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91 lines
No EOL
3.1 KiB
Python
91 lines
No EOL
3.1 KiB
Python
"""
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/* Copyright (c) 2023 Amazon
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Written by Jan Buethe */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
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OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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"""
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import torch
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from torch import nn
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import torch.nn.functional as F
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from utils.complexity import _conv1d_flop_count
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class LPCNetFeatureNet(nn.Module):
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def __init__(self,
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feature_dim=84,
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num_channels=256,
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upsamp_factor=2,
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lookahead=True):
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super().__init__()
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self.feature_dim = feature_dim
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self.num_channels = num_channels
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self.upsamp_factor = upsamp_factor
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self.lookahead = lookahead
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self.conv1 = nn.Conv1d(feature_dim, num_channels, 3)
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self.conv2 = nn.Conv1d(num_channels, num_channels, 3)
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self.gru = nn.GRU(num_channels, num_channels, batch_first=True)
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self.tconv = nn.ConvTranspose1d(num_channels, num_channels, upsamp_factor, upsamp_factor)
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def flop_count(self, rate=100):
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count = 0
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for conv in self.conv1, self.conv2, self.tconv:
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count += _conv1d_flop_count(conv, rate)
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count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate
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return count
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def forward(self, features, state=None):
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""" features shape: (batch_size, num_frames, feature_dim) """
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batch_size = features.size(0)
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if state is None:
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state = torch.zeros((1, batch_size, self.num_channels), device=features.device)
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features = features.permute(0, 2, 1)
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if self.lookahead:
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c = torch.tanh(self.conv1(F.pad(features, [1, 1])))
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c = torch.tanh(self.conv2(F.pad(c, [2, 0])))
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else:
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c = torch.tanh(self.conv1(F.pad(features, [2, 0])))
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c = torch.tanh(self.conv2(F.pad(c, [2, 0])))
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c = torch.tanh(self.tconv(c))
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c = c.permute(0, 2, 1)
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c, _ = self.gru(c, state)
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return c |