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test.py
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test.py
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import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
import os
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
from torch.utils.data import DataLoader
from tqdm import tqdm
import argparse
import json
import os
import torch
from scipy.ndimage import gaussian_filter
import cv2
# Importing from local modules
from tools import write2csv, setup_seed, Logger
from dataset import get_data, dataset_dict
from method import AdaCLIP_Trainer
from PIL import Image
import numpy as np
setup_seed(111)
def train(args):
assert os.path.isfile(args.ckt_path), f"Please check the path of pre-trained model, {args.ckt_path} is not valid."
# Configurations
batch_size = args.batch_size
image_size = args.image_size
device = 'cuda' if torch.cuda.is_available() else 'cpu'
save_fig = args.save_fig
# Logger
logger = Logger('log.txt')
# Print basic information
for key, value in sorted(vars(args).items()):
logger.info(f'{key} = {value}')
config_path = os.path.join('./model_configs', f'{args.model}.json')
# Prepare model
with open(config_path, 'r') as f:
model_configs = json.load(f)
# Set up the feature hierarchy
n_layers = model_configs['vision_cfg']['layers']
substage = n_layers // 4
features_list = [substage, substage * 2, substage * 3, substage * 4]
model = AdaCLIP_Trainer(
backbone=args.model,
feat_list=features_list,
input_dim=model_configs['vision_cfg']['width'],
output_dim=model_configs['embed_dim'],
learning_rate=0.,
device=device,
image_size=image_size,
prompting_depth=args.prompting_depth,
prompting_length=args.prompting_length,
prompting_branch=args.prompting_branch,
prompting_type=args.prompting_type,
use_hsf=args.use_hsf,
k_clusters=args.k_clusters
).to(device)
model.load(args.ckt_path)
if args.testing_model == 'dataset':
assert args.testing_data in dataset_dict.keys(), f"You entered {args.testing_data}, but we only support " \
f"{dataset_dict.keys()}"
save_root = args.save_path
csv_root = os.path.join(save_root, 'csvs')
image_root = os.path.join(save_root, 'images')
csv_path = os.path.join(csv_root, f'{args.testing_data}.csv')
image_dir = os.path.join(image_root, f'{args.testing_data}')
os.makedirs(image_dir, exist_ok=True)
test_data_cls_names, test_data, test_data_root = get_data(
dataset_type_list=args.testing_data,
transform=model.preprocess,
target_transform=model.transform,
training=False)
test_dataloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, shuffle=False)
save_fig_flag = save_fig
metric_dict = model.evaluation(
test_dataloader,
test_data_cls_names,
save_fig_flag,
image_dir,
)
for tag, data in metric_dict.items():
logger.info(
'{:>15} \t\tI-Auroc:{:.2f} \tI-F1:{:.2f} \tI-AP:{:.2f} \tP-Auroc:{:.2f} \tP-F1:{:.2f} \tP-AP:{:.2f}'.
format(tag,
data['auroc_im'],
data['f1_im'],
data['ap_im'],
data['auroc_px'],
data['f1_px'],
data['ap_px'])
)
for k in metric_dict.keys():
write2csv(metric_dict[k], test_data_cls_names, k, csv_path)
elif args.testing_model == 'image':
assert os.path.isfile(args.image_path), f"Please verify the input image path: {args.image_path}"
ori_image = cv2.resize(cv2.imread(args.image_path), (args.image_size, args.image_size))
pil_img = Image.open(args.image_path).convert('RGB')
img_input = model.preprocess(pil_img).unsqueeze(0)
img_input = img_input.to(model.device)
with torch.no_grad():
anomaly_map, anomaly_score = model.clip_model(img_input, [args.class_name], aggregation=True)
anomaly_map = anomaly_map[0, :, :]
anomaly_score = anomaly_score[0]
anomaly_map = anomaly_map.cpu().numpy()
anomaly_score = anomaly_score.cpu().numpy()
anomaly_map = gaussian_filter(anomaly_map, sigma=4)
anomaly_map = anomaly_map * 255
anomaly_map = anomaly_map.astype(np.uint8)
heat_map = cv2.applyColorMap(anomaly_map, cv2.COLORMAP_JET)
vis_map = cv2.addWeighted(heat_map, 0.5, ori_image, 0.5, 0)
vis_map = cv2.hconcat([ori_image, vis_map])
save_path = os.path.join(args.save_path, args.save_name)
print(f"Anomaly detection results are saved in {save_path}, with an anomaly of {anomaly_score:.3f} ")
cv2.imwrite(save_path, vis_map)
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
if __name__ == '__main__':
parser = argparse.ArgumentParser("AdaCLIP", add_help=True)
# Paths and configurations
parser.add_argument("--ckt_path", type=str, default='weights/pretrained_mvtec_colondb.pth',
help="Path to the pre-trained model (default: weights/pretrained_mvtec_colondb.pth)")
parser.add_argument("--testing_model", type=str, default="dataset", choices=["dataset", "image"],
help="Model for testing (default: 'dataset')")
# for the dataset model
parser.add_argument("--testing_data", type=str, default="visa", help="Dataset for testing (default: 'visa')")
# for the image model
parser.add_argument("--image_path", type=str, default="asset/img.png",
help="Model for testing (default: 'asset/img.png')")
parser.add_argument("--class_name", type=str, default="candle",
help="The class name of the testing image (default: 'candle')")
parser.add_argument("--save_name", type=str, default="test.png",
help="Model for testing (default: 'dataset')")
parser.add_argument("--save_path", type=str, default='./workspaces',
help="Directory to save results (default: './workspaces')")
parser.add_argument("--model", type=str, default="ViT-L-14-336",
choices=["ViT-B-16", "ViT-B-32", "ViT-L-14", "ViT-L-14-336"],
help="The CLIP model to be used (default: 'ViT-L-14-336')")
parser.add_argument("--save_fig", type=str2bool, default=False,
help="Save figures for visualizations (default: False)")
# Hyper-parameters
parser.add_argument("--batch_size", type=int, default=1, help="Batch size (default: 1)")
parser.add_argument("--image_size", type=int, default=518, help="Size of the input images (default: 518)")
# Prompting parameters
parser.add_argument("--prompting_depth", type=int, default=4, help="Depth of prompting (default: 4)")
parser.add_argument("--prompting_length", type=int, default=5, help="Length of prompting (default: 5)")
parser.add_argument("--prompting_type", type=str, default='SD', choices=['', 'S', 'D', 'SD'],
help="Type of prompting. 'S' for Static, 'D' for Dynamic, 'SD' for both (default: 'SD')")
parser.add_argument("--prompting_branch", type=str, default='VL', choices=['', 'V', 'L', 'VL'],
help="Branch of prompting. 'V' for Visual, 'L' for Language, 'VL' for both (default: 'VL')")
parser.add_argument("--use_hsf", type=str2bool, default=True,
help="Use HSF for aggregation. If False, original class embedding is used (default: True)")
parser.add_argument("--k_clusters", type=int, default=20, help="Number of clusters (default: 20)")
args = parser.parse_args()
if args.batch_size != 1:
raise NotImplementedError(
"Currently, only batch size of 1 is supported due to unresolved bugs. Please set --batch_size to 1.")
train(args)