clean IF args

Summary: continued - avoid duplicate inputs

Reviewed By: davnov134

Differential Revision: D38248827

fbshipit-source-id: 91ed398e304496a936f66e7a70ab3d189eeb5c70

projects/implicitron_trainer/configs/repro_base.yaml

@@ -43,7 +43,6 @@ model_factory_ImplicitronModelFactory_args:
       n_layers_xyz: 8
       append_xyz:
       - 5
-      latent_dim: 0
     raysampler_AdaptiveRaySampler_args:
       n_rays_per_image_sampled_from_mask: 1024
       scene_extent: 8.0

projects/implicitron_trainer/tests/experiment.yaml

@@ -313,7 +313,6 @@ model_factory_ImplicitronModelFactory_args:
         - AVG
         - STD
     implicit_function_IdrFeatureField_args:
-      feature_vector_size: 3
       d_in: 3
       d_out: 1
       dims:
@@ -331,15 +330,12 @@ model_factory_ImplicitronModelFactory_args:
       weight_norm: true
       n_harmonic_functions_xyz: 0
       pooled_feature_dim: 0
-      encoding_dim: 0
     implicit_function_NeRFormerImplicitFunction_args:
       n_harmonic_functions_xyz: 10
       n_harmonic_functions_dir: 4
       n_hidden_neurons_dir: 128
-      latent_dim: 0
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
-      color_dim: 3
       transformer_dim_down_factor: 2.0
       n_hidden_neurons_xyz: 80
       n_layers_xyz: 2
@@ -349,10 +345,8 @@ model_factory_ImplicitronModelFactory_args:
       n_harmonic_functions_xyz: 10
       n_harmonic_functions_dir: 4
       n_hidden_neurons_dir: 128
-      latent_dim: 0
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
-      color_dim: 3
       transformer_dim_down_factor: 1.0
       n_hidden_neurons_xyz: 256
       n_layers_xyz: 8
@@ -367,8 +361,6 @@ model_factory_ImplicitronModelFactory_args:
         n_layers_hypernet: 1
         in_features: 3
         out_features: 256
-        latent_dim_hypernet: 0
-        latent_dim: 0
         xyz_in_camera_coords: false
       pixel_generator_args:
         n_harmonic_functions: 4
@@ -385,7 +377,6 @@ model_factory_ImplicitronModelFactory_args:
         n_layers: 2
         in_features: 3
         out_features: 256
-        latent_dim: 0
         xyz_in_camera_coords: false
         raymarch_function: null
       pixel_generator_args:

pytorch3d/implicitron/models/generic_model.py

@@ -677,6 +677,18 @@ class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
         """
         pass
 
+    @classmethod
+    def implicit_function_tweak_args(cls, type, args: DictConfig) -> None:
+        """
+        We don't expose certain implicit_function fields because we want to set
+        them based on other inputs.
+        """
+        args.pop("feature_vector_size", None)
+        args.pop("encoding_dim", None)
+        args.pop("latent_dim", None)
+        args.pop("latent_dim_hypernet", None)
+        args.pop("color_dim", None)
+
     def _construct_implicit_functions(self):
         """
         After run_auto_creation has been called, the arguments
@@ -686,32 +698,31 @@ class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
         implicit function method. Then the required implicit
         function(s) are initialized.
         """
-        # nerf preprocessing
+        extra_args = {}
-        nerf_args = self.implicit_function_NeuralRadianceFieldImplicitFunction_args
+        if self.implicit_function_class_type in (
-        nerformer_args = self.implicit_function_NeRFormerImplicitFunction_args
+            "NeuralRadianceFieldImplicitFunction",
-        nerf_args["latent_dim"] = nerformer_args["latent_dim"] = (
+            "NeRFormerImplicitFunction",
-            self._get_viewpooled_feature_dim() + self._get_global_encoder_encoding_dim()
+        ):
-        )
+            extra_args["latent_dim"] = (
-        nerf_args["color_dim"] = nerformer_args[
+                self._get_viewpooled_feature_dim()
-            "color_dim"
+                + self._get_global_encoder_encoding_dim()
-        ] = self.render_features_dimensions
+            )
+            extra_args["color_dim"] = self.render_features_dimensions
 
-        # idr preprocessing
+        if self.implicit_function_class_type == "IdrFeatureField":
-        idr = self.implicit_function_IdrFeatureField_args
+            extra_args["feature_vector_size"] = self.render_features_dimensions
-        idr["feature_vector_size"] = self.render_features_dimensions
+            extra_args["encoding_dim"] = self._get_global_encoder_encoding_dim()
-        idr["encoding_dim"] = self._get_global_encoder_encoding_dim()
 
-        # srn preprocessing
+        if self.implicit_function_class_type == "SRNImplicitFunction":
-        srn = self.implicit_function_SRNImplicitFunction_args
+            extra_args["latent_dim"] = (
-        srn.raymarch_function_args.latent_dim = (
+                self._get_viewpooled_feature_dim()
-            self._get_viewpooled_feature_dim() + self._get_global_encoder_encoding_dim()
+                + self._get_global_encoder_encoding_dim()
-        )
+            )
 
         # srn_hypernet preprocessing
-        srn_hypernet = self.implicit_function_SRNHyperNetImplicitFunction_args
+        if self.implicit_function_class_type == "SRNHyperNetImplicitFunction":
-        srn_hypernet_args = srn_hypernet.hypernet_args
+            extra_args["latent_dim"] = self._get_viewpooled_feature_dim()
-        srn_hypernet_args.latent_dim_hypernet = self._get_global_encoder_encoding_dim()
+            extra_args["latent_dim_hypernet"] = self._get_global_encoder_encoding_dim()
-        srn_hypernet_args.latent_dim = self._get_viewpooled_feature_dim()
 
         # check that for srn, srn_hypernet, idr we have self.num_passes=1
         implicit_function_type = registry.get(
@@ -734,7 +745,7 @@ class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
         if config is None:
             raise ValueError(f"{config_name} not present")
         implicit_functions_list = [
-            ImplicitFunctionWrapper(implicit_function_type(**config))
+            ImplicitFunctionWrapper(implicit_function_type(**config, **extra_args))
             for _ in range(self.num_passes)
         ]
         return torch.nn.ModuleList(implicit_functions_list)

pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py

@@ -4,6 +4,7 @@
 from typing import Any, cast, Optional, Tuple
 
 import torch
+from omegaconf import DictConfig
 from pytorch3d.common.linear_with_repeat import LinearWithRepeat
 from pytorch3d.implicitron.third_party import hyperlayers, pytorch_prototyping
 from pytorch3d.implicitron.tools.config import Configurable, registry, run_auto_creation
@@ -327,6 +328,7 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
 @registry.register
 # pyre-fixme[13]: Uninitialized attribute
 class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
+    latent_dim: int = 0
     raymarch_function: SRNRaymarchFunction
     pixel_generator: SRNPixelGenerator
 
@@ -334,6 +336,17 @@ class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         super().__init__()
         run_auto_creation(self)
 
+    def create_raymarch_function(self) -> None:
+        self.raymarch_function = SRNRaymarchFunction(
+            latent_dim=self.latent_dim,
+            # pyre-ignore[32]
+            **self.raymarch_function_args,
+        )
+
+    @classmethod
+    def raymarch_function_tweak_args(cls, type, args: DictConfig) -> None:
+        args.pop("latent_dim", None)
+
     def forward(
         self,
         ray_bundle: RayBundle,
@@ -371,6 +384,8 @@ class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
     the cache.
     """
 
+    latent_dim_hypernet: int = 0
+    latent_dim: int = 0
     hypernet: SRNRaymarchHyperNet
     pixel_generator: SRNPixelGenerator
 
@@ -378,6 +393,19 @@ class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         super().__init__()
         run_auto_creation(self)
 
+    def create_hypernet(self) -> None:
+        self.hypernet = SRNRaymarchHyperNet(
+            latent_dim=self.latent_dim,
+            latent_dim_hypernet=self.latent_dim_hypernet,
+            # pyre-ignore[32]
+            **self.hypernet_args,
+        )
+
+    @classmethod
+    def hypernet_tweak_args(cls, type, args: DictConfig) -> None:
+        args.pop("latent_dim", None)
+        args.pop("latent_dim_hypernet", None)
+
     def forward(
         self,
         ray_bundle: RayBundle,

tests/implicitron/data/overrides.yaml

@@ -103,7 +103,6 @@ view_pooler_args:
     weight_by_ray_angle_gamma: 1.0
     min_ray_angle_weight: 0.1
 implicit_function_IdrFeatureField_args:
-  feature_vector_size: 3
   d_in: 3
   d_out: 1
   dims:
@@ -121,6 +120,5 @@ implicit_function_IdrFeatureField_args:
   weight_norm: true
   n_harmonic_functions_xyz: 1729
   pooled_feature_dim: 0
-  encoding_dim: 0
 view_metrics_ViewMetrics_args: {}
 regularization_metrics_RegularizationMetrics_args: {}

tests/implicitron/test_srn.py

@@ -55,9 +55,10 @@ class TestSRN(TestCaseMixin, unittest.TestCase):
     def test_srn_hypernet_implicit_function(self):
         # TODO investigate: If latent_dim_hypernet=0, why does this crash and dump core?
         latent_dim_hypernet = 39
-        hypernet_args = {"latent_dim_hypernet": latent_dim_hypernet}
         device = torch.device("cuda:0")
-        implicit_function = SRNHyperNetImplicitFunction(hypernet_args=hypernet_args)
+        implicit_function = SRNHyperNetImplicitFunction(
+            latent_dim_hypernet=latent_dim_hypernet
+        )
         implicit_function.to(device)
         global_code = torch.rand(_BATCH_SIZE, latent_dim_hypernet, device=device)
         bundle = self._get_bundle(device=device)