File size: 2,613 Bytes
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import torch.nn as nn
class Encoder(nn.Module):
def __init__(self, input_channels=2, cnn_channels=64, middle_features=128, output_features=256, n_fft=1024):
"""
input_channels: 入力チャンネル数（ここでは4）
cnn_channels (P): CNNの中間フィルタ数
output_features (Q): 最終的な特徴量次元
"""
super(Encoder, self).__init__()
assert (output_features % 2) == 0
self.output_features = output_features
self.cnn_block = nn.Sequential(
nn.Conv2d(input_channels, cnn_channels, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(cnn_channels),
nn.MaxPool2d(kernel_size=(1, 2)),
nn.Conv2d(cnn_channels, cnn_channels, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(cnn_channels),
nn.MaxPool2d(kernel_size=(1, 2)),
nn.Conv2d(cnn_channels, cnn_channels, kernel_size=3, padding=1),
nn.ReLU(),
nn.BatchNorm2d(cnn_channels),
nn.MaxPool2d(kernel_size=(1, 2)),
)
# CNN出力のfreq次元の縮小を正しく計算
self.freq_after_cnn = (n_fft // 2) // (2 ** 3) # (n_fft/2)を3回半分にする
self.middle_linear = nn.Linear(cnn_channels * self.freq_after_cnn, middle_features)
self.gru = nn.GRU(
input_size=middle_features,
hidden_size=output_features // 2,
num_layers=2,
batch_first=True,
bidirectional=True
)
def forward(self, x):
"""
x: (batch, time_frames, freq_bins, channels=2)
returns: (batch, time_frames, output_features=Q)
"""
batch_size, time_frames, freq_bins, channels = x.shape
# Prepare for CNN: permute to (batch, 2 * channels, time, freq)
x = x.permute(0, 3, 1, 2) # (batch, 2 * channels, time, freq)
# CNN
x = self.cnn_block(x) # (batch, cnn_channels, time, reduced_freq)
batch_size, cnn_channels, time_steps, freq_bins_reduced = x.size()
# Prepare for linear layer
x = x.permute(0, 2, 1, 3).contiguous() # (batch, time_steps, channels, freq_bins_reduced)
x = x.view(batch_size, time_steps, -1) # (batch, time_steps, channels * freq_bins_reduced)
# Project to output_features dimension (Q)
x = self.middle_linear(x) # (batch, time_steps, middle_features)
x, _ = self.gru(x) # (batch, time_steps, output_features)
x = torch.mean(x, dim=1)
return x
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