Further reduce atol for model comparison, move python 3.11 + torch 2.2 -> python 3.12 + torch 2.4.1

Author: rwightman

.github/workflows/tests.yml

@@ -16,11 +16,11 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python: ['3.10', '3.11']
-        torch: [{base: '1.13.0', vision: '0.14.0'}, {base: '2.1.0', vision: '0.16.0'}]
+        python: ['3.10', '3.12']
+        torch: [{base: '1.13.0', vision: '0.14.0'}, {base: '2.4.1', vision: '0.19.1'}]
         testmarker: ['-k "not test_models"', '-m base', '-m cfg', '-m torchscript', '-m features', '-m fxforward', '-m fxbackward']
         exclude:
-          - python: '3.11'
+          - python: '3.12'
             torch: {base: '1.13.0', vision: '0.14.0'}
     runs-on: ${{ matrix.os }}
 

tests/test_models.py

@@ -146,18 +146,18 @@ def test_model_inference(model_name, batch_size):
         rand_output = model(rand_tensors['input'])
         rand_features = model.forward_features(rand_tensors['input'])
         rand_pre_logits = model.forward_head(rand_features, pre_logits=True)
-        assert torch.allclose(rand_output, rand_tensors['output'], rtol=1e-3, atol=1e-5)
-        assert torch.allclose(rand_features, rand_tensors['features'], rtol=1e-3, atol=1e-5)
-        assert torch.allclose(rand_pre_logits, rand_tensors['pre_logits'], rtol=1e-3, atol=1e-5)
+        assert torch.allclose(rand_output, rand_tensors['output'], rtol=1e-3, atol=1e-4)
+        assert torch.allclose(rand_features, rand_tensors['features'], rtol=1e-3, atol=1e-4)
+        assert torch.allclose(rand_pre_logits, rand_tensors['pre_logits'], rtol=1e-3, atol=1e-4)
 
         def _test_owl(owl_input):
             owl_output = model(owl_input)
             owl_features = model.forward_features(owl_input)
             owl_pre_logits = model.forward_head(owl_features.clone(), pre_logits=True)
             assert owl_output.softmax(1).argmax(1) == 24  # owl
-            assert torch.allclose(owl_output, owl_tensors['output'], rtol=1e-3, atol=1e-5)
-            assert torch.allclose(owl_features, owl_tensors['features'], rtol=1e-3, atol=1e-5)
-            assert torch.allclose(owl_pre_logits, owl_tensors['pre_logits'], rtol=1e-3, atol=1e-5)
+            assert torch.allclose(owl_output, owl_tensors['output'], rtol=1e-3, atol=1e-4)
+            assert torch.allclose(owl_features, owl_tensors['features'], rtol=1e-3, atol=1e-4)
+            assert torch.allclose(owl_pre_logits, owl_tensors['pre_logits'], rtol=1e-3, atol=1e-4)
 
         _test_owl(owl_tensors['input'])  # test with original pp owl tensor
         _test_owl(pp(test_owl).unsqueeze(0))  # re-process from original jpg