91 lines
2.3 KiB
Python
91 lines
2.3 KiB
Python
from typing import bool
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from torch import nn
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class DeLSTM(nn.Module):
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def __init__(
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self,
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input_size:int,
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hidden_size:int,
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num_layers:int,
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bidirectional:bool=True
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):
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self.lstm = nn.LSTM(input_size, hidden_size, num_layers, bidirectional=bidirectional)
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dim = 2 if bidirectional else 1
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self.linear = nn.Linear(dim*hidden_size,hidden_size)
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def forward(self,x):
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output,(h,c) = self.lstm(x)
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output = self.linear(output)
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return output
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class Demus(nn.Module):
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def __init__(
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self,
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c_in:int=1,
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c_out:int=1,
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hidden:int=48,
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kernel_size:int=8,
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stride:int=4,
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growth_factor:int=2,
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depth:int = 6,
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glu:bool = True,
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bidirectional:bool=True,
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resample:int=2,
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):
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self.c_in = c_in
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self.c_out = c_out
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self.hidden = hidden
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self.growth_factor = growth_factor
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self.stride = stride
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self.kernel_size = kernel_size
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self.depth = depth
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self.bidirectional = bidirectional
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self.activation = nn.GLU(1) if glu else nn.ReLU()
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multi_factor = 2 if glu else 1
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## do resampling
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self.encoder = nn.ModuleList()
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self.decoder = nn.ModuleList()
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for layer in range(self.depth):
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encoder_layer = [nn.Conv1d(c_in,hidden,kernel_size,stride),
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nn.ReLU(),
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nn.Conv1d(hidden, hidden*multi_factor,kernel_size,1),
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self.activation]
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encoder_layer = nn.Sequential(encoder_layer)
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self.encoder.append(*encoder_layer)
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decoder_layer = [nn.Conv1d(hidden,hidden*multi_factor,kernel_size,1),
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self.activation,
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nn.ConvTranspose1d(hidden,c_out,kernel_size,stride)
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]
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if layer>0:
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decoder_layer.append(nn.ReLU())
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decoder_layer = nn.Sequential(*decoder_layer)
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self.decoder.insert(0,decoder_layer)
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c_out = hidden
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c_in = hidden
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hidden = self.growth_factor * hidden
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self.de_lstm = DeLSTM(input_size=c_in,hidden_size=c_in,num_layers=2,bidirectional=self.bidirectional)
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def forward(self,input):
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pass
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