Update README.md

README.md

@@ -3,11 +3,65 @@ tags:
 - model_hub_mixin
 - pytorch_model_hub_mixin
 ---
+from geopy.distance import geodesic
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision.models as models
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from huggingface_hub import PyTorchModelHubMixin
+
+model = CustomResNetModel.from_pretrained("5190final/model1")
+
+lat_mean = 39.951611366653395
+lat_std = 0.0006686190927448403
+lon_mean = -75.19145880459313
+lon_std = 0.0006484111794126842
+
+device = torch.device("mps" if torch.backends.mps.is_available() else "cpu")
+model.to(device)
+model.eval() 
+
+test_dataset = GPSImageDataset(
+    hf_dataset=dataset_test,
+    transform=inference_transform,
+    lat_mean=lat_mean,
+    lat_std=lat_std,
+    lon_mean=lon_mean,
+    lon_std=lon_std
+)
+test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
+
+all_preds = []
+all_actuals = []
+
+with torch.no_grad():  # Disable gradient calculations during inference
+    for images, gps_coords in test_dataloader:
+        images = images.to(device)
+        gps_coords = gps_coords.to(device)
+        
+        outputs = model(images)
+        logits = outputs.logits  # Extract the predictions
+
+        all_preds.extend(logits.cpu().numpy())  # Append predictions to the list
+        all_actuals.extend(gps_coords.cpu().numpy())  # Append actual values to the list
+
+all_preds = np.array(all_preds)
+all_actuals = np.array(all_actuals)
+
+all_preds_denorm = all_preds * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean])  # Assuming lat_std, etc., are lists or NumPy arrays
+all_actuals_denorm = all_actuals * np.array([lat_std, lon_std]) + np.array([lat_mean, lon_mean]) 
+
+squared_errors = []
+for pred, actual in zip(all_preds_denorm, all_actuals_denorm):
+    # Calculate geodesic distance between predicted and actual coordinates
+    distance = geodesic((actual[0], actual[1]), (pred[0], pred[1])).meters  
+    squared_errors.append(distance**2)  # Square the distance for RMSE
+
+rmse = np.sqrt(np.mean(squared_errors))
 
-lat_mean = [39.951611366653395],
-lat_std = [0.0006686190927448403],
-lon_mean = [-75.19145880459313],
-lon_std = [0.0006484111794126842]
 
 This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
 - Library: [More Information Needed]