diff --git a/DeepLense_Regression_Zhongchao_Guan/CNNT.py b/DeepLense_Regression_Zhongchao_Guan/CNNT.py
index 3cfb8d3..7cbb7bd 100644
--- a/DeepLense_Regression_Zhongchao_Guan/CNNT.py
+++ b/DeepLense_Regression_Zhongchao_Guan/CNNT.py
@@ -173,10 +173,10 @@ def __init__(self, num_classes, depth, heads, mlp_dim, pool='mean', dim_head=64,
         self.to_patch_embedding = CNN()
 
         # hyper-params
-        num_patches = 16 * 16
+       max_patches = 256  # maximum expected patches, avoids hardcoded assumption
         dim = 32
 
-        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.pos_embedding = nn.Parameter(torch.randn(1, max_patches + 1, dim))
         self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
         self.dropout = nn.Dropout(emb_dropout)
 
@@ -191,17 +191,26 @@ def __init__(self, num_classes, depth, heads, mlp_dim, pool='mean', dim_head=64,
         )
 
     def forward(self, img):
-        x = self.to_patch_embedding(img)
-
-        # b: batch size  n: patch number  _: dim of patch
-        b, n, _ = x.shape
-
-        cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b=b)
-        x = torch.cat((cls_tokens, x), dim=1)
-        x += self.pos_embedding[:, :(n + 1)]
-        x = self.dropout(x)
-        x = self.transformer(x)
-        x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]
-
-        x = self.to_latent(x)
-        return self.mlp_head(x)
+    x = self.to_patch_embedding(img)
+    b, n, _ = x.shape
+
+    cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b=b)
+    x = torch.cat((cls_tokens, x), dim=1)
+
+    # dynamically handle any patch size
+    if (n + 1) > self.pos_embedding.shape[1]:
+        pos_emb = torch.nn.functional.interpolate(
+            self.pos_embedding.permute(0, 2, 1),
+            size=(n + 1),
+            mode='linear',
+            align_corners=False
+        ).permute(0, 2, 1)
+    else:
+        pos_emb = self.pos_embedding[:, :(n + 1)]
+
+    x += pos_emb
+    x = self.dropout(x)
+    x = self.transformer(x)
+    x = x.mean(dim=1) if self.pool == 'mean' else x[:, 0]
+    x = self.to_latent(x)
+    return self.mlp_head(x)
\ No newline at end of file
diff --git a/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune.py b/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune.py
index cddae46..e0bb427 100644
--- a/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune.py
+++ b/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune.py
@@ -6,13 +6,18 @@
 from models.cnn_zoo import CustomResNet
 from utils.losses.contrastive_loss import ContrastiveLossEuclidean
 from utils.train import train_simplistic
+import argparse
 from utils.util import load_model_add_head
 from torchsummary import summary
 
 # Set device
 device = "cuda"  # torch.device("cuda" if torch.cuda.is_available() else "cpu")
 learning_method = "contrastive_embedding"
-saved_model_path = "/home/kartik/git/deepLense_transformer_ssl/output/pretrained_contrastive_embedding.pth"
+parser = argparse.ArgumentParser()
+parser.add_argument('--saved_model_path', type=str, required=True,
+                    help='Path to saved pretrained model checkpoint')
+args = parser.parse_args()
+saved_model_path = args.saved_model_path
 
 # Set hyperparameters
 batch_size = 128
diff --git a/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune_byol.py b/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune_byol.py
index c90729b..7909432 100644
--- a/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune_byol.py
+++ b/Transformers_Classification_DeepLense_Kartik_Sachdev/finetune_byol.py
@@ -14,6 +14,7 @@
     get_last_layer_features,
 )
 from torchsummary import summary
+import argparse
 from models.byol import BYOLSingleChannel, FinetuneModelByol
 import torchvision
 from models.utils.finetune_model import FinetuneModel
@@ -22,6 +23,11 @@
 device = "cuda"  # torch.device("cuda" if torch.cuda.is_available() else "cpu")
 learning_method = "contrastive_embedding"
 saved_model_path = "/home/kartik/git/DeepLense/Transformers_Classification_DeepLense_Kartik_Sachdev/logger/2023-07-23-13-30-24/checkpoint/Resnet_finetune_Model_II_2023-07-23-13-30-24.pt"
+parser = argparse.ArgumentParser()
+parser.add_argument('--saved_model_path', type=str, required=True,
+                    help='Path to saved BYOL model checkpoint')
+args = parser.parse_args()
+saved_model_path = args.saved_model_path
 
 # Set hyperparameters
 batch_size = 512
diff --git a/Transformers_Classification_DeepLense_Kartik_Sachdev/inference.py b/Transformers_Classification_DeepLense_Kartik_Sachdev/inference.py
index 5bda766..15d2a44 100644
--- a/Transformers_Classification_DeepLense_Kartik_Sachdev/inference.py
+++ b/Transformers_Classification_DeepLense_Kartik_Sachdev/inference.py
@@ -10,6 +10,7 @@
 from models.byol import BYOLSingleChannel, FinetuneModelByol
 import torchvision
 from torchsummary import summary
+import argparse
 
 
 def main():
@@ -19,8 +20,14 @@ def main():
     labels_map = {0: "axion", 1: "cdm", 2: "no_sub"}
     image_size = 224
     channels = 1
-    log_dir = "/home/kartik/git/DeepLense/Transformers_Classification_DeepLense_Kartik_Sachdev/logger/2023-07-23-13-30-24"
-    finetune_model_path = "/home/kartik/git/DeepLense/Transformers_Classification_DeepLense_Kartik_Sachdev/logger/2023-07-23-13-30-24/checkpoint/Resnet_finetune_Model_II.pt"
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--log_dir', type=str, required=True,
+                        help='Path to log directory')
+    parser.add_argument('--finetune_model_path', type=str, required=True,
+                        help='Path to finetuned model checkpoint')
+    args = parser.parse_args()
+    log_dir = args.log_dir
+    finetune_model_path = args.finetune_model_path
     batch_size = 512
     num_workers = 8