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import torch
from torch import nn
import torch.nn.functional as F
from lib import spec_utils
class Conv2DBNActiv(nn.Module):
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
super(Conv2DBNActiv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
nin, nout,
kernel_size=ksize,
stride=stride,
padding=pad,
dilation=dilation,
bias=False
),
nn.BatchNorm2d(nout),
activ()
)
def __call__(self, x):
return self.conv(x)
# class SeperableConv2DBNActiv(nn.Module):
# def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
# super(SeperableConv2DBNActiv, self).__init__()
# self.conv = nn.Sequential(
# nn.Conv2d(
# nin, nin,
# kernel_size=ksize,
# stride=stride,
# padding=pad,
# dilation=dilation,
# groups=nin,
# bias=False
# ),
# nn.Conv2d(
# nin, nout,
# kernel_size=1,
# bias=False
# ),
# nn.BatchNorm2d(nout),
# activ()
# )
# def __call__(self, x):
# return self.conv(x)
class Encoder(nn.Module):
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
super(Encoder, self).__init__()
self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ)
self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
def __call__(self, x):
h = self.conv1(x)
h = self.conv2(h)
return h
class Decoder(nn.Module):
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
super(Decoder, self).__init__()
self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
# self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ)
self.dropout = nn.Dropout2d(0.1) if dropout else None
def __call__(self, x, skip=None):
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
if skip is not None:
skip = spec_utils.crop_center(skip, x)
x = torch.cat([x, skip], dim=1)
h = self.conv1(x)
# h = self.conv2(h)
if self.dropout is not None:
h = self.dropout(h)
return h
class ASPPModule(nn.Module):
def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False):
super(ASPPModule, self).__init__()
self.conv1 = nn.Sequential(
nn.AdaptiveAvgPool2d((1, None)),
Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ)
)
self.conv2 = Conv2DBNActiv(
nin, nout, 1, 1, 0, activ=activ
)
self.conv3 = Conv2DBNActiv(
nin, nout, 3, 1, dilations[0], dilations[0], activ=activ
)
self.conv4 = Conv2DBNActiv(
nin, nout, 3, 1, dilations[1], dilations[1], activ=activ
)
self.conv5 = Conv2DBNActiv(
nin, nout, 3, 1, dilations[2], dilations[2], activ=activ
)
self.bottleneck = Conv2DBNActiv(
nout * 5, nout, 1, 1, 0, activ=activ
)
self.dropout = nn.Dropout2d(0.1) if dropout else None
def forward(self, x):
_, _, h, w = x.size()
feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
feat2 = self.conv2(x)
feat3 = self.conv3(x)
feat4 = self.conv4(x)
feat5 = self.conv5(x)
out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1)
out = self.bottleneck(out)
if self.dropout is not None:
out = self.dropout(out)
return out
class LSTMModule(nn.Module):
def __init__(self, nin_conv, nin_lstm, nout_lstm):
super(LSTMModule, self).__init__()
self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0)
self.lstm = nn.LSTM(
input_size=nin_lstm,
hidden_size=nout_lstm // 2,
bidirectional=True
)
self.dense = nn.Sequential(
nn.Linear(nout_lstm, nin_lstm),
nn.BatchNorm1d(nin_lstm),
nn.ReLU()
)
def forward(self, x):
N, _, nbins, nframes = x.size()
h = self.conv(x)[:, 0] # N, nbins, nframes
h = h.permute(2, 0, 1) # nframes, N, nbins
h, _ = self.lstm(h)
h = self.dense(h.reshape(-1, h.size()[-1])) # nframes * N, nbins
h = h.reshape(nframes, N, 1, nbins)
h = h.permute(1, 2, 3, 0)
return h
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