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use np.cumsum #9

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use np.cumsum
tosemml Aug 15, 2023
c9b59da
Update dcc_tf.py
tosemml Aug 15, 2023
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7 changes: 3 additions & 4 deletions src/training/dcc_tf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,8 @@
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np

from torchmetrics.functional import(
scale_invariant_signal_noise_ratio as si_snr,
signal_noise_ratio as snr,
Expand Down Expand Up @@ -80,10 +82,7 @@ def __init__(self, channels, num_layers, kernel_size=3):
for i in range(num_layers)]

# Compute buffer start indices for each layer
self.buf_indices = [0]
for i in range(num_layers - 1):
self.buf_indices.append(
self.buf_indices[-1] + self.buf_lengths[i])
self.buf_indices = [0] + np.cumsum(self.buf_lengths[:(num_layers - 1)]).tolist()

# Dilated causal conv layers aggregate previous context to obtain
# contexful encoded input.
Expand Down