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import os
from tqdm import tqdm
import torch
import pandas as pd
import json
import time
import numpy as np
from sklearn.metrics import roc_auc_score, accuracy_score
from networks import create_architecture, count_parameters
from utils.dataset import create_dataloader
from utils.processing import add_processing_arguments
from parser import get_parser
def test(loader, model, settings, device):
model.eval()
start_time = time.time()
# File paths
output_dir = f'./results/{settings.name}/data/{settings.data_keys}'
os.makedirs(output_dir, exist_ok=True)
csv_filename = os.path.join(output_dir, 'results.csv')
metrics_filename = os.path.join(output_dir, 'metrics.json')
image_results_filename = os.path.join(output_dir, 'image_results.json')
# Collect all results
all_scores = []
all_labels = []
all_paths = []
image_results = []
# Extract training dataset keys from model name (format: "training_keys_freeze_down" or "training_keys")
training_dataset_keys = []
model_name = settings.name
if '_freeze_down' in model_name:
training_name = model_name.replace('_freeze_down', '')
else:
training_name = model_name
if '&' in training_name:
training_dataset_keys = training_name.split('&')
else:
training_dataset_keys = [training_name]
# Write CSV header
with open(csv_filename, 'w') as f:
f.write(f"{','.join(['name', 'pro', 'flag'])}\n")
with torch.no_grad():
with tqdm(loader, unit='batch', mininterval=0.5) as tbatch:
tbatch.set_description(f'Validation')
for data_dict in tbatch:
data = data_dict['img'].to(device)
labels = data_dict['target'].to(device)
paths = data_dict['path']
scores = model(data).squeeze(1)
# Collect results
for score, label, path in zip(scores, labels, paths):
score_val = score.item()
label_val = label.item()
all_scores.append(score_val)
all_labels.append(label_val)
all_paths.append(path)
image_results.append({
'path': path,
'score': score_val,
'label': label_val
})
# Write to CSV (maintain backward compatibility)
with open(csv_filename, 'a') as f:
for score, label, path in zip(scores, labels, paths):
f.write(f"{path}, {score.item()}, {label.item()}\n")
# Calculate metrics
all_scores = np.array(all_scores)
all_labels = np.array(all_labels)
# Convert scores to predictions (threshold at 0, as used in train.py: y_pred > 0.0)
predictions = (all_scores > 0).astype(int)
# Calculate overall metrics
total_accuracy = accuracy_score(all_labels, predictions)
# TPR (True Positive Rate) = TP / (TP + FN) = accuracy on fake images (label==1)
fake_mask = all_labels == 1
if fake_mask.sum() > 0:
tpr = accuracy_score(all_labels[fake_mask], predictions[fake_mask])
else:
tpr = 0.0
# TNR per dataset key (True Negative Rate) = TN / (TN + FP) = accuracy on real images (label==0)
tnr_per_dataset = {}
# Calculate TNR on real images (label==0) in the test set
real_mask = all_labels == 0
if real_mask.sum() > 0:
# Overall TNR calculated on all real images in the test set
tnr = accuracy_score(all_labels[real_mask], predictions[real_mask])
else:
tnr = 0.0
# Map TNR to training dataset keys (as shown in the example JSON structure)
# The TNR is calculated on the test set, but organized by training dataset keys
#for training_key in training_dataset_keys:
# tnr_per_dataset[training_key] = overall_tnr
# AUC calculation (needs probabilities, so we'll use sigmoid on scores)
if len(np.unique(all_labels)) > 1: # Need both classes for AUC
# Apply sigmoid to convert scores to probabilities
probabilities = torch.sigmoid(torch.tensor(all_scores)).numpy()
auc = roc_auc_score(all_labels, probabilities)
else:
auc = 0.0
execution_time = time.time() - start_time
# Prepare metrics JSON
metrics = {
'TPR': float(tpr),
'TNR': float(tnr),
'Acc total': float(total_accuracy),
'AUC': float(auc),
'execution time': float(execution_time)
}
# Write metrics JSON
with open(metrics_filename, 'w') as f:
json.dump(metrics, f, indent=2)
# Write individual image results JSON
with open(image_results_filename, 'w') as f:
json.dump(image_results, f, indent=2)
print(f'\nMetrics saved to {metrics_filename}')
print(f'Image results saved to {image_results_filename}')
print(f'\nMetrics:')
print(f' TPR: {tpr:.4f}')
print(f' TNR: {tnr:.4f}')
print(f' Accuracy: {total_accuracy:.4f}')
print(f' AUC: {auc:.4f}')
print(f' Execution time: {execution_time:.2f} seconds')
if __name__ == '__main__':
parser = get_parser()
parser = add_processing_arguments(parser)
settings = parser.parse_args()
device = torch.device(settings.device if torch.cuda.is_available() else 'cpu')
test_dataloader = create_dataloader(settings, split='test')
model = create_architecture(settings.arch, pretrained=True, num_classes=1).to(device)
num_parameters = count_parameters(model)
print(f"Arch: {settings.arch} with #parameters {num_parameters}")
load_path = f'./checkpoint/{settings.name}/weights/best.pt'
print('loading the model from %s' % load_path)
model.load_state_dict(torch.load(load_path, map_location=device)['model'])
model.to(device)
test(test_dataloader, model, settings, device)
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