Added hubconf.py to allow models to be downloaded from torch.hub

Author: gmberton

hubconf.py

@@ -0,0 +1,44 @@
+
+dependencies = ['torch', 'torchvision']
+
+import torch
+from model import network
+
+
+AVAILABLE_MODELS = {
+    "VGG16":     [    64, 128, 256, 512],
+    "ResNet18":  [32, 64, 128, 256, 512],
+    "ResNet50":  [32, 64, 128, 256, 512, 1024, 2048],
+    "ResNet101": [32, 64, 128, 256, 512, 1024, 2048],
+    "ResNet152": [32, 64, 128, 256, 512, 1024, 2048],
+}
+
+
+def get_trained_model(backbone : str = "ResNet18", fc_output_dim : int = 32) -> torch.nn.Module:
+    """Return a model trained with CosPlace on San Francisco eXtra Large.
+    
+    Args:
+        backbone (str): which torchvision backbone to use. Must be VGG16 or a ResNet.
+        fc_output_dim (int): the output dimension of the last fc layer, equivalent to
+            the descriptors dimension. Must be between 32 and 2048, depending on model's availability.
+    
+    Return:
+        model (torch.nn.Module): a trained model.
+    """
+    print(f"Returning CosPlace model with backbone: {backbone} with features dimension {fc_output_dim}")
+    if backbone not in AVAILABLE_MODELS:
+        raise ValueError(f"Parameter `backbone` is set to {backbone} but it must be one of {list(AVAILABLE_MODELS.keys())}")
+    try:
+        fc_output_dim = int(fc_output_dim)
+    except:
+        raise ValueError(f"Parameter `fc_output_dim` must be an integer, but it is set to {fc_output_dim}")
+    if fc_output_dim not in AVAILABLE_MODELS[backbone]:
+        raise ValueError(f"Parameter `fc_output_dim` is set to {fc_output_dim}, but for backbone {backbone} "
+                         f"it must be one of {list(AVAILABLE_MODELS[backbone])}")
+    model = network.GeoLocalizationNet(backbone, fc_output_dim)
+    model.load_state_dict(
+        torch.hub.load_state_dict_from_url(
+            f'https://github.com/gmberton/CosPlace/releases/download/v0.1.0/{backbone}_{fc_output_dim}_cosplace.pth',
+        map_location=torch.device('cpu'))
+    )
+    return model