Chamfer example

examples/chamfer_loss/main.py

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+import argparse
+import torch
+from fml.nn import ChamferLoss
+
+if __name__ == '__main__':
+    # Parse input arguments
+    parser = argparse.ArgumentParser(
+            description='SikhornLoss between two batchs of points.')
+    parser.add_argument('--batch_size', '-bz', type=int, default=3,
+            help='Batch size.')
+    parser.add_argument('--set_size', '-sz', type=int, default=10,
+            help='Set size.')
+    parser.add_argument('--point_dim', '-pd', type=int, default=4,
+            help='Point dimension.')
+
+    args = parser.parse_args()
+
+    # Set the parameters
+    minibatch_size = args.batch_size
+    set_size = args.set_size
+    point_dim = args.point_dim
+
+    # Create two minibatches of point sets where each batch item set_a[k, :, :] is a set of `set_size` points
+    set_a = torch.rand([minibatch_size, set_size, point_dim])
+    set_b = torch.rand([minibatch_size, set_size, point_dim])
+
+    print('Set A')
+    print(set_a)
+
+    print('Set B')
+    print(set_b)
+
+    # Create a loss function module with default parameters. See the class documentation for optional parameters.
+    loss_fun = ChamferLoss()
+
+    # Compute the loss between each pair of sets in the minibatch
+    # loss is a tensor with [minibatch_size] elements which can be backpropagated through
+    loss = loss_fun(set_a, set_b)
+
+    print('Loss')
+    print(loss)
+