fix(#143): Replace hardcoded absolute paths with argparse + YAML config

Transformers_Classification_DeepLense_Kartik_Sachdev/config/yaml/finetune_byol_config.yaml

@@ -0,0 +1,15 @@
+device: "cuda"
+learning_method: "contrastive_embedding"
+dataset_name: "Model_II"
+batch_size: 512
+epochs: 10
+learning_rate: 0.0001
+margin: 1.0
+num_channels: 1
+temperature: 0.5
+num_classes: 3
+split_ratio: 0.25
+num_workers: 8
+
+# REQUIRED - set this to your actual path
+saved_model_path: ""

Transformers_Classification_DeepLense_Kartik_Sachdev/config/yaml/finetune_config.yaml

@@ -0,0 +1,13 @@
+device: "cuda"
+learning_method: "contrastive_embedding"
+batch_size: 128
+epochs: 10
+learning_rate: 0.001
+margin: 1.0
+num_channels: 1
+temperature: 0.5
+num_classes: 2
+num_workers: 4
+
+# REQUIRED - set this to your actual path
+saved_model_path: ""

Transformers_Classification_DeepLense_Kartik_Sachdev/config/yaml/inference_config.yaml

@@ -0,0 +1,15 @@
+device: "cuda"
+dataset_name: "Model_II"
+num_classes: 3
+image_size: 224
+channels: 1
+batch_size: 512
+num_workers: 8
+labels_map:
+  0: "axion"
+  1: "cdm"
+  2: "no_sub"
+
+# REQUIRED - set these to your actual paths
+log_dir: ""
+finetune_model_path: ""

Transformers_Classification_DeepLense_Kartik_Sachdev/finetune.py

@@ -1,3 +1,7 @@
+import os
+import argparse
+import yaml
+
 import torch
 import torch.nn as nn
 import torch.optim as optim
@@ -9,51 +13,24 @@
 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"
-
-# Set hyperparameters
-batch_size = 128
-epochs = 10
-learning_rate = 0.001
-margin = 1.0
-num_channels = 1
-temperature = 0.5
-num_classes = 2
-
-# Load dataset
-default_dataset_setup = DefaultDatasetSetup()
-train_dataset = default_dataset_setup.get_default_trainset_ssl()
-train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
-
-# Load pretrained model and add head
-pretrain_head = nn.Identity()
-pretrain_model = CustomResNet(num_channels, head=pretrain_head)
-pretrain_model.summarize()
-
-in_features = pretrain_model.get_last_layer_features()
-finetune_head = nn.Sequential(
-    nn.Linear(in_features, 256), nn.ReLU(), nn.Linear(256, num_classes)
-)
-
-model = load_model_add_head(
-    pretrain_model=pretrain_model,
-    saved_model_path=saved_model_path,
-    head=finetune_head,
-    freeze_pretrain_layers=True,
-)
-
-model.to(device)
-summary(model=model, input_size=(1, 1, 224, 224), device=device)
-
-
-# Define optimizer and loss function
-optimizer = optim.Adam(model.parameters(), lr=learning_rate)
-criterion = nn.CrossEntropyLoss()
 
-# Training loop
-train_simplistic(
-    epochs, model, device, train_loader, criterion, optimizer, saved_model_path
+# ── Argument parsing ───────────────────────────────────────────────────────────
+parser = argparse.ArgumentParser(description="Contrastive Finetuning for DeepLense")
+parser.add_argument(
+    "--config",
+    type=str,
+    default="config/yaml/finetune_config.yaml",
+    help="Path to YAML config file"
 )
+parser.add_argument("--saved_model_path", type=str, default=None, help="Path to pretrained model .pth file")
+parser.add_argument("--device",           type=str, default=None, help="cuda or cpu")
+args = parser.parse_args()
+
+# ── Load YAML config ───────────────────────────────────────────────────────────
+config = {}
+if os.path.exists(args.config):
+    with open(args.config, "r") as f:
+        config = yaml.safe_load(f) or {}
+
+# ── Settings (CLI overrides YAML) ──────────────────────────────────────────────
+device           = args.device           or config.get("dev

Transformers_Classification_DeepLense_Kartik_Sachdev/finetune_byol.py

@@ -1,10 +1,12 @@
+import os
+import argparse
+import yaml
+
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader
-from utils.dataset import DeepLenseDatasetSSL, DefaultDatasetSetupSSL
 from torch.utils.data import DataLoader, random_split
-
+from utils.dataset import DeepLenseDatasetSSL, DefaultDatasetSetupSSL
 from models.cnn_zoo import CustomResNet
 from utils.losses.contrastive_loss import ContrastiveLossEuclidean
 from utils.train import train_simplistic
@@ -18,78 +20,23 @@
 import torchvision
 from models.utils.finetune_model import FinetuneModel
 
-# 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/Transformers_Classification_DeepLense_Kartik_Sachdev/logger/2023-07-23-13-30-24/checkpoint/Resnet_finetune_Model_II_2023-07-23-13-30-24.pt"
-
-# Set hyperparameters
-batch_size = 512
-epochs = 10
-learning_rate = 0.0001
-margin = 1.0
-num_channels = 1
-temperature = 0.5
-num_classes = 3
 
-# Load dataset
-default_dataset_setup = DefaultDatasetSetupSSL(dataset_name="Model_II")
-train_dataset = default_dataset_setup.get_dataset()
-
-# split in train and valid set
-split_ratio = 0.25  # 0.25
-valid_len = int(split_ratio * len(train_dataset))
-train_len = len(train_dataset) - valid_len
-
-train_dataset, val_set = random_split(train_dataset, [train_len, valid_len])
-
-train_loader = DataLoader(
-    dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=8
+# ── Argument parsing ───────────────────────────────────────────────────────────
+parser = argparse.ArgumentParser(description="BYOL Finetuning for DeepLense")
+parser.add_argument(
+    "--config",
+    type=str,
+    default="config/yaml/finetune_byol_config.yaml",
+    help="Path to YAML config file"
 )
+parser.add_argument("--saved_model_path", type=str, default=None, help="Path to pretrained BYOL .pt file")
+parser.add_argument("--device",           type=str, default=None, help="cuda or cpu")
+args = parser.parse_args()
 
-val_loader = DataLoader(
-    dataset=val_set, batch_size=batch_size, shuffle=True, num_workers=8
-)
+# ── Load YAML config ───────────────────────────────────────────────────────────
+config = {}
+if os.path.exists(args.config):
+    with open(args.config, "r") as f:
+        config = yaml.safe_load(f) or {}
 
-
-# Load pretrained model and add head
-resnet = torchvision.models.resnet18()
-backbone = nn.Sequential(*list(resnet.children())[:-1])
-model = BYOLSingleChannel(backbone, num_ftrs=512)
-model.load_state_dict(torch.load(saved_model_path))
-print(">>>>>> Keys matched. Model loaded")
-model.to(device)
-# last_layer_num = get_last_layer_features(model=model, num_input=2)
-# print(last_layer_num)
-
-input_feature = 256
-finetune_head = nn.Sequential(
-    nn.Linear(input_feature, 512),
-    nn.BatchNorm1d(512),
-    nn.ReLU(),
-    nn.Linear(512, input_feature),
-    nn.BatchNorm1d(input_feature),
-    nn.ReLU(),
-    nn.Linear(input_feature, num_classes),
-)
-
-finetune_model = FinetuneModelByol(backbone=model, head=finetune_head)
-finetune_model.to(device=device)
-summary(finetune_model, input_size=(10, 1, 224, 224), device=device)
-
-
-# Define optimizer and loss function
-optimizer = optim.Adam(finetune_model.parameters(), lr=learning_rate)
-criterion = nn.CrossEntropyLoss()
-
-# Training loop
-train_simplistic(
-    epochs,
-    finetune_model,
-    device,
-    train_loader,
-    criterion,
-    optimizer,
-    saved_model_path,
-    valid_loader=val_loader,
-)
+# ── Settings (CLI overrides

Transformers_Classification_DeepLense_Kartik_Sachdev/inference.py

@@ -1,6 +1,8 @@
 from __future__ import print_function
 
-from turtle import down
+import os
+import argparse
+import yaml
 
 from utils.dataset import DefaultDatasetSetupSSL
 from utils.inference import InferenceSSL
@@ -12,17 +14,63 @@
 from torchsummary import summary
 
 
+def parse_args():
+    parser = argparse.ArgumentParser(description="BYOL Inference for DeepLense")
+    parser.add_argument(
+        "--config",
+        type=str,
+        default="config/yaml/inference_config.yaml",
+        help="Path to YAML config file"
+    )
+    parser.add_argument("--log_dir",            type=str, default=None, help="Path to log directory")
+    parser.add_argument("--finetune_model_path", type=str, default=None, help="Path to finetuned model .pt file")
+    parser.add_argument("--dataset_name",       type=str, default=None, help="Dataset name e.g. Model_II")
+    parser.add_argument("--device",             type=str, default=None, help="cuda or cpu")
+    return parser.parse_args()
+
+
 def main():
-    device = "cuda"
-    num_classes = 3
-    dataset_name = "Model_II"
-    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"
-    batch_size = 512
-    num_workers = 8
+    args = parse_args()
+
+    # Load YAML config
+    config = {}
+    if os.path.exists(args.config):
+        with open(args.config, "r") as f:
+            config = yaml.safe_load(f) or {}
+
+    # CLI args override YAML values
+    device              = args.device              or config.get("device",              "cuda")
+    num_classes         = config.get("num_classes",  3)
+    dataset_name        = args.dataset_name        or config.get("dataset_name",        "Model_II")
+    labels_map          = config.get("labels_map",   {0: "axion", 1: "cdm", 2: "no_sub"})
+    image_size          = config.get("image_size",   224)
+    channels            = config.get("channels",     1)
+    batch_size          = config.get("batch_size",   512)
+    num_workers         = config.get("num_workers",  8)
+    log_dir             = args.log_dir             or config.get("log_dir",             None)
+    finetune_model_path = args.finetune_model_path or config.get("finetune_model_path", None)
+
+    # Validate required paths
+    if not log_dir:
+        raise ValueError(
+            "log_dir is required.\n"
+            "Set it in config/yaml/inference_config.yaml or pass --log_dir <path>"
+        )
+    if not finetune_model_path:
+        raise ValueError(
+            "finetune_model_path is required.\n"
+            "Set it in config/yaml/inference_config.yaml or pass --finetune_model_path <path>"
+        )
+    if not os.path.exists(log_dir):
+        raise FileNotFoundError(
+            f"log_dir not found: {log_dir}\n"
+            "Please check your config/yaml/inference_config.yaml or --log_dir argument."
+        )
+    if not os.path.exists(finetune_model_path):
+        raise FileNotFoundError(
+            f"finetune_model_path not found: {finetune_model_path}\n"
+            "Please check your config/yaml/inference_config.yaml or --finetune_model_path argument."
+        )
 
     # Load pretrained model and add head
     resnet = torchvision.models.resnet18()
@@ -45,7 +93,6 @@ def main():
     print(">>>> Keys matched")
     summary(finetune_model, input_size=(10, 1, 224, 224), device=device)
 
-    # testset
     # setup default dataset
     default_dataset_setup = DefaultDatasetSetupSSL()
     default_dataset_setup.setup(dataset_name=dataset_name)
@@ -56,7 +103,7 @@ def main():
     default_dataset_setup.visualize_dataset(train_dataset)
 
     # split in train and valid set
-    split_ratio = 0.05  # 0.25
+    split_ratio = 0.05
     valid_len = int(split_ratio * len(train_dataset))
     train_len = len(train_dataset) - valid_len
 
@@ -84,12 +131,12 @@ def main():
         image_size=image_size,
         channels=channels,
         destination_dir="data",
-        log_dir=log_dir,  # log_dir
+        log_dir=log_dir,
     )
 
     infer_obj.infer_plot_roc()
     infer_obj.generate_plot_confusion_matrix()
 
 
 if __name__ == "__main__":
-    main()
+    main()