def test_epoch(self, dataset, trajectories_per_batch, nb_batchs=None):
""" Test the model with an epoch of the dataset.
"""
self.model.eval() # set the model in "testing mode"
with torch.no_grad():
loss_data = 0
rmsae_data = 0
n_trajectories = len(dataset)
if nb_batchs is None:
nb_batchs = n_trajectories // trajectories_per_batch
for idx in range(nb_batchs):
mic_sig_batch, acoustic_scene_batch = dataset.get_batch(
idx * trajectories_per_batch,
(idx + 1) * trajectories_per_batch)
x_batch, DOA_batch = self.data_transformation(
mic_sig_batch, acoustic_scene_batch)
DOA_batch_pred_cart = self.model(x_batch).contiguous()
DOA_batch = DOA_batch.contiguous()
DOA_batch_cart = sph2cart(DOA_batch)
loss_data += torch.nn.functional.mse_loss(
DOA_batch_pred_cart.view(-1, 3),
DOA_batch_cart.view(-1, 3))
DOA_batch_pred = cart2sph(DOA_batch_pred_cart)
rmsae_data += rms_angular_error_deg(DOA_batch.view(-1, 2),
DOA_batch_pred.view(-1, 2))
loss_data /= nb_batchs
rmsae_data /= nb_batchs
return loss_data, rmsae_data
def azim_proj(pos):
"""
Computes the Azimuthal Equidistant Projection of input point in 3D Cartesian Coordinates.
Imagine a plane being placed against (tangent to) a globe. If
a light source inside the globe projects the graticule onto
the plane the result would be a planar, or azimuthal, map
projection.
:param pos: position in 3D Cartesian coordinates
:return: projected coordinates using Azimuthal Equidistant Projection
"""
[r, elev, az] = cart2sph(pos[0], pos[1], pos[2])
return pol2cart(az, m.pi / 2 - elev)
def azim_proj(pos):
"""
Computes the Azimuthal Equidistant Projection of input point in 3D Cartesian Coordinates.
Imagine a plane being placed against (tangent to) a globe. If
a light source inside the globe projects the graticule onto
the plane the result would be a planar, or azimuthal, map
projection.
:param pos: position in 3D Cartesian coordinates
:return: projected coordinates using Azimuthal Equidistant Projection
"""
[r, elev, az] = cart2sph(pos[0], pos[1], pos[2])
return pol2cart(az, m.pi / 2 - elev)
def predict_batch(self, mic_sig_batch, vad_batch=None, return_x=False):
""" Perform the model inference for an input batch.
You can use return_x=True in order to return the input of the model in addition to the DOA estimation
"""
self.model.eval() # set the model in "testing mode"
n_trajectories = mic_sig_batch.shape[0]
trajectory_len = mic_sig_batch.shape[1]
x_batch = self.data_transformation(mic_sig_batch, vad_batch=vad_batch)
DOA_batch_pred = self.model(x_batch).cpu().detach()
DOA_batch_pred_cart = DOA_batch_pred.reshape(
(n_trajectories, trajectory_len, 3)) # For split trajectories
DOA_batch_pred = cart2sph(DOA_batch_pred_cart)
if return_x:
return DOA_batch_pred, x_batch.cpu().detach()
else:
return DOA_batch_pred
def azim_proj(pos): [r, elev, az] = cart2sph(pos[0], pos[1], pos[2]) return pol2cart(float(az), m.pi/2.0 - float(elev))
