thestalkers/

ImageToGPSproject_base_resnet18_v2

from huggingface_hub import hf_hub_download
import torch

repo_id = "thestalkers/ImageToGPSproject_base_resnet18_v2" 
filename = "resnet_gps_regressor_complete.pth"

model_path = hf_hub_download(repo_id=repo_id, filename=filename)

# Load the model using torch
model_test = torch.load(model_path)
model_test.eval()  # Set the model to evaluation mode

from datasets import load_dataset, Image

dataset_test = load_dataset("gydou/released_img", split="train")

inference_transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

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)

from sklearn.metrics import mean_absolute_error, mean_squared_error

# Initialize lists to store predictions and actual values
all_preds = []
all_actuals = []

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f'Using device: {device}')

with torch.no_grad():
    for images, gps_coords in test_dataloader:
        images, gps_coords = images.to(device), gps_coords.to(device)

        outputs = model_test(images)

        # Denormalize predictions and actual values
        preds = outputs.cpu() * torch.tensor([lat_std, lon_std]) + torch.tensor([lat_mean, lon_mean])
        actuals = gps_coords.cpu() * torch.tensor([lat_std, lon_std]) + torch.tensor([lat_mean, lon_mean])

        all_preds.append(preds)
        all_actuals.append(actuals)

# Concatenate all batches
all_preds = torch.cat(all_preds).numpy()
all_actuals = torch.cat(all_actuals).numpy()

# Compute error metrics
mae = mean_absolute_error(all_actuals, all_preds)
rmse = mean_squared_error(all_actuals, all_preds, squared=False)

print(f'Mean Absolute Error: {mae}')
print(f'Root Mean Squared Error: {rmse}')