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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 lib_v5 import spec_utils |
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class Conv2DBNActiv(nn.Module): |
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU): |
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super(Conv2DBNActiv, self).__init__() |
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self.conv = nn.Sequential( |
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nn.Conv2d( |
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nin, nout, |
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kernel_size=ksize, |
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stride=stride, |
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padding=pad, |
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dilation=dilation, |
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bias=False), |
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nn.BatchNorm2d(nout), |
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activ() |
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) |
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def __call__(self, x): |
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return self.conv(x) |
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class Encoder(nn.Module): |
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU): |
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super(Encoder, self).__init__() |
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self.conv1 = Conv2DBNActiv(nin, nout, ksize, stride, pad, activ=activ) |
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self.conv2 = Conv2DBNActiv(nout, nout, ksize, 1, pad, activ=activ) |
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def __call__(self, x): |
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h = self.conv1(x) |
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h = self.conv2(h) |
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return h |
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class Decoder(nn.Module): |
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def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False): |
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super(Decoder, self).__init__() |
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self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ) |
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self.dropout = nn.Dropout2d(0.1) if dropout else None |
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def __call__(self, x, skip=None): |
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x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) |
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if skip is not None: |
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skip = spec_utils.crop_center(skip, x) |
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x = torch.cat([x, skip], dim=1) |
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h = self.conv1(x) |
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if self.dropout is not None: |
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h = self.dropout(h) |
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return h |
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class ASPPModule(nn.Module): |
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def __init__(self, nin, nout, dilations=(4, 8, 12), activ=nn.ReLU, dropout=False): |
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super(ASPPModule, self).__init__() |
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self.conv1 = nn.Sequential( |
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nn.AdaptiveAvgPool2d((1, None)), |
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Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) |
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) |
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self.conv2 = Conv2DBNActiv(nin, nout, 1, 1, 0, activ=activ) |
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self.conv3 = Conv2DBNActiv( |
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nin, nout, 3, 1, dilations[0], dilations[0], activ=activ |
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) |
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self.conv4 = Conv2DBNActiv( |
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nin, nout, 3, 1, dilations[1], dilations[1], activ=activ |
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) |
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self.conv5 = Conv2DBNActiv( |
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nin, nout, 3, 1, dilations[2], dilations[2], activ=activ |
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) |
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self.bottleneck = Conv2DBNActiv(nout * 5, nout, 1, 1, 0, activ=activ) |
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self.dropout = nn.Dropout2d(0.1) if dropout else None |
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def forward(self, x): |
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_, _, h, w = x.size() |
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feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True) |
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feat2 = self.conv2(x) |
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feat3 = self.conv3(x) |
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feat4 = self.conv4(x) |
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feat5 = self.conv5(x) |
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out = torch.cat((feat1, feat2, feat3, feat4, feat5), dim=1) |
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out = self.bottleneck(out) |
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if self.dropout is not None: |
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out = self.dropout(out) |
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return out |
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class LSTMModule(nn.Module): |
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def __init__(self, nin_conv, nin_lstm, nout_lstm): |
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super(LSTMModule, self).__init__() |
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self.conv = Conv2DBNActiv(nin_conv, 1, 1, 1, 0) |
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self.lstm = nn.LSTM( |
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input_size=nin_lstm, |
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hidden_size=nout_lstm // 2, |
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bidirectional=True |
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) |
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self.dense = nn.Sequential( |
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nn.Linear(nout_lstm, nin_lstm), |
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nn.BatchNorm1d(nin_lstm), |
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nn.ReLU() |
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) |
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def forward(self, x): |
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N, _, nbins, nframes = x.size() |
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h = self.conv(x)[:, 0] |
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h = h.permute(2, 0, 1) |
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h, _ = self.lstm(h) |
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h = self.dense(h.reshape(-1, h.size()[-1])) |
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h = h.reshape(nframes, N, 1, nbins) |
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h = h.permute(1, 2, 3, 0) |
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return h |
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