provide cow dataset

Summary: Make a dummy single-scene dataset using the code from generate_cow_renders (used in existing NeRF tutorials)

Reviewed By: kjchalup

Differential Revision: D38116910

fbshipit-source-id: 8db6df7098aa221c81d392e5cd21b0e67f65bd70

tests/data/missing_usemtl/README.md

@@ -2,6 +2,6 @@
 
 This is copied version of docs/tutorials/data/cow_mesh with removed line 6159 (usemtl material_1) to test behavior without usemtl material_1 declaration.
 
-Thank you to Keenen Crane for allowing the cow mesh model to be used freely in the public domain.
+Thank you to Keenan Crane for allowing the cow mesh model to be used freely in the public domain.
 
 ###### Source: http://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/

tests/implicitron/data/data_source.yaml

@@ -90,6 +90,15 @@ dataset_map_provider_LlffDatasetMapProvider_args:
   n_known_frames_for_test: null
   path_manager_factory_PathManagerFactory_args:
     silence_logs: true
+dataset_map_provider_RenderedMeshDatasetMapProvider_args:
+  num_views: 40
+  data_file: null
+  azimuth_range: 180.0
+  resolution: 128
+  use_point_light: true
+  path_manager_factory_class_type: PathManagerFactory
+  path_manager_factory_PathManagerFactory_args:
+    silence_logs: true
 data_loader_map_provider_SequenceDataLoaderMapProvider_args:
   batch_size: 1
   num_workers: 0

tests/implicitron/test_data_cow.py

@@ -0,0 +1,57 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import os
+import unittest
+
+import torch
+from pytorch3d.implicitron.dataset.dataset_base import FrameData
+from pytorch3d.implicitron.dataset.rendered_mesh_dataset_map_provider import (
+    RenderedMeshDatasetMapProvider,
+)
+from pytorch3d.implicitron.tools.config import expand_args_fields
+from pytorch3d.renderer import FoVPerspectiveCameras
+from tests.common_testing import TestCaseMixin
+
+
+inside_re_worker = os.environ.get("INSIDE_RE_WORKER", False)
+
+
+class TestDataCow(TestCaseMixin, unittest.TestCase):
+    def test_simple(self):
+        if inside_re_worker:
+            return
+        expand_args_fields(RenderedMeshDatasetMapProvider)
+        self._runtest(use_point_light=True, num_views=4)
+        self._runtest(use_point_light=False, num_views=4)
+
+    def _runtest(self, **kwargs):
+        provider = RenderedMeshDatasetMapProvider(**kwargs)
+        dataset_map = provider.get_dataset_map()
+        known_matrix = torch.zeros(1, 4, 4)
+        known_matrix[0, 0, 0] = 1.7321
+        known_matrix[0, 1, 1] = 1.7321
+        known_matrix[0, 2, 2] = 1.0101
+        known_matrix[0, 3, 2] = -1.0101
+        known_matrix[0, 2, 3] = 1
+
+        self.assertIsNone(dataset_map.val)
+        self.assertIsNone(dataset_map.test)
+        self.assertEqual(len(dataset_map.train), provider.num_views)
+
+        value = dataset_map.train[0]
+        self.assertIsInstance(value, FrameData)
+
+        self.assertEqual(value.image_rgb.shape, (3, 128, 128))
+        self.assertEqual(value.fg_probability.shape, (1, 128, 128))
+        # corner of image is background
+        self.assertEqual(value.fg_probability[0, 0, 0], 0)
+        self.assertEqual(value.fg_probability.max(), 1.0)
+        self.assertIsInstance(value.camera, FoVPerspectiveCameras)
+        self.assertEqual(len(value.camera), 1)
+        self.assertIsNone(value.camera.K)
+        matrix = value.camera.get_projection_transform().get_matrix()
+        self.assertClose(matrix, known_matrix, atol=1e-4)