| from sklearn import metrics
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| import numpy as np
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| def parse_metric_for_print(metric_dict):
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| if metric_dict is None:
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| return "\n"
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| str = "\n"
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| str += "================================ Each dataset best metric ================================ \n"
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| for key, value in metric_dict.items():
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| if key != 'avg':
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| str= str+ f"| {key}: "
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| for k,v in value.items():
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| str = str + f" {k}={v} "
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| str= str+ "| \n"
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| else:
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| str += "============================================================================================= \n"
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| str += "================================== Average best metric ====================================== \n"
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| avg_dict = value
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| for avg_key, avg_value in avg_dict.items():
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| if avg_key == 'dataset_dict':
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| for key,value in avg_value.items():
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| str = str + f"| {key}: {value} | \n"
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| else:
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| str = str + f"| avg {avg_key}: {avg_value} | \n"
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| str += "============================================================================================="
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| return str
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| def get_test_metrics(y_pred, y_true, img_names):
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| def get_video_metrics(image, pred, label):
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| result_dict = {}
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| new_label = []
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| new_pred = []
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| for item in np.transpose(np.stack((image, pred, label)), (1, 0)):
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| s = item[0]
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| if '\\' in s:
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| parts = s.split('\\')
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| else:
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| parts = s.split('/')
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| a = parts[-2]
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| b = parts[-1]
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|
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| if a not in result_dict:
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| result_dict[a] = []
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| result_dict[a].append(item)
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| image_arr = list(result_dict.values())
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| for video in image_arr:
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| pred_sum = 0
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| label_sum = 0
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| leng = 0
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| for frame in video:
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| pred_sum += float(frame[1])
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| label_sum += int(frame[2])
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| leng += 1
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| new_pred.append(pred_sum / leng)
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| new_label.append(int(label_sum / leng))
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|
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| fpr, tpr, thresholds = metrics.roc_curve(new_label, new_pred)
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| v_auc = metrics.auc(fpr, tpr)
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| fnr = 1 - tpr
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| v_eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
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| prediction_class = (np.array(new_pred) > 0.5).astype(int)
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| correct = (prediction_class == np.array(new_label)).sum().item()
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| v_acc = correct / len(prediction_class)
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| return v_auc, v_eer, v_acc
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| y_pred = y_pred.squeeze()
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| fpr, tpr, thresholds = metrics.roc_curve(y_true, y_pred, pos_label=1)
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| auc = metrics.auc(fpr, tpr)
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| fnr = 1 - tpr
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| eer = fpr[np.nanargmin(np.absolute((fnr - fpr)))]
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| ap = metrics.average_precision_score(y_true, y_pred)
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| prediction_class = (y_pred > 0.5).astype(int)
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| correct = (prediction_class == np.clip(y_true, a_min=0, a_max=1)).sum().item()
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| acc = correct / len(prediction_class)
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| if type(img_names[0]) is not list:
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| try:
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| v_auc, v_eer, v_acc = get_video_metrics(img_names, y_pred, y_true)
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| return {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap, 'pred': y_pred, 'video_auc': v_auc, 'video_eer': v_eer, 'video_acc': v_acc, 'label': y_true}
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| except Exception as e:
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| print(e)
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| v_auc=auc
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| return {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap, 'pred': y_pred, 'label': y_true}
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| else:
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| v_auc=auc
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| v_eer=eer
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| v_acc=acc
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| return {'acc': acc, 'auc': auc, 'eer': eer, 'ap': ap, 'pred': y_pred, 'video_auc': v_auc, 'video_eer': v_eer, 'video_acc': v_acc, 'label': y_true}
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|
