Fix classification transformer runtime compatibility issues

DeepLense_Classification_Transformers_Archil_Srivastava/README.md

@@ -30,6 +30,15 @@ ___Note__: Axion files have extra data corresponding to mass of axion used in si
 
 <br>
 
+# __Setup__
+
+Install the Python dependencies for this project before running training or evaluation:
+```bash
+python3 -m pip install -r requirements.txt
+```
+
+If PyTorch is not installed yet, install a compatible `torch` and `torchvision` build for your platform first from the [official PyTorch install guide](https://pytorch.org/get-started/locally/), then install the remaining project requirements.
+
 # __Training__
 
 Use the train.py script to train a particular model (using timm model name). The script will ask for a WandB login key, hence a WandB account is needed. Example: 

DeepLense_Classification_Transformers_Archil_Srivastava/eval.py

@@ -56,9 +56,27 @@ def evaluate(model, data_loader, loss_fn, device):
     # Concatenate all results
     logits, y = torch.cat(logits), torch.cat(y)
     loss.append(loss_fn(logits, y))
-    accuracy.append(accuracy_fn(logits, y, num_classes=NUM_CLASSES))
-    class_auroc.append(auroc_fn(logits, y, num_classes=NUM_CLASSES, average=None))
-    macro_auroc.append(auroc_fn(logits, y, num_classes=NUM_CLASSES, average="macro"))
+    accuracy.append(
+        accuracy_fn(logits, y, task="multiclass", num_classes=NUM_CLASSES)
+    )
+    class_auroc.append(
+        auroc_fn(
+            logits,
+            y,
+            task="multiclass",
+            num_classes=NUM_CLASSES,
+            average=None,
+        )
+    )
+    macro_auroc.append(
+        auroc_fn(
+            logits,
+            y,
+            task="multiclass",
+            num_classes=NUM_CLASSES,
+            average="macro",
+        )
+    )
 
     result = {
         "ground_truth": y,

DeepLense_Classification_Transformers_Archil_Srivastava/requirements.txt

@@ -0,0 +1,13 @@
+# Install PyTorch and torchvision for your platform first if needed:
+# https://pytorch.org/get-started/locally/
+
+einops>=0.6,<1
+matplotlib>=3.7,<4
+numpy>=1.24,<3
+scikit-learn>=1.3,<2
+timm>=0.9,<1
+torch>=2.1,<3
+torchmetrics>=1.3,<2
+torchvision>=0.16,<1
+tqdm>=4.66,<5
+wandb>=0.16,<1

DeepLense_Classification_Transformers_Archil_Srivastava/train.py

@@ -319,7 +319,7 @@ def train(
         # Scheduler
         if run_config.decay_lr:
             scheduler = CosineAnnealingWarmRestarts(
-                optimizer, T_0=15, T_mult=1, eta_min=1e-6, verbose=True
+                optimizer, T_0=15, T_mult=1, eta_min=1e-6
             )
         else:
             scheduler = None

DeepLense_Classification_Transformers_Archil_Srivastava/utils.py

@@ -29,7 +29,11 @@ def get_device(device):
         return xm.xla_device()
     if (device == "cuda" or device == "best") and torch.cuda.is_available():
         return "cuda"
-    if (device == "mps" or device == "best") and torch.has_mps:
+    if (
+        (device == "mps" or device == "best")
+        and hasattr(torch.backends, "mps")
+        and torch.backends.mps.is_available()
+    ):
         return "mps"
     if device == "cpu" or device == "best":
         return "cpu"