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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_resize = False
# val_cfg.aug_crop = False
val_cfg.aug_flip = False
val_cfg.serial_batches = True
val_cfg.jpg_method = ["pil"]
# Currently assumes jpg_prob, blur_prob 0 or 1
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
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