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| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from . 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 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 | |