Merge branch 'main' into generative

tests/field_components/test_encodings.py

@@ -154,10 +154,9 @@ def test_tensor_hash_encoder():
     )
     assert encoder.get_out_dim() == out_dim
 
-    in_tensor = torch.zeros((10, 3))
-    in_tensor[..., 1] = 1
-    encoded = encoder(in_tensor)
-    assert encoded.shape == (10, out_dim)
+    in_tensor = torch.rand((10, 3))
+    encoded_torch = encoder(in_tensor)
+    assert encoded_torch.shape == (10, out_dim)
 
     encoder = encodings.HashEncoding(
         num_levels=num_levels,
@@ -165,10 +164,8 @@ def test_tensor_hash_encoder():
         log2_hashmap_size=5,
         implementation="tcnn",
     )
-    in_tensor = torch.zeros((10, 3))
-    in_tensor[..., 1] = 1
-    encoded = encoder(in_tensor)
-    assert encoded.shape == (10, out_dim)
+    encoded_tcnn = encoder(in_tensor).float().cpu()
+    assert encoded_tcnn.shape == (10, out_dim)
 
 
 def test_kplane_encoder():

tests/test_train.py

@@ -20,7 +20,6 @@ BLACKLIST = [
     "semantic-nerfw",
     "instant-ngp",
     "instant-ngp-bounded",
-    "nerfacto",
     "nerfacto-big",
     "volinga",
     "phototourism",
@@ -35,7 +34,11 @@ BLACKLIST = [
 
 def set_reduced_config(config: TrainerConfig):
     """Reducing the config settings to speedup test"""
-    config.machine.num_gpus = 0
+    config.machine.device_type = "cpu"
+    if hasattr(config.pipeline.model, "implementation"):
+        setattr(config.pipeline.model, "implementation", "torch")
+    config.mixed_precision = False
+    config.use_grad_scaler = False
     config.max_num_iterations = 2
     # reduce dataset factors; set dataset to test
     config.pipeline.datamanager.dataparser = BlenderDataParserConfig(data=Path("tests/data/lego_test"))