc)
edit = edit_score(y_pred, y_true, True, bg_class)
metrics.append(edit)
# print(f"Acc: {Acc} edit:{edit}", end=" ")
for s in range(len(overlap)):
precision = tp[s] / float(tp[s]+fp[s])
recall = tp[s] / float(tp[s]+fn[s])
f1 = 2.0 * (precision*recall) / (precision+recall)
f1 = np.nan_to_num(f1)*100
metrics.append(f1)
# print('F1@%0.2f: %.4f' % (overlap[s], f1), end=" ")
return metrics
if __name__ == "__main__":
pred = np.zeros(1000)
true = np.zeros(1000)
true[10:100] = 1
true[105:125] = 1
true[205:225] = 1
true[305:335] = 1
print(get_all_metrics(pred,true))