Update describe.py

describe.py

@@ -10,7 +10,7 @@ from PIL import Image
 from tqdm import tqdm
 import torchvision.transforms as transforms
 
-from CircumSpect.model.densecap import densecap_resnet50_fpn
+from model.densecap import densecap_resnet50_fpn
 
 model = None
 first_run = True
@@ -224,11 +224,11 @@ def process_image(image, folder_location):
     parser.add_argument('--check', action='store_true', help='whether to validate box feat by regenerate sentences')
     args = argparse.Namespace()
 
-    args.config_json = f'{folder_location}CircumSpect/model_params/train_all_val_all_bz_2_epoch_10_inject_init/config.json'
-    args.lut_path = f'{folder_location}CircumSpect/data/VG-regions-dicts-lite.pkl'
-    args.model_checkpoint = f'{folder_location}models/train_all_val_all_bz_2_epoch_10_inject_init.pth.tar'
+    args.config_json = f'{folder_location}/model_params/train_all_val_all_bz_2_epoch_10_inject_init/config.json'
+    args.lut_path = f'{folder_location}/data/VG-regions-dicts-lite.pkl'
+    args.model_checkpoint = f'{folder_location}model_params/train_all_val_all_bz_2_epoch_10_inject_init.pth.tar'
     args.img_path = image
-    args.result_dir = f'{folder_location}CircumSpect/'
+    args.result_dir = f'{folder_location}'
     args.box_per_img = 100
     args.batch_size = 2
     args.extract = False