fixing shape

test.py

@@ -10,6 +10,7 @@ import argparse
 import json
 import os
 import torch
+
 # from torch.utils.data import Subset
 # from scipy.ndimage import gaussian_filter
 # import cv2
@@ -17,15 +18,20 @@ import numpy as np
 
 # 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
-from datasets.rayan_dataset import RayanDataset  
+from datasets.rayan_dataset import RayanDataset
 from utils.dump_scores import DumpScores
+from torchvision import transforms
 
 setup_seed(111)
 
+IMAGENET_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_STD = [0.229, 0.224, 0.225]
+
 
 def get_available_class_names(data_path):
     all_items = os.listdir(data_path)
@@ -41,7 +47,7 @@ def train(args):
         args.ckt_path
     ), f"Please check the path of pre-trained model, {args.ckt_path} is not valid."
 
-    data_path = "./data/"  
+    data_path = "./data/"
     class_names = get_available_class_names(data_path)
 
     # Configurations
@@ -97,15 +103,23 @@ def train(args):
         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)
-        
+
         dumper = DumpScores()
 
         for classname in class_names:
             test_data = RayanDataset(
                 source=data_path,
                 classname=classname,
+                external_transform=transforms.Compose(
+                    [
+                        transforms.Resize((224, 224)),
+                        transforms.CenterCrop(224),
+                        transforms.ToTensor(),
+                        transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD),
+                    ]
+                ),
             )
-            
+
             test_dataloader = torch.utils.data.DataLoader(
                 test_data, batch_size=1, shuffle=False
             )