Raysampler as pluggable

Summary:
This converts raysamplers to ReplaceableBase so that users can hack their own raysampling impls.

Context: Andrea tried to implement TensoRF within implicitron but could not due to the need to implement his own raysampler.

Reviewed By: shapovalov

Differential Revision: D36016318

fbshipit-source-id: ef746f3365282bdfa9c15f7b371090a5aae7f8da

projects/implicitron_trainer/configs/repro_base.yaml

@@ -49,10 +49,8 @@ generic_model_args:
     append_xyz:
     - 5
     latent_dim: 0
-  raysampler_args:
+  raysampler_AdaptiveRaySampler_args:
     n_rays_per_image_sampled_from_mask: 1024
-    min_depth: 0.0
-    max_depth: 0.0
     scene_extent: 8.0
     n_pts_per_ray_training: 64
     n_pts_per_ray_evaluation: 64

projects/implicitron_trainer/configs/repro_multiseq_idr_ad.yaml

@@ -54,7 +54,7 @@ generic_model_args:
       n_harmonic_functions_dir: 4
       pooled_feature_dim: 0
       weight_norm: true
-  raysampler_args:
+  raysampler_AdaptiveRaySampler_args:
     n_rays_per_image_sampled_from_mask: 1024
     n_pts_per_ray_training: 0
     n_pts_per_ray_evaluation: 0

projects/implicitron_trainer/configs/repro_multiseq_nerf_wce.yaml

@@ -6,5 +6,5 @@ clip_grad: 1.0
 generic_model_args:
   chunk_size_grid: 16000
   view_pooler_enabled: true
-  raysampler_args:
+  raysampler_AdaptiveRaySampler_args:
     n_rays_per_image_sampled_from_mask: 850

projects/implicitron_trainer/configs/repro_multiseq_nerformer.yaml

@@ -4,7 +4,7 @@ defaults:
 - _self_
 generic_model_args:
   chunk_size_grid: 16000
-  raysampler_args:
+  raysampler_AdaptiveRaySampler_args:
     n_rays_per_image_sampled_from_mask: 800
     n_pts_per_ray_training: 32
     n_pts_per_ray_evaluation: 32

projects/implicitron_trainer/configs/repro_multiseq_nerformer_angle_w.yaml

@@ -1,17 +1,6 @@
 defaults:
-- repro_multiseq_base.yaml
-- repro_feat_extractor_transformer.yaml
+- repro_multiseq_nerformer.yaml
 - _self_
 generic_model_args:
-  chunk_size_grid: 16000
-  raysampler_args:
-    n_rays_per_image_sampled_from_mask: 800
-    n_pts_per_ray_training: 32
-    n_pts_per_ray_evaluation: 32
-  renderer_MultiPassEmissionAbsorptionRenderer_args:
-    n_pts_per_ray_fine_training: 16
-    n_pts_per_ray_fine_evaluation: 16
-  implicit_function_class_type: NeRFormerImplicitFunction
-  view_pooler_enabled: true
   view_pooler_args:
     feature_aggregator_class_type: AngleWeightedIdentityFeatureAggregator

projects/implicitron_trainer/configs/repro_multiseq_srn_ad_hypernet.yaml

@@ -16,11 +16,11 @@ generic_model_args:
   sequence_autodecoder_args:
     encoding_dim: 256
     n_instances: 20000
-  raysampler_args:
+  raysampler_class_type: NearFarRaySampler
+  raysampler_NearFarRaySampler_args:
     n_rays_per_image_sampled_from_mask: 2048
     min_depth: 0.05
     max_depth: 0.05
-    scene_extent: 0.0
     n_pts_per_ray_training: 1
     n_pts_per_ray_evaluation: 1
     stratified_point_sampling_training: false

projects/implicitron_trainer/configs/repro_multiseq_srn_wce.yaml

@@ -13,11 +13,11 @@ generic_model_args:
     loss_prev_stage_mask_bce: 0.0
     loss_autodecoder_norm: 0.0
     depth_neg_penalty: 10000.0
-  raysampler_args:
+  raysampler_class_type: NearFarRaySampler
+  raysampler_NearFarRaySampler_args:
     n_rays_per_image_sampled_from_mask: 2048
     min_depth: 0.05
     max_depth: 0.05
-    scene_extent: 0.0
     n_pts_per_ray_training: 1
     n_pts_per_ray_evaluation: 1
     stratified_point_sampling_training: false

projects/implicitron_trainer/configs/repro_singleseq_idr.yaml

@@ -49,7 +49,7 @@ generic_model_args:
       n_harmonic_functions_dir: 4
       pooled_feature_dim: 0
       weight_norm: true
-  raysampler_args:
+  raysampler_AdaptiveRaySampler_args:
     n_rays_per_image_sampled_from_mask: 1024
     n_pts_per_ray_training: 0
     n_pts_per_ray_evaluation: 0

projects/implicitron_trainer/configs/repro_singleseq_nerf.yaml

@@ -1,4 +1,3 @@
 defaults:
 - repro_singleseq_base
 - _self_
-exp_dir: ./data/nerf_single_apple/

projects/implicitron_trainer/configs/repro_singleseq_nerf_wce.yaml

@@ -5,5 +5,5 @@ defaults:
 generic_model_args:
   chunk_size_grid: 16000
   view_pooler_enabled: true
-  raysampler_args:
+  raysampler_AdaptiveRaySampler_args:
     n_rays_per_image_sampled_from_mask: 850

projects/implicitron_trainer/configs/repro_singleseq_nerformer.yaml

@@ -6,7 +6,7 @@ generic_model_args:
   chunk_size_grid: 16000
   view_pooler_enabled: true
   implicit_function_class_type: NeRFormerImplicitFunction
-  raysampler_args:
+  raysampler_AdaptiveRaySampler_args:
     n_rays_per_image_sampled_from_mask: 800
     n_pts_per_ray_training: 32
     n_pts_per_ray_evaluation: 32

projects/implicitron_trainer/configs/repro_singleseq_srn.yaml

@@ -12,11 +12,11 @@ generic_model_args:
     loss_prev_stage_mask_bce: 0.0
     loss_autodecoder_norm: 0.0
     depth_neg_penalty: 10000.0
-  raysampler_args:
+  raysampler_class_type: NearFarRaySampler
+  raysampler_NearFarRaySampler_args:
     n_rays_per_image_sampled_from_mask: 2048
     min_depth: 0.05
     max_depth: 0.05
-    scene_extent: 0.0
     n_pts_per_ray_training: 1
     n_pts_per_ray_evaluation: 1
     stratified_point_sampling_training: false

projects/implicitron_trainer/configs/repro_singleseq_srn_wce.yaml

@@ -13,11 +13,11 @@ generic_model_args:
     loss_prev_stage_mask_bce: 0.0
     loss_autodecoder_norm: 0.0
     depth_neg_penalty: 10000.0
-  raysampler_args:
+  raysampler_class_type: NearFarRaySampler
+  raysampler_NearFarRaySampler_args:
     n_rays_per_image_sampled_from_mask: 2048
     min_depth: 0.05
     max_depth: 0.05
-    scene_extent: 0.0
     n_pts_per_ray_training: 1
     n_pts_per_ray_evaluation: 1
     stratified_point_sampling_training: false

pytorch3d/implicitron/models/generic_model.py

@@ -19,7 +19,7 @@ from pytorch3d.implicitron.tools.config import (
     run_auto_creation,
 )
 from pytorch3d.implicitron.tools.rasterize_mc import rasterize_mc_samples
-from pytorch3d.implicitron.tools.utils import cat_dataclass
+from pytorch3d.implicitron.tools.utils import cat_dataclass, setattr_if_hasattr
 from pytorch3d.renderer import RayBundle, utils as rend_utils
 from pytorch3d.renderer.cameras import CamerasBase
 from visdom import Visdom
@@ -46,7 +46,7 @@ from .renderer.base import (
 )
 from .renderer.lstm_renderer import LSTMRenderer  # noqa
 from .renderer.multipass_ea import MultiPassEmissionAbsorptionRenderer  # noqa
-from .renderer.ray_sampler import RaySampler
+from .renderer.ray_sampler import RaySamplerBase
 from .renderer.sdf_renderer import SignedDistanceFunctionRenderer  # noqa
 from .resnet_feature_extractor import ResNetFeatureExtractor
 from .view_pooler.view_pooler import ViewPooler
@@ -160,6 +160,8 @@ class GenericModel(ImplicitronModelBase, torch.nn.Module):  # pyre-ignore: 13
             the scene such as multiple objects or morphing objects. It is up to the implicit
             function definition how to use it, but the most typical way is to broadcast and
             concatenate to the other inputs for the implicit function.
+        raysampler_class_type: The name of the raysampler class which is available
+            in the global registry.
         raysampler: An instance of RaySampler which is used to emit
             rays from the target view(s).
         renderer_class_type: The name of the renderer class which is available in the global
@@ -204,7 +206,8 @@ class GenericModel(ImplicitronModelBase, torch.nn.Module):  # pyre-ignore: 13
     sequence_autodecoder: Autodecoder
 
     # ---- raysampler
-    raysampler: RaySampler
+    raysampler_class_type: str = "AdaptiveRaySampler"
+    raysampler: RaySamplerBase
 
     # ---- renderer configs
     renderer_class_type: str = "MultiPassEmissionAbsorptionRenderer"
@@ -262,11 +265,6 @@ class GenericModel(ImplicitronModelBase, torch.nn.Module):  # pyre-ignore: 13
         super().__init__()
         self.view_metrics = ViewMetrics()
 
-        self._check_and_preprocess_renderer_configs()
-        self.raysampler_args["sampling_mode_training"] = self.sampling_mode_training
-        self.raysampler_args["sampling_mode_evaluation"] = self.sampling_mode_evaluation
-        self.raysampler_args["image_width"] = self.render_image_width
-        self.raysampler_args["image_height"] = self.render_image_height
         run_auto_creation(self)
 
         self._implicit_functions = self._construct_implicit_functions()
@@ -339,7 +337,7 @@ class GenericModel(ImplicitronModelBase, torch.nn.Module):  # pyre-ignore: 13
         )
 
         # (1) Sample rendering rays with the ray sampler.
-        ray_bundle: RayBundle = self.raysampler(
+        ray_bundle: RayBundle = self.raysampler(  # pyre-fixme[29]
             target_cameras,
             evaluation_mode,
             mask=mask_crop[:n_targets]
@@ -565,19 +563,52 @@ class GenericModel(ImplicitronModelBase, torch.nn.Module):  # pyre-ignore: 13
             else 0
         )
 
-    def _check_and_preprocess_renderer_configs(self):
+    def create_raysampler(self):
+        raysampler_args = getattr(
+            self, "raysampler_" + self.raysampler_class_type + "_args"
+        )
+        setattr_if_hasattr(
+            raysampler_args, "sampling_mode_training", self.sampling_mode_training
+        )
+        setattr_if_hasattr(
+            raysampler_args, "sampling_mode_evaluation", self.sampling_mode_evaluation
+        )
+        setattr_if_hasattr(raysampler_args, "image_width", self.render_image_width)
+        setattr_if_hasattr(raysampler_args, "image_height", self.render_image_height)
+        self.raysampler = registry.get(RaySamplerBase, self.raysampler_class_type)(
+            **raysampler_args
+        )
+
+    def create_renderer(self):
+        raysampler_args = getattr(
+            self, "raysampler_" + self.raysampler_class_type + "_args"
+        )
         self.renderer_MultiPassEmissionAbsorptionRenderer_args[
             "stratified_sampling_coarse_training"
-        ] = self.raysampler_args["stratified_point_sampling_training"]
+        ] = raysampler_args["stratified_point_sampling_training"]
         self.renderer_MultiPassEmissionAbsorptionRenderer_args[
             "stratified_sampling_coarse_evaluation"
-        ] = self.raysampler_args["stratified_point_sampling_evaluation"]
+        ] = raysampler_args["stratified_point_sampling_evaluation"]
         self.renderer_SignedDistanceFunctionRenderer_args[
             "render_features_dimensions"
         ] = self.render_features_dimensions
-        self.renderer_SignedDistanceFunctionRenderer_args.ray_tracer_args[
-            "object_bounding_sphere"
-        ] = self.raysampler_args["scene_extent"]
+
+        if self.renderer_class_type == "SignedDistanceFunctionRenderer":
+            if "scene_extent" not in raysampler_args:
+                raise ValueError(
+                    "SignedDistanceFunctionRenderer requires"
+                    + " a raysampler that defines the 'scene_extent' field"
+                    + " (this field is supported by, e.g., the adaptive raysampler - "
+                    + " self.raysampler_class_type='AdaptiveRaySampler')."
+                )
+            self.renderer_SignedDistanceFunctionRenderer_args.ray_tracer_args[
+                "object_bounding_sphere"
+            ] = self.raysampler_AdaptiveRaySampler_args["scene_extent"]
+
+        renderer_args = getattr(self, "renderer_" + self.renderer_class_type + "_args")
+        self.renderer = registry.get(BaseRenderer, self.renderer_class_type)(
+            **renderer_args
+        )
 
     def create_view_pooler(self):
         """

pytorch3d/implicitron/models/renderer/ray_sampler.py

@@ -9,16 +9,48 @@ from typing import Optional, Tuple
 
 import torch
 from pytorch3d.implicitron.tools import camera_utils
-from pytorch3d.implicitron.tools.config import Configurable
+from pytorch3d.implicitron.tools.config import ReplaceableBase, registry
 from pytorch3d.renderer import NDCMultinomialRaysampler, RayBundle
 from pytorch3d.renderer.cameras import CamerasBase
 
 from .base import EvaluationMode, RenderSamplingMode
 
 
-class RaySampler(Configurable, torch.nn.Module):
+class RaySamplerBase(ReplaceableBase):
+    """
+    Base class for ray samplers.
     """
 
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        cameras: CamerasBase,
+        evaluation_mode: EvaluationMode,
+        mask: Optional[torch.Tensor] = None,
+    ) -> RayBundle:
+        """
+        Args:
+            cameras: A batch of `batch_size` cameras from which the rays are emitted.
+            evaluation_mode: one of `EvaluationMode.TRAINING` or
+                `EvaluationMode.EVALUATION` which determines the sampling mode
+                that is used.
+            mask: Active for the `RenderSamplingMode.MASK_SAMPLE` sampling mode.
+                Defines a non-negative mask of shape
+                `(batch_size, image_height, image_width)` where each per-pixel
+                value is proportional to the probability of sampling the
+                corresponding pixel's ray.
+
+        Returns:
+            ray_bundle: A `RayBundle` object containing the parametrizations of the
+                sampled rendering rays.
+        """
+        raise NotImplementedError()
+
+
+class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
+    """
     Samples a fixed number of points along rays which are in turn sampled for
     each camera in a batch.
 
@@ -29,46 +61,19 @@ class RaySampler(Configurable, torch.nn.Module):
     for training and evaluation by setting `self.sampling_mode_training`
     and `self.sampling_mode_training` accordingly.
 
-    The class allows two modes of sampling points along the rays:
-        1) Sampling between fixed near and far z-planes:
-            Active when `self.scene_extent <= 0`, samples points along each ray
-            with approximately uniform spacing of z-coordinates between
-            the minimum depth `self.min_depth` and the maximum depth `self.max_depth`.
-            This sampling is useful for rendering scenes where the camera is
-            in a constant distance from the focal point of the scene.
-        2) Adaptive near/far plane estimation around the world scene center:
-            Active when `self.scene_extent > 0`. Samples points on each
-            ray between near and far planes whose depths are determined based on
-            the distance from the camera center to a predefined scene center.
-            More specifically,
-            `min_depth = max(
-                (self.scene_center-camera_center).norm() - self.scene_extent, eps
-            )` and
-            `max_depth = (self.scene_center-camera_center).norm() + self.scene_extent`.
-            This sampling is ideal for object-centric scenes whose contents are
-            centered around a known `self.scene_center` and fit into a bounding sphere
-            with a radius of `self.scene_extent`.
-
-        Similar to the sampling mode, the sampling parameters can be set separately
-        for training and evaluation.
+    The class allows to adjust the sampling points along rays by overwriting the
+    `AbstractMaskRaySampler._get_min_max_depth_bounds` function which returns
+    the near/far planes (`min_depth`/`max_depth`) `NDCMultinomialRaysampler`.
 
     Settings:
         image_width: The horizontal size of the image grid.
         image_height: The vertical size of the image grid.
-        scene_center: The xyz coordinates of the center of the scene used
-            along with `scene_extent` to compute the min and max depth planes
-            for sampling ray-points.
-        scene_extent: The radius of the scene bounding sphere centered at `scene_center`.
-            If `scene_extent <= 0`, the raysampler samples points between
-            `self.min_depth` and `self.max_depth` depths instead.
         sampling_mode_training: The ray sampling mode for training. This should be a str
             option from the RenderSamplingMode Enum
         sampling_mode_evaluation: Same as above but for evaluation.
         n_pts_per_ray_training: The number of points sampled along each ray during training.
         n_pts_per_ray_evaluation: The number of points sampled along each ray during evaluation.
         n_rays_per_image_sampled_from_mask: The amount of rays to be sampled from the image grid
-        min_depth: The minimum depth of a ray-point. Active when `self.scene_extent > 0`.
-        max_depth: The maximum depth of a ray-point. Active when `self.scene_extent > 0`.
         stratified_point_sampling_training: if set, performs stratified random sampling
             along the ray; otherwise takes ray points at deterministic offsets.
         stratified_point_sampling_evaluation: Same as above but for evaluation.
@@ -77,24 +82,17 @@ class RaySampler(Configurable, torch.nn.Module):
 
     image_width: int = 400
     image_height: int = 400
-    scene_center: Tuple[float, float, float] = field(
-        default_factory=lambda: (0.0, 0.0, 0.0)
-    )
-    scene_extent: float = 0.0
     sampling_mode_training: str = "mask_sample"
     sampling_mode_evaluation: str = "full_grid"
     n_pts_per_ray_training: int = 64
     n_pts_per_ray_evaluation: int = 64
     n_rays_per_image_sampled_from_mask: int = 1024
-    min_depth: float = 0.1
-    max_depth: float = 8.0
     # stratified sampling vs taking points at deterministic offsets
     stratified_point_sampling_training: bool = True
     stratified_point_sampling_evaluation: bool = False
 
     def __post_init__(self):
         super().__init__()
-        self.scene_center = torch.FloatTensor(self.scene_center)
 
         self._sampling_mode = {
             EvaluationMode.TRAINING: RenderSamplingMode(self.sampling_mode_training),
@@ -108,8 +106,8 @@ class RaySampler(Configurable, torch.nn.Module):
                 image_width=self.image_width,
                 image_height=self.image_height,
                 n_pts_per_ray=self.n_pts_per_ray_training,
-                min_depth=self.min_depth,
-                max_depth=self.max_depth,
+                min_depth=0.0,
+                max_depth=0.0,
                 n_rays_per_image=self.n_rays_per_image_sampled_from_mask
                 if self._sampling_mode[EvaluationMode.TRAINING]
                 == RenderSamplingMode.MASK_SAMPLE
@@ -121,8 +119,8 @@ class RaySampler(Configurable, torch.nn.Module):
                 image_width=self.image_width,
                 image_height=self.image_height,
                 n_pts_per_ray=self.n_pts_per_ray_evaluation,
-                min_depth=self.min_depth,
-                max_depth=self.max_depth,
+                min_depth=0.0,
+                max_depth=0.0,
                 n_rays_per_image=self.n_rays_per_image_sampled_from_mask
                 if self._sampling_mode[EvaluationMode.EVALUATION]
                 == RenderSamplingMode.MASK_SAMPLE
@@ -132,6 +130,9 @@ class RaySampler(Configurable, torch.nn.Module):
             ),
         }
 
+    def _get_min_max_depth_bounds(self, cameras: CamerasBase) -> Tuple[float, float]:
+        raise NotImplementedError()
+
     def forward(
         self,
         cameras: CamerasBase,
@@ -169,12 +170,7 @@ class RaySampler(Configurable, torch.nn.Module):
                 mode="nearest",
             )[:, 0]
 
-        if self.scene_extent > 0.0:
-            # Override the min/max depth set in initialization based on the
-            # input cameras.
-            min_depth, max_depth = camera_utils.get_min_max_depth_bounds(
-                cameras, self.scene_center, self.scene_extent
-            )
+        min_depth, max_depth = self._get_min_max_depth_bounds(cameras)
 
         # pyre-fixme[29]:
         #  `Union[BoundMethod[typing.Callable(torch.Tensor.__getitem__)[[Named(self,
@@ -183,8 +179,75 @@ class RaySampler(Configurable, torch.nn.Module):
         ray_bundle = self._raysamplers[evaluation_mode](
             cameras=cameras,
             mask=sample_mask,
-            min_depth=float(min_depth[0]) if self.scene_extent > 0.0 else None,
-            max_depth=float(max_depth[0]) if self.scene_extent > 0.0 else None,
+            min_depth=min_depth,
+            max_depth=max_depth,
         )
 
         return ray_bundle
+
+
+@registry.register
+class AdaptiveRaySampler(AbstractMaskRaySampler):
+    """
+    Adaptively samples points on each ray between near and far planes whose
+    depths are determined based on the distance from the camera center
+    to a predefined scene center.
+
+    More specifically,
+    `min_depth = max(
+        (self.scene_center-camera_center).norm() - self.scene_extent, eps
+    )` and
+    `max_depth = (self.scene_center-camera_center).norm() + self.scene_extent`.
+
+    This sampling is ideal for object-centric scenes whose contents are
+    centered around a known `self.scene_center` and fit into a bounding sphere
+    with a radius of `self.scene_extent`.
+
+    Args:
+        scene_center: The xyz coordinates of the center of the scene used
+            along with `scene_extent` to compute the min and max depth planes
+            for sampling ray-points.
+        scene_extent: The radius of the scene bounding box centered at `scene_center`.
+    """
+
+    scene_extent: float = 8.0
+    scene_center: Tuple[float, float, float] = (0.0, 0.0, 0.0)
+
+    def __post_init__(self):
+        super().__post_init__()
+        if self.scene_extent <= 0.0:
+            raise ValueError("Adaptive raysampler requires self.scene_extent > 0.")
+        self._scene_center = torch.FloatTensor(self.scene_center)
+
+    def _get_min_max_depth_bounds(self, cameras: CamerasBase) -> Tuple[float, float]:
+        """
+        Returns the adaptivelly calculated near/far planes.
+        """
+        min_depth, max_depth = camera_utils.get_min_max_depth_bounds(
+            cameras, self._scene_center, self.scene_extent
+        )
+        return float(min_depth[0]), float(max_depth[0])
+
+
+@registry.register
+class NearFarRaySampler(AbstractMaskRaySampler):
+    """
+    Samples a fixed number of points between fixed near and far z-planes.
+    Specifically, samples points along each ray with approximately uniform spacing
+    of z-coordinates between the minimum depth `self.min_depth` and the maximum depth
+    `self.max_depth`. This sampling is useful for rendering scenes where the camera is
+    in a constant distance from the focal point of the scene.
+
+    Args:
+        min_depth: The minimum depth of a ray-point.
+        max_depth: The maximum depth of a ray-point.
+    """
+
+    min_depth: float = 0.1
+    max_depth: float = 8.0
+
+    def _get_min_max_depth_bounds(self, cameras: CamerasBase) -> Tuple[float, float]:
+        """
+        Returns the stored near/far planes.
+        """
+        return self.min_depth, self.max_depth

pytorch3d/implicitron/tools/utils.py

@@ -157,6 +157,15 @@ def cat_dataclass(batch, tensor_collator: Callable):
     return type(elem)(**collated)
 
 
+def setattr_if_hasattr(obj, name, value):
+    """
+    Same as setattr(obj, name, value), but does nothing in case `name` is
+    not an attribe of `obj`.
+    """
+    if hasattr(obj, name):
+        setattr(obj, name, value)
+
+
 class Timer:
     """
     A simple class for timing execution.