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"""
/* Copyright (c) 2023 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import sys
sys.path.append('../dnntools')
import numbers
import torch
from torch import nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
from utils.complexity import _conv1d_flop_count
from dnntools.quantization.softquant import soft_quant
from dnntools.sparsification import mark_for_sparsification
class SilkFeatureNetPL(nn.Module):
""" feature net with partial lookahead """
def __init__(self,
feature_dim=47,
num_channels=256,
hidden_feature_dim=64,
softquant=False,
sparsify=True,
sparsification_density=0.5,
apply_weight_norm=False):
super(SilkFeatureNetPL, self).__init__()
if isinstance(sparsification_density, numbers.Number):
sparsification_density = 4 * [sparsification_density]
self.feature_dim = feature_dim
self.num_channels = num_channels
self.hidden_feature_dim = hidden_feature_dim
norm = weight_norm if apply_weight_norm else lambda x, name=None: x
self.conv1 = norm(nn.Conv1d(feature_dim, self.hidden_feature_dim, 1))
self.conv2 = norm(nn.Conv1d(4 * self.hidden_feature_dim, num_channels, 2))
self.tconv = norm(nn.ConvTranspose1d(num_channels, num_channels, 4, 4))
gru_input_dim = num_channels + self.repeat_upsamp_dim if self.repeat_upsamp else num_channels
self.gru = norm(norm(nn.GRU(gru_input_dim, num_channels, batch_first=True), name='weight_hh_l0'), name='weight_ih_l0')
if softquant:
self.conv2 = soft_quant(self.conv2)
if not self.repeat_upsamp: self.tconv = soft_quant(self.tconv)
self.gru = soft_quant(self.gru, names=['weight_hh_l0', 'weight_ih_l0'])
if sparsify:
mark_for_sparsification(self.conv2, (sparsification_density[0], [8, 4]))
if not self.repeat_upsamp: mark_for_sparsification(self.tconv, (sparsification_density[1], [8, 4]))
mark_for_sparsification(
self.gru,
{
'W_ir' : (sparsification_density[2], [8, 4], False),
'W_iz' : (sparsification_density[2], [8, 4], False),
'W_in' : (sparsification_density[2], [8, 4], False),
'W_hr' : (sparsification_density[3], [8, 4], True),
'W_hz' : (sparsification_density[3], [8, 4], True),
'W_hn' : (sparsification_density[3], [8, 4], True),
}
)
def flop_count(self, rate=200):
count = 0
for conv in [self.conv1, self.conv2] if self.repeat_upsamp else [self.conv1, self.conv2, self.tconv]:
count += _conv1d_flop_count(conv, rate)
count += 2 * (3 * self.gru.input_size * self.gru.hidden_size + 3 * self.gru.hidden_size * self.gru.hidden_size) * rate
return count
def forward(self, features, state=None):
""" features shape: (batch_size, num_frames, feature_dim) """
batch_size = features.size(0)
num_frames = features.size(1)
if state is None:
state = torch.zeros((1, batch_size, self.num_channels), device=features.device)
features = features.permute(0, 2, 1)
# dimensionality reduction
c = torch.tanh(self.conv1(features))
# frame accumulation
c = c.permute(0, 2, 1)
c = c.reshape(batch_size, num_frames // 4, -1).permute(0, 2, 1)
c = torch.tanh(self.conv2(F.pad(c, [1, 0])))
# upsampling
c = torch.tanh(self.tconv(c))
c = c.permute(0, 2, 1)
c, _ = self.gru(c, state)
return c
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