import torch from torch import nn import torch.nn.functional as F from lib_v5 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