def test_sisnr_implementations(n_batches=1,
bs=5,
n_sources=1,
length=16000,
improvement=True):
cpu_timer, gpu_timer = 0., 0.
gpu_results = torch.zeros((n_batches, bs - 1, n_sources)).float()
cpu_results = np.zeros((n_batches, bs - 1, n_sources))
cpu_batches = random_batch_creator(n_batches=n_batches,
bs=bs,
n_sources=n_sources,
length=length)
gpu_batches = torch.from_numpy(cpu_batches)
torch.set_printoptions(precision=8)
for b_ind in np.arange(cpu_batches.shape[0]):
before = time()
cpu_results[b_ind, :, :] = \
naive_sisnr(cpu_batches[b_ind, 1:, :, :],
cpu_batches[b_ind, 1:, :, :],
n_sources,
improvement=improvement)
now = time()
cpu_timer += now - before
gpu_sisnr = sisdr_l.PermInvariantSISDR(batch_size=bs,
n_sources=n_sources,
zero_mean=True,
backward_loss=False,
improvement=improvement)
for b_ind in np.arange(gpu_batches.shape[0]):
before = time()
gpu_results[b_ind, :, :] = \
gpu_sisnr(gpu_batches[b_ind, 1:, :, :],
gpu_batches[b_ind, 1:, :, :])
now = time()
gpu_timer += now - before
print("CPU")
print(cpu_results)
print("GPU")
print(gpu_results.data.cpu().numpy())
print("DIFF")
print(np.abs(cpu_results - gpu_results.data.cpu().numpy()))
hparams["fs"],
hparams["bs"],
hparams["n_sources"])
else:
audio_logger = None
# define data loaders
train_gen, val_gen, tr_val_gen = dataset_specific_params.get_data_loaders(hparams)
# define the losses that are going to be used
back_loss_tr_loss_name, back_loss_tr_loss = (
'tr_back_loss_mask_SISDR',
sisdr_lib.PermInvariantSISDR(batch_size=hparams['bs'],
n_sources=hparams['n_sources'],
zero_mean=True,
backward_loss=True,
improvement=True))
val_losses = dict([
('val_SISDRi', sisdr_lib.PermInvariantSISDR(batch_size=hparams['bs'],
n_sources=hparams['n_sources'],
zero_mean=True,
backward_loss=False,
improvement=True,
return_individual_results=True)),
])
val_loss_name = 'val_SISDRi'
tr_val_losses = dict([
('tr_SISDRi', sisdr_lib.PermInvariantSISDR(batch_size=hparams['bs'],