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