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/torchaudio_unittest/prototype/functional/functional_test_impl.py

@@ -412,6 +412,258 @@ 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):
+        if self.dtype == torch.float64:
+            import unittest
+
+            raise unittest.SkipTest("float64 is not supported yet")
+
+        room = torch.tensor([3., 4., 5.], dtype=self.dtype)
+        source = torch.tensor([0., 0., 0.], dtype=self.dtype)
+        mic = torch.tensor([[1., 2., 3.]], dtype=self.dtype)
+
+        _ = F.ray_tracing(room=room, source=source, mic_array=mic, num_rays=10)
+
+        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 = 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)
+
+        with self.assertRaises(ValueError) as cm:
+            F.ray_tracing(
+                room=room.to(torch.float64),
+                source=source.to(torch.float32),
+                mic_array=mic,
+                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)
+
+        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_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_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_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_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)
+
+        abs_ = torch.randn((7, 6), dtype=self.dtype)
+        scat = torch.randn((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.randn((5, 6), dtype=self.dtype),
+        )
+        F.ray_tracing(
+            room=room,
+            source=source,
+            mic_array=mic,
+            num_rays=10,
+            absorption=torch.randn((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(

test/torchaudio_unittest/prototype/functional/pyroomacoustics_compatibility_test.py

@@ -1,3 +1,4 @@
+import numpy as np
 import torch
 import torchaudio.prototype.functional as F
 
@@ -91,3 +92,76 @@ def test_simulate_rir_ism_multi_band(self, channel):
             expected[i, 0 : room.rir[i][0].shape[0]] = torch.from_numpy(room.rir[i][0])
         actual = F.simulate_rir_ism(room_dim, source, mic_array, max_order, absorption)
         self.assertEqual(expected, actual, atol=1e-3, rtol=1e-3)
+
+    @parameterized.expand(
+        [
+            ([20, 25, 30], [1, 10, 5], [[8, 8, 22]], 1_000),  # 3D with 1 mic
+        ]
+    )
+    def test_ray_tracing_same_results_as_pyroomacoustics(self, room_dim, source, mic_array, num_rays):
+
+        walls = ["west", "east", "south", "north", "floor", "ceiling"]
+        num_walls = len(walls)
+        num_bands = 6
+
+        energy_thres = 1e-7
+        time_thres = 10.0
+        hist_bin_size = 0.004
+        mic_radius = 0.5
+        sound_speed = 343.0
+
+        absorption = torch.rand(num_bands, num_walls, dtype=self.dtype)
+        scattering = torch.rand(num_bands, num_walls, dtype=self.dtype)
+        room_dim = torch.tensor(room_dim, dtype=self.dtype)
+        source = torch.tensor(source, dtype=self.dtype)
+        mic_array = torch.tensor(mic_array, dtype=self.dtype)
+
+        room = pra.ShoeBox(
+            room_dim.tolist(),
+            ray_tracing=True,
+            materials={
+                walls[i]: pra.Material(
+                    energy_absorption={
+                        "coeffs": absorption[:, i].reshape(-1).detach().numpy(),
+                        "center_freqs": 125 * 2 ** np.arange(num_bands),
+                    },
+                    scattering={
+                        "coeffs": scattering[:, i].reshape(-1).detach().numpy(),
+                        "center_freqs": 125 * 2 ** np.arange(num_bands),
+                    },
+                )
+                for i in range(num_walls)
+            },
+            air_absorption=False,
+            max_order=0,  # Make sure PRA doesn't use the hybrid method (we just want ray tracing)
+        )
+        room.add_microphone_array(mic_array.T.tolist())
+        room.add_source(source.tolist())
+        room.set_ray_tracing(
+            n_rays=num_rays,
+            energy_thres=energy_thres,
+            time_thres=time_thres,
+            hist_bin_size=hist_bin_size,
+            receiver_radius=mic_radius,
+        )
+        room.set_sound_speed(sound_speed)
+        room.compute_rir()
+        hist_pra = np.array(room.rt_histograms, dtype=np.double)[:, 0, 0]
+
+        hist = F.ray_tracing(
+            room=room_dim,
+            source=source,
+            mic_array=mic_array,
+            num_rays=num_rays,
+            absorption=absorption,
+            scattering=scattering,
+            sound_speed=sound_speed,
+            mic_radius=mic_radius,
+            energy_thres=energy_thres,
+            time_thres=time_thres,
+            hist_bin_size=hist_bin_size,
+        )
+
+        assert hist.ndim == 3
+        assert hist.shape == hist_pra.shape
+        self.assertEqual(hist, hist_pra)

test/torchaudio_unittest/prototype/functional/torchscript_consistency_test_impl.py

@@ -112,3 +112,42 @@ def test_simulate_rir_ism_multi_band(self, channel):
             F.simulate_rir_ism,
             (room_dim, source, mic_array, max_order, absorption, None, 81, center_frequency, 343.0, 16000.0),
         )
+
+    @parameterized.expand(
+        [
+            ([20, 25, 30], [1, 10, 5], [[8, 8, 22]], 500),  # 3D with 1 mic
+        ]
+    )
+    def test_ray_tracing(self, room_dim, source, mic_array, num_rays):
+        num_walls = 4 if len(room_dim) == 2 else 6
+        num_bands = 3
+
+        absorption = torch.rand(num_bands, num_walls, dtype=torch.float32)
+        scattering = torch.rand(num_bands, num_walls, dtype=torch.float32)
+
+        energy_thres = 1e-7
+        time_thres = 10.0
+        hist_bin_size = 0.004
+        mic_radius = 0.5
+        sound_speed = 343.0
+
+        room_dim = torch.tensor(room_dim, dtype=self.dtype)
+        source = torch.tensor(source, dtype=self.dtype)
+        mic_array = torch.tensor(mic_array, dtype=self.dtype)
+
+        self._assert_consistency(
+            F.ray_tracing,
+            (
+                room_dim,
+                source,
+                mic_array,
+                num_rays,
+                absorption,
+                scattering,
+                mic_radius,
+                sound_speed,
+                energy_thres,
+                time_thres,
+                hist_bin_size,
+            ),
+        )