Rename Metric and use double in computation (#350)

Signed-off-by: Walter Hugo Lopez Pinaya <ianonimato@hotmail.com>

Author: Warvito

generative/metrics/__init__.py

@@ -11,7 +11,7 @@
 
 from __future__ import annotations
 
-from .fid import FID
+from .fid import FIDMetric
 from .mmd import MMD
 from .ms_ssim import MultiScaleSSIMMetric
 from .ssim import SSIMMetric

generative/metrics/fid.py

@@ -35,7 +35,7 @@
 from monai.metrics.metric import Metric
 
 
-class FID(Metric):
+class FIDMetric(Metric):
     """
     Frechet Inception Distance (FID). The FID calculates the distance between two distributions of feature vectors.
     Based on: Heusel M. et al. "Gans trained by a two time-scale update rule converge to a local nash equilibrium."
@@ -56,8 +56,8 @@ def __call__(self, y_pred: torch.Tensor, y: torch.Tensor):
 
 
 def get_fid_score(y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
-    y = y.float()
-    y_pred = y_pred.float()
+    y = y.double()
+    y_pred = y_pred.double()
 
     if y.ndimension() > 2:
         raise ValueError("Inputs should have (number images, number of features) shape.")

tests/test_compute_fid_metric.py

@@ -16,19 +16,19 @@
 import numpy as np
 import torch
 
-from generative.metrics import FID
+from generative.metrics import FIDMetric
 
 
 class TestMMDMetric(unittest.TestCase):
     def test_results(self):
         x = torch.Tensor([[1, 2], [1, 2], [1, 2]])
         y = torch.Tensor([[2, 2], [1, 2], [1, 2]])
-        results = FID()(x, y)
+        results = FIDMetric()(x, y)
         np.testing.assert_allclose(results.cpu().numpy(), 0.4433, atol=1e-4)
 
     def test_input_dimensions(self):
         with self.assertRaises(ValueError):
-            FID()(torch.ones([3, 3, 144, 144]), torch.ones([3, 3, 145, 145]))
+            FIDMetric()(torch.ones([3, 3, 144, 144]), torch.ones([3, 3, 145, 145]))
 
 
 if __name__ == "__main__":