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Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| class ResNetSE(nn.Module): | |
| def __init__(self, block, layers, num_filters, nOut, encoder_type='SAP', n_mels=80, n_mel_T=1, log_input=True, **kwargs): | |
| super(ResNetSE, self).__init__() | |
| print('Embedding size is %d, encoder %s.' % (nOut, encoder_type)) | |
| self.inplanes = num_filters[0] | |
| self.encoder_type = encoder_type | |
| self.n_mels = n_mels | |
| self.log_input = log_input | |
| self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.bn1 = nn.BatchNorm2d(num_filters[0]) | |
| self.layer1 = self._make_layer(block, num_filters[0], layers[0]) | |
| self.layer2 = self._make_layer(block, num_filters[1], layers[1], stride=(2, 2)) | |
| self.layer3 = self._make_layer(block, num_filters[2], layers[2], stride=(2, 2)) | |
| self.layer4 = self._make_layer(block, num_filters[3], layers[3], stride=(2, 2)) | |
| self.instancenorm = nn.InstanceNorm1d(n_mels) | |
| outmap_size = int(self.n_mels * n_mel_T / 8) | |
| self.attention = nn.Sequential( | |
| nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1), | |
| nn.ReLU(), | |
| nn.BatchNorm1d(128), | |
| nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1), | |
| nn.Softmax(dim=2), | |
| ) | |
| if self.encoder_type == "SAP": | |
| out_dim = num_filters[3] * outmap_size | |
| elif self.encoder_type == "ASP": | |
| out_dim = num_filters[3] * outmap_size * 2 | |
| else: | |
| raise ValueError('Undefined encoder') | |
| self.fc = nn.Linear(out_dim, nOut) | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') | |
| elif isinstance(m, nn.BatchNorm2d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def _make_layer(self, block, planes, blocks, stride=1): | |
| downsample = None | |
| if stride != 1 or self.inplanes != planes * block.expansion: | |
| downsample = nn.Sequential( | |
| nn.Conv2d(self.inplanes, planes * block.expansion, | |
| kernel_size=1, stride=stride, bias=False), | |
| nn.BatchNorm2d(planes * block.expansion), | |
| ) | |
| layers = [] | |
| layers.append(block(self.inplanes, planes, stride, downsample)) | |
| self.inplanes = planes * block.expansion | |
| for i in range(1, blocks): | |
| layers.append(block(self.inplanes, planes)) | |
| return nn.Sequential(*layers) | |
| def new_parameter(self, *size): | |
| out = nn.Parameter(torch.FloatTensor(*size)) | |
| nn.init.xavier_normal_(out) | |
| return out | |
| def forward(self, x): | |
| # with torch.no_grad(): | |
| # x = self.torchfb(x) + 1e-6 | |
| # if self.log_input: x = x.log() | |
| # x = self.instancenorm(x).unsqueeze(1) | |
| x = self.conv1(x) | |
| x = self.relu(x) | |
| x = self.bn1(x) | |
| x = self.layer1(x) | |
| x = self.layer2(x) | |
| x = self.layer3(x) | |
| x = self.layer4(x) | |
| x = x.reshape(x.size()[0], -1, x.size()[-1]) | |
| w = self.attention(x) | |
| if self.encoder_type == "SAP": | |
| x = torch.sum(x * w, dim=2) | |
| elif self.encoder_type == "ASP": | |
| mu = torch.sum(x * w, dim=2) | |
| sg = torch.sqrt((torch.sum((x ** 2) * w, dim=2) - mu ** 2).clamp(min=1e-5)) | |
| x = torch.cat((mu, sg), 1) | |
| x = x.view(x.size()[0], -1) | |
| x = self.fc(x) | |
| return x | |
| class SEBasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8): | |
| super(SEBasicBlock, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.se = SELayer(planes, reduction) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.relu(out) | |
| out = self.bn1(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.se(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class SEBottleneck(nn.Module): | |
| expansion = 4 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8): | |
| super(SEBottleneck, self).__init__() | |
| self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) | |
| self.bn1 = nn.BatchNorm2d(planes) | |
| self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, | |
| padding=1, bias=False) | |
| self.bn2 = nn.BatchNorm2d(planes) | |
| self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) | |
| self.bn3 = nn.BatchNorm2d(planes * 4) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.se = SELayer(planes * 4, reduction) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.bn1(out) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| out = self.bn2(out) | |
| out = self.relu(out) | |
| out = self.conv3(out) | |
| out = self.bn3(out) | |
| out = self.se(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class SELayer(nn.Module): | |
| def __init__(self, channel, reduction=8): | |
| super(SELayer, self).__init__() | |
| self.avg_pool = nn.AdaptiveAvgPool2d(1) | |
| self.fc = nn.Sequential( | |
| nn.Linear(channel, channel // reduction), | |
| nn.ReLU(inplace=True), | |
| nn.Linear(channel // reduction, channel), | |
| nn.Sigmoid() | |
| ) | |
| def forward(self, x): | |
| b, c, _, _ = x.size() | |
| y = self.avg_pool(x).view(b, c) | |
| y = self.fc(y).view(b, c, 1, 1) | |
| return x * y | |