WIP: Add Ray Tracing (#3604)

Summary:
Revamped version of #3234 (which was also revamp of #2850)

Differential Revision: D49197174

Pulled By: mthrok

Author: mthrok

docs/source/prototype.functional.rst

@@ -35,4 +35,5 @@ Room Impulse Response Simulation
    :toctree: generated
    :nosignatures:
 
+   ray_tracing
    simulate_rir_ism

test/cpp/rir/wall_collision.cpp

@@ -3,22 +3,24 @@
 
 using namespace torchaudio::rir;
 
+using DTYPE = double;
+
 struct CollisionTestParam {
   // Input
   torch::Tensor origin;
   torch::Tensor direction;
   // Expected
   torch::Tensor hit_point;
   int next_wall_index;
-  float hit_distance;
+  DTYPE hit_distance;
 };
 
 CollisionTestParam par(
-    torch::ArrayRef<float> origin,
-    torch::ArrayRef<float> direction,
-    torch::ArrayRef<float> hit_point,
+    torch::ArrayRef<DTYPE> origin,
+    torch::ArrayRef<DTYPE> direction,
+    torch::ArrayRef<DTYPE> hit_point,
     int next_wall_index,
-    float hit_distance) {
+    DTYPE hit_distance) {
   auto dir = torch::tensor(direction);
   return {
       torch::tensor(origin),
@@ -50,7 +52,7 @@ TEST_P(Simple3DRoomCollisionTest, CollisionTest3D) {
 
   auto param = GetParam();
   auto [hit_point, next_wall_index, hit_distance] =
-      find_collision_wall<float>(room, param.origin, param.direction);
+      find_collision_wall<DTYPE>(room, param.origin, param.direction);
 
   EXPECT_EQ(param.next_wall_index, next_wall_index);
   EXPECT_FLOAT_EQ(param.hit_distance, hit_distance);
@@ -100,3 +102,41 @@ INSTANTIATE_TEST_CASE_P(
         par({.5, .5, 1}, {0.0, -1., -1.}, {.5, .0, .5}, 2, ISQRT2),
         par({.5, .5, 1}, {0.0, 1.0, -1.}, {.5, 1., .5}, 3, ISQRT2),
         par({.5, .5, 1}, {0.0, 0.0, -1.}, {.5, .5, .0}, 4, 1.0)));
+
+
+INSTANTIATE_TEST_CASE_P(
+    EdgeCollisionTest,
+    Simple3DRoomCollisionTest,
+    ::testing::Values(
+        par({1, 1, 0}, {1., 1., 0.}, {1., 1., 0.}, 1, 0.0),
+        par({1, 1, 0}, {-1., 1., 0.}, {1., 1., 0.}, 3, 0.0),
+        //
+        par({1, 1, 1}, {1., 1., 1.}, {1., 1., 1.}, 1, 0.0),
+        par({1, 1, 1}, {-1., 1., 1.}, {1., 1., 1.}, 3, 0.0),
+        par({1, 1, 1}, {-1., -1., 1.}, {1., 1., 1.}, 5, 0.0)
+                      ));
+
+class Simple3DRoomCollisionTest2
+    : public ::testing::TestWithParam<CollisionTestParam> {};
+
+TEST_P(Simple3DRoomCollisionTest2, CollisionTest3D) {
+  auto room = torch::tensor({3, 4, 5});
+
+  auto param = GetParam();
+  auto [hit_point, next_wall_index, hit_distance] =
+      find_collision_wall<DTYPE>(room, param.origin, param.direction);
+
+  EXPECT_EQ(param.next_wall_index, next_wall_index);
+  EXPECT_FLOAT_EQ(param.hit_distance, hit_distance);
+  EXPECT_NEAR(param.hit_point[0].item<DTYPE>(), hit_point[0].item<DTYPE>(), 1e-5);
+  EXPECT_NEAR(param.hit_point[1].item<DTYPE>(), hit_point[1].item<DTYPE>(), 1e-5);
+  EXPECT_NEAR(param.hit_point[2].item<DTYPE>(), hit_point[2].item<DTYPE>(), 1e-5);
+}
+
+
+INSTANTIATE_TEST_CASE_P(
+    EdgeCollisionTest2,
+    Simple3DRoomCollisionTest2,
+    ::testing::Values(
+        par({3., 4., 4.6542}, {-0.9798, 0.1733, 0.1000}, {3., 4., 0.}, 3, 0.0)
+                      ));

test/torchaudio_unittest/prototype/functional/functional_test_impl.py

@@ -412,6 +412,269 @@ def test_exp_sigmoid_input_diff(self, linspace_input_values, exp_sigmoid_paramet
 
         self.assertEqual(torch_out, torch.tensor(np_out))
 
+    @parameterized.expand(
+        [
+            # both float
+            (0.1, 0.2, (2, 1, 2500)),
+            # Per-wall
+            ((6,), 0.2, (2, 1, 2500)),
+            (0.1, (6,), (2, 1, 2500)),
+            ((6,), (6,), (2, 1, 2500)),
+            # Per-band and per-wall
+            ((3, 6), 0.2, (2, 3, 2500)),
+            (0.1, (5, 6), (2, 5, 2500)),
+            ((7, 6), (7, 6), (2, 7, 2500)),
+        ]
+    )
+    def test_ray_tracing_output_shape(self, abs_, scat_, expected_shape):
+        if isinstance(abs_, float):
+            absorption = abs_
+        else:
+            absorption = torch.rand(abs_, dtype=self.dtype)
+        if isinstance(scat_, float):
+            scattering = scat_
+        else:
+            scattering = torch.rand(scat_, dtype=self.dtype)
+
+        room_dim = torch.tensor([3, 4, 5], dtype=self.dtype)
+        mic_array = torch.tensor([[0, 0, 0], [1, 1, 1]], dtype=self.dtype)
+        source = torch.tensor([1, 2, 3], dtype=self.dtype)
+        num_rays = 100
+
+        hist = F.ray_tracing(
+            room=room_dim,
+            source=source,
+            mic_array=mic_array,
+            num_rays=num_rays,
+            absorption=absorption,
+            scattering=scattering,
+        )
+        assert hist.shape == expected_shape
+
+    def test_ray_tracing_input_errors(self):
+        room = torch.tensor([3.0, 4.0, 5.0], dtype=self.dtype)
+        source = torch.tensor([0.0, 0.0, 0.0], dtype=self.dtype)
+        mic = torch.tensor([[1.0, 2.0, 3.0]], dtype=self.dtype)
+
+        # baseline. This should not raise
+        _ = F.ray_tracing(room=room, source=source, mic_array=mic, num_rays=10)
+
+        # invlaid room shape
+        for invalid in ([[4, 5]], [4, 5, 4, 5]):
+            invalid = torch.tensor(invalid, dtype=self.dtype)
+            with self.assertRaises(ValueError) as cm:
+                F.ray_tracing(room=invalid, source=source, mic_array=mic, num_rays=10)
+
+            error = str(cm.exception)
+            self.assertIn("`room` must be a 1D Tensor with 3 elements.", error)
+            self.assertIn(str(invalid.shape), error)
+
+        # invalid microphone shape
+        invalid = torch.tensor([[[3, 4]]], dtype=self.dtype)
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(room=room, source=source, mic_array=invalid, num_rays=10)
+
+        error = str(cm.exception)
+        self.assertIn("`mic_array` must be a 2D Tensor with shape (num_channels, 3).", error)
+        self.assertIn(str(invalid.shape), error)
+
+        # incompatible dtypes
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(
+                room=room.to(torch.float64),
+                source=source.to(torch.float32),
+                mic_array=mic.to(torch.float32),
+                num_rays=10,
+            )
+        error = str(cm.exception)
+        self.assertIn("dtype of `room`, `source` and `mic_array` must match.", error)
+        self.assertIn("`room` (torch.float64)", error)
+        self.assertIn("`source` (torch.float32)", error)
+        self.assertIn("`mic_array` (torch.float32)", error)
+
+        # invalid time configuration
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(
+                room=room,
+                source=source,
+                mic_array=mic,
+                num_rays=10,
+                time_thres=10,
+                hist_bin_size=11,
+            )
+        error = str(cm.exception)
+        self.assertIn("`time_thres` must be greater than `hist_bin_size`.", error)
+        self.assertIn("hist_bin_size=11", error)
+        self.assertIn("time_thres=10", error)
+
+        # invalid absorption shape 1D
+        invalid_abs = torch.tensor([1, 2, 3], dtype=self.dtype)
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(
+                room=room,
+                source=source,
+                mic_array=mic,
+                num_rays=10,
+                absorption=invalid_abs,
+            )
+        error = str(cm.exception)
+        self.assertIn("The shape of `absorption` must be (6,) when", error)
+        self.assertIn(str(invalid_abs.shape), error)
+
+        # invalid absorption shape 2D
+        invalid_abs = torch.tensor([[1, 2, 3]], dtype=self.dtype)
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(room=room, source=source, mic_array=mic, num_rays=10, absorption=invalid_abs)
+        error = str(cm.exception)
+        self.assertIn("The shape of `absorption` must be (NUM_BANDS, 6) when", error)
+        self.assertIn(str(invalid_abs.shape), error)
+
+        # invalid scattering shape 1D
+        invalid_scat = torch.tensor([1, 2, 3], dtype=self.dtype)
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(
+                room=room,
+                source=source,
+                mic_array=mic,
+                num_rays=10,
+                scattering=invalid_scat,
+            )
+        error = str(cm.exception)
+        self.assertIn("The shape of `scattering` must be (6,) when", error)
+        self.assertIn(str(invalid_scat.shape), error)
+
+        # invalid scattering shape 2D
+        invalid_scat = torch.tensor([[1, 2, 3]], dtype=self.dtype)
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(room=room, source=source, mic_array=mic, num_rays=10, scattering=invalid_scat)
+        error = str(cm.exception)
+        self.assertIn("The shape of `scattering` must be (NUM_BANDS, 6) when", error)
+        self.assertIn(str(invalid_scat.shape), error)
+
+        # Invalid absorption value
+        for invalid_val in [-1., torch.tensor([i - 1. for i in range(6)])]:
+            with self.assertRaises(ValueError) as cm:
+                F.ray_tracing(room=room, source=source, mic_array=mic, num_rays=10, absorption=invalid_val)
+
+                error = str(cm.exception)
+                self.assertIn("`absorption` must be non-negative`")
+
+        # Invalid scattering value
+        for invalid_val in [-1., torch.tensor([i - 1. for i in range(6)])]:
+            with self.assertRaises(ValueError) as cm:
+                F.ray_tracing(room=room, source=source, mic_array=mic, num_rays=10, scattering=invalid_val)
+
+                error = str(cm.exception)
+                self.assertIn("`scattering` must be non-negative`")
+
+        # incompatible scattering and absorption
+        abs_ = torch.zeros((7, 6), dtype=self.dtype)
+        scat = torch.zeros((5, 6), dtype=self.dtype)
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(
+                room=room,
+                source=source,
+                mic_array=mic,
+                num_rays=10,
+                absorption=abs_,
+                scattering=scat,
+            )
+        error = str(cm.exception)
+        self.assertIn(
+            "`absorption` and `scattering` must be broadcastable to the same number of bands and walls", error
+        )
+        self.assertIn(f"absorption={abs_.shape}", error)
+        self.assertIn(f"scattering={scat.shape}", error)
+
+        # Make sure passing different shapes for absorption or scattering doesn't raise an error
+        # float and tensor
+        F.ray_tracing(
+            room=room,
+            source=source,
+            mic_array=mic,
+            num_rays=10,
+            absorption=0.1,
+            scattering=torch.rand((5, 6), dtype=self.dtype),
+        )
+        F.ray_tracing(
+            room=room,
+            source=source,
+            mic_array=mic,
+            num_rays=10,
+            absorption=torch.rand((7, 6), dtype=self.dtype),
+            scattering=0.1,
+        )
+        # per-wall only and per-band + per-wall
+        F.ray_tracing(
+            room=room,
+            source=source,
+            mic_array=mic,
+            num_rays=10,
+            absorption=torch.rand(6, dtype=self.dtype),
+            scattering=torch.rand(7, 6, dtype=self.dtype),
+        )
+        F.ray_tracing(
+            room=room,
+            source=source,
+            mic_array=mic,
+            num_rays=10,
+            absorption=torch.rand(7, 6, dtype=self.dtype),
+            scattering=torch.rand(6, dtype=self.dtype),
+        )
+
+    def test_ray_tracing_per_band_per_wall_absorption(self):
+        """Check that when the value of absorption and scattering are the same
+        across walls and frequency bands, the output histograms are:
+        - all equal across frequency bands
+        - equal to simply passing a float value instead of a (num_bands, D) or
+        (D,) tensor.
+        """
+
+        room_dim = torch.tensor([20, 25, 5], dtype=self.dtype)
+        mic_array = torch.tensor([[2, 2, 0], [8, 8, 0]], dtype=self.dtype)
+        source = torch.tensor([7, 6, 0], dtype=self.dtype)
+        num_rays = 1_000
+        ABS, SCAT = 0.1, 0.2
+
+        absorption = torch.full(fill_value=ABS, size=(7, 6), dtype=self.dtype)
+        scattering = torch.full(fill_value=SCAT, size=(7, 6), dtype=self.dtype)
+        hist_per_band_per_wall = F.ray_tracing(
+            room=room_dim,
+            source=source,
+            mic_array=mic_array,
+            num_rays=num_rays,
+            absorption=absorption,
+            scattering=scattering,
+        )
+        absorption = torch.full(fill_value=ABS, size=(6,), dtype=self.dtype)
+        scattering = torch.full(fill_value=SCAT, size=(6,), dtype=self.dtype)
+        hist_per_wall = F.ray_tracing(
+            room=room_dim,
+            source=source,
+            mic_array=mic_array,
+            num_rays=num_rays,
+            absorption=absorption,
+            scattering=scattering,
+        )
+
+        absorption = ABS
+        scattering = SCAT
+        hist_single = F.ray_tracing(
+            room=room_dim,
+            source=source,
+            mic_array=mic_array,
+            num_rays=num_rays,
+            absorption=absorption,
+            scattering=scattering,
+        )
+        self.assertEqual(hist_per_band_per_wall.shape, (2, 7, 2500))
+        self.assertEqual(hist_per_wall.shape, (2, 1, 2500))
+        self.assertEqual(hist_single.shape, (2, 1, 2500))
+        torch.testing.assert_close(hist_single, hist_per_wall)
+
+        hist_single = hist_single.expand(hist_per_band_per_wall.shape)
+        torch.testing.assert_close(hist_single, hist_per_band_per_wall)
+
 
 class Functional64OnlyTestImpl(TestBaseMixin):
     @nested_params(