| import math |
| import os |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
| import torch |
| import torch.nn as nn |
|
|
| from utils1.config import CONFIGCLASS |
| from utils1.utils import to_cuda |
|
|
|
|
| def get_val_cfg(cfg: CONFIGCLASS, split="val", copy=True): |
| if copy: |
| from copy import deepcopy |
|
|
| val_cfg = deepcopy(cfg) |
| else: |
| val_cfg = cfg |
| val_cfg.dataset_root = os.path.join(val_cfg.dataset_root, split) |
| val_cfg.datasets = cfg.datasets_test |
| val_cfg.isTrain = False |
| |
| |
| val_cfg.aug_flip = False |
| val_cfg.serial_batches = True |
| val_cfg.jpg_method = ["pil"] |
| |
| if len(val_cfg.blur_sig) == 2: |
| b_sig = val_cfg.blur_sig |
| val_cfg.blur_sig = [(b_sig[0] + b_sig[1]) / 2] |
| if len(val_cfg.jpg_qual) != 1: |
| j_qual = val_cfg.jpg_qual |
| val_cfg.jpg_qual = [int((j_qual[0] + j_qual[-1]) / 2)] |
| return val_cfg |
|
|
| def validate(model: nn.Module, cfg: CONFIGCLASS): |
| from sklearn.metrics import accuracy_score, average_precision_score, roc_auc_score |
|
|
| from utils1.datasets import create_dataloader |
|
|
| data_loader = create_dataloader(cfg) |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| with torch.no_grad(): |
| y_true, y_pred = [], [] |
| for data in data_loader: |
| img, label, meta = data if len(data) == 3 else (*data, None) |
| in_tens = to_cuda(img, device) |
| meta = to_cuda(meta, device) |
| predict = model(in_tens, meta).sigmoid() |
| y_pred.extend(predict.flatten().tolist()) |
| y_true.extend(label.flatten().tolist()) |
|
|
| y_true, y_pred = np.array(y_true), np.array(y_pred) |
| r_acc = accuracy_score(y_true[y_true == 0], y_pred[y_true == 0] > 0.5) |
| f_acc = accuracy_score(y_true[y_true == 1], y_pred[y_true == 1] > 0.5) |
| acc = accuracy_score(y_true, y_pred > 0.5) |
| ap = average_precision_score(y_true, y_pred) |
| results = { |
| "ACC": acc, |
| "AP": ap, |
| "R_ACC": r_acc, |
| "F_ACC": f_acc, |
| } |
| return results |
|
|
