Spaces:
Running
Running
File size: 5,743 Bytes
9c4b1c4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 |
# ----------------------------------------------------------------------------
# IMPORTS
# ----------------------------------------------------------------------------
import os
import torch
import pandas as pd
from tqdm import tqdm
import json
import time
import numpy as np
from sklearn.metrics import roc_auc_score, accuracy_score
from networks import ImageClassifier
from parser import get_parser
from dataset import create_dataloader
def test(loader, model, settings, device):
model.eval()
start_time = time.time()
# File paths
output_dir = f'./results/{settings.name}/{settings.data_keys}/data/'
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 = []
# Parse dataset keys from settings.data_keys (format: "key1&key2&..." or single "key")
dataset_keys = settings.data_keys.split('&') if '&' in settings.data_keys else [settings.data_keys]
# Extract training dataset keys from model name (format: "training_keys_freeze_down" or "training_keys")
# The model name typically contains the training dataset keys used for training
training_dataset_keys = []
model_name = settings.name
# Remove common suffixes like "_freeze_down"
if '_freeze_down' in model_name:
training_name = model_name.replace('_freeze_down', '')
else:
training_name = model_name
# Split by & to get individual training dataset keys
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, labels, paths) in tbatch:
data = data.to(device)
labels = labels.to(device)
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)
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
# 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
# AUC calculation (needs probabilities, so we'll use sigmoid on scores)
if len(np.unique(all_labels)) > 1:
# 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()
settings = parser.parse_args()
device = torch.device(settings.device if torch.cuda.is_available() else 'cpu')
test_dataloader = create_dataloader(settings, split='test')
model = ImageClassifier(settings)
model.to(device)
path_weight = f'./checkpoint/{settings.name}/weights/best.pt'
state_dict = torch.load(path_weight)
model.load_state_dict(state_dict)
test(test_dataloader, model, settings, device) |