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