collate_batched_meshes for datasets

Summary: Adding collate_batched_meshes for datasets.utils: takes in a list of dictionaries and merge them into one dictionary (while adding a merged mesh to the dictionary).

Reviewed By: nikhilaravi

Differential Revision: D22180404

fbshipit-source-id: f811f9a140f09638f355ad5739bffa6ee415819f

pytorch3d/datasets/__init__.py

 # Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
 
 from .shapenet import ShapeNetCore
+from .utils import collate_batched_meshes
 
 
 __all__ = [k for k in globals().keys() if not k.startswith("_")]

pytorch3d/datasets/utils.py

+# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+
+from typing import Dict, List
+
+from pytorch3d.structures import Meshes
+
+
+def collate_batched_meshes(batch: List[Dict]):
+    """
+    Take a list of objects in the form of dictionaries and merge them
+    into a single dictionary. This function can be used with a Dataset
+    object to create a torch.utils.data.Dataloader which directly
+    returns Meshes objects.
+    TODO: Add support for textures.
+
+    Args:
+        batch: List of dictionaries containing information about objects
+            in the dataset.
+    Returns:
+        collated_dict: Dictionary of collated lists. If batch contains both
+            verts and faces, a collated mesh batch is also returned.
+    """
+    if batch is None or len(batch) == 0:
+        return None
+    collated_dict = {}
+    for k in batch[0].keys():
+        collated_dict[k] = [d[k] for d in batch]
+
+    collated_dict["mesh"] = None
+    if {"verts", "faces"}.issubset(collated_dict.keys()):
+        collated_dict["mesh"] = Meshes(
+            verts=collated_dict["verts"], faces=collated_dict["faces"]
+        )
+
+    return collated_dict

tests/test_shapenet_core.py

@@ -10,13 +10,14 @@ import numpy as np
 import torch
 from common_testing import TestCaseMixin, load_rgb_image
 from PIL import Image
-from pytorch3d.datasets import ShapeNetCore
+from pytorch3d.datasets import ShapeNetCore, collate_batched_meshes
 from pytorch3d.renderer import (
     OpenGLPerspectiveCameras,
     PointLights,
     RasterizationSettings,
     look_at_view_transform,
 )
+from torch.utils.data import DataLoader
 
 
 # Set the SHAPENET_PATH to the local path to the dataset
@@ -110,6 +111,38 @@ class TestShapenetCore(TestCaseMixin, unittest.TestCase):
         ]
         self.assertEqual(len(shapenet_subset), sum(subset_model_nums))
 
+    def test_collate_models(self):
+        """
+        Test collate_batched_meshes returns items of the correct shapes and types.
+        Check that when collate_batched_meshes is passed to Dataloader, batches of
+        the correct shapes and types are returned.
+        """
+        # Load ShapeNetCore without specifying any particular categories.
+        shapenet_dataset = ShapeNetCore(SHAPENET_PATH)
+        # Randomly retrieve several objects from the dataset.
+        rand_idxs = torch.randint(len(shapenet_dataset), (6,))
+        rand_objs = [shapenet_dataset[idx] for idx in rand_idxs]
+
+        # Collate the randomly selected objects
+        collated_meshes = collate_batched_meshes(rand_objs)
+        verts, faces = (collated_meshes["verts"], collated_meshes["faces"])
+        self.assertEqual(len(verts), 6)
+        self.assertEqual(len(faces), 6)
+
+        # Pass the custom collate_fn function to DataLoader and check elements
+        # in batch have the correct shape.
+        batch_size = 12
+        shapenet_core_loader = DataLoader(
+            shapenet_dataset, batch_size=batch_size, collate_fn=collate_batched_meshes
+        )
+        it = iter(shapenet_core_loader)
+        object_batch = next(it)
+        self.assertEqual(len(object_batch["synset_id"]), batch_size)
+        self.assertEqual(len(object_batch["model_id"]), batch_size)
+        self.assertEqual(len(object_batch["label"]), batch_size)
+        self.assertEqual(object_batch["mesh"].verts_padded().shape[0], batch_size)
+        self.assertEqual(object_batch["mesh"].faces_padded().shape[0], batch_size)
+
     def test_catch_render_arg_errors(self):
         """
         Test rendering ShapeNetCore with invalid model_ids, categories or indices,