更改
This commit is contained in:
lee
105
test_ori.py
105
test_ori.py
@ -11,6 +11,7 @@ import matplotlib.pyplot as plt
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# from config import config as conf
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from tools.dataset import get_transform
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from tools.image_joint import merge_imgs
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from configs import trainer_tools
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import yaml
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@ -22,6 +23,7 @@ embedding_size = conf["base"]["embedding_size"]
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img_size = conf["transform"]["img_size"]
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device = conf["base"]["device"]
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def unique_image(pair_list: str) -> Set[str]:
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unique_images = set()
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try:
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@ -38,7 +40,7 @@ def unique_image(pair_list: str) -> Set[str]:
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except IOError as e:
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print(f"Error reading pair list file: {e}")
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raise
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return unique_images
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@ -56,11 +58,11 @@ def group_image(images: Set[str], batch_size: int) -> List[List[str]]:
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image_list = list(images)
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num_images = len(image_list)
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batches = []
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for i in range(0, num_images, batch_size):
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batch_end = min(i + batch_size, num_images)
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batches.append(image_list[i:batch_end])
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return batches
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@ -89,21 +91,21 @@ def test_preprocess(images: list, transform) -> torch.Tensor:
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def featurize(
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images: List[str],
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transform: callable,
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net: nn.Module,
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device: torch.device,
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train: bool = False
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images: List[str],
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transform: callable,
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net: nn.Module,
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device: torch.device,
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train: bool = False
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) -> Dict[str, torch.Tensor]:
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try:
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# Select appropriate preprocessing
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preprocess_fn = _preprocess if train else test_preprocess
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# Preprocess and move to device
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data = preprocess_fn(images, transform)
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data = data.to(device)
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net = net.to(device)
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# Extract features with automatic mixed precision
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with torch.no_grad():
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if conf['models']['half']:
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@ -111,12 +113,16 @@ def featurize(
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features = net(data)
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# Create path-to-feature mapping
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return {img: feature for img, feature in zip(images, features)}
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except Exception as e:
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print(f"Error in feature extraction: {e}")
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raise
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def cosin_metric(x1, x2):
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return np.dot(x1, x2) / (np.linalg.norm(x1) * np.linalg.norm(x2))
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def threshold_search(y_score, y_true):
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y_score = np.asarray(y_score)
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y_true = np.asarray(y_true)
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@ -179,22 +185,23 @@ def compute_accuracy_recall(score, labels):
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f_labels = (labels == 0)
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TN = np.sum(np.logical_and(f_score, f_labels))
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FP = np.sum(np.logical_and(np.logical_not(f_score), f_labels))
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print("Threshold:{} TP:{},FP:{},TN:{},FN:{}".format(th, TP, FP, TN, FN))
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# print("Threshold:{} TP:{},FP:{},TN:{},FN:{}".format(th, TP, FP, TN, FN))
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PrecisePos.append(0 if TP / (TP + FP) == 'nan' else TP / (TP + FP))
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PreciseNeg.append(0 if TN == 0 else TN / (TN + FN))
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recall.append(0 if TP == 0 else TP / (TP + FN))
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recall_TN.append(0 if TN == 0 else TN / (TN + FP))
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Correct.append(0 if TP == 0 else (TP + TN) / (TP + FP + TN + FN))
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print("Threshold:{} PrecisePos:{},recall:{},PreciseNeg:{},recall_TN:{}".format(th, PrecisePos[-1], recall[-1],
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PreciseNeg[-1], recall_TN[-1]))
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showHist(Same, Cross)
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showgrid(recall, recall_TN, PrecisePos, PreciseNeg, Correct)
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def compute_accuracy(
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feature_dict: Dict[str, torch.Tensor],
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pair_list: str,
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test_root: str
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feature_dict: Dict[str, torch.Tensor],
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pair_list: str,
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test_root: str
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) -> Tuple[float, float]:
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try:
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with open(pair_list, 'r') as f:
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@ -205,37 +212,43 @@ def compute_accuracy(
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similarities = []
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labels = []
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for pair in pairs:
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pair = pair.strip()
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if not pair:
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continue
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try:
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img1, img2, label = pair.split()
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img1_path = osp.join(test_root, img1)
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img2_path = osp.join(test_root, img2)
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# Verify features exist
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if img1_path not in feature_dict or img2_path not in feature_dict:
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raise ValueError(f"Missing features for image pair: {img1_path}, {img2_path}")
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# Get features and compute similarity
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feat1 = feature_dict[img1_path].cpu().numpy()
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feat2 = feature_dict[img2_path].cpu().numpy()
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similarity = cosin_metric(feat1, feat2)
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similarities.append(similarity)
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labels.append(int(label))
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except Exception as e:
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print(f"Skipping invalid pair: {pair}. Error: {e}")
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continue
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# try:
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img1, img2, label = pair.split()
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img1_path = osp.join(test_root, img1)
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img2_path = osp.join(test_root, img2)
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# Verify features exist
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if img1_path not in feature_dict or img2_path not in feature_dict:
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raise ValueError(f"Missing features for image pair: {img1_path}, {img2_path}")
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# Get features and compute similarity
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feat1 = feature_dict[img1_path].cpu().numpy()
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feat2 = feature_dict[img2_path].cpu().numpy()
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similarity = cosin_metric(feat1, feat2)
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print('{} vs {}: {}'.format(img1_path, img2_path, similarity))
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if conf['data']['save_image_joint']:
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merge_imgs(img1_path,
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img2_path,
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conf['data']['image_joint_pth'],
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similarity,
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label)
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similarities.append(similarity)
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labels.append(int(label))
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# except Exception as e:
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# print(f"Skipping invalid pair: {pair}. Error: {e}")
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# continue
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# Find optimal threshold and accuracy
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accuracy, threshold = threshold_search(similarities, labels)
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compute_accuracy_recall(np.array(similarities), np.array(labels))
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return accuracy, threshold
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@ -267,10 +280,10 @@ def compute_group_accuracy(content_list_read):
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d = featurize(group[0], conf.test_transform, model, conf.device)
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one_group_list.append(d.values())
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if data_loaded[-1] == '1':
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similarity = deal_group_pair(one_group_list[0], one_group_list[1])
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similarity = abs(deal_group_pair(one_group_list[0], one_group_list[1]))
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Same.append(similarity)
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else:
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similarity = deal_group_pair(one_group_list[0], one_group_list[1])
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similarity = abs(deal_group_pair(one_group_list[0], one_group_list[1]))
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Cross.append(similarity)
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allLabel.append(data_loaded[-1])
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allSimilarity.extend(similarity)
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@ -291,7 +304,17 @@ def init_model():
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print('load model {} '.format(conf['models']['backbone']))
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if torch.cuda.device_count() > 1 and conf['base']['distributed']:
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model = nn.DataParallel(model).to(conf['base']['device'])
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###############正常模型加载################
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model.load_state_dict(torch.load(conf['models']['model_path'], map_location=conf['base']['device']))
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#######################################
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####### 对比学习模型临时运用###
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# state_dict = torch.load(conf['models']['model_path'], map_location=conf['base']['device'])
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# new_state_dict = {}
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# for k, v in state_dict.items():
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# new_key = k.replace("module.base_model.", "module.")
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# new_state_dict[new_key] = v
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# model.load_state_dict(new_state_dict, strict=False)
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###########################
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if conf['models']['half']:
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model.half()
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first_param_dtype = next(model.parameters()).dtype
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