FID pytorch Old version and Windows Fix (#2061)

* Old version error fix * Changed to 6 digit precision checking * Update pytorch-version-tests.yml * Changed rel_err to 1e-5 * Removed manual trigger Co-authored-by: vfdev <[email protected]>
pytorch · Jun 18, 2021 · f240e6e · f240e6e
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Showing 3 changed files with 11 additions and 5 deletions.
diff --git a/ignite/metrics/gan/fid.py b/ignite/metrics/gan/fid.py
@@ -165,7 +165,10 @@ def _get_covariance(self, sigma: torch.Tensor, total: torch.Tensor) -> torch.Ten
         r"""
         Calculates covariance from mean and sum of products of variables
         """
-        sub_matrix = torch.outer(total, total)
+        if LooseVersion(torch.__version__) <= LooseVersion("1.7.0"):
+            sub_matrix = torch.ger(total, total)
+        else:
+            sub_matrix = torch.outer(total, total)
         sub_matrix = sub_matrix / self._num_examples
         return (sigma - sub_matrix) / (self._num_examples - 1)
 

diff --git a/requirements-dev.txt b/requirements-dev.txt
@@ -7,7 +7,7 @@ pytest-xdist
 dill
 # Test contrib dependencies
 scipy
-pytorch_fid
+pytorch_fid==0.1.1
 tqdm
 scikit-learn
 matplotlib

diff --git a/tests/ignite/metrics/gan/test_fid.py b/tests/ignite/metrics/gan/test_fid.py
@@ -71,7 +71,7 @@ def test_fid_function():
 
     sigma1 = torch.tensor(sigma1, dtype=torch.float64)
     sigma2 = torch.tensor(sigma2, dtype=torch.float64)
-    assert pytest.approx(fid_score(mu1, mu2, sigma1, sigma2)) == pytorch_fid_score.calculate_frechet_distance(
+    assert pytest.approx(fid_score(mu1, mu2, sigma1, sigma2), rel=1e-5) == pytorch_fid_score.calculate_frechet_distance(
         mu1, sigma1, mu2, sigma2
     )
 
@@ -86,7 +86,10 @@ def test_compute_fid_from_features():
     mu1, sigma1 = train_samples.mean(axis=0), cov(train_samples, rowvar=False)
     mu2, sigma2 = test_samples.mean(axis=0), cov(test_samples, rowvar=False)
 
-    assert pytest.approx(pytorch_fid_score.calculate_frechet_distance(mu1, sigma1, mu2, sigma2)) == fid_scorer.compute()
+    assert (
+        pytest.approx(pytorch_fid_score.calculate_frechet_distance(mu1, sigma1, mu2, sigma2), rel=1e-5)
+        == fid_scorer.compute()
+    )
 
 
 def test_compute_fid_sqrtm():
@@ -187,7 +190,7 @@ def update(_, i):
         evaluator = pytorch_fid_score.calculate_frechet_distance
         mu1, sigma1 = y_pred.mean(axis=0).to("cpu"), cov(y_pred.to("cpu"), rowvar=False)
         mu2, sigma2 = y_true.mean(axis=0).to("cpu"), cov(y_true.to("cpu"), rowvar=False)
-        assert pytest.approx(evaluator(mu1, sigma1, mu2, sigma2)) == m.compute()
+        assert pytest.approx(evaluator(mu1, sigma1, mu2, sigma2), rel=1e-5) == m.compute()
 
     metric_devices = [torch.device("cpu")]
     if device.type != "xla":